Role of the primate ventral striatum as a neural hub bridging option valuation and action selection

Domain: biology
Published: 2026-03-28
Editorial outcome: minor_revision

Abstract
Making appropriate decisions relies on the brain’s capacity to evaluate the expected outcomes of available options and select the most rewarding action. The ventral striatum and midbrain dopamine neurons have been implicated in the option valuation process, consistent with the brain’s reinforcement learning theory in which these brain structures encode and update value representations of expected outcomes. Extending beyond this framework, we found that the dopamine–ventral striatum system plays a more proactive role in action selection. We recorded single-unit activity from ventral striatum neurons in macaque monkeys as they sequentially evaluated an option, decided whether to perform an action to choose it, and expressed that motor action. The activity of these neurons initially reflected the value of the option but gradually shifted to reflect monkey’s action selection, as if the ventral striatum translates the value information into the action. Moreover, optogenetic facilitation of dopamine input to the ventral striatum as well as electrical stimulation of this region altered monkey’s action selection. Our findings reveal a previously unappreciated function of the ventral striatum as a neural hub that bridges option valuation and action selection, and demonstrate the contribution of dopamine in the process leading to action selection within this region.

Introduction
To select the most rewarding action in a given environment, the brain is thought to rely on principles of reinforcement learning1,2. A central concept in this framework is value function, which represents the expected reward value associated with each available option. This value representation is continuously updated based on the difference between actual and expected outcomes, a process driven by reward prediction errors (RPEs). RPE, which encodes the outcome difference, works as a teaching signal for the update. The more accurately the value function represents the true reward value of each option, the more effectively an agent can select the most beneficial action.

The striatum, particularly its ventral subdivision, and midbrain dopamine neurons have been identified as key neural substrates supporting the value representation and its update. Neurons in the ventral striatum encode expected reward values3–6 and adjust these representations in accordance with past reward experiences7,8, mirroring the value function computational mechanisms proposed by reinforcement learning theory. Midbrain dopamine neurons exhibit phasic increases in firing in response to outcomes that exceed expectations, and decreases when outcomes fall short, consistent with their role in signaling RPEs9–13. Since dopamine neurons project densely to the ventral striatum, RPE signals are transmitted to this region and are thought to update value representations through dopaminergic modulation of synaptic plasticity14.

According to the reinforcement learning framework, such precise value representations in the ventral striatum enable the brain to select the most valuable action in a given environment. However, a critical question remains: how does the brain link these internal value representations to the actual selection of action? A different line of research may provide insight into this question. As part of the limbic network, which integrates cortical and subcortical regions involved in reward processing15,16, the ventral striatum projects to the globus pallidus17–19, which in turn connects to the motor and premotor systems20–22. This anatomical organization has led to the idea that the ventral striatum serves as a limbic–motor interface, translating motivational and emotional signals into motor outputs23–25. Supporting this notion, previous studies have shown that the ventral striatum contributes to effortful motor behaviors, such as precision gripping in animals with spinal cord injuries, by integrating motivational drive with motor coordination26.

The ventral striatum's role as a limbic–motor interface may also be relevant to decision-making contexts. The functional linkage between the limbic and the motor systems suggests that the ventral striatum not only represents the value of available options but may also help bridge the value representation and action selection. It could, for instance, provide the premotor system, including prefrontal and parietal cortical areas, with information about the value of available options and support its selection of the most valuable action. Alternatively, it may play a more active role in converting the value information directly into the decision through interaction with the premotor system. To explore these possibilities, we employed a value-based decision-making task in which macaque monkeys evaluated an option, decided whether to perform an action to select it, and executed that action. We recorded single-unit activity from ventral striatum neurons in monkeys performing this task. The activity of these neurons reflected the value of the option immediately after it was presented. Over time, however, their activity progressively shifted to reflect monkey’s action selection (i.e., whether to perform the action to choose the option). This neural dynamics suggests that the value signal in the ventral striatum gradually evolved into the action selection signal as the decision was being made. Moreover, optogenetic facilitation of dopamine input to the ventral striatum as well as electrical stimulation of this region altered monkey’s action selection, demonstrating a causal role of the ventral striatum and its dopaminergic modulation in decision-making. Our findings reveal a previously unappreciated function of the ventral striatum as a neural hub that bridges option valuation and action selection. They further highlight the contribution of dopamine to the process leading to action selection within this region.

Results
Value-based decision-making task and monkey’s behavior
To examine the roles of the dopamine-ventral striatum system in option valuation and action selection, we employed a value-based decision-making task in which macaque monkeys evaluated an option and then immediately decided whether to choose it (Fig. 1a). We trained three monkeys (monkey E: Macaca mulatta, male, 11.7kg, 15 years old; monkey A: Macaca mulatta, male, 8.4kg, 11 years old; monkey M: Macaca fuscata, female, 6.3kg, 6 years old) to perform this task. At the beginning of each trial, the monkey gazed at a central fixation point and pressed a button in front. Six visual objects were associated with different amounts of a liquid reward (value 1 = 0.12 ml, value 2 = 0.18 ml, value 3 = 0.24 ml, value 4 = 0.30 ml, value 5 = 0.36 ml, value 6 = 0.42 ml), and two of these objects were sequentially presented. The first object was presented as an option, and the monkey was required to decide whether to choose or not to choose the first object. If the monkey released the button during the presentation of the first object, this was interpreted as a decision to choose that object. Conversely, if the monkey kept the button pressed down, it was considered a decision not to choose the object. Thus, the monkey’s decision was regarded as whether to perform a specific action (i.e., button release) to choose the first option. After this decision, the second object was presented. If the monkey had decided to choose the first object, the animal was unable to choose the second object, even if the second object was better than the first object. Then, the reward associated with the first object was delivered. If the monkey had decided not to choose the first object, the animal had to release the button within the presentation of the second object. Then, the reward associated with the second object was delivered.

We confirmed that the three monkeys decided whether to release the button to choose the first object based on its value (Fig. 1b). The higher the value of the first object, the more likely the monkeys were to choose it (i.e., the logistic regression slope between the value and the choice rate was significantly larger than zero; monkey E: n = 54 sessions, mean ± SEM = 11.5 ± 2.4, z = 7.3, p = 2.4 × 10−13; monkey A: n = 71 sessions, mean ± SEM = 13.5 ± 3.3, z = 6.4, p = 1.3 × 10−4; monkey M: n = 18 sessions, mean ± SEM = 3.3 ± 0.2, z = 3.7, p = 1.9 × 10−4; two-tailed Wilcoxon signed-rank test). The latency of button release to choose the first object was also influenced by the value of the first object (Fig. 1c). It significantly decreased as the object value increased (i.e., the regression slope between the value and the latency was significantly smaller than zero; monkey E: n = 54 sessions, mean ± SEM = −27.6 ± 1.0, z = −7.3, p = 2.4 × 10−13; monkey A: n = 71 sessions, mean ± SEM = −20.7 ± 2.6, z = −5.6, p = 2.7 × 10−8; monkey M: n = 18 sessions, mean ± SEM = −18.0 ± 2.3, z = −3.7, p = 2.0 × 10−4; two-tailed Wilcoxon signed-rank test).

Because the task design allowed the monkeys to either commit early to the first object or wait for the second one, their decisions could, in principle, be influenced by individual risk attitudes. For example, risk-seeking monkeys might reject the first object more often in anticipation of receiving a larger reward from the second object. We quantified risk attitude as the value of the first object that was chosen with 50% probability. A higher value indicated a stronger risk-seeking tendency. However, no significant differences in risk attitude were observed across the three monkeys (n = 54, 71, and 18 sessions for monkeys E, A and M, respectively, p = 0.07, F = 2.78; one-way ANOVA) (Supplementary Fig. 1a). This indicates that the observed choice patterns were not influenced by individual differences in risk preference but were instead a consistent consequence of option valuation and action selection within the task.

Notably, the button-release latency was significantly longer when the monkey released the button for the first object than when the monkey released the button for the second object (monkey E: n = 54 sessions, df = 322, F = 873.24, p = 1.0 × 10−93; monkey A: n = 71 sessions, df = 424, F = 399.61, p = 4.1 × 10−63; monkey M: n = 18 sessions, df = 106, F = 596.89, p = 2.4 × 10−45; one-way ANOVA) (Fig. 1c). This result is consistent with our task design, which required the monkey to make a decision when releasing the button for the first object but only to respond reflexively to the appearance of the second object. Even for the second object, the button-release latency decreased as its value increased, although this trend was not statistically significant in one monkey (regression slope; monkey E: n = 54 sessions, mean ± SEM = −16.4 ± 0.7, z = −7.3, p = 2.4 × 10−13; monkey A: n = 71 sessions, mean ± SEM = −11.7 ± 1.0, z = −6.3, p = 2.8 × 10−10; monkey M: n = 18 sessions, mean ± SEM = −1.5 ± 1.2, z = −1.0, p = 0.33; two-tailed Wilcoxon signed-rank test) (Fig. 1c). These data indicate that the monkey’s decision was influenced by the value of the objects, and that the monkeys properly linked option valuation to action selection in our value-based decision-making task.

We also found that the monkey’s decision was influenced by recent experience. Specifically, the value of the second object in the previous trial negatively predicted whether the monkey would choose the first object in the current trial. A logistic regression analysis revealed a weak but significantly negative regression coefficient between the value of the previous second object and the choice rate of the current first object (n = 143 sessions, z = −7.2, p = 8.4 × 10−13; two-tailed Wilcoxon signed-rank test), while the regression coefficient for the current first object value was significantly positive (n = 143 sessions, z = 10.4, p = 3.2 × 10−25; two-tailed Wilcoxon signed-rank test) (Supplementary Fig. 1b). Thus, the higher the value of the previous second object, the less likely the monkeys were to choose the current first object. This opposing effect of the current and previous values suggests that when the previous second object had been highly rewarding, the monkeys anticipated the possibility of another valuable second object and were therefore more inclined to reject the current first object.

Neural signatures of option valuation and action selection in the ventral striatum
While monkeys E and A were performing the decision-making task, we recorded single unit activity from 125 neurons in the ventral striatum (monkey E: n = 54 neurons; monkey A: n = 71 neurons) (Supplementary Fig. 2). In this task, option valuation, action selection, and action execution occurred in close succession during the presentation of the first object, providing an opportunity to examine the neural mechanisms underlying each process and the transitions between them. We analyzed neuronal activity during this period.

Although the ventral striatum has attracted attention for its role in signaling reward value information, we found that ventral striatum neurons did not simply represent reward value in our decision-making task. For example, a representative neuron shown in Fig. 2a increased the activity as the value of the first object increased but only when the monkey would decide to choose this object (i.e., only when the monkey decided to release the button to obtain the reward associated with this object), indicating that the activity reflected not only the first object value but also the monkey’s action selection. These neurons seem to represent a decision variable called “chosen value”, which has been reported to be signaled in prefrontal areas27, because they represented the value of the “chosen” first object. The activity of some other recorded neurons reflected the monkey’s action selection but not the first object value. These neurons changed their activity depending on whether the monkey would decide to release the button to choose the first object (see Fig. 2b for a representative neuron). Especially, when the value of the first object was 4, the monkey sometimes chose the object and sometimes did not. In this value condition, the activity changed depending on the action selection regardless of the same value.

As mentioned above, many of the recorded ventral striatum neurons did not simply represent the value. Their activity reflected the monkey’s action selection to varying degrees for each neuron. We further found that their activity changed over time. For example, a representative neuron shown in Fig. 2c increased its activity as the first object value increased, independent of the monkey’s action selection, immediately after the onset of the first object, suggesting that the activity of this neuron simply reflected the value during the early period. However, its activity gradually shifted to reflect not only the first object value but also the monkey’s action selection. Its activity increased as the first object value increased, but only when the monkey decided to release the button to choose the object during the later period.

To statistically characterize the activity of ventral striatum neurons, we conducted a model comparison analysis, in which we fitted the activity of each neuron with two models representing value and choice (Fig. 2d). We did not use a conventional multiple regression approach with value and choice as predictors in this main analysis, because these variables were highly correlated (see Fig. 1b), making it difficult to obtain reliable parameter estimates (called “multicollinearity” problem). The model comparison analysis, which is not affected by this issue, has been successfully used in our previous study to characterize neuronal activity in other brain regions28. In this analysis, the value model assumes that neuronal activity reflects the value of the first object, while the choice model assumes that neuronal activity reflects whether the monkey chose to release the button to select the first object. We compared their coefficients of determination (R-square) for each neuron (Fig. 2e). If neurons showed a significantly better fit with the value model than the choice model, their activity was considered to be more largely modulated by the value of the first object (p < 0.05; two-tailed Monte Carlo test) (blue area in Fig. 2e, hereafter called “value-modulated” signal). If neurons showed a significantly better fit with the choice model than the value model, their activity was considered to be more largely modulated by the monkey’s action selection (p < 0.05; two-tailed Monte Carlo test) (red area in Fig. 2e, hereafter called “choice-modulated” signal). If neurons did not show a significantly better fit with either model (p > 0.05; two-tailed Monte Carlo test) but showed significant fits with both models (p < 0.05; two-tailed F test), their activity was considered to exhibit an intermediate modulation between the value and the choice models (cyan area in Fig. 2e, hereafter called “intermediate” signal). To examine the temporal profile of neuronal activity, this model comparison analysis was conducted throughout the presentation of the first object using a 150-ms sliding window with a 1-ms step. Consequently, we identified 42 neurons with the value-modulated signal, 56 neurons with the choice-modulated signal, and 81 neurons with the intermediate signal (Fig. 2f, see also Supplementary Fig. 3a and 3b for individual monkeys). Notably, some of the recorded neurons exhibited two or three types of signals because these neurons represented distinct signals for different periods during the object presentation.

To confirm the validity of this classification, we conducted an additional analysis using “ridge regression”, which can partly mitigate the issue of multicollinearity among predictors. In this model, the firing rate of each neuron was predicted by the value of the first object (value) and whether the monkey released the button to choose the object (choice), with an additional regularization term in the loss function that penalizes large coefficient estimates arising from correlated predictors. This regularization stabilizes parameter estimation and enables a more reliable evaluation of each predictor’s influence. We fitted the ridge model separately to the activity of each neuron classified as value- or choice-modulated based on the model comparison analysis. The activity was measured within the time window during which each neuron exhibited the value- or choice-modulated signal. The neuronal activity classified as value-modulated showed significantly larger regression coefficients for value than did the activity classified as choice-modulated (z = 3.1, p = 2.3 × 10−3; two-tailed Wilcoxon rank-sum test), whereas the neuronal activity classified as choice-modulated showed significantly larger coefficients for choice than did the activity classified as value-modulated (z = 3.2, p = 1.4 × 10−3; two-tailed Wilcoxon rank-sum test) (Supplementary Fig. 4). These results demonstrate that our model comparison approach reliably dissociates the value- and choice-modulated signals, despite their inherent correlation. The consistency of these findings with the ridge regression further supports the robustness of our model comparison approach.

Using another ridge regression, we tested whether the modulation of each neuron could be accounted for by the variation in reaction time (i.e., the latency of button release). Here, the firing rate of each neuron was predicted by the value of the first object (value) and the button-release latency (latency), which were also correlated (Fig. 1c). We excluded the predictor of whether the monkey released the button to choose the first object (choice) from this analysis, because the button-release latency existed only when the monkey released the button (i.e., when the monkey did not release the button, making it impossible to include such trials in the regression analysis). Whereas the proportion of neurons with a significant regression coefficient for value was significantly higher than chance for all calculation time windows (each 150-ms window after the first object onset) except immediately after the onset (p > 0.05; two-tailed Monte Carlo test) (Supplementary Fig. 5a), the proportion of neurons with a significant regression coefficient for latency was very close to chance over time, slightly above for some time windows (p < 0.05; two-tailed Monte Carlo test) but below for others (p > 0.05; two-tailed Monte Carlo test) (Supplementary Fig. 5b). These results suggest that the influence of button-release latency on neural activity, if any, was minimal.

We calculated the latencies of the value-modulated, choice-modulated, and intermediate signals for each neuron (Fig. 2g, see also Supplementary Fig. 3c and d for individual monkeys). The order of these latencies corresponded to the time course of the decision-making process in which the monkey evaluated the first object and then decided whether to release the button to choose it. That is, the value-modulated signal appeared earliest, followed by the intermediate signal (statistical significance of the latency difference between the value-modulated signal and the intermediate signal; z = 2.1, p = 0.03; two-tailed Wilcoxon rank-sum test). Then, the choice-modulated signal appeared (statistical significance of the latency difference between the intermediate signal and the choice-modulated signal; z = 2.9, p = 0.004; two-tailed Wilcoxon rank-sum test). Furthermore, many of the neurons with the choice-modulated signal exhibited an earlier onset of their signal than the onset of the monkey’s action execution (i.e., the onset of button release) (Fig. 2h). These results suggest that the signal dynamics of ventral striatum neurons reproduced the process from option valuation to action selection in value-based decision-making, and that this dynamics occurred within the period during which a decision was being made.

The signal dynamics corresponding to the time course of the decision-making process was observed even in individual neurons. For example, the representative neuron shown in Fig. 2c first represented the value-modulated signal, then the intermediate signal. Here, we calculated the transition probability among the value-modulated, intermediate and choice-modulated signals of the neurons that represented more than two of these signals (Fig. 2i). For the transition between the value-modulated and intermediate signals, the value-to-intermediate and intermediate-to-value transition probabilities were the same (i.e., 50%). However, for the transition between the intermediate and choice-modulated signals and the transition between the value-modulated and choice-modulated signals, the intermediate-to-choice and the value-to-choice transition probabilities were more than 70%. Furthermore, the time at which the transition occurred (i.e., the latency of the later signal) was shorter for the value-to-intermediate transition compared to the intermediate-to-choice and value-to-choice transitions. However, these differences did not reach statistical significance, possibly due to the small sample size (value-to-intermediate: n = 14, mean ± SD = 491 ± 209 ms; intermediate-to-choice: n = 27, mean ± SD = 497 ± 191 ms; value-to-choice: n = 8, mean ± SD = 543 ± 192 ms; value-to-intermediate vs. intermediate-to-choice: z = −0.11, p = 0.91; value-to-intermediate vs. value-to-choice: z = −0.72, p = 0.47; two-tailed Wilcoxon rank-sum test) (Supplementary Fig. 6). These signal transitions correspond to the time course of the decision-making process that computes the value of the first object and then decides whether to choose it based on the value information. The equal transition probability between the value-modulated and intermediate signals suggests that these two states may not form a strictly sequential process but could reflect bidirectional or overlapping computations during value valuation. This transition is therefore likely to be coordinated at the population level rather than within single neurons.

Although the neurons with the choice-modulated signal seem to represent whether the monkey would decide to choose or not to choose the first object, it remains possible that these neurons reflect a simple motor action (i.e., button release) associated with choosing the first object. To test this possibility, we recorded the activity of 56 neurons with the choice-modulated signal while the monkey was performing a simple button-release motor task (Fig. 3a). In this task, the monkey was not required to make decisions and was required just to release the button. We observed that only 7 of these 56 neurons exhibited a significant modulation around the button release onset in the button-release task (p < 0.05; two-tailed Wilcoxon signed-rank test). On average, these 56 neurons exhibited a smaller modulation in the button-release task compared to the decision-making task (see Fig. 3b and c for 33 neurons with positive modulation and Supplementary Fig. 7a, b for 23 neurons with negative modulation). The difference in modulation magnitude between the tasks was statistically significant (neurons with positive modulation: z = 2.46, p = 0.014; neurons with negative modulation: z = 2.10, p = 0.036; two-tailed Wilcoxon signed-rank test) (Fig. 3d and Supplementary Fig. 7c). These results suggest that the choice-modulated signal do not merely reflect the motor action of button release.

To understand the neural mechanisms underlying option valuation, action selection, and their transition, we analyzed neuronal activity encoding the value of the first object and whether the monkey decided to release the button to choose it during the decision-making period (i.e., during the presentation of the first object). However, other neural processes contributing to decision formation might also occur in the ventral striatum during the same period. For example, we observed that not only the value of the first object, but also the value of the second object in the previous trial influenced the monkey’s action selection (Supplementary Fig. 1b), suggesting that valuation or maintenance of the previous second object might also take place in the ventral striatum during decision making. To test this possibility, we conducted another ridge regression analysis in which the firing rate of each neuron was predicted by the value of the previous second object (previous value), as well as the value of the current first object (current value) and whether the monkey chose the first object (choice). As expected from the model comparison analysis, the proportions of neurons with significant regression coefficients for current value and choice increased after the first object onset and became significantly higher than chance (p < 0.05, two-tailed Monte Carlo test) (Supplementary Fig. 8a and b). On the other hand, the proportion of neurons with a significant regression coefficient for previous value was close to the chance level over time, slightly above for some time windows (p < 0.05; two-tailed Monte Carlo test) but below for others (p > 0.05; two-tailed Monte Carlo test) (Supplementary Fig. 8c), suggesting that the ventral striatum may not strongly represent or maintain the value of the previous second object. However, at the behavioral level, the influence of the previous second object value on the monkey’s choices was much weaker than that of the current first object value. Therefore, it may be difficult to detect neuronal activity corresponding to such a weak behavioral effect of the previous second object value in the ventral striatum.

So far, we have focused on the neuronal modulation evoked by the first object and identified ventral striatum neurons with value-modulated, intermediate, and choice-modulated signals. How do these neurons with different types of signals respond to the second object? We did not observe clear differences in their modulations evoked by the second object across the three neuron groups (Supplementary Fig. 9). When the monkey did not choose the first object, i.e., when the second object was available and predicted the reward outcome, neuronal activity in all three groups reflected the value of the second object (Supplementary Fig. 9a–f). The magnitude of this modulation, measured as the regression coefficient between neuronal activity and the second object value, did not differ significantly across the three groups (neurons with positive modulation: p = 0.41, F = 0.90; neurons with negative modulation: p = 0.41, F = 0.89; one-way ANOVA) (Supplementary Fig. 9g and h). In contrast to the clear modulation evoked by the available second object, when the monkey chose the first object, i.e., when the second object was unavailable and no longer predicted reward, neurons of all groups showed little modulation (Supplementary Fig. 9a–h). Together, these results suggest that ventral striatum neurons flexibly change their roles depending on context, such as free-choice situations (when the monkey decided whether to choose the first object) versus forced-choice situations (when the monkey had no alternative but to obtain the reward associated with the second object).

Effect of electrical stimulation of the ventral striatum on monkey’s action selection
We next examined the causal contribution of neuronal activity in the ventral striatum to the monkey’s action selection using monkeys E and A. We electrically stimulated the ventral striatum during the presentation of the first object, coinciding with the period when the monkey was making a decision (Fig. 4a and b). During this period, we found that ventral striatum neurons exhibited value-modulated, intermediate, and choice-modulated signals, which dynamically evolved as the decision process progressed from option valuation to action selection. The stimulation was applied in half of the trials, while the remaining trials served as controls without stimulation. We conducted the electrical stimulation experiment at 56 sites of the ventral striatum (monkey E: n = 20 sites; monkey A: n = 36 sites) and observed that the electrical stimulation influenced the monkey’s action selection. At some stimulation sites, the monkey was more likely to release the button to choose the first object in stimulation trials compared to non-stimulation trials (Fig. 4c for a representative stimulation site). At other sites, the monkey was less likely to release the button for the choice in stimulation trials (Fig. 4d for a representative stimulation site). Notably, these positive and negative effects were particularly evident in trials in which the first object had a medium value (i.e., value 4). Under this value condition, the monkey’s decision fluctuated, sometimes selecting the first object and sometimes not. The stimulation sites that positively or negatively influenced the choice rate were distributed throughout the ventral striatum without clustering in any specific region (Supplementary Fig. 10a). However, the negative effect was slightly but significantly stronger in the posterior portion (correlation between Δchoice rate and anterior-posterior position; n = 56 sites, r = 0.328, p = 0.014; two-tailed Pearson’s correlation test) (Supplementary Fig. 10b).

To statistically validate the effect of electrical stimulation on the monkey’s action selection, we calculated the difference in choice rate between stimulation and non-stimulation trials (Δchoice rate) for each object value. In trials in which the first object value was 4, both positive and negative effects were observed, so the difference in choice rate did not significantly differ from zero as population (z = 0.90, p = 0.37; two-tailed Wilcoxon signed-rank test) (Fig. 4e). We confirmed that the direction of these positive and negative effects was consistent across the early and late halves of the trials for each stimulation site (Supplementary Fig. 11), suggesting that these effects were not due to random trial-by-trial fluctuations in the monkey’s action selection. We calculated the absolute difference in choice rate, which reflects the net effect of electrical stimulation on the monkey’s action selection (even if positive and negative effects are mixed), and observed that this absolute difference was significantly larger when the first object value was 4 compared with when the value was other than 4 (n = 56 sites, df = 335, F = 74.9, p = 3.0 × 10−52; one-way ANOVA and post-hoc test) (Fig. 4f, see also Supplementary Fig. 12a and b for individual monkeys). Furthermore, the distribution width of the choice rate difference, which also reflects the net effect, was significantly larger than the chance-level distribution width calculated by shuffling stimulation and non-stimulation trials when the first object value was 4 (p = 0.006; two-tailed Monte Carlo test) (Fig. 4g, see also Supplementary Fig. 12c and d for individual monkeys). On the other hand, when the first object value was other than 4, the distribution width was not significantly different from the chance-level distribution width (p > 0.05; two-tailed Monte Carlo test). These results suggest that electrical stimulation of the ventral striatum influenced the monkey’s action selection specifically when the monkey was uncertain about choosing the medium-value option.

Although we observed that electrical stimulation of the ventral striatum influenced the choice rate of the first object when its value was 4, it remains possible that this effect could be attributed to the impact of the electrical stimulation on motor action rather than decision-making. The monkey’s decisions were indicated by either releasing the button or keeping the button pressed down, so it is conceivable that the stimulation influenced the hand movements, which in turn altered the choice rate. To test this possibility, we conducted an electrical stimulation experiment using the simple button-release motor task, in which the monkey was required only to release the button and did not need to make decisions. Electrical stimulation with the same parameters (including duration, current and frequency) was applied to the ventral striatum from 1000 ms to 0 ms before the go signal in half of the trials, while no stimulation was delivered in the other half (Supplementary Fig. 13a). We calculated the difference in button-release latency between stimulation and non-stimulation trials to quantify the effect of electrical stimulation on the motor action. The latency difference was not significantly different from zero as population (z = −0.45, p = 0.65; two-tailed Wilcoxon signed-rank test) (Supplementary Fig. 13b). Notably, there was no significant correlation between the difference in button-release latency and the difference in choice rate for the first object with value 4 (n = 56 sites, r = −0.13, p = 0.34; two-tailed Pearson’s correlation test) (Supplementary Fig. 13c), indicating that the effect of electrical stimulation on the motor action (if any) and the effect on the monkey’s action selection were independent. These results suggest that the effect of electrical stimulation of the ventral striatum on the monkey’s action selection cannot be explained by its impact on the motor action. Rather, neuronal activity in the ventral striatum appears to play a causal role in value-based decision-making.

Even in the decision-making task, electrical stimulation did not clearly affect button-release latency (Supplementary Fig. 14a). Although the latency difference between stimulation and non-stimulation trials reached marginal significance at the population level (n = 56 sites, z = −2.45, p = 0.014; two-tailed Wilcoxon signed-rank test), the difference was small, and only one of the 56 stimulation sites showed a significant effect (p < 0.05; two-tailed Wilcoxon rank-sum test). Moreover, there was no significant correlation between the button-release latency difference in the decision-making task and the choice-rate difference for the first object with value 4 (n = 56 sites, r = 0.08, p = 0.54; two-tailed Pearson’s correlation test) (Supplementary Fig. 14b). These results suggest that electrical stimulation of the ventral striatum might weakly influence the latency to reach a decision, but its impact (if any) is minimal and independent of which action the monkey selects.

Effect of optogenetic facilitation of dopamine input to the ventral striatum on monkey’s action selection
One of the main sources of input to the ventral striatum originates from midbrain dopamine neurons29,30. Within the framework of reinforcement learning in the brain, these neurons are thought to encode RPEs9–13 and contribute to updating value representations in the ventral striatum14. Such reinforcement effects typically occur when animals obtain a reward (i.e., after a decision has been made) and shape future decision-making. However, our recent findings show that dopamine neurons are also activated at the time of option presentation (i.e., prior to decision-making) and encode multiple decision-related variables, including the value of the option and the monkey’s action selection28. The ventral striatum likely receives these dopamine signals while a decision is being made. To test whether these dopamine signals influence decision-related processing in the ventral striatum, we conducted optogenetics experiments in which dopamine projections to the ventral striatum were selectively stimulated (Fig. 5a). To deliver a red-light-sensitive channelrhodopsin gene (ChRmine) into dopamine neurons, we injected an adeno-associated virus vector (AAV2.1-hTHp2.6S-ChRmine-HA) into the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) of monkeys E and M. We subsequently confirmed that dopamine neurons near the injection sites were activated when these neurons were stimulated with a 635-nm red laser light via optrode (an optic fiber coupled with a recording electrode) (Fig. 5c). At the end of the optogenetics experiments, we histologically verified the expression of the ChRmine-HA chimeric protein in dopamine neuron cell bodies (Fig. 5d) and dopamine projections in the ventral striatum (Fig. 5e).

In the optogenetics experiments, a 635-nm red laser light was delivered into the ventral striatum to stimulate dopamine projections during the presentation of the first object, coinciding with the period when the monkey was making a decision (Fig. 5b). Laser light stimulation was applied in half of the trials, while the remaining trials served as controls without stimulation. This stimulation schedule was the same as that of the electrical stimulation experiment. We conducted the optogenetics experiment at 50 sites within the ventral striatum (monkey E: n = 32 sites; monkey M: n = 18 sites) and observed that the laser light stimulation influenced the monkey’s action selection. The laser light stimulation of dopamine projections in the ventral striatum evoked effects similar to those of electrical stimulation of the ventral striatum. At some stimulation sites, the monkey was more likely to release the button to choose the first object in stimulation trials compared to non-stimulation trials (Fig. 5f for a representative stimulation site). At other sites, the monkey was less likely to release the button for the choice in stimulation trials (Fig. 5g for a representative stimulation site). These positive and negative effects were particularly evident in trials in which the first object value was 4. The stimulation sites that positively or negatively influenced the choice rate were distributed throughout the ventral striatum and did not cluster in any specific region (Supplementary Fig. 15a).

We performed the same analyses to statistically validate the effect of laser light stimulation on the monkey’s action selection as we did for electrical stimulation. In trials in which the object value was 4, the difference in choice rate between stimulation and non-stimulation trials (Δchoice rate) did not significantly differ from zero as population because the stimulation induced both positive and negative effects across stimulation sites (z = 0.35, p = 0.72; two-tailed Wilcoxon signed-rank test) (Fig. 5h). We confirmed that the direction of these positive and negative effects was consistent across the early and late halves of the trials for each stimulation site (Supplementary Fig. 16), suggesting that these effects were not due to random trial-by-trial fluctuations in the monkey’s action selection. The absolute difference in choice rate, which reflects the net effect of laser light stimulation, was significantly larger when the first object value was 4 compared with when the value was other than 4 (n = 50 sites, df = 299, F = 51.0, p = 6.2 × 10−38; one-way ANOVA and post-hoc test) (Fig. 5i, see also Supplementary Fig. 17a and b for individual monkeys). Furthermore, the distribution width of the choice rate difference, which also reflects the net effect, was significantly larger than the chance-level distribution width calculated by shuffling stimulation and non-stimulation trials when the first object value was 4 (p = 0.002; two-tailed Monte Carlo test) (Fig. 5j, see also Supplementary Fig. 17c and d for individual monkeys). On the other hand, when the first object value was other than 4, the distribution width was not significantly different from the chance-level distribution width (p > 0.05; two-tailed Monte Carlo test). Thus, as observed in the electrical stimulation experiments, laser light stimulation of dopamine projections in the ventral striatum influenced the monkey’s action selection specifically when the monkey was uncertain about choosing the medium-value option.

We confirmed that the effect of laser light stimulation could not be attributed to its impact on motor action, as we demonstrated in the electrical stimulation experiment by the simple button-release motor task. A laser light stimulation with the same parameters was applied to stimulated dopamine projections in the ventral striatum from 1000 ms to 0 ms before the go signal in half of the trials, while no stimulation was delivered in the other half (Supplementary Fig. 18a). The difference in button-release latency between stimulation and non-stimulation trials was not significantly different from zero as population (z = 0.56, p = 0.57; two-tailed Wilcoxon signed-rank test) (Supplementary Fig. 18b). Please note that there was no significant correlation between the difference in button-release latency and the difference in choice rate of the first object with value of 4, although the weak negative correlation was observed (n = 50 sites, r = −0.23, p = 0.11; two-tailed Pearson’s correlation test) (Supplementary Fig. 18c). This subtle trend indicates only a limited association, if any, between the effect of laser light stimulation on the motor action and the effect on the monkey’s action selection. These results therefore suggest that the effect of laser light stimulation on dopamine projections on the monkey’s action selection is unlikely to be fully explained by its impact on motor action.

As in the button-release task, laser light stimulation did not affect button-release latency in the decision-making task (Supplementary Fig. 19a). The latency difference between stimulation and non-stimulation trials was not significant (n = 50 sites, z = −0.56, p = 0.57; two-tailed Wilcoxon signed-rank test), and no stimulation sites showed a significant effect (p < 0.05; two-tailed Wilcoxon rank-sum test). Moreover, there was no significant correlation between the button-release latency difference and the choice-rate difference for the first object with value 4 (n = 50 sites, r = −0.03, p = 0.86; two-tailed Pearson’s correlation test) (Supplementary Fig. 19b). These results suggest that laser light stimulation of dopamine projections does not influence the latency to reach a decision, regardless of its effect on the monkey’s action selection.

Methods
Animals
Three adult macaque monkeys were used in this study (monkey E: Macaca mulatta, male, 11.7kg, 15 years old; monkey A: Macaca mulatta, male, 8.4kg, 11 years old; monkey M: Macaca fuscata, female, 6.3kg, 6 years old). Single-unit recording and electrical stimulation experiments were conducted in monkeys E and A. Optogenetics experiments were conducted in monkeys E and M. All animal care and experimental procedures were approved by the Animal Experiment Committee and Genetic Modification Experiment Safety Committee at the University of Tsukuba (permission number, 17-167).

Behavioral Tasks
Behavioral tasks and data collection were conducted using the TEMPO system (Reflective Computing, WA, USA). The monkeys were seated in a primate chair, facing a computer monitor in a sound-attenuated and electrically shielded room. Eye movements were tracked with an infrared eye-tracking system (Eyelink, SR Research, Ontario, Canada) sampling at 500 Hz.

The monkeys were trained to perform a value-based decision-making task (Fig. 1a). Six visual objects were associated with different amounts of a liquid reward (water; 0.12, 0.18, 0.24, 0.30, 0.36, and 0.42 ml). For monkey E and monkey M, the visual objects were monochrome fractal images (5.2° in width and height), whereas for monkey A, they were bar stimuli (5.3° wide, 2.3° high) comprising green and magenta areas, with the color proportions indicating the reward size. Each trial began with a central fixation point (0.5° diameter) on the monitor, and the monkey was required to fixate on the point and press a button with its contralateral hand to the recording/stimulation hemisphere. Once the monkey maintained fixation and held the button for 750 ms, the fixation point disappeared, and one of the six visual objects was randomly displayed as the “first object” for 1000 ms at the center of the monitor. The monkey then had to decide whether to choose this first object by either releasing the button or keeping it pressed down. Releasing the button indicated the decision to choose the object, while keeping the button pressed down indicated the decision not to choose it. When the monkey released the button, a red open rectangle (6.3° width and height) appeared around the first object as feedback. The first object and red rectangle disappeared 1000 ms after the onset of the first object. Then, after a 400-ms fixation period, one of the six visual objects was displayed as the “second object” for 1000 ms. If the monkey had chosen the first object, it received the reward associated with it after the second object presentation. If the monkey had not chosen the first object, it could receive the reward associated with the second object by releasing the button during its presentation. When the monkey released the button, the red open rectangle appeared around the second object as feedback. Both the first and second objects were pseudo-randomly selected from the six visual objects with equal probability and independently of each other, so that the same object could appear as both the first and second objects on a given trial. Correct behavior was indicated by a 1-kHz tone and followed by the reward delivery. Trials were aborted under the following conditions: failure to start fixation or press the button within 4000 ms, breaking fixation, releasing the button during inappropriate periods, pressing the button twice, or failing to release the button during a trial. Errors were signaled by a 100-Hz beep tone. The intertrial interval (ITI) was randomly set between 2000 and 3000 ms. The monkeys were overtrained to perform this decision-making task until their action selection became stable across daily sessions, ensuring that the associations between each visual object and its corresponding reward outcome were well learned. Therefore, no further learning was expected to occur during the recording or stimulation sessions.

The monkeys also performed a simple button-release motor task in which the monkey was not required to make decisions and was required just to release the button (Fig. 3a). Each trial began with a central fixation point (0.5° diameter) on the monitor, and the monkey was required to fixate on the point and press a button with its contralateral hand to the recording/stimulation hemisphere. After a 1000-ms fixation and button-press period, the fixation point color changed from white to gray. This color change was a go signal and the monkey was required to release the button within 2000 ms after the onset of the go signal. Correct behavior was indicated by a 1-kHz tone and followed by the reward delivery. The reward amount in the control task was set to be equivalent to value 3 or 4 in the decision-making task, which approximately matched the expected reward magnitude in the decision-making task (3.5). Trials were aborted under the following conditions: failure to start fixation or press the button within 4000 ms, breaking fixation or releasing the button during the 1000-ms fixation and button press period, or failure to release the button within 2000 ms after the go signal onset. Errors were signaled by a 100-Hz beep tone. The ITI was randomly set between 2500 and 3500 ms.

Single-unit recording
A plastic head holder and three recording chambers were securely fixed to the skull under general anesthesia and sterile surgical conditions. Two of the chambers were placed over the frontoparietal lobes on both hemispheres, tilted laterally by 35°, and targeted to the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA). The third chamber was positioned over the midline of the frontal lobes, aimed at the ventral striatum in both hemispheres. The head holder and recording chambers were embedded in dental acrylic, which covered the top of the skull and was firmly anchored using plastic screws. After implanting the head holder and recording chambers, the monkeys underwent a magnetic resonance image (MRI) scan to determine the position of therecording electrode.

Single-unit recordings were carried out using tungsten electrodes with a single channel (impedance: 0.7 to 2.5 MΩ) (FHC, Bowdoin, USA), which were guided into the brain through a stainless-steel guide tube by an oil-driven micromanipulator (MO-97-S, Narishige, Tokyo, Japan). A grid system was used to specify recording sites, enabling recordings at 1-mm intervals between penetration points. For finer neuron mapping, a complementary grid was employed, allowing electrode penetrations between the original grid holes. On the basis of the obtained MRI and using the grid system, we inserted the tungsten electrodes into the ventral striatum (Supplementary Fig. 2). The recording sites ranged from A22 to A28 mm in monkey E and from A23 to A30 mm in monkey A along the anterior-posterior axis.

During each daily experiment, one electrode was inserted into the ventral striatum. Recording of one neuron was counted as one recording session. Therefore, zero to a few recording sessions (i.e., zero to a few neurons) were conducted per daily experiment. In total, 125 recording sessions were performed (corresponding to 125 neurons), and these data were included in the analyses.

Single-unit potentials were amplified and band-pass filtered (100 Hz to 8 kHz) using a multichannel processor (MCP-Plus 8, Alpha Omega, Nazareth, Israel). Action potentials were isolated in real-time using a voltage-time window discrimination system (ASD, Alpha Omega, Nazareth, Israel), and the timing of each action potential was recorded with a resolution of 1 ms.

Electrical stimulation
Electrical stimulation was generated by a stimulator (STG4000, Multi Channel Systems, Reutlingen, Germany) and applied to the ventral striatum using the same tungsten electrodes that were used for single-unit recordings. The stimulation protocol consisted of 200-µs pulses delivered at a frequency of 333 Hz. Each pulse was biphasic, with a current magnitude of 100 µA. In the value-based decision-making task, electrical stimulation was administered throughout the presentation of the first object, lasting 1000 ms. In the button-release task, stimulation was delivered for 1000 ms following the onset of the go signal. These stimulations were applied in half of the trials, while the remaining trials served as controls. The stimulation sites ranged from A23 to A25 mm in monkey E and from A24 to A28 mm in monkey A along the anterior-posterior axis, which overlapped with the electrophysiological recording sites.

Viral vector injection
To deliver the red-light-sensitive channelrhodopsin gene (ChRmine) into dopamine neurons, we used an AAV2.1-hTHp2.6S-ChRmine-HA vector (1.0 × 10¹³ genome copies per ml) that was produced via the helper-free triple transfection method and purified by affinity chromatography (GE Healthcare). AAV2.1 is a viral vector developed by a research group that includes authors of this study57. It efficiently infects neurons in macaque monkeys and enables high-level expression of target genes in these cells. To determine the viral vector injection sites, we first identified the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) using MRI and mapped dopamine neuron locations within these structures through single-unit recordings. Putative dopamine neurons were identified based on established electrophysiological characteristics: a low baseline firing rate (~5 Hz), a broad spike potential that stood out from nearby neurons with high background firing rates in the substantia nigra pars reticulata, and a phasic excitation in response to a free reward. After identifying these neurons, the viral vector was injected into the SNc and VTA under general anesthesia using a microsyringe (1701RN, Hamilton, Reno, USA) equipped with a 28-gauge needle (28G/75mm/PT2, Hamilton, Reno, USA). Six injections were made in each hemisphere of the SNc and VTA, with injection sites spaced at least 1 mm apart. For each injection site, we introduced 0.7 to 2.0 µl of the vector at one to three different depths at a rate of 0.2 µl per minute.

Laser light stimulation
To stimulate dopamine projections in the ventral striatum with laser light, we used an optrode (OR-250/125, BioResearch Center, Nagoya, Japan), consisting of a 250-μm-diameter optical fiber attached to a tungsten recording electrode (impedance: 0.5–2.0 MΩ, diameter: 125 μm) that extended 500 μm beyond the fiber tip. This optrode was inserted into the ventral striatum. A red laser light (638 nm wavelength, <210 mW/mm²) was delivered through the optrode into the ventral striatum using a laser light source (LuxX+® 638-100, Omicron Laserage, Rodgau-Dudenhofen, Germany). The stimulation protocol consisted of 10-ms pulses delivered at a frequency of 25 Hz. In the value-based decision-making task, laser light stimulation was administered throughout the presentation of the first object, lasting 1000 ms. In the button-release task, stimulation was delivered for 1000 ms following the onset of the go signal. These stimulations were applied in half of the trials, while the remaining trials served as controls without stimulation. The stimulation sites ranged from A22 to A25 mm in monkey E and from A21.5 to A24.5 mm in monkey M along the anterior-posterior axis, which overlapped with the electrophysiological recording sites.

When the laser was simply turned on, light weakly leaked from the connection between the optrode and the optical fiber, which could potentially affect monkey behavior. To prevent this unintended leakage, we covered the connection with a black plastic tube and wrapped the entire assembly with a black opaque sheet. These precautions ensured that no light leakage was visible in the experimental room.

To verify the activation of dopamine neurons by red laser stimulation, we inserted the optrode into the SNc or VTA and recorded single-unit activity from putative dopamine neurons, identified by their established electrophysiological characteristics, as described above. The red laser light was then applied, and we analyzed the responses of these putative dopamine neurons to laser stimulation.

Data analysis
To assess how the value of the first object influences the monkey’s decision to release the button to choose it, we modeled the choice rate for the first object using the following logistic function:

P = 1 / (1 + exp(−(β0 + β1 × V)))

where P denotes the choice rate of the first object, V represents the value of the first object, and β0 and β1 are coefficients determined by logistic regression.

To evaluate the effects of the second object value in the previous trial on the monkey’s decision to release the button to choose the first object, we modeled the choice rate for the first object using the following logistic function:

P_t = 1 / (1 + exp(−(β0 + β1 × V2_t-1 + β2 × V1_t)))

where P_t denotes the choice rate of the first object in trial t, V2_t-1 represents the value of the second object in trial t-1, V1_t represents the value of the first object in trial t, and β0, β1 and β2 are coefficients determined by logistic regression.

To calculate spike density functions (SDFs), each spike was replaced by a Gaussian kernel with σ = 30 ms.

To statistically characterize the activity of ventral striatum neurons, we fitted each neuron’s activity to both value and choice models (Fig. 2d). The value model is given by:

F = β0 + β1 × V

where F represents the firing rate of each neuron, V denotes the value of the first object, and β0 and β1 are coefficients determined by linear regression. The choice model is represented by:

F = β0 + β1 × C

where C indicates whether the monkey chose the first object (1 for chosen trials, 0 for unchosen trials). To evaluate the fit of each model, we compared the coefficient of determination (R-square) for each neuron. We used a Monte Carlo procedure to determine whether the R-square differed significantly between the two models. For each neuron, we generated a shuffled dataset by randomly reassigning the firing rate of each trial to another trial. We then fitted the baseline firing rate (0–200 ms before the first object onset) of this shuffled dataset to both models and calculated the baseline R-square difference between the models. This baseline R-square difference of the shuffled dataset was compared to the R-square difference of the original dataset during a targeted time window, and this shuffling and comparison process was repeated 1000 times. If the R-squared difference of the original dataset exceeded that of the shuffled dataset in more than 975 out of 1000 repetitions, this indicated that the value model had a significantly higher R-square than the choice model (p < 0.05; two-tailed Monte Carlo test). If the value model also significantly fit the neuron’s activity (p < 0.05; two-tailed F-test), then the neuron’s activity was considered to be more strongly modulated by the value of the first object (blue area in Fig. 2e). Conversely, if the R-squared difference of the original dataset was smaller than that of the shuffled dataset in over 975 repetitions, the choice model was deemed to have a significantly better fit than the value model (p < 0.05; two-tailed Monte Carlo test). If the choice model also significantly fit the neuron’s activity (p < 0.05; two-tailed F-test), the neuron’s activity was considered to be more strongly modulated by the monkey’s choice (red area in Fig. 2e). If there was no significant difference in R-square between the models (p > 0.05; two-tailed Monte Carlo test), but both models still significantly fit the activity (p < 0.05; two-tailed F-test), then the neuron was considered to show an intermediate modulation between the value and the choice models (cyan area in Fig. 2e).

We performed this model comparison for each of the 150-ms sliding windows with a 1-ms step. If a neuron showed a significantly higher R-square for the value model than the choice model (p < 0.05; two-tailed Monte Carlo test) for 40 consecutive windows and simultaneously exhibited a significant fit with the value model across the same 40 windows (p < 0.05; two-tailed F-test), the activity of this period was classified as a value-modulated signal. Conversely, if a neuron showed a significantly higher R-square for the choice model than the value model (p < 0.05; two-tailed Monte Carlo test) for 40 consecutive windows and simultaneously exhibited a significant fit with the choice model across the same 40 windows (p < 0.05; two-tailed F-test), the activity of this period was classified as a choice-modulated signal. If a neuron showed no significant difference in R-square between the models (p > 0.05; two-tailed Monte Carlo test) but both models significantly fit the activity (p < 0.05; two-tailed F-test) across 40 consecutive windows, the activity of this period was classified as an intermediate signal. Some neurons represented two or even three signals because their activity was classified as distinct signals during different time periods.

The model comparison analysis was also used to determine the latencies of value-modulated, intermediate, and choice-modulated signals. These latencies were defined as the start time of the first of the 40 consecutive 150-ms sliding windows with the 1-ms step where each modulation type was identified.

To confirm the validity of the classification based on the model comparison analysis, we applied a ridge regression approach, which can explicitly address multicollinearity between predictors, such as value and choice in this study. The ridge regression model was expressed as:

F = β0 + β1 × V + β2 × C

where F represents the firing rate of each neuron, V denotes the value of the first object, C indicates whether the monkey chose the first object (1 for chosen trials, 0 for unchosen trials). Ridge regression includes a regularization term, λ(β1^2 + β2^2), in the loss function, which penalizes large coefficient estimates that can arise from correlated predictors. This regularization effectively stabilizes parameter estimation and allows for a more reliable assessment of each predictor’s relative influence, even when V and C are correlated. This ridge regression model was fitted separately to the activity of each neuron classified as value- or choice-modulated based on the model comparison analysis. The activity (F) was measured within the time window during which each neuron exhibited the value- or choice-modulated signal.

To test whether the modulation of each neuron could be accounted for by the variation in reaction time (the latency of button release), we used another ridge regression model:

F = β0 + β1 × V + β2 × RT

where F represents the firing rate of each neuron, V denotes the value of the first object, RT indicates the latency of button release to choose the first object. The activity (F) was measured within each 150-ms window after the first object onset. To test whether the proportion of neurons showing a significant regression coefficient was larger than expected by chance, we performed a permutation-based Monte Carlo analysis. For each neuron, we shuffled the firing rates across trials and assigned them to another trial at random to generate surrogate datasets; the same ridge regression analysis was applied to each shuffled dataset. The proportion of neurons with a significant regression coefficient was then computed for each predictor. To address the multiple-comparison problem arising from repeated tests across time windows, we adopted a max-statistic permutation procedure. In each of 1000 permutations, the maximum proportion of significant neurons across all 150-ms windows was obtained and used to construct the null distribution of the chance level. The observed proportion in each window was then compared with this null distribution to determine statistical significance while controlling the family-wise error rate (FWER).

To examine the effect of the second object value in the previous trial, we used another ridge regression model:

F = β0 + β1 × V2_t-1 + β2 × V1_t + β3 × C_t

where F represents the firing rate of each neuron, V2_t-1 denotes the value of the second object in the previous trial (t-1), V1_t represents the value of the first object in the current trial (t), and C_t indicates whether the monkey chose the first object in the current trial (t) (1 for chosen trials, 0 for unchosen trials). The activity (F) was measured within each 150-ms window after the first object onset. To test whether the proportion of neurons showing a significant regression coefficient was larger than expected by chance, we performed the same permutation-based Monte Carlo analysis described above.

To evaluate the effects of electrical stimulation of the ventral striatum and optogenetic facilitation of dopamine projections in the ventral striatum, we calculated the difference in choice rate between stimulation and non-stimulation trials for each object value (Δchoice rate). The statistical significance of Δchoice rate was evaluated using a chi-square test for each stimulation site.

At the population level, we compared the width of the distribution of Δchoice rate, which reflects the net effect of stimulation on the monkey’s action selection (even if positive and negative effects are mixed), with a chance-level distribution width using a Monte Carlo procedure. To determine the chance-level distribution width, we shuffled the stimulation and no-stimulation conditions for each trial at random and created a new dataset for each stimulation site. Using this shuffled dataset, we calculated the Δchoice rate for each stimulation site and measured the distribution width of the Δchoice rate as the chance-level distribution width. We then compared the original distribution width with the chance-level distribution width. This process of shuffling and comparison was repeated 1000 times. If the original distribution width exceeded the chance-level distribution width in more than 975 out of 1000 repetitions, this was taken as evidence that the stimulation had a statistically significant effect on the monkey’s action selection, independent of whether it increased or decreased the choice rate (p < 0.05; two-tailed Monte Carlo test).

Histology
Upon completion of all experiments, monkeys E and M were deeply anesthetized with secobarbital sodium (150 mg/kg) and transcardially perfused with phosphate-buffered saline (PBS), followed by 10% formalin in PBS (pH 7.4). The removed brains were postfixed in the same fresh fixative overnight at 4°C and saturated with 30% sucrose at 4°C. Coronal sections were cut serially at a 50-µm thickness on a freezing microtome and grouped into ten series. Every tenth section was used for individual histological analyses.

To visualize projection fibers of virus-transfected neurons, a series of every tenth sections was subjected to immunohistochemical staining for hemagglutinin (HA) protein, utilized as an optogenetic tag, using the standard avidin-biotin-peroxidase complex method with 3,3’-diaminobenzidine (DAB) as the chromogen. A primary antibody [1:500, mouse anti-HA.11 clone 16B12 monoclonal IgG (901513, BioLegend, San Diego, USA)] and a biotinylated secondary antibody [1:1000, donkey anti-mouse IgG biotin-SP-conjugated antibody (715-065-150, Jackson ImmunoResearch Laboratories, West Grove, USA)] were employed. These sections were mounted onto gelatin-coated glass slides, counterstained with Neutral red, and coverslipped after DAB staining.

To test whether virus-transfected neurons are dopaminergic, another series of every tenth section was subject to immunofluorescence staining to examine the co-expression of HA protein and Tyrosine Hydroxylase (TH), a marker for dopamine neurons. Primary antibodies [1:300, mouse anti-HA.11 clone 16B12 monoclonal IgG (901513, BioLegend, San Diego, USA); 1:1000, rabbit anti-TH polyclonal IgG (AB-152, Chemicon, Temecula, USA); 1:3000, guinea pig anti-NeuN polyclonal IgG (ABN90, Millipore, St. Louis, USA)] and secondary antibodies [1:400, donkey anti-mouse IgG conjugated with Alexa Fluor 488 (A21202, Jackson ImmunoResearch Laboratories, West Grove, USA); 1:400, donkey anti-rabbit IgG conjugated with Alexa Fluor 555 (A31572, Invitrogen, Waltham, USA); 1:200, donkey anti-guinea pig IgG conjugated with Alexa Fluor 647 (706-605-148, Jackson ImmunoResearch Laboratories, West Grove, USA)] were employed. Following staining, these sections were mounted onto gelatin-coated glass slides. Fluorescent images were acquired using a fluorescence microscope (BZ-X700, Keyence, Osaka, Japan).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript reports activity patterns of ventral striatum neurons recorded while monkeys performed a two-step reward-guided decision task. Neurons encoded values of choice options, action selection and their combination. Electrical stimulation in the ventral striatum, directly and indirectly via optogentic dopamine stimulation, biased behavioral action selection. The authors suggest these data indicate a broader role of ventral striatum in reinforcement learning that extends to valuation and action selection.\nThis is an interesting study that can make an important contribution to the literature and advance our understanding of the little explored primate ventral striatum. The experiments are conducted at high quality and the data seem robust, although the number of reported neurons (N = 125) is relatively low. The stimulation experiments are impressive and together with the motor-preparation controls add important insights. Overall, I am very supportive of publication. However, there are a few issues that should be addressed in a revision.\nMajor comments\n1. There are some features related to the task design that make it an unusual model of decision-making, and that in my view limit the conclusions that can be made about decision computations. The monkey does not have to wait for the second stimulus to engage in decision-making but can already make and execute a decision at the first stimulus. This process likely involves comparing the first stimulus value against the expected value of the second option, which is the mean value of all remaining stimuli. Thus, at the time of the first stimulus, one would expect neurons to code the value of the first option, the prospective value of the second option, perhaps a correlate of the individual monkey’s risk attitude (preference or aversion toward the more uncertain second stimulus), and this might transition into a choice signal. Importantly, however, the choice signal would necessarily be referenced to the button push, i.e., an action-referenced choice signal, rather than a stimulus-specific choice signal. The task therefore allows identification of option values and action choices, but not of stimulus choices, as it conflates stimulus choice with the motor response. It also does not temporally separate the internal processes of value comparison and decision-making from the process of action selection—this would require additional task periods.\nThe abstract and manuscript should be rephrased to reflect this ambiguity. For example:\nAbstract, line 23: “deciding which option to choose” should be reworded to clarify that what is investigated is action selection.\nAbstract, line 27: “reflect the monkey’s choice”, and “derives the choice”; these expressions should be amended to clarify that the choice that is demonstrated in the study is whether or not to perform a specific action (as this cannot be dissociated from stimulus choice in the present task design).\nDiscussion, line 406: “encoded the value of the option (i.e., the value of the first object)”; this should be rephrased to reflect the task design, which cannot distinguish object value coding from action value coding\n2. The authors should also report the results of analysing the second-option period, as this will help clarify the specific information encoded by striatal neurons. For example, action value neurons would code the action of the button press both at first and second stimulus, whereas option coding neurons.\n3. I was surprised that two pioneering studies on action value coding in primate striatum were not cited and discussed (Samejima et al., 2005; Lau and Glimcher, 2008).\n4. As some striatal neurons showed mixtures of value and choice coding, it would be more appropriate to conduct a multiple regression model that includes both variables and thus allow them to compete directly to explain variance in neuronal activity.\n5. It was unclear from the methods description how the critical p-value for the F-test of model fit in the sliding window analysis was determined. This should likely be based on a similar permutation method as the comparison of R-values but I could not find this information. Did the authors impose a criterion of a critical number of significant F-tests in consecutive sliding windows?\n6. The authors’ interpretation that the ventral striatum might directly derive choice from value should be discussed in light of findings that neurons in the orbitofrontal cortex (Padoa-Schioppa and Assad, 2006; Ballesta and Padoa-Schioppa, 2019) and amygdala (Grabenhorst et al., 2012; Grabenhorst et al., 2023) encode subjective values for stimuli, chosen values, stimulus choices, but not necessarily chosen actions (this includes data from sequential decision tasks). Specifically, value-to-choice transitions as reported here have previously been reported in primate amygdala (Grabenhorst et al., 2012; Grabenhorst et al., 2023). Both OFC and amygdala provide direct inputs to ventral striatum. Thus, it is possible the presently observed signals could be inherited from these structures. Alternatively, if the authors can demonstrate that ventral striatal neurons code the value and choice for action (i.e., the same neurons signalling choice at first and second stimulus), irrespective of stimulus identity, this might add an interesting new aspect to our understanding of value and choice coding in this network\n7. The behavioral regression in its current form is perhaps not providing a full picture of the monkeys’ decision process: in addition to the value of the first option, the expected value of the second option should also be taken into account. Similarly, the monkeys’ decision-making might be influenced by individual risk attitudes. It would be helpful to model these influences on choice in the regression, or, in the case of risk attitude, at least discuss whether this variable might have influenced the monkeys’ choices.\n8. In addition to value-to-choice transition (better labelled value-to-action transition), the authors should examine other indicators of a decision computation: (i) stronger choice signals for easier decisions (unsigned value difference) compared to difficult decisions; (ii) correlates of value comparison, e.g., transient relationship between firing rate and value difference between options.\nRelated to this issue: Abstract, line 32: “modulating decision computations”; decision computation has not been sufficiently demonstrated in the study; unless further evidence for decision computation in the neural data is provided, this should be changed to “bias action selection” or something similar\nMinor comments.\n9. Data from neurons recorded from the primate ventral striatum in decision tasks are rare. It is a shame that the authors did not document these neurons in more detail with raster plots, to show the response pattern and recording quality, as they do for the dopamine neuron in Fig. 5c, and for which the authors have a distinguished track record.\n10. Fig. 3b should include standard error/deviation in addition to the mean response. It would also be helpful to additionally see these data aligned to the onset of the first stimulus, rather than button press, as this is more consistent with prior studies indicating stimulus-based choice signals\n11. Line 149, typo: “stratum”\nReferences\nBallesta S, Padoa-Schioppa C (2019) Economic Decisions through Circuit Inhibition. Current biology : CB 29:3814-3824\ne3815.\nGrabenhorst F, Hernadi I, Schultz W (2012) Prediction of economic choice by primate amygdala neurons. Proc Natl Acad\nSci U S A 109:18950-18955.\nGrabenhorst F, Ponce-Alvarez A, Battaglia-Mayer A, Deco G, Schultz W (2023) A view-based decision mechanism for\nrewards in the primate amygdala. Neuron 111:3871-3884 e3814.\nLau B, Glimcher PW (2008) Value representations in the primate striatum during matching behavior. Neuron 58:451-463.\nPadoa-Schioppa C, Assad JA (2006) Neurons in the orbitofrontal cortex encode economic value. Nature 441:223-226.\nSamejima K, Ueda Y, Doya K, Kimura M (2005) Representation of action-specific reward values in the striatum. Science\n310:1337-1340.", "reviewer_2": "Summary\nIn this study, Neijme and colleagues record and manipulate neural activity in the ventral striatum (VS) of two macaque monkeys to study the region’s role in a sequential value-based choice task. They show that the VS has an important role in the transition from option valuation to action selection signals. Two experimental manipulations using electrical stimulation and optogenetics are used to support this finding with causal evidence.\nThis is an interesting study that examines the role of the ventral striatum in value and choice/action coding using a combination of recording and causal approaches. It involves an impressive set of experiments. It also uses control tasks and is conducted to a high standard overall. I have a few major and minor comments that I believe would strengthen the conclusions of the work.\nMajor\n1. By definition, in real-life, value and choice are correlated, yet the ability to dissociate the two processes is critical for concluding that the VS is responsible for the choice-to-action transformation. How did the authors address this issue? Did they simulate data to show that choice value coding and action coding can be dissociated in their study using their paradigm? It would be good to see evidence of this.\n2. It seems a shame that choice is not modulated in a predictable direction when using optogenetic or electrical stimulation. However, it should be possible to show that the direction was consistent for a given recording site/area. Could you test whether the modulation is consistent across repeat stimulations for a single stimulation site to provide evidence that the choice modulation effects go beyond simply adding noise but show a consistent direction as long as the same location is studied? Related to this, why does it seem as though there an anterior-posterior effect for the electric but not optogenetic experiment? Were the sites comparable in the two experiments or could you discuss why they might differ? Related to this analysis, I was not sure why there are seven sites in figure 4d but in the xyz figure with location there are many more sites? Could you just show the xyz plot for value 4 to match with Fig 4d?\n3. I am not an expert in optogenetics stimulation, but it is commonly discussed that this technique comes with significant challenges in macaque monkeys/larger NHPs. What was done to overcome these limitations here? How can we be sure that the conclusions can be trusted? Relatedly, could the animals (for either stimulation method) possibly tell when the stimulation was on? Or if not, how can you rule out simple confound effects of the stimulation?\n4. In Fig.2i, it would be interesting to have more information of the temporal dynamics. For example, did most of the transitions from value or intermediate neurons to choice neurons happen in the late period? Is it possible to provide more detail on how the three types of neurons switch over time? These dynamics seem to be at the core of what the paper can contribute.\n5. Finally, does the stimulation of VS (with either electrical or optogenetic methods) influence decision reaction times (RTs) and is there any correlation between ∆Release latency in the button-release task and ∆decision RT in the decision task (Figs 4 & 5)?\nMinor\n1. Several studies have demonstrated the role of the VS in cost-benefit decision making, e.g., VS lesions or VS dopamine depletion reduces animals’ likelihood of choosing a high-effort choice option (Hauber & Sommer, 2009; Nicola, 2010; Salamone et al. 1994). Does this relate at all to the ideas presented in this manuscript or if not, why not?\n2. Please specify in the Methods which pairings of objects were used how frequently.\n3. Please plot the data from each monkey separately or in separate lines in the key figures.\n4. While it is brilliant to see that control experiments using simple motor tasks were performed, there is more than one difference between the task of interest and the control task. For example, in the simple motor task, there is no choice, no value processing and expectation, no modulation of outcome size and thus overall less reward on average. It would be good to acknowledge and discuss these differences in the discussion.\n5. In Fig.1, please show the duration for each stage of the task in Fig.1a.\n6. Why did the authors pick ‘release’ as their response modality?", "reviewer_3": "This manuscript addresses an important and timely topic: how the ventral striatum contributes to value-based decision-making. The authors used a combination of single-unit recordings, electrical stimulation, and optogenetics in nonhuman primates. The monkeys performed a sequential choice task between pairs of visual stimuli associated with different amounts of reward, and the authors report that ventral striatum activity transitions from value encoding to action selection. They further show that stimulation of the ventral striatum or its dopaminergic inputs can bias choice behavior. The multi-method approach is a strength, and the effort to dissect decision-making phases is valuable. However, several key conceptual and interpretive issues need to be addressed. In particular, the study is often framed within a reinforcement learning framework, but this is not well supported by the task design or data. Sample sizes of recorded neurons are relatively small, and the observed effects of stimulation, while interesting, are also limited. Some analyses can be improved. I believe some of these concerns can be addressed through reframing and clarification, as well as the introduction of further analysis. Below are specific comments. I hope the authors find them useful.\nMajor points:\n1) Figure 2 shows firing rate differences that may reflect variations in reaction times, as well as other task/behavioral variables, rather than “value.” Have the authors tested for correlations between firing rates and reaction times? In general, reaction times should be considered as a covariate or explanatory variable in the analysis.\nThe author can, for example, build a single regression model to predict the neuronal activity of single neurons over time based on different predictors, including reaction times, reward amount, and choice, and estimate the variation in coefficients for each predictor over time, as well as the fraction of neurons that are significant. This would give better estimate of what task/behavioral variable influence neuronal activity. It would be actually also interesting to include the chosen stimulus identity in the model.\nI think this approach can be helpful for assessing temporal transitions in encoding, which is exactly what the authors claim is happening (from value to choice over time).\n2) I appreciate the ambition of the authors to connect their findings to a broader theoretical framework, but this is not a reinforcement learning task. Monkeys are basically choosing between overlearned stimuli. Thus, there is no learning. I think this is a very important point. The manuscript repeatedly attempts to position the findings within a reinforcement learning framework, but this interpretation is not adequately supported by the data. As a result, several claims appear overextended, which diminishes the overall strength of the study. A more cautious, data-driven interpretation would improve the clarity and impact of the work.\nSome examples from the manuscript:\n- ”Notably, the transitions, encoding from state value to action value and further to action, were often observed within single neurons, rather than across segregated neuronal populations.” The concepts of state value and action value are not applicable in this task.\n- “This finding challenges the classical notion that the ventral striatum primarily encodes value-related signals in the reinforcement learning framework”. It is unclear how the present results challenge the encoding of value-related signals in the VS, especially given the lack of learning.\n- “Instead, our data suggest a broader role of the ventral striatum potentially as a neural substrate not only for value representations, but also for some aspects of policy.” This seems a bit vague. What specific aspects of “policy” are meant here?\n- Also related, the authors often refer to reward prediction errors, which cannot be measured in the present study because animals were likely overtrained, and the reward was not probabilistic.\n3) The effects of ventral striatum and dopaminergic neuron stimulation were absent on over 80% of trials and appeared restricted to conditions of uncertainty. This limited efficacy should be acknowledged and discussed, as it has implications for interpreting the causal role of these signals. I think that the weak effects are likely due to the absence of learning in the task. Therefore, it is unclear to what extent these results can be generalized.\n4) I think the authors should clearly state that the monkeys were overtrained and that there was no learning in the task. This clarification is critical for contextualizing the neural signals and avoiding overinterpretation in terms of reinforcement learning.\nMinor points:\n1) I do not understand the following sentence and the data to support it: “During this period, we observed the signal dynamics of ventral striatum neurons corresponding to the time course of the decision-making process.” (line 260-261)\n2) Please clarify how many electrodes were used per session, how many recording sessions were included in the analysis, and whether ventral striatal activity differed depending on whether recordings were made in the hemisphere ipsilateral or contralateral to the arm used to perform the task.\n3) Caption in Figure 1: “Error bars in (b) and (c) indicate SEM, which are very small and hidden in most cases”. I really cannot see any error bar. Maybe reducing the size of the black circles can help?", "reviewer_4": "Nejime and colleagues investigated the role of the primate ventral striatum in value-based decision-making, specifically examining its function in the transformation of option valuation into action selection. The authors challenge the traditional view that the VS primarily encodes expected value, proposing instead a more active role in the decision process itself.\nThe key findings are:\nVS neurons exhibit dynamic encoding, initially representing the value of an offered option (offer 1) and subsequently shifting their activity to reflect the monkey's upcoming choice (to accept or reject offer 1). This transition often occurs within individual neurons.\nBoth electrical stimulation of the VS and optogenetic facilitation of dopamine input to the VS during the decision period (presentation of offer 1) significantly altered the monkeys' choices, particularly when they were uncertain (i.e., for options of intermediate value). These manipulations could bias choices both towards and away from selecting the option, depending on the specific stimulation site.\nControl experiments suggest these effects are not merely due to influences on motor execution.\nThe authors conclude that the VS acts as a crucial neural hub that actively bridges option valuation and action selection, with dopamine playing a critical role in modulating these decision computations in real-time, extending beyond its classical role in reward prediction error signaling.\nThe idea that the VS itself might be involved in deriving the choice, rather than just passing value information to other regions, is particularly intriguing.The study employs a sophisticated combination of state-of-the-art techniques in non-human primates. Optogenetics in primates is still challenging, and its successful application here to dissect the role of dopamine is commendable.\nWhile this study addresses an important question in decision neuroscience and employs sophisticated methodologies, I have substantial concerns outlined as follows.\nMajor comments：\n1. The authors state that the task temporally separates option evaluation, decision commitment, and motor execution. However, during the presentation of the first object (offer 1), the animals must evaluate offer 1, make an accept/reject decision, and execute a motor response (hold or release the button). This conflation of evaluation, decision, and motor execution for offer 1 should be acknowledged more clearly. The primary temporal separation is between the decision on offer 1 and the revelation/receipt of offer\n2. The manuscript would benefit from a more explicit model of the cognitive operations underlying the monkey's choice. The decision to accept/reject offer 1 is presumably based on a comparison of its value, V(offer 1), against an expected value of offer 2, E[V(offer 2)], which is learned from past experience. The paper does not detail how E[V(offer 2)] is operationalized or assumed to be updated. This is crucial because VS activity might reflect components of this expectation.\n3. To better understand how E[V(offer 2)] might be formed and whether it's encoded, the authors could test if V(offer 2) from trial n-1 influences choice behavior for offer 1 in trial n (e.g., by splitting trials based on previous V(offer 2) being high vs. low) and if this variable is encoded in VS activity during the offer 1 period of trial n.\n4. Consequently, during the offer 1 period, VS neurons could be encoding multiple variables beyond just V(offer 1) and the impending choice. These potentially include: E[V(offer 2)] (possibly influenced by V(offer 2) from trial n-1 or a running average), the decision variable itself (e.g., V(offer 1) - E[V(offer 2)]), and motor preparation signals related to holding or releasing the button. This complexity makes the unique attribution of neural activity changes and stimulation effects to a single cognitive component challenging.\n5. The authors use a 150ms sliding window with a 1ms step to classify neural response modulations (value, choice, intermediate). For a typical analysis window (e.g., 500ms or more), this results in a large number of statistical tests per neuron. It is crucial to clarify if and how corrections for multiple comparisons (e.g., Bonferroni or FDR) were applied. Without such corrections, the reported proportions of neuron types could be inflated.\n6. As a robustness check and to complement the dynamic analysis, the authors should consider reporting results from analyses using a fixed, a priori defined time window for value encoding (e.g., early after offer 1 onset) and choice encoding (e.g., later, pre-response). This is common practice and can help validate the sliding window findings.\n7. The text sometimes refers to classifying \"neurons\" (e.g., \"value-modulated neurons\"). It's more precise to state that specific responses or epochs of activity within a neuron's firing pattern are classified, as a single neuron can show different types of modulation over time, which the authors indeed report.\n8. The bidirectional effects (increasing or decreasing choice probability) are reasonably attributed to potential mixed activation of direct and indirect pathway neurons. This remains speculative without cell-type specific manipulations.\n9. The bidirectional effects here are more puzzling. Dopamine typically excites D1-expressing direct pathway neurons and inhibits D2-expressing indirect pathway neurons, both actions generally promoting approach. The manuscript's discussion of differential modulation of distinct VS populations is a starting point, but this warrants deeper exploration or acknowledgment of its unresolved nature. Could it relate to different baseline states, targeted VS subregions, non-canonical dopamine effects at the stimulation parameters used, or interaction with the complex encoding during offer 1 (see point 4)?\n10. Given the multiple cognitive and motor-related processes potentially occurring during offer 1 presentation (as noted in point 4), interpreting precisely which aspect of the decision process is being altered by electrical or optogenetic stimulation is challenging.\n11. The simple button-release task is used to argue that VS choice signals are not purely motor. However, details regarding the reward structure in this control task are missing. If the reward magnitude or expectancy in the control task significantly differed from the decision-making task, it could influence baseline VS activity and the observed modulation strength, potentially confounding the comparison in Fig. 3c. This needs to be clarified.\nMinor Comments：\n1. Plotting trial-by-trial choice behavior for value 1 = 4 under stimulation vs. no-stimulation conditions could reveal finer-grained dynamics, such as adaptation or sequential dependencies in the stimulation effect.\n2. For correlations that are \"close to significance\" (e.g., Fig S6c, p=0.11 for opto-stim effect on choice vs. motor latency), it's advisable to acknowledge the trend and be cautious about definitively concluding \"no relationship,\" especially with modest sample sizes for correlational analyses.\n3. The manuscript should specify the time windows or criteria used to define a neuron's state (value, intermediate, choice) for calculating the transition probabilities shown in Fig 2i.\n4. The 50% transition probability between \"value-modulated\" and \"intermediate\" states (and vice-versa) warrants discussion. What does this bidirectionality imply about the underlying neural process compared to the more directed transitions towards \"choice-modulated\" states?\n5. The discussion acknowledges the role of prefrontal cortex (PFC) in transforming value to action. The findings beg the question of how VS computations are coordinated with PFC inputs. Is the VS transforming value signals received from regions like OFC/vmPFC, or is it part of a more iterative processing loop? A more nuanced discussion of these potential network interactions would enrich the paper.\n6. To further situate the current findings within the field, the authors may wish to consider additional relevant studies on primate ventral striatum (VS) function in reward processing and decision-making. For example, incorporating discussion of works such as:\nhttps://doi.org/10.1523/JNEUROSCI.2430-24.2025\nhttps://doi.org/10.1016/j.neuron.2010.11.041", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to the above comment\nWe are very pleased to hear the supportive comment from the reviewer. Thank you so\nmuch.\nMajor comments\n1. There are some features related to the task design that make it an unusual model of\ndecision-making, and that in my view limit the conclusions that can be made about\ndecision computations. The monkey does not have to wait for the second stimulus to\nengage in decision-making but can already make and execute a decision at the first\nstimulus. This process likely involves comparing the first stimulus value against the\nexpected value of the second option, which is the mean value of all remaining stimuli.\nThus, at the time of the first stimulus, one would expect neurons to code the value of the\nfirst option, the prospective value of the second option, perhaps a correlate of the\nindividual monkey’s risk attitude (preference or aversion toward the more uncertain\nsecond stimulus), and this might transition into a choice signal. Importantly, however,\nthe choice signal would necessarily be referenced to the button push, i.e., an action-\nreferenced choice signal, rather than a stimulus-specific choice signal. The task therefore\n1\nallows identification of option values and action choices, but not of stimulus choices, as\nit conflates stimulus choice with the motor response. It also does not temporally separate\nthe internal processes of value comparison and decision-making from the process of\naction selection—this would require additional task periods. The abstract and\nmanuscript should be rephrased to reflect this ambiguity. For example:\nAbstract, line 23: “deciding which option to choose” should be reworded to clarify that\nwhat is investigated is action selection.\nAbstract, line 27: “reflect the monkey’s choice”, and “derives the choice”; these\nexpressions should be amended to clarify that the choice that is demonstrated in the\nstudy is whether or not to perform a specific action (as this cannot be dissociated from\nstimulus choice in the present task design).\nDiscussion, line 406: “encoded the value of the option (i.e., the value of the first object)”;\nthis should be rephrased to reflect the task design, which cannot distinguish object value\ncoding from action value coding\n> Response to the above comment\nWe agree with the reviewer that our task design imposes limitations and does not\nallow us to distinguish between stimulus choice coding and action choice coding, nor\nbetween object value coding and action value coding. As suggested, we have revised\nthe relevant descriptions throughout the manuscript.\nAbstract, line 23: “deciding which option to choose” should be reworded to clarify that\nwhat is investigated is action selection.\n>The original sentence, “we found that the dopamine–ventral striatum system plays a\nmore proactive role in deciding which option to choose.”, has been revised as follows\n(new line 21-22):\n“we found that the dopamine–ventral striatum system plays a more proactive role in\naction selection.”\nAbstract, line 27: “reflect the monkey’s choice”, and “derives the choice”; these\nexpressions should be amended to clarify that the choice that is demonstrated in the\nstudy is whether or not to perform a specific action (as this cannot be dissociated\nfrom stimulus choice in the present task design).\n>We changed “reflect the monkey’s choice” and “derives the choice from value\ninformation” as follows (new line 26 and 27, respectively):\n2\n“reflect monkey’s action selection”\n“translates the value information into the action”\nDiscussion, line 406: “encoded the value of the option (i.e., the value of the first\nobject)”; this should be rephrased to reflect the task design, which cannot distinguish\nobject value coding from action value coding\n>We revised “encoded the value of the option (i.e., the value of the first object)” as\nfollows (new line 552):\n“reflected the value of the first object”, which we believe is a more neutral description\nof the observed phenomenon.\nIn addition, we have systematically revised the introduction, results, and\ndiscussion to maintain consistency. For example, phrases such as “select option” or\n“choose option” were replaced with “select action”, “decided whether to choose the\noption” was changed to “decided whether to perform a specific action to choose the option”,\nand “choice behavior” was replaced with “action selection”. Numerous other related\nexpressions were similarly adjusted.\nFinally, to clearly acknowledge the limitations of the study, we added the following\nparagraph to the discussion (new line 609-628):\n“It is also important to note that neuroscience studies on decision-making have further\ncategorized value-related signals into distinct subtypes. For example, neurons in the dorsal\nstriatum have been shown to encode “action value,” the value associated with a potential\naction45,46, whereas neurons in other brain regions encode “chosen value,” the value of the\noption ultimately selected27,47. Because the task used in the present study was not\ndesigned to disentangle these subtypes, we cannot determine whether the value-\nmodulated signal observed in the ventral striatum corresponded to action value, chosen\nvalue, or another value. Similarly, although we observed choice-modulated signal in the\nventral striatum, it remains unclear whether this signal reflects the choice of option or the\nchoice of action. Ideally, task designs should enable the construction of a single regression\nmodel incorporating these value- and choice-related variables, as well as other decision\nvariables such as the sensory identity of each option and the motor components of choice,\nto achieve a comprehensive understanding of the neural representations underlying\ndecision-making. Thus, while our findings establish the ventral striatum as a neural hub\nwhere value signals evolve into choice signals, the precise nature of these computations\nremains to be clarified. Future studies using tasks explicitly designed to dissociate these\ndifferent forms of value- and choice-related signals—and other decision variables—will be\n3\nessential for elucidating how the ventral striatum contributes to the full process leading to\naction selection.”\n2. The authors should also report the results of analysing the second-option period, as\nthis will help clarify the specific information encoded by striatal neurons. For example,\naction value neurons would code the action of the button press both at first and second\nstimulus, whereas option coding neurons.\n> Response to the above comment\nWe appreciate the reviewer’s suggestion to analyze neuronal activity during the\nsecond object period. Following this advice, we examined how the three neuron types\n(value-modulated, intermediate, and choice-modulated) identified from the first object\nperiod responded to the second object (new Supplementary Fig. 8). To address the\nreviewer’s example, we compared neuronal activity between trials in which the\nmonkey chose the first object (i.e., did not release the button during the second object\nperiod) (right panels in Supplementary Fig. 8a to c) and trials in which the monkey\nchose the second object (i.e., released the button during the second object period)\n(left panels in the figures). We observed that neurons of all three types exhibited clear\nmodulation when the monkey released the button, but little modulation when the\nbutton was not released.\nThis modulation pattern likely reflects task context rather than the motor act of\nbutton release itself. In trials where the first object was chosen, the second object was\nunavailable and did not predict future reward, which explains the weak modulation\nevoked by the second object. By contrast, when the first object was not chosen, the\nsecond object remained available and predictive of reward, and neurons of all three\ntypes consistently reflected the second object value. If these neurons primarily\nencoded the motor act of button release, their activity should have increased\nindependently of the second object value, which was not the case.\nBecause the value-modulated, intermediate, and choice-modulated neuron types\nwere originally classified based on their responses to the first object, the consistent\nvalue coding across neuron types for the second object suggests that ventral striatum\nneurons flexibly adjust their roles depending on context, for example, free-choice\nsituations (when the monkey decided whether to choose the first object) versus\nforced-choice situations (when the monkey had no alternative but to accept the\nsecond object). We have added these results to the revised manuscript as follows\n4\n(new line 348-366):\n“So far, we have focused on the neuronal modulation evoked by the first object and\nidentified ventral striatum neurons with value-modulated, intermediate, and choice-\nmodulated signals. How do these neurons with different types of signals respond to the\nsecond object? We did not observe clear differences in their modulations evoked by the\nsecond object across the three neuron groups (Supplementary Fig. 8). When the monkey\ndid not choose the first object, i.e., when the second object was available and predicted the\nreward outcome, neuronal activity in all three groups reflected the value of the second\nobject (Supplementary Fig. 8a to 8f). The magnitude of this modulation, measured as the\nregression coefficient between neuronal activity and the second object value, did not differ\nsignificantly across the three groups (neurons with positive modulation: p = 0.41, F = 0.90;\nneurons with negative modulation: p = 0.41, F = 0.89; one-way ANOVA) (Supplementary\nFig. 8g and 8h). In contrast to the clear modulation evoked by the available second object,\nwhen the monkey chose the first object, i.e., when the second object was unavailable and\nno longer predicted reward, neurons of all groups showed little modulation (Supplementary\nFig. 8a to 8h). Together, these results suggest that ventral striatum neurons flexibly change\ntheir roles depending on context, such as free-choice situations (when the monkey decided\nwhether to choose the first object) versus forced-choice situations (when the monkey had\nno alternative but to obtain the reward associated with the second object).”\n3. I was surprised that two pioneering studies on action value coding in primate striatum\nwere not cited and discussed (Samejima et al., 2005; Lau and Glimcher, 2008).\n> Response to the above comment\nThank you very much for pointing out these landmark studies. As suggested, we now\ncite Samejima et al. (2005) and Lau and Glimcher (2008) and incorporate them into the\ndiscussion of action value and option value. Specifically, we added the following\nparagraph, which was included in response to Comment 1 above, to the discussion\n(new line 609-628) (Ref 45 is Samejima et al. (2005), and Ref 46 is Lau and Glimcher\n(2008)):\n“It is also important to note that neuroscience studies on decision-making have further\ncategorized value-related signals into distinct subtypes. For example, neurons in the dorsal\nstriatum have been shown to encode “action value,” the value associated with a potential\naction45,46, whereas neurons in other brain regions encode “chosen value,” the value of the\noption ultimately selected27,47. Because the task used in the present study was not\n5\ndesigned to disentangle these subtypes, we cannot determine whether the value-\nmodulated signal observed in the ventral striatum corresponded to action value, chosen\nvalue, or another value. Similarly, although we observed choice-modulated signal in the\nventral striatum, it remains unclear whether this signal reflects the choice of option or the\nchoice of action. Ideally, task designs should enable the construction of a single regression\nmodel incorporating these value- and choice-related variables, as well as other decision\nvariables such as the sensory identity of each option and the motor components of choice,\nto achieve a comprehensive understanding of the neural representations underlying\ndecision-making. Thus, while our findings establish the ventral striatum as a neural hub\nwhere value signals evolve into choice signals, the precise nature of these computations\nremains to be clarified. Future studies using tasks explicitly designed to dissociate these\ndifferent forms of value- and choice-related signals—and other decision variables—will be\nessential for elucidating how the ventral striatum contributes to the full process leading to\naction selection.”\n4. As some striatal neurons showed mixtures of value and choice coding, it would be more\nappropriate to conduct a multiple regression model that includes both variables and thus\nallow them to compete directly to explain variance in neuronal activity.\n> Response to the above comment\nWe appreciate the reviewer’s constructive suggestion to use a multiple regression\nmodel including both value and choice variables. However, in our dataset, these two\nvariables were highly correlated (see Fig. 1b), making it statistically inappropriate to\ninclude them simultaneously in the same regression model. Under such conditions,\nregression coefficients cannot be reliably estimated, and any apparent variance\npartitioning between the two variables would be unstable and difficult to interpret.\nFor this reason, we primarily analyzed the contributions of value- and choice-\nrelated signals using a model comparison approach, in which the goodness of fit was\ncompared between models representing value and choice. While we agree that it\nwould be ideal to statistically dissociate the two signals within a single model, the\nstrong correlation between these predictors in our task design makes this infeasible.\nWe have clarified this point in the revised manuscript as follows (new line 205-212):\n“To statistically characterize the activity of ventral striatum neurons, we conducted a\nmodel comparison analysis, in which we fitted the activity of each neuron with two models\nrepresenting value and choice (Fig. 2d). We did not use a conventional multiple regression\n6\napproach with value and choice as predictors in this main analysis, because these variables\nwere highly correlated (see Fig. 1b), making it difficult to obtain reliable parameter estimates\n(called “multicollinearity” problem). The model comparison analysis, which is not affected by\nthis issue, has been successfully used in our previous study to characterize neuronal activity\nin other brain regions28.”\nOn the other hand, in order to respond to this comment and other reviewers’\ncomments, we additionally applied a “ridge regression” approach, which can partly,\nbut not perfectly, mitigate the issue of multicollinearity among predictors, to verify the\nresults obtained from the model comparison analysis. This complementary analysis\nyielded consistent results, as described below (new line 235-254):\n“To confirm the validity of this classification, we conducted an additional analysis using\n“ridge regression”, which can partly mitigate the issue of multicollinearity among predictors.\nIn this model, the firing rate of each neuron was predicted by the value of the first object\n(value) and whether the monkey released the button to choose the object (choice), with an\nadditional regularization term in the loss function that penalizes large coefficient estimates\narising from correlated predictors. This regularization stabilizes parameter estimation and\nenables a more reliable valuation of each predictor’s influence. We fitted the ridge model\nseparately to the activity of each neuron classified as value- or choice-modulated based on\nthe model comparison analysis. The activity was measured within the time window during\nwhich each neuron exhibited the value- or choice-modulated signal. The neuronal activity\nclassified as value-modulated showed significantly larger regression coefficients for value\nthan did the activity classified as choice-modulated (z = 3.1, p = 0.002; two-tailed Wilcoxon\nrank-sum test), whereas the neuronal activity classified as choice-modulated showed\nsignificantly larger coefficients for choice than did the activity classified as value-modulated\n(z = 3.2, p = 0.001; two-tailed Wilcoxon rank-sum test) (Supplementary Fig. 4). These\nresults demonstrate that our model comparison approach reliably dissociates the value-\nand choice-modulated signals, despite their inherent correlation. The consistency of these\nfindings with the ridge regression further supports the robustness of our model comparison\napproach.”\nPlease note that ridge regression approach only partly mitigates the issue of\nmulticollinearity among predictors. We therefore used this approach just to verify our\nmain results obtained by the model comparison approach.\n5. It was unclear from the methods description how the critical p-value for the F-test of\nmodel fit in the sliding window analysis was determined. This should likely be based on\n7\na similar permutation method as the comparison of R-values but I could not find this\ninformation. Did the authors impose a criterion of a critical number of significant F-tests\nin consecutive sliding windows?\n> Response to the above comment\nWe thank the reviewer for raising this important point. Using the same sliding window\nprocedure, we directly applied the F-test in each window to determine whether the\nvalue and choice models explained significant variance in neuronal activity.\nRegarding the reviewer’s question about the requirement of a minimum number\nof significant windows in sequence, we required the significance of F-test (p < 0.05) to\nbe maintained for at least 40 consecutive windows to avoid spurious results due to\nnoise fluctuations. This criterion is the same as the criterion for the comparison of R-\nvalues. These methodological details have now been clarified in the revised\nmanuscript as follows (new line 991-1004):\n“We performed this model comparison for each of 150-ms sliding window with a 1-ms\nstep. If a neuron showed a significantly higher R-square for the value model than the\nchoice model (p < 0.05; two-tailed Monte Carlo test) for 40 consecutive windows and\nsimultaneously exhibited a significant fit with the value model across the same 40 windows\n(p < 0.05; two-tailed F-test), the activity of this period was classified as a value-modulated\nsignal. Conversely, if a neuron showed a significantly higher R-square for the choice model\nthan the value model (p < 0.05; two-tailed Monte Carlo test) for 40 consecutive windows\nand simultaneously exhibited a significant fit with the choice model across the same 40\nwindows (p < 0.05; two-tailed F-test), the activity of this period was classified as a choice-\nmodulated signal. If a neuron showed no significant difference in R-square between the two\nmodels but significant fit with both for 40 consecutive windows, the activity during this\nperiod was classified as an intermediate signal. Some neurons represented two or even\nthree signals because their activity was classified as distinct signals during different time\nperiods.”\n6. The authors’ interpretation that the ventral striatum might directly derive choice from\nvalue should be discussed in light of findings that neurons in the orbitofrontal cortex\n(Padoa-Schioppa and Assad, 2006; Ballesta and Padoa-Schioppa, 2019) and amygdala\n(Grabenhorst et al., 2012; Grabenhorst et al., 2023) encode subjective values for stimuli,\nchosen values, stimulus choices, but not necessarily chosen actions (this includes data\nfrom sequential decision tasks). Specifically, value-to-choice transitions as reported here\n8\nhave previously been reported in primate amygdala (Grabenhorst et al., 2012;\nGrabenhorst et al., 2023). Both OFC and amygdala provide direct inputs to ventral\nstriatum. Thus, it is possible the presently observed signals could be inherited from these\nstructures. Alternatively, if the authors can demonstrate that ventral striatal neurons\ncode the value and choice for action (i.e., the same neurons signalling choice at first and\nsecond stimulus), irrespective of stimulus identity, this might add an interesting new\naspect to our understanding of value and choice coding in this network\n> Response to the above comment:\nThank you very much for pointing out the important studies in the orbitofrontal cortex\n(OFC) and amygdala, and for suggesting the new analysis to further clarify the\nprinciples of value and choice coding in the ventral striatum. In the response to major\ncomment 2, which is described above, we conducted the suggested analysis\nexamining how ventral striatum neurons classified during the first object period\nresponded to the second object. We found that value-modulated, intermediate, and\nchoice-modulated neurons all consistently encoded the value of the second object\nwhen it was available and predictive of reward, but showed little modulation when the\nsecond object was unavailable and no longer associated with future reward.\nImportantly, this pattern indicates that ventral striatum neurons flexibly switch\nbetween value and choice coding depending on task context. As mentioned in the\nresponse to major comment 2, we have added these results to the revised manuscript\n(new line 348-366).\nRegarding to the comment, “Both OFC and amygdala provide direct inputs to\nventral striatum. Thus, it is possible the presently observed signals could be inherited\nfrom these structures.”, we totally agree this possibility. We therefore revised a\ndiscussion paragraph to raise this possibility in the revised manuscript, in which the\nsuggested studies are cited, as follows (new line 581-608):\n“The neural dynamics we observed in the ventral striatum emphasizes its role as a\nneural hub that bridges option valuation and action selection. Previous studies have also\nexplored the neural mechanism that bridges option valuation and action selection. These\nstudies mainly dealt with the prefrontal and parietal cortical areas32-34. For example, it has\nbeen shown that option value signals are encoded in the ventromedial prefrontal cortex and\nthen transmitted to the dorsomedial prefrontal cortex and the intraparietal sulcus area, where\nneuronal activity is involved in action selection by comparing the value of each option34. In\ncontrast, we found that neurons in the ventral striatum are involved in both value signaling\nand action selection. Their activity gradually shifted from value representation to action\n9\nselection encoding, suggesting that the ventral striatum may directly derive action selection\nfrom value information. At the same time, it is important to note that neurons in the\norbitofrontal cortex have been reported to encode value- and choice-related variables in\ndecision-making situation27,35, and that similar value-to-choice transitions have been\nobserved in the amygdala36,37. Because both the orbitofrontal cortex and amygdala project\ndirectly to the ventral striatum38,39, the signals we observed in the ventral striatum might in\npart be inherited from these upstream regions. Particularly, however, the relationship\nbetween the ventral striatum and prefrontal regions requires further considerations. Both\nregions participate in cortico-basal ganglia loop circuits, often referred to as the “limbic\nloop”40,41, yet little is known about the nature of the signals exchanged between them.\nComputational studies have suggested that cortico-basal ganglia loop circuits, which\nsometimes include the dopamine system and amygdala, implement more than simple\nfeedforward transmission of signals and highlighted the importance of iterative and recurrent\nprocessing within these circuits42-44. In light of our findings on the dynamic transformation\nfrom value representation to action selection encoding in the ventral striatum, future studies\nshould investigate how such iterative interactions between the ventral striatum and prefrontal\nregions contribute to decision-making, for example, through simultaneous recordings or\ncausal perturbations of their communication.”\n7. The behavioral regression in its current form is perhaps not providing a full picture of\nthe monkeys’ decision process: in addition to the value of the first option, the expected\nvalue of the second option should also be taken into account. Similarly, the monkeys’\ndecision-making might be influenced by individual risk attitudes. It would be helpful to\nmodel these influences on choice in the regression, or, in the case of risk attitude, at least\ndiscuss whether this variable might have influenced the monkeys’ choices.\n> Response to the above comment:\nThank you very much for pointing out the possibility that the expected value of\nthe second object and individual risk attitudes influence the monkey’s action selection.\nWe first examined the effect of the expected value. We presume that the monkey’s\nexpectation of the second object value depends on its recent experience on the\nsecond object. Using a logistic linear regression with the second object value in the\nprevious trial (t-1) and the first object value in the current trial (t), we found that the\nvalue of the previous second object affected the monkey’s action selection in a\nmanner opposite to the value of the current first object. That is, the higher the value\nof the previous second object, the less likely the monkey was to choose the current\nfirst object. This opposing effect of the current and previous values suggests that\nwhen the previous second object had been highly rewarding, the monkeys anticipated\nthe possibility of another valuable second object and were therefore more inclined to\nreject the current first object. We have added these results to the revised manuscript\nas follows (new line 157-169):\n“We also found that the monkey’s decision was influenced by recent experience.\nSpecifically, the value of the second object in the previous trial negatively predicted whether\nthe monkey would choose the first object in the current trial. A logistic regression analysis\nrevealed a weak but significantly negative regression coefficient between the value of the\nprevious second object and the choice rate of the current first object (n = 143 sessions, z =\n-7.2, p = 8.4 × 10-13; two-tailed Wilcoxon signed-rank test), while the regression coefficient for\nthe current first object value was significantly positive (n = 143 sessions, z = 10.4, p = 3.2 ×\n10-25; two-tailed Wilcoxon signed-rank test) (Supplementary Fig. 1b). Thus, the higher the\nvalue of the previous second object, the less likely the monkeys were to choose the current\nfirst object. This opposing effect of the current and previous values suggests that when the\nprevious second object had been highly rewarding, the monkeys anticipated the possibility\nof another valuable second object and were therefore more inclined to reject the current first\nobject.”\nWe next examined whether individual risk attitudes affected choice behavior. In\nprinciple, risk-seeking monkeys may reject the first object more often in anticipation\nof receiving a larger reward from the second object. We therefore quantified risk\nattitude as the first object value that was chosen with 50% probability, with a higher\nvalue indicating greater risk seeking. However, no significant differences in risk\nattitude were observed across the three monkeys. This suggests that the observed\nchoice patterns were not driven by individual differences in risk preference, but rather\nreflected a consistent process of option evaluation and action selection within the task.\nWe have added these results to the revised manuscript as follows (new line 130-140):\n“Because the task design allowed the monkeys to either commit early to the first object\nor wait for the second one, their decisions could, in principle, be influenced by individual risk\nattitudes. For example, risk-seeking monkeys might reject the first object more often in\nanticipation of receiving a larger reward from the second object. We quantified risk attitude\nas the value of the first object that was chosen with 50% probability. A higher value indicated\na stronger risk-seeking tendency. However, no significant differences in risk attitude were\nobserved across the three monkeys (n = 54, 71, and 18 sessions for monkeys E, A and M,\nrespectively, p = 0.07, F = 2.78; one-way ANOVA) (Supplementary Fig. 1a). This indicates\n11\nthat the observed choice patterns were not influenced by individual differences in risk\npreference but were instead a consistent consequence of option valuation and action\nselection within the task.”\n8. In addition to value-to-choice transition (better labelled value-to-action transition), the authors should examine other indicators of a decision computation: (i) stronger choice signals for easier decisions (unsigned value difference) compared to difficult decisions; (ii) correlates of value comparison, e.g., transient relationship between firing rate and value difference between options.\nRelated to this issue: Abstract, line 32: “modulating decision computations”; decision computation has not been sufficiently demonstrated in the study; unless further evidence for decision computation in the neural data is provided, this should be changed to “bias action selection” or something similar.\n> Response to the above comment:\nThank you very much for raising this important point. In response to major comment\n7 described above, we found that the value of the second object in the previous trial,\nwhich could influence the expected value of the second object in the current trial,\naffected the monkeys’ choice of the current first object. Specifically, the higher the\nvalue of the previous second object, the less likely the monkeys were to choose the\ncurrent first object. This finding suggests that when the previous second object had\nbeen highly rewarding, the monkeys anticipated the possibility of another valuable\nsecond object and were therefore more inclined to reject the current first object by\ncomparing this first object with the potential second object.\nTo address the reviewer’s suggestion regarding decision computations, we\nanalyzed trials in which the value of the first object was 4. In these trials, the monkeys\nsometimes chose and sometimes rejected the first object, reflecting difficult decision\nsituations. In contrast, in other value conditions, the monkeys almost always either\nchose or rejected the first object. Thus, only the condition with the first object value\nof 4 allowed us to examine neural correlates of difficult decision as the reviewer\nsuggested.\nWe calculated the magnitude of the choice-modulated signal evoked by the first\nobject with value 4, separately for each second object value in the previous trial (see\nthe figure below). The magnitude was also shown separately for the 33 choice-\nmodulated neurons with positive modulation (activity increased when the monkey\n12\nchose the first object) (a and b in the figure) and the 23 neurons with negative\nmodulation (activity decreased when the monkey chose the first object) (c and d in the\nfigure). The time window to calculate the magnitude (gray shaded area in a and c, 500–\n1000 ms) was determined based on the averaged activity pattern.\nWhen the previous second object value was small (e.g., value = 1), the decision\nabout whether to choose the current first object with value 4 could be easier (unsigned\nvalue difference = 3). In contrast, when the previous second-object value was 4, the\ndecision could be more difficult (unsigned value difference = 0). However, the\nmagnitude of the choice-modulated signal was not significantly influenced by the\ndecision difficulty (i.e., by the previous second object value) for either neurons with\npositive modulation or those with negative modulation (p > 0.05; one-way ANOVA).\nWe also calculated the correlation between the magnitude of neuronal activity and\nthe value difference for these positively and negatively modulated neurons using trials\nin the same situation (i.e., first object chosen trials with its value of 4). We selected\nthese trials because the monkey’s action selection fluctuated (i.e., difficult decisions\nwere included). The magnitude of neuronal activity was calculated using the same\ntime window (500–1000 ms). Of the 33 choice-modulated neurons with positive\nmodulation, only 3 neurons exhibited a significant correlation coefficient between the\nmagnitude of neuronal activity and the value difference (p < 0.05; correlation analysis).\nOf the 23 choice-modulated neurons with negative modulation, only 2 neurons\n13\nexhibited a significant correlation coefficient (p < 0.05; correlation analysis).\nThese results suggest that ventral striatum neurons did not explicitly represent\nthe value difference between the first object in the current trial and the second object\nin the previous trial, which could influence the expected value of the second object in\nthe current trial. Behaviorally, we observed that the value of the previous second\nobject affected the monkey’s choice of the current first object, indicating that the\nmonkeys may perform such value comparison computations to some extent. However,\nthe behavioral effect of the previous second object value was relatively weak\ncompared with the effect of the current first object value, which may explain the\nabsence of a detectable neural correlate under our recording conditions.\nAlthough we do not plan to include these negative results in the main text due to\ntheir limited statistical power and interpretability, we would be willing to include\nthem in the revised manuscript if the reviewer strongly recommends doing so.\nFurthermore, to address the reviewer’s comment regarding the phrase\n“modulating decision computations” in the abstract, we revised the sentence to more\naccurately reflect our findings, as follows (new line 29-32):\n“Our findings reveal a novel function of the ventral striatum as a neural hub that bridges\noption valuation and action selection, and demonstrate the contribution of dopamine in the\nprocess leading to action selection within this region.”\nWe have also revised related sentences throughout the manuscript accordingly.\nMinor comments.\n9. Data from neurons recorded from the primate ventral striatum in decision tasks are rare. It is a shame that the authors did not document these neurons in more detail with raster plots, to show the response pattern and recording quality, as they do for the dopamine neuron in Fig. 5c, and for which the authors have a distinguished track record.\n> Response to the above comment:\nWe fully agree with the reviewer that raster plots demonstrate the quality of our data.\nAccordingly, we have included raster plots in Fig. 2a–2c of the revised manuscript.\n10. Fig. 3b should include standard error/deviation in addition to the mean response. It\nwould also be helpful to additionally see these data aligned to the onset of the first\nstimulus, rather than button press, as this is more consistent with prior studies\n14\nindicating stimulus-based choice signals\n> Response to the above comment:\nAs the reviewer suggested, we have included SEM and SDFs aligned at the first object\nonset (at the go signal onset in the button-release task) in Fig. 3 and Supplementary\nFig. 6 of the revised manuscript.\n11. Line 149, typo: “stratum”\n> Response to the above comment:\nThank you very much for carefully checking our manuscript. We corrected the typo in\nthe revised manuscript.\nSummary\nIn this study, Neijme and colleagues record and manipulate neural activity in the ventral\nstriatum (VS) of two macaque monkeys to study the region’s role in a sequential value-\nbased choice task. They show that the VS has an important role in the transition from\noption valuation to action selection signals. Two experimental manipulations using\nelectrical stimulation and optogenetics are used to support this finding with causal\nevidence.\nThis is an interesting study that examines the role of the ventral striatum in value and\nchoice/action coding using a combination of recording and causal approaches. It involves\nan impressive set of experiments. It also uses control tasks and is conducted to a high\nstandard overall. I have a few major and minor comments that I believe would\nstrengthen the conclusions of the work.\n> Response to the above comment\nWe are very pleased to hear the supportive comment from the reviewer. Thank you so\nmuch.\nMajor\n1. By definition, in real-life, value and choice are correlated, yet the ability to dissociate\nthe two processes is critical for concluding that the VS is responsible for the choice-to-\naction transformation. How did the authors address this issue? Did they simulate data\nto show that choice value coding and action coding can be dissociated in their study using\ntheir paradigm? It would be good to see evidence of this.\n> Response to the above comment\nWe greatly appreciate the reviewer’s insightful comment on this critical issue. In our\ntask, the monkeys were required to decide whether to accept a single option (i.e., the\nfirst object) presented on each trial (Fig. 1a). As expected, the higher the value of the\nfirst object, the more likely the monkey was to accept it (Fig. 1b). Consequently, the\nfirst object value and the monkey’s choice were positively correlated. Such situations\n16\nnaturally arise in real-life decision-making contexts. This correlation, however, poses\nan analytical challenge: when two explanatory variables are highly correlated,\nconventional multiple regression analyses cannot reliably estimate their independent\ncontributions because of “multicollinearity”.\nTo address this issue in our original manuscript, we avoided using a standard\nmultiple regression including both value and choice as predictors in the main analysis.\nInstead, we employed a model comparison approach, in which each neuron’s activity\nwas separately fitted with two competing models, a value model and a choice model\n(see the figure below and Fig. 2d in our manuscript), and their explanatory power was\nstatistically compared. This approach allowed us to identify whether neuronal activity\nwas better explained by the value of the first object or by the monkey’s choice, without\nassuming independence between the two predictors. Using this procedure, we\nclassified the activity of each recorded neuron into three types: value-modulated,\nchoice-modulated, and intermediate signals\nAs the reviewer noted, this model comparison procedure is original, and therefore\nits robustness should be validated. To this end, we conducted an additional analysis\nusing “ridge regression”, which can partly address multicollinearity between\npredictors. The ridge regression model was expressed as:\nF = β0 + β1 × V + β2 × C\nwhere F represents the firing rate of each neuron, V denotes the value of the first\nobject, C indicates whether the monkey chose the first object (1 for chosen trials, 0\nfor unchosen trials). Ridge regression includes a regularization term, λ(β12 + β22), in\nthe loss function, which penalizes large coefficient estimates that can arise from\ncorrelated predictors. This regularization effectively stabilizes parameter estimation\nand allows for a more reliable assessment of each predictor’s relative influence, even\n17\nwhen V and C are correlated.\nWe fitted the ridge model separately to the activity of each neuron classified as\nvalue- or choice-modulated based on the model comparison analysis. The activity was\nmeasured within the time window during which each neuron exhibited the value- or\nchoice-modulated signal. The neuronal activity classified as value-modulated showed\nsignificantly larger regression coefficients for value than did the activity classified as\nchoice-modulated (z = 3.1, p = 0.002; two-tailed Wilcoxon rank-sum test), whereas the\nneuronal activity classified as choice-modulated showed significantly larger\ncoefficients for choice than did the activity classified as value-modulated (z = 3.2, p =\n0.001; two-tailed Wilcoxon rank-sum test) (see Supplementary Fig. 4).\nThese results confirm that our model comparison approach reliably distinguishes\nneuronal signals modulated by the value of the first object and those modulated by\nthe monkey’s choice, despite the inherent correlation between the two variables. The\nconsistency of results obtained using ridge regression further supports the validity\nand robustness of our analytical framework for dissociating value- and choice-\nmodulated signals in ventral striatum neurons. We have included this new analysis\nand its results in the revised manuscript as follows (new line 235-254):\n“To confirm the validity of this classification, we conducted an additional analysis using\n“ridge regression”, which can partly mitigate the issue of multicollinearity among predictors.\nIn this model, the firing rate of each neuron was predicted by the value of the first object\n(value) and whether the monkey released the button to choose the object (choice), with an\nadditional regularization term in the loss function that penalizes large coefficient estimates\narising from correlated predictors. This regularization stabilizes parameter estimation and\nenables a more reliable valuation of each predictor’s influence. We fitted the ridge model\nseparately to the activity of each neuron classified as value- or choice-modulated based on\nthe model comparison analysis. The activity was measured within the time window during\nwhich each neuron exhibited the value- or choice-modulated signal. The neuronal activity\nclassified as value-modulated showed significantly larger regression coefficients for value\nthan did the activity classified as choice-modulated (z = 3.1, p = 0.002; two-tailed Wilcoxon\nrank-sum test), whereas the neuronal activity classified as choice-modulated showed\nsignificantly larger coefficients for choice than did the activity classified as value-modulated\n(z = 3.2, p = 0.001; two-tailed Wilcoxon rank-sum test) (Supplementary Fig. 4). These results\ndemonstrate that our model comparison approach reliably dissociates the value- and choice-\nmodulated signals, despite their inherent correlation. The consistency of these findings with\nthe ridge regression further supports the robustness of our model comparison approach.”\n18\n2. It seems a shame that choice is not modulated in a predictable direction when using\noptogenetic or electrical stimulation. However, it should be possible to show that the\ndirection was consistent for a given recording site/area. Could you test whether the\nmodulation is consistent across repeat stimulations for a single stimulation site to\nprovide evidence that the choice modulation effects go beyond simply adding noise but\nshow a consistent direction as long as the same location is studied? Related to this, why\ndoes it seem as though there an anterior-posterior effect for the electric but not\noptogenetic experiment? Were the sites comparable in the two experiments or could you\ndiscuss why they might differ? Related to this analysis, I was not sure why there are\nseven sites in figure 4d but in the xyz figure with location there are many more sites?\nCould you just show the xyz plot for value 4 to match with Fig 4d?\n> Response to the above comment\nWe thank the reviewer for this valuable suggestion. In order to respond to the\ncomment, “Could you test whether the modulation is consistent across repeat\nstimulations for a single stimulation site to provide evidence that the choice\nmodulation effects go beyond simply adding noise but show a consistent direction as\nlong as the same location is studied?”, we performed an additional analysis to test\nwhether the direction of choice modulation was consistent for each single stimulation\nsite. Specifically, we examined whether the positive or negative effects of stimulation\nwere maintained across the early and late halves of the trials for each site. The results\nshowed that the direction of these effects was consistent within individual sites (see\nSupplementary Fig. 10 for electrical stimulation and Supplementary Fig. 15 for\noptogenetic stimulation), suggesting that the observed modulation reflects stable site-\nspecific effects rather than random trial-by-trial fluctuations. The revised text reads as\nfollows (new line 398-401 for electrical stimulation and new line 501-505 for\noptogenetic stimulation; the statistical significance is shown in Supplementary Fig.\n10 and 15, respectively):\n“We confirmed that the direction of these positive and negative effects was consistent\nacross the early and late halves of the trials for each stimulation site (Supplementary Fig. 10),\nsuggesting that these effects were not due to random trial-by-trial fluctuations in the monkey’s\naction selection.”\n“We confirmed that the direction of these positive and negative effects was consistent\nacross the early and late halves of the trials for each stimulation site (Supplementary Fig. 15),\nsuggesting that these effects were not due to random trial-by-trial fluctuations in the monkey’s\n19\naction selection.”\nIn order to respond to the comment, “Related to this, why does it seem as though\nthere is an anterior–posterior effect for the electric but not optogenetic experiment?\nWere the sites comparable in the two experiments or could you discuss why they\nmight differ?”, we have added a discussion paragraph emphasizing the difference\nbetween electrical stimulation (which directly modulates cell body activity) and\noptogenetic stimulation (which affects dopamine release that modulates synaptic\nefficacy onto striatal neurons). We also referred to a pharmacological study that\ninactivated the ventral striatum and reported a similar anterior–posterior gradient,\nsupporting the notion that such functional heterogeneity may extend to decision-\nrelated processes. We believe that the sites of electrical stimulation (A23–A28) and\nthose of optogenetic stimulation (A21.5–A25) are largely comparable, although the\nelectrical stimulation covered a slightly more anterior portion of the ventral striatum,\nwhich might explain the observed gradient. The newly added discussion paragraph\nreads as follows (new line 725-745):\n“Please note that, although both electrical stimulation of the ventral striatum and\noptogenetic facilitation of dopamine inputs to this region produced bidirectional effects on the\nmonkey’s action selection, only the electrical stimulation exhibited a weak but significant\nanterior–posterior gradient. The positive effects tended to appear in the anterior portion,\nwhereas the negative effects were more pronounced in the posterior portion. A recent\npharmacological study in macaque monkeys also reported functional heterogeneity along\nthis axis of the ventral striatum, showing that inactivation of its anterior portion induced\nhypoactive, “resting” behavior, whereas inactivation of the posterior portion elicited\ncompulsive-like “checking” behaviors56. Our results raise the possibility that such anterior–\nposterior functional differentiation may also extend to decision-related processes. However,\nit remains unclear why optogenetic facilitation of dopamine inputs did not exhibit a similar\ngradient. One possible explanation is that electrical stimulation and pharmacological\nmanipulation directly modulate the cell body activity of ventral striatal neurons, whereas\noptogenetic facilitation affects dopamine release from axon terminals. Since dopamine\nrelease mainly modulates synaptic efficacy onto striatal neurons rather than their intrinsic\nexcitability, the anterior–posterior gradient observed with electrical stimulation might reflect\nthe influence of non-dopaminergic afferents that directly impact neuronal firing. Additionally,\nthe electrical stimulation sites (A23–A28) covered a slightly more anterior portion of the\nventral striatum compared with the optogenetic facilitation sites (A21.5–A25), which might\nhave contributed to the observed gradient.”\nThe reviewer raised the additional concern, “Related to this analysis, I was not\n20\nsure why there are seven sites in figure 4d but in the xyz figure with location there are\nmany more sites? Could you just show the xyz plot for value 4 to match with Fig 4d?”\nWe completely agree that the original xyz plot could be confusing to readers. In the\ninitial version, both significant and non-significant data were plotted in the same\nformat without explicit distinction. This was because we assumed that the statistical\nsignificance at individual sites might have been underestimated due to the limited\nnumber of analyzable trials (approximately 25–50 trials per site in which the first object\nwith value 4 was presented and electrical stimulation was delivered). Therefore, we\ndecided to display all data equally in order to illustrate the overall trend of the spatial\ndistribution of stimulation effects. Accordingly, our main focus was placed on the\npopulation-level analyses shown in Figures 4f and 4g, and Figures 5i and 5j. However,\nas the reviewer rightly pointed out, the lack of visual distinction between significant\nand non-significant data could cause confusion and create an apparent discrepancy\nbetween the number of sites shown in Figure 4e and 5h and the xyz plot. To address\nthis concern, we have revised the xyz plots so that significant and non-significant\nsites are now clearly differentiated, as shown in the updated versions of\nSupplementary Figures 9a and 14a.\n3. I am not an expert in optogenetics stimulation, but it is commonly discussed that this\ntechnique comes with significant challenges in macaque monkeys/larger NHPs. What\nwas done to overcome these limitations here? How can we be sure that the conclusions\ncan be trusted? Relatedly, could the animals (for either stimulation method) possibly tell\nwhen the stimulation was on? Or if not, how can you rule out simple confound effects of\nthe stimulation?\n> Response to the above comment\nWe appreciate the reviewer’s important point regarding the challenges of applying\noptogenetic stimulation in macaque monkeys. We agree that this issue was not\nsufficiently explained in the original manuscript. One of the major difficulties in\nmacaques, compared with rodents, is the relatively low infection efficiency of viral\nvectors commonly used in optogenetics. To address this limitation, a research group\nthat includes authors of the present study developed AAV2.1, a viral vector that\nefficiently infects neurons in the macaque brain and achieves robust expression of\ntarget genes. In this study, we used this vector system. We have added this\ninformation to the revised manuscript, citing the original report of AAV2.1 (new line\n21\n907-909):\n“AAV2.1 is a viral vector developed by a research group that includes authors of this\nstudy57. It efficiently infects neurons in macaque monkeys and enables high-level expression\nof target genes in these cells.”\nThe reviewer also raised an important concern about whether the animals could\ndetect when stimulation was applied, for example through unintended leakage of the\nstimulation laser light into the experimental room. Indeed, if the laser were simply\nturned on, light could weakly leak at the connection between the stimulation probe\n(optrode) and the optical fiber (see the following picture).\nWeak leakage of stimulation laser light\nTip of stimulation probe\nTo prevent this, we covered the connection with a black tube and wrapped the\nentire assembly with a black sheet, as illustrated in the following pictures. These\nprecautions ensured that no light leakage was visible in the experimental room. We\ntherefore believe that confounding effects due to light leakage can be ruled out, and\nwe are not aware of other possible confounds related to the stimulation.\nBlack tube\nBlack sheet\n4. In Fig.2i, it would be interesting to have more information of the temporal dynamics.\nneurons happen in the late period? Is it possible to provide more detail on how the three\ntypes of neurons switch over time? These dynamics seem to be at the core of what the\npaper can contribute.\n> Response to the above comment\nWe thank the reviewer for this valuable and insightful suggestion. Following the\nreviewer’s advice, we examined the temporal dynamics of the transitions among the\nthree functional neuron types by calculating the latencies of signal transitions from\nvalue to intermediate, from intermediate to choice, and from value to choice (see the\nfigure below). The transition latency was defined as the start time of each second\nsignal.\nAs expected, we observed that the transition latency was shorter for value-to-\nintermediate transitions than for intermediate-to-choice or value-to-choice transitions\n(see the vertical dotted line in each figure, indicating the mean ± SD of the latency).\nHowever, these differences were small and did not reach statistical significance (p >\n0.05, Wilcoxon rank-sum test).\nThe most likely reason for the lack of statistical significance is the limited number\nof neurons that exhibited these signal transitions. While the observed tendency is\nconsistent with a sequential flow of information from value to choice representations,\nthe current dataset does not allow a definitive temporal characterization of these\ntransitions. Although we do not plan to include this negative result in the main text\ndue to the limited sample size, we would be happy to incorporate the detailed analysis\nanalysis\nand corresponding figure in the revised manuscript if the reviewer considers it\nimportant for completeness.\n5. Finally, does the stimulation of VS (with either electrical or optogenetic methods)\n23\ninfluence decision reaction times (RTs) and is there any correlation between ∆Release\nlatency in the button-release task and ∆decision RT in the decision task (Figs 4 & 5)?\n> Response to the above comment\nWe appreciate the reviewer’s insightful comment regarding the potential effects of\nventral striatum (VS) stimulation on decision reaction time (the latency of button\nrelease in the decision-making task). To address this point, we analyzed the effects of\nboth electrical and optogenetic stimulations on the button-release latency to choose\nan option in the decision-making task.\nFor electrical stimulation, we found that stimulation did not clearly affect button-\nrelease latency. Here we analyzed the button-release latency only for the first object\nwith value 4 because the electrical stimulation affected the monkey’s action selection\nonly for this value condition. Although a marginally significant difference was\nobserved at the population level, the magnitude of this effect was small, and only one\nstimulation site showed a significant effect. Furthermore, there was no significant\ncorrelation between the effect on the button-release latency and the effect on the\nmonkey’s action selection. These results suggest that electrical stimulation of the\nventral striatum may weakly influence the latency to reach a decision, but its effect (if\nany) is minimal and independent of which action the monkey selects. We have added\nthese data to the revised manuscript as follows (new line 441-452):\n“Even in the decision-making task, electrical stimulation did not clearly affect button-\nrelease latency (Supplementary Fig. 13a). Although the latency difference between\nstimulation and non-stimulation trials reached marginal significance at the population level\n(n = 56 sites, z = -2.45, p = 0.0144; two-tailed Wilcoxon signed-rank test), the difference\nwas small, and only one of the 56 stimulation sites showed a significant effect (p < 0.05;\ntwo-tailed Wilcoxon rank-sum test). Moreover, there was no significant correlation between\nthe button-release latency difference in the decision-making task and the choice-rate\ndifference for the first object with value 4 (n = 56 sites, r = 0.08, p = 0.54; two-tailed\nPearson’s correlation test) (Supplementary Fig. 13b). These results suggest that electrical\nstimulation of the ventral striatum might weakly influence the latency to reach a decision,\nbut its impact (if any) is minimal and independent of which action the monkey selects.”\nSimilarly, optogenetic stimulation of dopamine projections did not significantly\nalter button-release latency in the decision-making task, and no individual stimulation\nsites exhibited a significant effect. There was no significant correlation between the\neffect on the button-release latency and the effect on the monkey’s action selection.\nWe have added these data to the revised manuscript as follows (new line 536-545):\n24\n“As in the button-release task, laser light stimulation did not affect button-release\nlatency in the decision-making task (Supplementary Fig. 18a). The latency difference\nbetween stimulation and non-stimulation trials was not significant (n = 50 sites, z = -0.56, p\n= 0.57; two-tailed Wilcoxon signed-rank test), and no stimulation sites showed a significant\neffect (p < 0.05; two-tailed Wilcoxon rank-sum test). Moreover, there was no significant\ncorrelation between the button-release latency difference and the choice-rate difference for\nthe first object with value 4 (n = 50 sites, r = -0.03, p = 0.86; two-tailed Pearson’s\ncorrelation test) (Supplementary Fig. 18b). These results suggest that laser light stimulation\nof dopamine projections does not influence the latency to reach a decision, regardless of its\neffect on the monkey’s action selection.”\nFinally, as the reviewer suggested, we examined “the correlation between\nΔrelease latency in the button-release task and Δdecision RT in the decision-making\ntask”. No significant correlation was found for either electrical or optogenetic\nstimulation (see the figure below). This result is consistent with the absence or\nweakness of stimulation effects on button-release latency in both tasks. We did not\ninclude this additional result in the revised manuscript because they are largely\nredundant with the analyses already presented, but we would be happy to include\nthem if the reviewer considers them useful for completeness.\nMinor\n1. Several studies have demonstrated the role of the VS in cost-benefit decision making,\n25\ne.g., VS lesions or VS dopamine depletion reduces animals’ likelihood of choosing a high-\neffort choice option (Hauber & Sommer, 2009; Nicola, 2010; Salamone et al. 1994). Does\nthis relate at all to the ideas presented in this manuscript or if not, why not?\n> Response to the above comment\nWe thank the reviewer for raising this important point and for referring to studies\ndemonstrating the involvement of the ventral striatum (VS) in cost–benefit decision\nmaking (Hauber & Sommer, 2009; Nicola, 2010; Salamone et al., 1994). We fully agree\nthat these studies are highly relevant to our research in highlighting the VS’s role in\nregulating choice behavior.\nHowever, our study focuses on a different aspect of decision making, specifically,\nthe transformation from option valuation to action selection. To investigate this\nprocess, it is essential to record and manipulate neuronal activity with precise\ntemporal control during the decision period itself. In contrast, studies employing VS\nlesions or dopamine depletion lack such temporal specificity and thus cannot directly\naddress the dynamic neural mechanisms that mediate value-to-action transformation\nduring an ongoing decision. Instead, these studies seem to primarily assess the\neffects on general motivation, which can manifest independently of specific decision\ntiming.\nIt is widely accepted that dopamine neurons convey reward prediction error (RPE)\nsignals, which are elicited by outcomes after a decision and are thought to influence\nfuture decision processes. The studies cited by the reviewer do not exclude the\npossibility that the observed behavioral effects partly reflect alterations in such\ndopamine-mediated learning or motivational mechanisms, because these studies lack\nneuronal recording and manipulation techniques with precise temporal resolution. By\ncontrast, our work investigates how the dopamine–VS system influences the ongoing\ndecision process itself, before any reward feedback occurs. In this sense, our study\naddresses a complementary but conceptually distinct question regarding the\nimmediate, within-trial contribution of dopamine to decision formation.\nBecause our main objective is to elucidate this temporally specific mechanism,\nwe have not included this discussion in the revised manuscript. However, if the\nreviewer considers that explicitly contrasting our findings with prior lesion or\ndopamine depletion studies would strengthen the manuscript’s conceptual framing,\nwe would be happy to add a brief discussion highlighting this distinction in the revised\nversion.\n26\n2. Please specify in the Methods which pairings of objects were used how frequently.\n> Response to the above comment\nThis information is important for readers to understand the task design. We have\nadded it to the Methods section of the revised manuscript as follows (new line 827-\n830):\n“Both the first and second objects were pseudo-randomly selected from the six visual\nobjects with equal probability and independently of each other, so that the same object could\nappear as both the first and second objects on a given trial.”\n3. Please plot the data from each monkey separately or in separate lines in the key\nfigures.\n> Response to the above comment\nWe appreciate this valuable suggestion. Showing the data for each monkey is indeed\nimportant for demonstrating the reproducibility of our findings. In the revised\nmanuscript, we have now presented key figures separately for each monkey,\nillustrating the proportions of neurons encoding value-modulated, choice-modulated,\nand intermediate signals (Supplementary Fig. 3a and 3b), as well as the latencies of\nthese signals (Supplementary Fig. 3c and 3d). These data are critical for\ndemonstrating the transition from option valuation representation to action selection\nencoding in the ventral striatum.\nIn addition, we have separately plotted, for each monkey, the effects of electrical\nand optogenetic stimulations (Supplementary Fig. 11 and Supplementary Fig. 16,\nrespectively), which are essential to show the causal contributions of the ventral\nstriatum and dopaminergic inputs to decision-making. We found that the results were\nconsistent across monkeys, supporting the robustness and reproducibility of our\nconclusions.\n4. While it is brilliant to see that control experiments using simple motor tasks were\nperformed, there is more than one difference between the task of interest and the control\ntask. For example, in the simple motor task, there is no choice, no value processing and\nexpectation, no modulation of outcome size and thus overall less reward on average. It\nwould be good to acknowledge and discuss these differences in the discussion.\n> Response to the above comment\nWe thank the reviewer for this insightful comment. We fully agree that the control task\ndiffered from the decision-making task in several respects beyond the presence or\nabsence of choice, and that these differences could influence the observed neuronal\nmodulation. The control task was intentionally designed to remove the choice process\nin order to isolate motor-related neural activity in the ventral striatum. However, in the\ndecision-making task, the identity and value of the first object varied unpredictably\nacross trials, introducing uncertainty about which option would appear and what\nreward could be obtained. This trial-by-trial uncertainty likely engaged reward\nexpectation and prediction error processes that were absent in the control task, where\nthe stimulus–reward association was fixed and fully predictable. Such differences in\nuncertainty and reward expectation may explain why neuronal modulation was\nstronger in the decision-making task.\nWe also note that the reward structure was comparable across the two tasks. The\nreward amount per trial in the control task was set to be equivalent to value 3 or 4 in\nthe decision-making task, matching the expected reward magnitude in the decision-\nmaking task (3.5). Because each trial in the control task was shorter, the overall reward\nrate was actually higher, which could lead to greater motivation. Therefore, the weaker\nneuronal modulation in the control task is unlikely to be due to reduced motivation.\nWe have added these discussions to the revised manuscript as follows (new line 629-\n648):\n“We confirmed that the choice-modulated signal discussed above was not simply caused\nby the motor action (i.e., button release) associated with choosing the first object, using the\ncontrol task in which the monkey was required only to release the button. This control task\nwas designed to eliminate the process of choice in order to isolate motor-related neuronal\nactivity in the ventral striatum. However, it also differed from the decision-making task in other\nimportant respects. In the decision-making task, the identity and value of the first object\npresented as an option varied unpredictably across trials, introducing uncertainty about which\nobject would appear and what reward could be obtained. This trial-by-trial uncertainty likely\nevoked reward expectation and prediction error processes that were absent in the control\ntask, where the stimulus–reward association was fixed and fully predictable. Such differences\nin uncertainty and reward expectation may account for the stronger neuronal modulation\nobserved during the decision-making task. In addition to these differences, we also note that\nthe reward structure was comparable across tasks.\nThe reward amount per trial in the control task was set to be equivalent to value 3 or 4 in\nthe decision-making task, which matched the expected reward magnitude in the decision-\nmaking task (3.5). However, because the duration of each trial in the control task was\nshorter, the overall reward rate was actually higher, potentially leading to greater\nmotivation. Therefore, the weaker neuronal modulation observed in the control task is\nunlikely to reflect reduced motivation.”\n5. In Fig.1, please show the duration for each stage of the task in Fig.1a.\n> Response to the above comment\nWe have added the durations in Fig. 1a of the revised manuscript.\n6. Why did the authors pick ‘release’ as their response modality?\n> Response to the above comment\nWe appreciate the reviewer’s question regarding the choice of “release” as the\nresponse modality. In the early stages of training, the monkeys were initially trained\nto make their choice by pressing a button. However, under this design, they\nsometimes attempted to press the button again when a highly valuable second object\nappeared, even after having already chosen the first object. To prevent such double-\nchoice behavior and to ensure a clear and unambiguous response, we adopted a\n“button release” as the choice action. Once the monkey releases the button, it cannot\nmake a second release within the same trial, which effectively enforces a single,\ntemporally well-defined choice. This modification greatly improved task performance.\nThis manuscript addresses an important and timely topic: how the ventral striatum\ncontributes to value-based decision-making. The authors used a combination of single-\nunit recordings, electrical stimulation, and optogenetics in nonhuman primates. The\nmonkeys performed a sequential choice task between pairs of visual stimuli associated\nwith different amounts of reward, and the authors report that ventral striatum activity\ntransitions from value encoding to action selection. They further show that stimulation\nof the ventral striatum or its dopaminergic inputs can bias choice behavior. The multi-\nmethod approach is a strength, and the effort to dissect decision-making phases is\nvaluable. However, several key conceptual and interpretive issues need to be addressed.\nIn particular, the study is often framed within a reinforcement learning framework, but\nthis is not well supported by the task design or data. Sample sizes of recorded neurons\nare relatively small, and the observed effects of stimulation, while interesting, are also\nlimited. Some analyses can be improved. I believe some of these concerns can be\naddressed through reframing and clarification, as well as the introduction of further\nanalysis. Below are specific comments. I hope the authors find them useful.\n> Response to the above comment\nWe are very pleased to hear the supportive comment and appreciate the following\nthoughtful comments, which have helped us improve our study.\nMajor points:\n1) Figure 2 shows firing rate differences that may reflect variations in reaction times, as\nwell as other task/behavioral variables, rather than “value.” Have the authors tested for\ncorrelations between firing rates and reaction times? In general, reaction times should\nbe considered as a covariate or explanatory variable in the analysis.\nThe author can, for example, build a single regression model to predict the neuronal\nactivity of single neurons over time based on different predictors, including reaction\ntimes, reward amount, and choice, and estimate the variation in coefficients for each\npredictor over time, as well as the fraction of neurons that are significant. This would\ngive better estimate of what task/behavioral variable influence neuronal activity. It\nwould be actually also interesting to include the chosen stimulus identity in the model.\nI think this approach can be helpful for assessing temporal transitions in encoding,\n30\nwhich is exactly what the authors claim is happening (from value to choice over time).\n> Response to the above comment\nWe thank the reviewer for this important and constructive comment. To examine\nwhether the observed neuronal modulation could be explained by reaction time (RT),\nwe conducted an additional analysis using “ridge regression”, a regularized\nregression method that can partly mitigate the problem of multicollinearity between\ncorrelated predictors. In our dataset, the first object value and RT (the latency of\nbutton release to choose the first object) were correlated (Fig. 1c), making it\nstatistically inappropriate to include both in a standard multiple regression model.\nRidge regression introduces a small penalty term to the loss function, stabilizing\ncoefficient estimation and allowing a more reliable assessment of each predictor’s\ncontribution under such correlated conditions.\nThe results showed that the influence of RT on neuronal activity was minimal,\nconfirming that the value-related modulation reported in Figure 2 cannot be explained\nby variations in reaction time. The corresponding description has been added to the\nrevised manuscript (new line 255-270):\n“Using another ridge regression, we tested whether the modulation of each neuron could\nbe accounted for by the variation in reaction time (i.e., the latency of button release). Here,\nthe firing rate of each neuron was predicted by the value of the first object (value) and the\nbutton-release latency (latency), which were also correlated (Fig. 1c). We excluded the\npredictor of whether the monkey released the button to choose the first object (choice) from\nthis analysis, because the button-release latency existed only when the monkey released the\nbutton (i.e., when the monkey did not release the button, making it impossible to include such\ntrials in the regression analysis). Whereas the proportion of neurons with a significant\nregression coefficient for value was significantly higher than chance for all calculation time\nwindows (each 150-ms window after the first object onset) except immediately after the onset\n(p > 0.05; two-tailed Monte Carlo test) (Supplementary Fig. 5a), the proportion of neurons\nwith a significant regression coefficient for latency was very close to chance over time, slightly\nabove for some time windows (p < 0.05; two-tailed Monte Carlo test) but below for others (p\n> 0.05; two-tailed Monte Carlo test) (Supplementary Fig. 5b). These results suggest that the\ninfluence of button-release latency on neural activity, if any, was minimal.”\nThe reviewer further suggested building a single regression model including\nmultiple behavioral and task variables (e.g., reaction times, reward amount, choice,\nand stimulus identity) to better assess the temporal dynamics of encoding. We fully\nagree that such a unified model could provide valuable insights. However, several\n31\naspects of our task design and data structure make it difficult to include all these\npredictors in a single model without introducing severe statistical and interpretational issues. First, reward value and choice were strongly correlated in our task (Fig. 1b), resulting in severe multicollinearity, which prevents reliable estimation of regression coefficients and interpretation of their relative contributions. Second, reaction time (RT) existed only in trials where the monkey released the button to accept the first object, and thus RT and choice could not be included simultaneously, as reject trials had no measurable RT. Third, because each visual object was uniquely associated with a specific reward amount, the “stimulus identity” and “reward value” variables were perfectly confounded, making it impossible to separate their effects statistically.\nDespite these inherent limitations, our task design offers a critical advantage. In\nthis task, option valuation, action selection, and action execution occurred in close\ntemporal succession during the presentation of the first object. This structure\nprovided a unique opportunity to examine the neural mechanisms underlying each of\nthese processes and the temporal transitions between them within a single,\ncontinuous decision period. Thus, while statistical separation of certain behavioral\nvariables was not feasible, this tight coupling between valuation and action processes\nallowed us to capture the natural evolution of neural signals from value representation\nto action selection coding—precisely the phenomenon our study aims to characterize.\nWe again agree that the single regression model including multiple behavioral and\ntask variables suggested by the reviewer could, in principle, provide further insights\ninto the neuronal representations underlying decision-making. Therefore, despite the\nconstraints, we performed two complementary analyses designed to address the\nreviewer’s concern while maintaining statistical validity:\n(1) As described above, we examined whether neuronal modulation could be\nexplained by variations in RT using a ridge regression model that included value\nand latency as predictors. The results showed minimal RT influence on neuronal\nactivity.\n(2) We also tested whether neurons represented the value of the second object in the\nprevious trial, in addition to the value of the first object in the current trial and the\nmonkey’s choice, to assess whether past valuation processes contributed to\ncurrent decision-related activity, using a ridge regression model that included\nthese variables as predictors. At the behavioral level, the value of the previous second\nobject influenced the monkey’s decisions (Supplementary Fig. 1b): the\nhigher the value of the previous second object, the less likely the monkeys were\nto choose the current first object. This suggests that when the previous second\nobject had been highly rewarding, the monkeys anticipated the possibility of\nanother valuable second object and therefore more inclined to reject the current first\nobject. The ridge regression analysis showed that while the proportions of\nneurons significantly encoding the current value and choice were above chance,\nthe proportion for the previous value remained close to the chance level, indicating weak representation of the previous second object value. The relevant\ndescription has been added to the revised manuscript as follows (new line 322-\n347):\n“To understand the neural mechanisms underlying option valuation, action selection,\nand the transition between them, we have analyzed neuronal activity encoding the value of\nthe first object and whether the monkey decided to release the button to choose this object\nduring the decision-making period (i.e., during the presentation of the first object). However,\nother neural processes contributing to decision formation might also occur in the ventral\nstriatum during the same period. For example, we observed that not only the value of the\nfirst object, but also the value of the second object in the previous trial influenced the\nmonkey’s action selection (Supplementary Fig. 1b), suggesting that valuation or\nmaintenance of the previous second object might also take place in the ventral striatum\nduring decision making. To test this possibility, we conducted another ridge regression\nanalysis in which the firing rate of each neuron was predicted by the value of the previous\nsecond object (previous value), as well as the value of the current first object (current\nvalue) and whether the monkey chose the first object (choice). As expected from the model\ncomparison analysis, the proportions of neurons with significant regression coefficients for\ncurrent value and choice increased after the first object onset and became significantly\nhigher than chance (p < 0.05, two-tailed Monte Carlo test) (Supplementary Fig. 7a and 7b).\nOn the other hand, the proportion of neurons with a significant regression coefficient for\nprevious value was close to the chance level over time, slightly above for some time\nwindows (p < 0.05; two-tailed Monte Carlo test) but below for others (p > 0.05; two-tailed\nMonte Carlo test) (Supplementary Fig. 7c), suggesting that the ventral striatum may not\nstrongly represent or maintain the value of the previous second object. However, at the\nbehavioral level, the influence of the previous second object value on the monkey’s choices\nwas much weaker than that of the current first object value. Therefore, it may be difficult to\ndetect neuronal activity corresponding to such a weak behavioral effect of the previous\nsecond object value in the ventral striatum.”\nBoth of the above analyses used ridge regression. While ridge regression cannot\nfully eliminate multicollinearity, its regularization term allows us to “partially” mitigate\nthis problem and obtain more stable coefficient estimates under correlated conditions.\nrather than definitive variance-partitioning tests. Nonetheless, the consistency of the\nridge regression results with those obtained from the model comparison analysis\nreinforces the robustness of our main conclusions.\nIn these ridge regression analyses, we presented the proportion of neurons with\na significant regression coefficient for each predictor, but not the coefficients\nthemselves. We did not display the regression coefficients because individual\nneurons exhibited both positive and negative coefficients, reflecting heterogeneous\ncoding directions (e.g., some neurons increased their activity with higher value, while\nothers decreased it). Averaging such coefficients across neurons would therefore\ncancel out meaningful effects and obscure the underlying coding diversity. In addition,\nsince the main conclusions of this analysis were already well supported by the\nproportions of significantly modulated neurons, presenting the regression\ncoefficients was not necessary for further interpretation. However, we would be happy\nto provide these coefficients if the reviewer considers them informative.\nRecognizing the importance of the reviewer’s suggestion, we have also noted this\npoint in the revised Discussion section. As described there, although our task design\nmakes it difficult to include all relevant variables (e.g., value, choice, reaction time,\nand stimulus identity) within a single regression model due to strong correlations and\nmissing data in certain conditions, we fully agree that such an approach would provide\na more comprehensive understanding of the neural representations underlying\ndecision-making. Accordingly, we added the following statement to the Discussion\n(new line 618–624):\n“Ideally, task designs should enable the construction of a single regression model\nincorporating these value- and choice-related variables, as well as other decision variables\nsuch as the sensory identity of each option and the motor components of choice, to achieve\na comprehensive understanding of the neural representations underlying decision-making.\nThus, while our findings establish the ventral striatum as a neural hub where value signals\nevolve into choice signals, the precise nature of these computations remains to be clarified.”\n2) I appreciate the ambition of the authors to connect their findings to a broader theoretical\nframework, but this is not a reinforcement learning task. Monkeys are basically choosing\nbetween overlearned stimuli. Thus, there is no learning. I think this is a very important point.\nThe manuscript repeatedly attempts to position the findings within a reinforcement learning\nframework, but this interpretation is not adequately supported by the data. As a result, several\nclaims appear overextended, which diminishes the overall strength of the study. A more\ncautious, data-driven interpretation would improve the clarity and impact of the work.\n> Response to the above comment\nWe appreciate the reviewer’s insightful comment. We agree that our task does not\ninvolve a learning component, and therefore it is not appropriate to directly interpret\nour findings within a reinforcement learning framework. In line with this concern, we\nhave removed suggestions, discussions, and conclusions that explicitly linked our\ndata to reinforcement learning. Below, we provide examples of the revisions we made\nin response to the reviewer’s points.\nSome examples from the manuscript:\n- ”Notably, the transitions, encoding from state value to action value and further to action,\nwere often observed within single neurons, rather than across segregated neuronal\npopulations.” The concepts of state value and action value are not applicable in this task.\n> As the reviewer correctly noted, the concepts of state value and action value are not\napplicable to this task. We have deleted the paragraph in the discussion section in\nwhich we referred to the relationship between our findings and the reinforcement\nlearning framework, including the terms “state value” and “action value.” These\nterms have also been removed entirely from the manuscript.\n- “This finding challenges the classical notion that the ventral striatum primarily encodes\nvalue-related signals in the reinforcement learning framework”. It is unclear how the present\nresults challenge the encoding of value-related signals in the VS, especially given the lack of\nlearning.\n> We have deleted the discussion paragraph including this sentence.\n- “Instead, our data suggest a broader role of the ventral striatum potentially as a neural\nsubstrate not only for value representations, but also for some aspects of policy.” This seems\na bit vague. What specific aspects of “policy” are meant here?\n> We thank the reviewer for pointing out this issue. In the original manuscript, we used\nthe term “policy” in the context of reinforcement learning to suggest that ventral\n35\nstriatal activity might relate to decision strategies beyond value representation.\nHowever, as the reviewer correctly noted, our task does not involve learning, and thus\nthe use of this term was misleading. To avoid overinterpretation, we have deleted the\ndiscussion paragraph containing this sentence, and we have removed any mention of\n“policy” from the manuscript.\n- Also related, the authors often refer to reward prediction errors, which cannot be measured\nin the present study because animals were likely overtrained, and the reward was not\nprobabilistic.\n> We agree with the reviewer’s assessment that reward prediction errors cannot be\nproperly evaluated in our task, as the animals were overtrained and the reward\nschedule was deterministic. In the revised manuscript, “reward prediction error”\nappeared only in a general context because dopamine neurons are widely known to\nencode reward prediction errors. We did not intend to link our own results directly to\nreward prediction error signals. We now restrict our discussion to interpretations that\nare directly supported by our data.\n3) The effects of ventral striatum and dopaminergic neuron stimulation were absent on\nover 80% of trials and appeared restricted to conditions of uncertainty. This limited\nefficacy should be acknowledged and discussed, as it has implications for interpreting\nthe causal role of these signals. I think that the weak effects are likely due to the absence\nof learning in the task. Therefore, it is unclear to what extent these results can be\ngeneralized.\n> Response to the above comment\nWe thank the reviewer for this thoughtful comment regarding the limited effects of\nelectrical and optogenetic stimulations and their potential relationship to learning. We\nagree that the selective effects observed under uncertain conditions have important\nimplications for interpreting the causal role of the ventral striatum and dopamine\ninputs and for the generalizability of our findings. We have now included the following\nparagraph in the Discussion to address this issue (new line 746-763):\n“Both electrical stimulation and optogenetic facilitation of dopamine inputs influenced\nthe monkey’s action selection specifically when the monkey was uncertain about whether to\nchoose the medium-value first object (value = 4). When the object value was sufficiently\nhigh or low, the decision process became more deterministic and thus less susceptible to\nexternal perturbations. This stability likely reflects the fact that the monkeys had already\nlearned, through extensive training, the associations between each visual object and its\ncorresponding reward outcome. By contrast, during ambiguous decisions, the neural\ncircuits integrating value and choice signals are likely to be in a transient, metastable state\nor susceptible phase, in which brief perturbations can more effectively bias the final\nbehavioral outcome. Indeed, the medium-value first object elicited intermediate-level\nresponses in ventral striatum neurons showing value-modulated activity. It is also possible\nthat if the associations between visual objects and reward outcomes had not been well\nlearned, the effects of electrical stimulation and optogenetic facilitation of dopamine inputs\nmight have been broader and stronger. Future studies combining electrical or optogenetic\nstimulation with learning paradigms or systematically varying the level of decision\nuncertainty could further elucidate the specific computational roles of the dopamine-ventral\nstriatum system in adaptive decision-making.”\nThis additional discussion explicitly acknowledges the limited efficacy of\nstimulation effects and clarifies their potential dependence on both learning and\ndecision uncertainty. While our current findings highlight how the dopamine–ventral\nstriatum system contributes to ongoing decision-making under well-learned\nconditions, future studies manipulating learning state or uncertainty level will be\nessential to determine how broadly these mechanisms generalize across different\nbehavioral contexts.\n4) I think the authors should clearly state that the monkeys were overtrained and that\nthere was no learning in the task. This clarification is critical for contextualizing the\nneural signals and avoiding overinterpretation in terms of reinforcement learning.\n> Response to the above comment\nWe appreciate the reviewer’s helpful suggestion. We fully agree that clarifying the\ntraining status of the monkeys is important to appropriately contextualize the\nobserved neural signals and to avoid overinterpretation in terms of reinforcement\nlearning processes. As suggested, we have now clearly stated in the revised\nmanuscript that the monkeys were overtrained and that no further learning occurred\nduring the task. The following sentence has been added to the revised manuscript\n(new line 835-839):\n“The monkeys were overtrained to perform this decision-making task until their action\nselection became stable across daily sessions, ensuring that the associations between each\nvisual object and its corresponding reward outcome were well learned. Therefore, no further\nlearning was expected to occur during the recording or stimulation sessions.”\nWe also included additional discussion regarding the learning state, as suggested\nin the previous review comment (see our response to Comment 3).\nMinor points:\n1) I do not understand the following sentence and the data to support it: “During this period,\nwe observed the signal dynamics of ventral striatum neurons corresponding to the time\ncourse of the decision-making process.” (line 260-261)\n> Response to the above comment\nWe thank the reviewer for pointing out this unclear sentence. We agree that the\noriginal wording was ambiguous and did not clearly convey our intended meaning. To\nimprove clarity, we have revised the sentence as follows (new line 373-376):\n“During this period, we found that ventral striatum neurons exhibited value-modulated,\nintermediate, and choice-modulated signals, which dynamically evolved as the decision\nprocess progressed from option valuation to action selection.”\n2) Please clarify how many electrodes were used per session, how many recording\nsessions were included in the analysis, and whether ventral striatal activity differed\ndepending on whether recordings were made in the hemisphere ipsilateral or\ncontralateral to the arm used to perform the task.\n> Response to the above comment\nWe thank the reviewer for this helpful request for clarification. We used one single-\nchannel electrode in each daily experiment. Recording of one neuron was counted as\none recording session. Therefore, zero to a few recording sessions (i.e., zero to a few\nneurons) were conducted per daily experiment. In total, 125 recording sessions were\nperformed (corresponding to 125 neurons), and these data were included in the\nanalyses. We have added this information to the revised manuscript as follows (line\n878-882):\n“During each daily experiment, one electrode was inserted into the ventral striatum.\nrecording sessions (i.e., zero to a few neurons) were conducted per daily experiment. In\ntotal, 125 recording sessions were performed (corresponding to 125 neurons), and these\ndata were included in the analyses.”\nRegarding the question about ipsilateral and contralateral differences, we\nconducted single-unit recordings (as well as stimulation experiments) exclusively in\nthe hemisphere contralateral to the arm used to perform the task (as described on new\nline 843-844 of the manuscript). The contralateral hemisphere was targeted because it\nis generally considered to have a stronger functional coupling with motor output. Even\nwithin the ventral striatum, Sawada et al. (2015) Science, which is cited in our\nmanuscript, reported that the ventral striatum contributes to effortful motor behaviors\n(precision gripping in animals with spinal cord injuries) by integrating motivational\ndrive with motor coordination, and that this effect was observed only on the\ncontralateral side. Therefore, we did not examine hemispheric differences between\ncontralateral and ipsilateral recordings in this study.\nHowever, as far as we know, there is currently no evidence that the ventral\nstriatum does not contribute to ipsilateral action selection in decision-making\ncontexts. Future studies directly comparing both hemispheres will be valuable to\ndetermine whether similar or distinct patterns of neural activity and causal effects are\npresent in the ipsilateral ventral striatum. Although we have not included this\ndiscussion in the revised manuscript to maintain focus on the main findings, we would\nbe happy to incorporate a brief statement on this point if the reviewer considers it\nimportant for completeness.\n3) Caption in Figure 1: “Error bars in (b) and (c) indicate SEM, which are very small and\nhidden in most cases”. I really cannot see any error bar. Maybe reducing the size of the\nblack circles can help?\n> Response to the above comment\nWe reduced the size. Now, the error bars are visible.\nNejime and colleagues investigated the role of the primate ventral striatum in value-\nbased decision-making, specifically examining its function in the transformation of\noption valuation into action selection. The authors challenge the traditional view that\nthe VS primarily encodes expected value, proposing instead a more active role in the\ndecision process itself.\nThe key findings are:\nVS neurons exhibit dynamic encoding, initially representing the value of an offered\noption (offer 1) and subsequently shifting their activity to reflect the monkey's upcoming\nchoice (to accept or reject offer 1). This transition often occurs within individual neurons.\nBoth electrical stimulation of the VS and optogenetic facilitation of dopamine input to\nthe VS during the decision period (presentation of offer 1) significantly altered the\nmonkeys' choices, particularly when they were uncertain (i.e., for options of intermediate\nvalue). These manipulations could bias choices both towards and away from selecting\nthe option, depending on the specific stimulation site.\nControl experiments suggest these effects are not merely due to influences on motor\nexecution.\nThe authors conclude that the VS acts as a crucial neural hub that actively bridges option\nvaluation and action selection, with dopamine playing a critical role in modulating these\ndecision computations in real-time, extending beyond its classical role in reward\nprediction error signaling.\nThe idea that the VS itself might be involved in deriving the choice, rather than just\npassing value information to other regions, is particularly intriguing. The study employs\na sophisticated combination of state-of-the-art techniques in non-human primates.\nOptogenetics in primates is still challenging, and its successful application here to\ndissect the role of dopamine is commendable.\nWhile this study addresses an important question in decision neuroscience and employs\nsophisticated methodologies, I have substantial concerns outlined as follows.\n40\n> Response to the above comment:\nWe sincerely appreciate the following thoughtful comments, which have helped us\nimprove our study.\nMajor comments：\n1. The authors state that the task temporally separates option evaluation, decision\ncommitment, and motor execution. However, during the presentation of the first object\n(offer 1), the animals must evaluate offer 1, make an accept/reject decision, and execute\na motor response (hold or release the button). This conflation of evaluation, decision, and\nmotor execution for offer 1 should be acknowledged more clearly. The primary temporal\nseparation is between the decision on offer 1 and the revelation/receipt of offer\n> Response to the above comment:\nThank you very much for pointing out this important issue. We agree that our original\ndescription was misleading, as the option evaluation, decision commitment, and\nmotor execution processes cannot be demonstrated to be temporally separated in our\ntask. Instead, a key advantage of the task design is that these processes occur in close\nsuccession during the presentation of the first object. This temporal structure allows\nus to investigate not only the neural mechanisms underlying each process but also\nthe transitions between them. To address this concern, we have removed sentences\nreferring to temporal separation and revised the text to emphasize the advantage of\nour task as follows (new line 174-177):\n“In this task, option valuation, action selection, and action execution occurred in close\nsuccession during the presentation of the first object, providing an opportunity to examine\nthe neural mechanisms underlying each process and the transitions between them.”\n2. The manuscript would benefit from a more explicit model of the cognitive operations\nunderlying the monkey's choice. The decision to accept/reject offer 1 is presumably based\non a comparison of its value, V(offer 1), against an expected value of offer 2, E[V(offer 2)],\nwhich is learned from past experience. The paper does not detail how E[V(offer 2)] is\noperationalized or assumed to be updated. This is crucial because VS activity might\nreflect components of this expectation.\n41\n> Response to the above comment:\nWe thank the reviewer for this insightful comment. We agree that the monkey’s\ndecision to accept or reject the first object (offer 1) is likely based on a comparison\nbetween its value, V(offer 1), and an expected value of the second object, E[V(offer 2)],\nwhich may be formed from past experience. Indeed, our behavioral analyses revealed\nthat the value of the second object in the “previous trial” influenced the monkey’s\nchoice behavior in a manner consistent with such comparison. The corresponding\ndescription has been added to the revised manuscript as follows (new line 157-169):\n“We also found that the monkey’s decision was influenced by recent experience.\nSpecifically, the value of the second object in the previous trial negatively predicted whether\nthe monkey would choose the first object in the current trial. A logistic regression analysis\nrevealed a weak but significantly negative regression coefficient between the value of the\nprevious second object and the choice rate of the current first object (n = 143 sessions, z =\n-7.2, p = 8.4 × 10-13; two-tailed Wilcoxon signed-rank test), while the regression coefficient for\nthe current first object value was significantly positive (n = 143 sessions, z = 10.4, p = 3.2 ×\n10-25; two-tailed Wilcoxon signed-rank test) (Supplementary Fig. 1b). Thus, the higher the\nvalue of the previous second object, the less likely the monkeys were to choose the current\nfirst object. This opposing effect of the current and previous values suggests that when the\nprevious second object had been highly rewarding, the monkeys anticipated the possibility\nof another valuable second object and were therefore more inclined to reject the current first\nobject.”\nTo further examine whether the ventral striatum represented this previous second\nobject value during the decision-making period in the current trial (i.e., during the\npresentation of the current first object), we performed a “ridge regression” analysis in\nwhich the firing rate of each neuron was predicted by the value of the previous second\nobject, the value of the current first object, and whether the monkey chose this first\nobject. We used ridge regression because these predictors were correlated, making it\nstatistically inappropriate to include them in a standard multiple regression model.\nUnder such correlated conditions, regression coefficients cannot be reliably\nestimated, as any apparent variance partitioning between predictors becomes\nunstable due to “multicollinearity”. Ridge regression introduces a small regularization\nterm to the loss function, which stabilizes coefficient estimation and allows partial,\nbut not perfect, mitigation of this issue. Therefore, this analysis should be regarded\nas a complementary validation rather than a primary variance-partitioning test.\nThe results showed that, while many ventral striatum neurons significantly\nencoded the current first object value and the monkey’s choice (as expected from our\n42\noriginal analyses), only a few neurons represented the previous second object value.\nThe corresponding description has been added to the revised manuscript as follows\n(new line 322-347):\n“To understand the neural mechanisms underlying option valuation, action selection,\nand the transition between them, we have analyzed neuronal activity encoding the value of\nthe first object and whether the monkey decided to release the button to choose this object\nduring the decision-making period (i.e., during the presentation of the first object). However,\nother neural processes contributing to decision formation might also occur in the ventral\nstriatum during the same period. For example, we observed that not only the value of the\nfirst object, but also the value of the second object in the previous trial influenced the\nmonkey’s action selection (Supplementary Fig. 1b), suggesting that valuation or\nmaintenance of the previous second object might also take place in the ventral striatum\nduring decision making. To test this possibility, we conducted another ridge regression\nanalysis in which the firing rate of each neuron was predicted by the value of the previous\nsecond object (previous value), as well as the value of the current first object (current\nvalue) and whether the monkey chose the first object (choice). As expected from the model\ncomparison analysis, the proportions of neurons with significant regression coefficients for\ncurrent value and choice increased after the first object onset and became significantly\nhigher than chance (p < 0.05, two-tailed Monte Carlo test) (Supplementary Fig. 7a and 7b).\nOn the other hand, the proportion of neurons with a significant regression coefficient for\nprevious value was close to the chance level over time, slightly above for some time\nwindows (p < 0.05; two-tailed Monte Carlo test) but below for others (p > 0.05; two-tailed\nMonte Carlo test) (Supplementary Fig. 7c), suggesting that the ventral striatum may not\nstrongly represent or maintain the value of the previous second object. However, at the\nbehavioral level, the influence of the previous second object value on the monkey’s choices\nwas much weaker than that of the current first object value. Therefore, it may be difficult to\ndetect neuronal activity corresponding to such a weak behavioral effect of the previous\nsecond object value in the ventral striatum.”\nThese analyses also address the points raised in Comment 3 below.\n3. To better understand how E[V(offer 2)] might be formed and whether it's encoded, the\nauthors could test if V(offer 2) from trial n-1 influences choice behavior for offer 1 in trial\nn (e.g., by splitting trials based on previous V(offer 2) being high vs. low) and if this\nvariable is encoded in VS activity during the offer 1 period of trial n.\n> Response to the above comment:\nwhether the value of the second object in the previous trial (previous V(offer 2))\ninfluenced the monkey’s choice behavior and whether this information was\nrepresented in ventral striatal activity during the offer 1 period (i.e., the presentation\nof the first object) of the current trial. The detailed results are presented in our\nresponse to Comment 2 above.\nBriefly, a logistic regression analysis showed that the previous second object\nvalue negatively predicted the probability of choosing the current first object,\nindicating that monkeys anticipated the potential value of a future second object\nbased on past experience. At the neural level, a ridge regression analysis—including\nthe value of the second object in the previous trial, the value of the first object in the\ncurrent trial, and the monkey’s choice—revealed that while many neurons significantly\nencoded the current first object value and the choice, only a small proportion (near\nchance level) encoded the previous second object value. Please refer to the response\nto Comment 2 for further details and interpretation.\n4. Consequently, during the offer 1 period, VS neurons could be encoding multiple\nvariables beyond just V(offer 1) and the impending choice. These potentially include:\nE[V(offer 2)] (possibly influenced by V(offer 2) from trial n-1 or a running average),\nthe decision variable itself (e.g., V(offer 1) - E[V(offer 2)]), and motor preparation\nsignals related to holding or releasing the button. This complexity makes the unique\nattribution of neural activity changes and stimulation effects to a single cognitive component\nchallenging.\n> Response to the above comment:\nWe appreciate the reviewer’s thoughtful comment regarding the potential complexity\nof neural representations during the offer 1 period. We fully agree that ventral striatal\n(VS) neurons could, in principle, encode multiple decision-related variables such as\nV(offer 1), E[V(offer 2)], the decision variable V(offer 1)−E[V(offer 2)], and motor\npreparation signals related to the forthcoming action. However, in our dataset, these\nvariables were highly correlated with one another. Specifically, V(offer 1), E[V(offer 2)]\n(approximated by V(offer 2) from trial n−1), and the decision variable V(offer\n1)−E[V(offer 2)] are mathematically and empirically interdependent, making it\nstatistically inappropriate to include all of them simultaneously in a single regression\nmodel due to severe multicollinearity.\nresponses to Comments 2 and 3 above, we examined whether the value of the\nprevious second object V(offer 2) influenced both behavior and neuronal activity.\nn−1\nAlthough behavioral analyses revealed a weak but significant negative effect of V(offer\n2) on the current choice, neural analyses showed that only a very small proportion\nn−1\nof neurons encoded this variable, with the proportion remaining close to chance level.\nBased on this result, it is reasonable to expect that the representation of the\ncomparison signal V(offer 1)−E[V(offer 2)] would also be weak in the ventral striatum,\nsince its computation depends on the reliable encoding of both terms. Indeed, when\nwe explicitly tested this possibility using a separate regression model in which the\nfiring rate of each neuron was predicted only by V(offer 1)−E[V(offer 2)], only 9 out of\n125 neurons (7.2%) showed significant regression coefficients, a proportion not\nsignificantly different from chance (p > 0.05, two-tailed Monte Carlo test). These results\nsuggest that ventral striatum neurons primarily encode the current object value and\nchoice-related activity, rather than an explicit decision variable computed as V(offer\n1)−E[V(offer 2)].\nAs seen in the response to Comments 2 and 3 above, we described that ventral\nstriatum neurons only weakly (if any) represented E[V(offer 2)] (approximated by\nV(offer 2) from trial n−1). But we did not show the data from the regression model in\nwhich the firing rate of each neuron was predicted only by V(offer 1)−E[V(offer 2)],\nbecause the results can be expected from the weak representation of E[V(offer 2)].\nHowever, if the reviewer considers this additional analysis important for clarity, we\nwould be happy to include it in the revised manuscript.\nFurthermore, we examined whether ventral striatum activity reflected motor\npreparation signals, such as the latency of button release (reaction time) when\nchoosing the first object (we have already analyzed the effect of holding or releasing\nthe button on neuronal activity in Fig. 3). Using a ridge regression model that included\nthe current object value and button-release latency as predictors, we found that the\nproportion of neurons with significant regression coefficients for latency was close to\nthe chance level across time (Supplementary Fig. 5b). These results indicate that the\nventral striatum activity does not reflect motor preparation or execution. We described\nthese results in the revised manuscript as follows (new line 255-270):\n“Using another ridge regression, we tested whether the modulation of each neuron could\nbe accounted for by the variation in reaction time (i.e., the latency of button release). Here,\nthe firing rate of each neuron was predicted by the value of the first object (value) and the\nbutton-release latency (latency), which were also correlated (Fig. 1c). We excluded the\npredictor of whether the monkey released the button to choose the first object (choice) from\nthis analysis, because the button-release latency existed only when the monkey released the\nbutton (i.e., when the monkey did not release the button, making it impossible to include such\ntrials in the regression analysis). Whereas the proportion of neurons with a significant\nregression coefficient for value was significantly higher than chance for all calculation time\nwindows (each 150-ms window after the first object onset) except immediately after the onset\n(p > 0.05; two-tailed Monte Carlo test) (Supplementary Fig. 5a), the proportion of neurons\nwith a significant regression coefficient for latency was very close to chance over time, slightly\nabove for some time windows (p < 0.05; two-tailed Monte Carlo test) but below for others (p\n> 0.05; two-tailed Monte Carlo test) (Supplementary Fig. 5b). These results suggest that the\ninfluence of button-release latency on neural activity, if any, was minimal.”\nTogether, these analyses address the reviewer’s concern and support the\nconclusion that ventral striatum neurons predominantly represent value- and choice-\nrelated signals during decision formation, with minimal influence from prior value\nexpectations or motor-related factors.\n5. The authors use a 150ms sliding window with a 1ms step to classify neural response\nmodulations (value, choice, intermediate). For a typical analysis window (e.g., 500ms or\nmore), this results in a large number of statistical tests per neuron. It is crucial to clarify\nif and how corrections for multiple comparisons (e.g., Bonferroni or FDR) were applied.\nWithout such corrections, the reported proportions of neuron types could be inflated.\n> Response to the above comment:\nAs the reviewer correctly pointed out, we used a 150-ms sliding window with a 1-ms\nstep and tested statistical significance for each window using a threshold of p < 0.05.\nThis method was chosen to examine the temporal dynamics of neuronal activity,\nspecifically, when and how each neuron exhibited different types of modulation.\nFor this analysis, we further applied an additional criterion requiring that at least\n40 consecutive windows exhibit the same type of significant modulation (i.e., value-\ntype, choice-type, or intermediate-type modulation). This consecutive-window\ncriterion effectively reduces the likelihood of false positives that could arise from\nmultiple comparisons across overlapping windows. Similar approaches have been\nadopted in many previous studies (e.g., Grabenhorst and Báez-Mendoza, Nat.\nCommun., 2025), where consecutive significant windows are treated as a cluster-\nbased correction method.\nWe note that the rationale behind this approach is that adjacent sliding windows\nare not statistically independent, because neural activity typically changes gradually\nover time. Consequently, applying conventional multiple-comparison corrections\nsuch as Bonferroni or FDR, which assume statistical independence among tests,\nwould be overly conservative and substantially increase the likelihood of false\nnegatives.\nTo emphasize ‘40 consecutive windows’, we revised the original manuscript as\nfollows (new line 991-1004):\n“We performed this model comparison for each of 150-ms sliding window with a 1-ms\nstep. If a neuron showed a significantly higher R-square for the value model than the\nchoice model (p < 0.05; two-tailed Monte Carlo test) for 40 consecutive windows and\nsimultaneously exhibited a significant fit with the value model across the same 40 windows\n(p < 0.05; two-tailed F-test), the activity of this period was classified as a value-modulated\nsignal. Conversely, if a neuron showed a significantly higher R-square for the choice model\nthan the value model (p < 0.05; two-tailed Monte Carlo test) for 40 consecutive windows\nand simultaneously exhibited a significant fit with the choice model across the same 40\nwindows (p < 0.05; two-tailed F-test), the activity of this period was classified as a choice-\nmodulated signal. If a neuron showed no significant difference in R-square between the two\nmodels but significant fit with both for 40 consecutive windows, the activity during this\nperiod was classified as an intermediate signal. Some neurons represented two or even\nthree signals because their activity was classified as distinct signals during different time\nperiods.”\n6. As a robustness check and to complement the dynamic analysis, the authors should consider\nreporting results from analyses using a fixed, a priori defined time window for value encoding\n(e.g., early after offer 1 onset) and choice encoding (e.g., later, pre-response). This is common\npractice and can help validate the sliding window findings.\n> Response to the above comment:\nWe appreciate the reviewer’s valuable suggestion. As a robustness check to\ncomplement our dynamic analysis, we conducted an additional analysis using fixed,\na priori–defined time windows. Specifically, we divided the 1000-ms period of the first\nobject presentation into four equal quadrants (0–250, 250–500, 500–750, and 750–1000\nms) and applied the same model comparison procedure to each window. This allowed\nus to classify recorded neurons as value-modulated, intermediate, or choice-\nmodulated within each fixed time window. Please see the figure below, in which the\nproportions of value-modulated, intermediate, and choice-modulated neurons are\nshown for each quadrant.\nThe proportion of value-modulated neurons was the largest in the 0–250 ms\nwindow and decreased thereafter, whereas the proportion of intermediate neurons\npeaked in the 250–500 ms window and then declined. The proportion of choice-\nmodulated neurons gradually increased and became dominant in the 500–750 ms\nwindow. These temporal patterns, obtained using the fixed time windows, consistently\nreproduced the value-to-choice transition observed in our original sliding-window\nanalysis, supporting the robustness of our findings.\nWhile we believe this additional analysis validates the main results, we feel that\nincluding it in the manuscript would be somewhat redundant, given the already\nextensive supplementary materials (18 supplementary figures). However, if the\nreviewer considers this additional analysis important for clarity, we would be happy\nto include it in the revised manuscript.\n7. The text sometimes refers to classifying \"neurons\" (e.g., \"value-modulated neurons\").\nIt's more precise to state that specific responses or epochs of activity within a neuron's\nfiring pattern are classified, as a single neuron can show different types of modulation\nover time, which the authors indeed report.\n> Response to the above comment:\nWe fully agree with the reviewer’s point. In the revised manuscript, we now classify\nneuronal activity, not the neurons themselves, into value-modulated, intermediate, or\nchoice-modulated signals, as follows (new line 216-234):\n“If neurons showed a significantly better fit with the value model than the choice model,\ntheir activity was considered to be more largely modulated by the value of the first object (p\n< 0.05; two-tailed Monte Carlo test) (blue area in Fig. 2e, hereafter called “value-modulated”\nsignal). If neurons showed a significantly better fit with the choice model than the value model,\ntheir activity was considered to be more largely modulated by the monkey’s action selection\n(p < 0.05; two-tailed Monte Carlo test) (red area in Fig. 2e, hereafter called “choice-modulated”\nsignal). If neurons did not show a significantly better fit with either model (p > 0.05; two-tailed\nMonte Carlo test) but showed significant fits with both models (p < 0.05; two-tailed F test),\ntheir activity was considered to exhibit an intermediate modulation between the value and\nthe choice models (cyan area in Fig. 2e, hereafter called “intermediate” signal). To examine\nthe temporal profile of neuronal activity, this model comparison analysis was conducted\nthroughout the presentation of the first object using a 150-ms sliding window with a 1-ms\nstep. Consequently, we identified 42 neurons with the value-modulated signal, 56 neurons\nwith the choice-modulated signal, and 81 neurons with the intermediate signal (Fig. 2f, see\nalso Supplementary Fig. 3a and 3b for individual monkeys). Notably, some of the recorded\nneurons exhibited two or three types of signals because these neurons represented distinct\nsignals for different periods during the object presentation.”\nWe have revised all relevant descriptions throughout the manuscript to\nconsistently refer to neuronal activity or signals, rather than to neurons themselves.\n8. The bidirectional effects (increasing or decreasing choice probability) are reasonably\nattributed to potential mixed activation of direct and indirect pathway neurons. This\nremains speculative without cell-type specific manipulations.\n> Response to the above comment:\nWe agree with the reviewer that the interpretation regarding mixed activation of direct\nand indirect pathway neurons remains speculative without cell-type–specific\nmanipulations. To clarify this point, we have added the following description to the\nrelevant paragraph to acknowledge this limitation and to highlight directions for future\nresearch, as follows (new line 667-672):\n“Future studies are called for to directly test this interpretation using cell-type–specific\nmanipulations. Although optogenetic or chemogenetic approaches have enabled selective\nactivation of direct- and indirect-pathway neurons in rodents55, these techniques have not yet\nbeen fully established in non-human primates. The development of such cell-type–specific\ntools in primates will be essential for causally identifying the contributions of these pathways\nto action selection.”\n9. The bidirectional effects here are more puzzling. Dopamine typically excites D1-expressing\ndirect pathway neurons and inhibits D2-expressing indirect pathway neurons, both actions\ngenerally promoting approach. The manuscript's discussion of differential modulation of\ndistinct VS populations is a starting point, but this warrants deeper exploration or\nacknowledgment of its unresolved nature. Could it relate to different baseline states,\ntargeted VS subregions, non-canonical dopamine effects at the stimulation parameters used,\nor interaction with the complex encoding during offer 1 (see point 4)?\n> Response to the above comment:\nWe thank the reviewer for this insightful comment. We agree that the bidirectional\nbehavioral effects of optogenetic facilitation of dopamine inputs are indeed puzzling\nand cannot be fully explained by canonical dopaminergic mechanisms. In the revised\nmanuscript, we have expanded the discussion to explore potential explanations and\nto explicitly acknowledge the unresolved nature of these effects. The revised\nparagraph now reads as follows (new line 704-724):\n“…. Taken together, the dopamine inputs to both direct and indirect pathway neurons\nmight likely increase the animal’s trend to approach motivational stimuli.\nNevertheless, our results indicate that optogenetic facilitation of dopamine input to the\nventral striatum produced bidirectional behavioral effects. This apparent contradiction\nsuggests that the functional consequences of dopamine input may not be uniform across\nventral striatal circuits. Several non-mutually exclusive mechanisms could underlie this\nheterogeneity. First, the effects of dopamine input might depend on the baseline state of the\nlocal network, such that the same dopaminergic facilitation could either enhance or suppress\nneuronal firing depending on ongoing activity patterns. Second, subtle differences in the\nsubregions of the ventral striatum targeted by optogenetic stimulation (e.g., shell versus core)\nmight contribute to the divergent behavioral effects. Third, non-canonical effects of dopamine\nunder the specific stimulation parameters used, such as spillover activation of other receptor\ntypes or modulation of presynaptic terminals, cannot be excluded. Finally, the optogenetic\nmanipulation was applied during the presentation of the first object, when ventral striatum\nneurons encode multiple signals related to value and choice. The dopaminergic facilitation\nmight have interacted with these complex, time-varying representations, leading to variable\nbehavioral consequences. Future studies combining cell-type-specific and temporally\nprecise manipulations with simultaneous neuronal recordings will be essential to elucidate\nhow dopamine input dynamically modulates value- and choice-related signals in the ventral\nstriatum.”\n10. Given the multiple cognitive and motor-related processes potentially occurring\nduring offer 1 presentation (as noted in point 4), interpreting precisely which aspect of\nthe decision process is being altered by electrical or optogenetic stimulation is\nchallenging.\n> Response to the above comment:\nWe fully agree with the reviewer that multiple cognitive and motor-related processes\nlikely occur during the presentation of the first object, making it challenging to\ndetermine which specific component of the decision-making process was directly\naffected by electrical or optogenetic stimulation. To address this point, we have\nrevised the discussion to explicitly acknowledge this limitation and clarify our\ninterpretation, as follows (page 764-776):\n“It should be noted again that our electrophysiological recordings suggest that the ventral\nstriatum contributes not only to option valuation and action selection, but also to integrating\nthese processes as a neural hub. The ventral striatum may also participate in other processes\nessential for decision-making. Therefore, although electrical stimulation and optogenetic\nfacilitation of dopamine inputs during the decision-making period altered the monkey’s action\nselection, it remains challenging to precisely determine which component of the decision-\nmaking process was directly affected by these manipulations. Given that multiple cognitive\nand motor-related processes occur during the presentation of the first object, disentangling\ntheir respective contributions will require experimental designs that can temporally and\nfunctionally separate these processes. Future studies combining targeted perturbations with\nsimultaneous neuronal recordings will be essential for clarifying how ventral striatal signals\ncausally contribute to each stage of the decision-making process.”\n11. The simple button-release task is used to argue that VS choice signals are not purely\nmotor. However, details regarding the reward structure in this control task are missing.\nIf the reward magnitude or expectancy in the control task significantly differed from the\ndecision-making task, it could influence baseline VS activity and the observed\nmodulation strength, potentially confounding the comparison in Fig. 3c. This needs to be\nclarified.\n> Response to the above comment:\nWe thank the reviewer for pointing out this important clarification. We fully agree that\nthe difference in expected reward magnitude between the decision-making and control\ntasks could confound the comparison of neuronal modulation. To minimize this\npossibility, we used comparable reward magnitudes across the two tasks. Specifically,\nin the control task, the amount of reward per trial was set to be equivalent to that of\nvalue 3 or 4 in the decision-making task, which approximately matched the expected\nvalue of the decision-making task (3.5). Therefore, we believe that the stronger\nneuronal modulation observed in the decision-making task cannot be attributed to the\ndifference in expected reward magnitude. We have added the following description to\nthe Methods section (page 848-850):\n“The reward amount in the control task was set to be equivalent to value 3 or 4 in the\ndecision-making task, which approximately matched the expected reward magnitude in the\ndecision-making task (3.5).”\nWe also added a discussion related to this issue as follows (new line 629-648):\n“We confirmed that the choice-modulated signal discussed above was not simply caused\nby the motor action (i.e., button release) associated with choosing the first object, using the\ncontrol task in which the monkey was required only to release the button. This control task\nwas designed to eliminate the process of choice in order to isolate motor-related neuronal\nactivity in the ventral striatum. However, it also differed from the decision-making task in other\nimportant respects. In the decision-making task, the identity and value of the first object\npresented as an option varied unpredictably across trials, introducing uncertainty about which\nobject would appear and what reward could be obtained. This trial-by-trial uncertainty likely\nevoked reward expectation and prediction error processes that were absent in the control\ntask, where the stimulus–reward association was fixed and fully predictable. Such differences\nin uncertainty and reward expectation may account for the stronger neuronal modulation\nobserved during the decision-making task. In addition to these differences, we also note that\nthe reward structure was comparable across tasks. The reward amount per trial in the control\ntask was set to be equivalent to value 3 or 4 in the decision-making task, which matched the\nexpected reward magnitude in the decision-making task (3.5). However, because the\nduration of each trial in the control task was shorter, the overall reward rate was actually\nhigher, potentially leading to greater motivation. Therefore, the weaker neuronal modulation\nobserved in the control task is unlikely to reflect reduced motivation.”\nMinor Comments：\n1. Plotting trial-by-trial choice behavior for value 1 = 4 under stimulation vs. no-\nstimulation conditions could reveal finer-grained dynamics, such as adaptation or\nsequential dependencies in the stimulation effect.\n> Response to the above comment:\nWe thank the reviewer for this helpful suggestion. Following the comment, we have\nnow visualized the trial-by-trial choice behavior for first object value = 4 under\nstimulation and no-stimulation conditions as raster plots in the new Figures 4 and 5.\nThese plots allow readers to observe the temporal dynamics of choice modulation,\nincluding potential adaptation or sequential dependencies related to stimulation.\n2. For correlations that are \"close to significance\" (e.g., Fig S6c, p=0.11 for opto-stim\neffect on choice vs. motor latency), it's advisable to acknowledge the trend and be\ncautious about definitively concluding \"no relationship,\" especially with modest sample\nsizes for correlational analyses.\n> Response to the above comment:\nWe thank the reviewer for the helpful suggestion. In the revised manuscript, we have\nmodified the description to acknowledge the modest negative trend in the correlation\nwhile avoiding an overly definitive statement of “no relationship.” Specifically, we now\ndescribe that although the correlation between the difference in button-release latency\nand the difference in choice rate of the first object with value of 4 did not reach\nstatistical significance, a weak negative trend was observed. The revised text reads\nas follows (new line 527-535):\n“Please note that there was no significant correlation between the difference in button-\nrelease latency and the difference in choice rate of the first object with value of 4, although\nthe weak negative correlation was observed (n = 50 sites, r = -0.23, p = 0.11; two-tailed\nPearson’s correlation test) (Supplementary Fig. 17c). This subtle trend indicates only a\nlimited association, if any, between the effect of laser light stimulation on the motor action\nand the effect on the monkey’s action selection. These results therefore suggest that the\neffect of laser light stimulation on dopamine projections on the monkey’s action selection is\nunlikely to be fully explained by its impact on motor action.”\n3. The manuscript should specify the time windows or criteria used to define a neuron's\nstate (value, intermediate, choice) for calculating the transition probabilities shown in\nFig 2i.\n> Response to the above comment:\nWe appreciate the reviewer’s insightful comment. The criteria and time windows used\nto define each neuronal state (value-modulated, choice-modulated, or intermediate)\nwere originally described in the Methods section. However, we agree that this\ndescription could be made clearer. In the revised manuscript, we have rewritten this\npart to improve clarity and readability. Specifically, we now provide a concise\nexplanation that each neuron’s activity was statistically evaluated by comparing the\nfits of a value model and a choice model using R-square values obtained from 150-ms\nsliding windows with a 1-ms step, assessed by Monte Carlo and F-tests. Based on the\nresults over 40 consecutive windows, neuronal activity was classified as value-\nmodulated, choice-modulated, or intermediate. These criteria were also used to\ndetermine the latencies of each modulation type. The revised text now reads as\nfollows (new line 991-1008):\n“We performed this model comparison for each of 150-ms sliding window with a 1-ms\nstep. If a neuron showed a significantly higher R-square for the value model than the\nchoice model (p < 0.05; two-tailed Monte Carlo test) for 40 consecutive windows and\nsimultaneously exhibited a significant fit with the value model across the same 40 windows\n(p < 0.05; two-tailed F-test), the activity of this period was classified as a value-modulated\nsignal. Conversely, if a neuron showed a significantly higher R-square for the choice model\nthan the value model (p < 0.05; two-tailed Monte Carlo test) for 40 consecutive windows\nand simultaneously exhibited a significant fit with the choice model across the same 40\nwindows (p < 0.05; two-tailed F-test), the activity of this period was classified as a choice-\nmodulated signal. If a neuron showed no significant difference in R-square between the two\nmodels but significant fit with both for 40 consecutive windows, the activity during this\nperiod was classified as an intermediate signal. Some neurons represented two or even\nthree signals because their activity was classified as distinct signals during different time\nperiods.\nThe model comparison analysis was also used to determine the latencies of value-\nmodulated, intermediate, and choice-modulated signals. These latencies were defined as\nthe start time of the first of the 40 consecutive 150-ms sliding windows with the 1-ms step\nwhere each modulation type was identified.”\n4. The 50% transition probability between \"value-modulated\" and \"intermediate\" states\n(and vice-versa) warrants discussion. What does this bidirectionality imply about the\nunderlying neural process compared to the more directed transitions towards \"choice-\nmodulated\" states?\n> Response to the above comment:\nWe appreciate the reviewer’s insightful comment regarding the bidirectional transition\nbetween the value-modulated and intermediate signals. We agree that this finding\nwarrants clarification. In the revised manuscript, we now note that the equal transition\nprobability between these two states suggests that they may not form a strictly\nsequential process, but could instead reflect bidirectional or overlapping\ncomputations during value evaluation. We also mention that such transitions are likely\ncoordinated at the population level rather than within single neurons (we found that\nthe latency of the value-modulated signal was shorter than that of the intermediate\nsignal at the population level). The revised text now reads as follows (new line 300-\n304):\n“The equal transition probability between the value-modulated and intermediate signals\nsuggests that these two states may not form a strictly sequential process but could reflect\nbidirectional or overlapping computations during value valuation. This transition is therefore\nlikely to be coordinated at the population level rather than within single neurons.”\n5. The discussion acknowledges the role of prefrontal cortex (PFC) in transforming value\nto action. The findings beg the question of how VS computations are coordinated with\nPFC inputs. Is the VS transforming value signals received from regions like OFC/vmPFC,\nor is it part of a more iterative processing loop? A more nuanced discussion of these\npotential network interactions would enrich the paper.\n> Response to the above comment:\nWe thank the reviewer for this insightful comment regarding the network-level\ninteractions between the ventral striatum (VS) and the prefrontal cortex (PFC). In the\nrevised Discussion, we have expanded this section to incorporate a more nuanced\nconsideration of how VS computations may be coordinated with cortical inputs.\nSpecifically, we now note that both the VS and PFC are components of cortico-basal\nganglia loop circuits, and that the nature of the signals exchanged between these\nregions remains poorly understood. We further emphasize that computational studies\nhave proposed that cortico-basal ganglia loops implement more than simple\nfeedforward transmission and instead support iterative and recurrent processing\nwithin the loop. This addition provides a framework in which the VS could both\ntransform value signals received from cortical regions such as the OFC/vmPFC and\nactively participate in iterative value–choice computations. We also added that future\nstudies should investigate these interactions by simultaneously recording from both\nregions or by causally perturbing their communication. The revised text reads as\nfollows (new line 581-608):\n“The neural dynamics we observed in the ventral striatum emphasizes its role as a\nneural hub that bridges option valuation and action selection. Previous studies have also\nexplored the neural mechanism that bridges option valuation and action selection. These\nstudies mainly dealt with the prefrontal and parietal cortical areas32-34. For example, it has\nbeen shown that option value signals are encoded in the ventromedial prefrontal cortex and\nthen transmitted to the dorsomedial prefrontal cortex and the intraparietal sulcus area,\nwhere neuronal activity is involved in action selection by comparing the value of each\noption34. In contrast, we found that neurons in the ventral striatum are involved in both\nvalue signaling and action selection. Their activity gradually shifted from value\nrepresentation to action selection encoding, suggesting that the ventral striatum may\ndirectly derive action selection from value information. At the same time, it is important to\nnote that neurons in the orbitofrontal cortex have been reported to encode value- and\nchoice-related variables in decision-making situation27,35, and that similar value-to-choice\ntransitions have been observed in the amygdala36,37. Because both the orbitofrontal cortex\nand amygdala project directly to the ventral striatum38,39, the signals we observed in the\nventral striatum might in part be inherited from these upstream regions. Particularly,\nhowever, the relationship between the ventral striatum and prefrontal regions requires\nfurther considerations. Both regions participate in cortico-basal ganglia loop circuits, often\nreferred to as the “limbic loop”40,41, yet little is known about the nature of the signals\nexchanged between them. Computational studies have suggested that cortico-basal\nganglia loop circuits, which sometimes include the dopamine system and amygdala,\nimplement more than simple feedforward transmission of signals and highlighted the\nimportance of iterative and recurrent processing within these circuits42-44. In light of our\nfindings on the dynamic transformation from value representation to action selection\nencoding in the ventral striatum, future studies should investigate how such iterative\ninteractions between the ventral striatum and prefrontal regions contribute to decision-\nmaking, for example, through simultaneous recordings or causal perturbations of their\ncommunication.”\n6. To further situate the current findings within the field, the authors may wish to\nconsider additional relevant studies on primate ventral striatum (VS) function in reward\nprocessing and decision-making. For example, incorporating discussion of works such as:\nhttps://doi.org/10.1523/JNEUROSCI.2430-24.2025\nhttps://doi.org/10.1016/j.neuron.2010.11.041\n> Response to the above comment:\nWe thank the reviewer for this insightful suggestion. In the revised manuscript,\nwe have expanded the Discussion section to more comprehensively situate our findings\nwithin the broader literature on the primate ventral striatum (VS). Specifically, we now\nrefer to and discuss the studies by Cai et al. (2011) Neuron (Ref 31 in our manuscript)\nand Iwaoki et al. (2025) J Neurosci (Ref 56 in our manuscript) as follows (new line 564-580 and 725-742, respectively):\n“The ventral striatum has been widely recognized as a key structure for processing\nreward value information and regulating motivational states3-6. For example, a previous\nstudy demonstrated its role in representing temporally discounted values by integrating\ninformation about both reward magnitude and delay during decision-making31. Importantly,\nthis study reported that ventral striatal neurons encoded the sum of discounted values\nacross options, which may reflect a general motivational drive, whereas neurons in the\ndorsal striatum encoded the difference between these values, suggesting a more pivotal\nrole for the dorsal striatum in action selection. In contrast, our present results indicate that\nthe ventral striatum itself can also participate directly in action selection. The gradual\ntransition we observed from the value-modulated to choice-modulated signals suggests\nthat, beyond its established role in value integration, the ventral striatum may actively\ncontribute to transforming option value information into action selection. However, to fully\nunderstand the division of labor between the ventral and dorsal striatum, it will be essential\nto examine their activity under identical behavioral conditions. Future studies that\nsimultaneously record neuronal activity in both regions during the same decision-making\ntask will be crucial for elucidating how these striatal subregions cooperate to translate value\ninto action.”\n“Please note that, although both electrical stimulation of the ventral striatum and\noptogenetic facilitation of dopamine inputs to this region produced bidirectional effects on the\nmonkey’s action selection, only the electrical stimulation exhibited a weak but significant\nanterior–posterior gradient. The positive effects tended to appear in the anterior portion,\nwhereas the negative effects were more pronounced in the posterior portion. A recent\npharmacological study in macaque monkeys also reported functional heterogeneity along\nthis axis of the ventral striatum, showing that inactivation of its anterior portion induced\nhypoactive, “resting” behavior, whereas inactivation of the posterior portion elicited\ncompulsive-like “checking” behaviors56. Our results raise the possibility that such anterior–\nposterior functional differentiation may also extend to decision-related processes. However,\nit remains unclear why optogenetic facilitation of dopamine inputs did not exhibit a similar\ngradient. One possible explanation is that electrical stimulation and pharmacological\nmanipulation directly modulate the cell body activity of ventral striatal neurons, whereas\noptogenetic facilitation affects dopamine release from axon terminals. Since dopamine\nrelease mainly modulates synaptic efficacy onto striatal neurons rather than their intrinsic\nexcitability, the anterior–posterior gradient observed with electrical stimulation might reflect\nthe influence of non-dopaminergic afferents that directly impact neuronal firing.”\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nThe authors have addressed all my comments.\n> Response to the above comment\nWe appreciate your positive feedback and are glad that our revisions have addressed\nall of your comments.\nReviewer #2 (Remarks to the Author):\nThanks for all your work in addressing my concerns. I believe the manuscript has been\nsignificantly strengthened as a result. I have no major comments left and my minor\ncomments below are optional small points.\n> Response to the above comment\nWe appreciate your positive feedback and are glad that our revisions have addressed\nyour concerns.\nRelated to my previous comment #3, I wondered if it would make sense to add the\nprecautions to prevent light leaking into the experimental room during optogenetic\nstimulation to the Methods in one or two sentences (e.g. to mention the black tube &\nblack sheet).\n> Response to the above comment\nWe agree that describing these precautions is important for the reproducibility of the\nstudy. We have therefore added the following text to the Methods section (new lines\n947–951):\n“When the laser was simply turned on, light weakly leaked from the connection between\nthe optrode and the optical fiber, which could potentially affect monkey behavior. To prevent\nthis unintended leakage, we covered the connection with a black plastic tube and wrapped\nthe entire assembly with a black opaque sheet. These precautions ensured that no light\nleakage was visible in the experimental room.”\nI also think that the figure shown in response to my previous comment #4 showing\ntransitions from value to intermediate and intermediate to choice neurons in terms of\nlatency could make an interesting supplementary figure. I leave it up to the authors to\ninclude it or not, according to their preference.\n> Response to the above comment\nWe agree that readers may find the latency of transitions of interest. In response to\nthe reviewer’s suggestion, we have included this data as Supplementary Fig. 6 in the\nrevised manuscript.\nReviewer #3 (Remarks to the Author):\nThe authors have adequately addressed all of my concerns, and I appreciate the inclusion\nof the additional analyses. The manuscript has improved and is now more coherently\nframed in light of the evidence provided. I have no further comments.\n> Response to the above comment\nWe appreciate your positive feedback and are glad that our revisions have addressed\nall of your concerns.\nReviewer #4 (Remarks to the Author):\nThe authors have addressed all of my concerns with the original manuscript.\n> Response to the above comment\nWe appreciate your positive feedback and are glad that our revisions have addressed\nall of your concerns.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts."}]

Decision letter