Enhanced reward coding and condition-independent dynamics in optogenetically identified corticostriatal neurons in monkeys

Domain: biology
Published: 2026-04-03
Editorial outcome: major_revision

Abstract
The basal ganglia integrate cortical inputs with dopaminergic signals to potentiate and select actions. The reward-related activity of dopamine neurons is well-studied, but the coding properties of cortical inputs to the basal ganglia remain largely unknown. We examined the activity of neurons in the frontal eye field of monkeys that were optogenetically identified as projecting to the basal ganglia. We found that the projecting neurons contained information about expected rewards and selected actions. The reward-related signal and modulations independent of task condition were stronger in optogenetically identified projecting neurons than in other neurons in the same area. These findings indicate that reward, choice, and sensorimotor information are already integrated into the cortical inputs to the basal ganglia, suggesting that the basal ganglia network integrates reward from both cortical and dopaminergic inputs rather than relying on a dopaminergic source alone.

Introduction
One of the widespread assumptions about basal ganglia function is that the striatum integrates reward information from dopaminergic inputs with cortical information to potentiate movement and select actions1–3. While studies have extensively characterized dopaminergic inputs to the striatum4–11, the content of the cortical input to the basal ganglia remains less well understood12,13. Cortical neurons have been shown to integrate reward, choice and sensorimotor information14–19, but neurons projecting to the basal ganglia may not contain reward-related signals. Further, other sensory and movement signals transmitted from the cortex to the basal ganglia aside from reward-related signals remain poorly characterized.

To better understand the content of cortico-striatal signaling, we focused on the monkey eye movement system. In this system, the frontal eye field (FEF) projects to the caudate nucleus in the striatum20,21, and both areas are causally linked to eye movements22,23. The FEF is a central hub for processing visual and movement signals that drive eye movements18,24.

We aimed to functionally characterize the signal transmitted from the FEF to the caudate in eye movement tasks that included reward and choice manipulations. In earlier research protocols, antidromic electrical stimulation, where axons of neurons are stimulated at the target site and detected at the source has been used to identify input12,25. However, the yield of this method is often low, as it is difficult to stimulate and record from the same neuron at two sites simultaneously. Viruses that code channelrhodopsin and are transported retrogradely from the injection site (e.g., the caudate) to the source (e.g., FEF) provide an effective alternative26,27. Using this method, input neurons can be identified by their responses to light stimulation at the recording site28–30, a method termed opto-tagging.

We opto-tagged neurons projecting from the FEF to the caudate and recorded their activity during eye movement tasks where reward size was manipulated. We found that the neurons that were opto-tagged encoded strong reward and choice information and exhibited more pronounced modulations in activity averaged across task conditions (termed condition-independent modulations) than other neurons, thus implying that the inputs to the basal ganglia have already integrated reward, choice and sensorimotor information. These findings thus suggest that the basal ganglia rely on a combination of reward signals from multiple sources rather than dopaminergic input alone.

Results
Reward impacts monkeys’ behavior in eye movement tasks
Two monkeys were engaged in three eye movement tasks in which we manipulated the reward size (Fig. 1A). In these tasks, the monkeys were presented with color-coded targets that indicated whether they would receive a large or small reward at the end of the trial. The monkeys observed a colored target that appeared in a central (the pursuit task) or a peripheral location (the saccade task). After a variable delay, the monkeys were instructed to shift their gaze to the target (Go). On the saccade trials, the monkeys moved their eyes ballistically to the eccentric location (Fig. 1A and B, left), whereas on the pursuit trials the monkeys smoothly followed a continuously moving target (Fig. 1A and B, middle). The saccade and pursuit movements were directed to one of the four cardinal directions. On the choice task, two targets appeared and the monkeys had to select one (Fig. 1A and B right). At the end of each trial, the monkeys received either a small or large reward based on the color of the single or selected target. We refer to the epoch during which the monkeys observed the cues but were not allowed to move as the cue epoch, the epoch during which they moved their eyes to the colored target as the movement epoch, and the epoch during which they received the reward as the outcome epoch.

The monkeys’ behavior indicated that they successfully associated the colored target with the upcoming reward. We analyzed behavior on days when we recorded neural activity, after the monkeys were well-trained on the task. On the saccade tasks, the monkeys moved earlier in the large reward target (Fig. 1C, p=8*10^-5 and 10^-6 for monkeys H and K, Wilcoxon signed-rank test). On the pursuit trials, in one monkey, the eye velocity at movement initiation was consistently faster for the large reward (Fig. 1D, eye velocity at 200-250 ms after target motion onset, p = 8*10^-7 and p =0.98 for monkeys H and K, Wilcoxon signed-rank test). The monkeys’ preferences were confirmed on a choice task in which both monkeys almost always selected the target that was associated with the larger reward (Fig. 1E p = 5*10^-7 and p=8*10^-6 for monkeys H and K, Wilcoxon signed-rank test for differences from a chance level of 0.5), demonstrating that the monkeys associated color and reward size. Thus, the monkeys’ behavior indicated that they were expecting the upcoming reward, which provided the foundation for studying reward-related signals.

Opto-tagging of neurons in the FEF projecting to the striatum
To identify neurons in the FEF projecting to the basal ganglia, we injected an AAVretro-ChR2 virus26 into the eye movement areas of the caudate (Fig. 1F and Fig. S1). This virus is transported retrogradely from the axons in the caudate to the cell bodies in the cortex. As a result, in the cortex, only cortico-striatal projection neurons express ChR2. We confirmed the injection location over the course of several days during which we injected a solution with Mn+2 and imaged the injection site with an MRI scan (Fig. S1). We waited four weeks for viral expression and then on each recording day we lowered an optrode with 4 closely spaced recording contacts attached to a fiber for light stimulation (Thomas Recordings) into the FEF (Fig. S2). We searched for neurons that responded consistently with short latencies (see Methods) to brief light stimulations (durations of 5 ms at 10, 20 and 50 Hz). Figure 1G shows an example of the extracellular activity in one contact of the optrodes while we applied the light stimulation. The recorded neuron fired sparsely before the stimulation and very consistently with a very short latency during the stimulation (Fig. 1G, H). Based on the response to stimulation (see Methods) we categorized the neurons into (1) light responding neurons that were highly likely to be cortico-striatal projection neurons, (2) neurons that were recorded in the same location as the responding neurons but did not respond to the stimulation, or their response was inconsistent, and (3) neurons in which none of the neurons in the recording session responded consistently to the stimulation. A response to light was considered strong evidence for the cell to be a cortico-striatal neuron, but a lack of response was harder to interpret. For this reason, we classified the neurons into three groups: tagged (n=108), untagged (n=266) and area-untagged (n=138). We performed an offline analysis to confirm the consistency of the extracellular waveforms of the connected neurons across the stimulation and behavioral sessions (Fig. S3 and Methods).

Factoring the response of single neurons to the experimental variables
To study the coding properties of the neurons, we factored the single-neuron activity into components related to direction, reward, and those independent of the task condition. We depict this factorization in Figure 2A-E which shows the response of an example neuron to the appearance of an eccentric color cue in the saccade task. Averaging across either the reward size or direction of the visual cue (and upcoming movement) (Fig. 2C and D) indicated that the neuron was modulated by the direction and the reward size. Averaging across both reward size and direction showed that the neuron exhibited overall temporal modulations Fig. 2E) which we term condition-independent modulations.

To quantify the contribution of these different experimental variables to the overall activity, we calculated the partial \omega2 effect size (\omega2, see Methods)3 1–33. \omega2 is an unbiased estimator of the variability explained by an experimental variable, normalized by the same variance plus a noise term (Fig. 2F). The noise term is an estimator of the variance that is unaccounted for by any variable in the experiment; i.e., the trial-by-trial variability within task conditions. Note that we term this ‘noise’, but it may still contain meaningful trial-by-trial activity. \omega2 is distributed around zero (unbiased) when a neuron does not respond to the experimental variable and has a value of 1 when all the trial-by-trial variability is explained by an experimental variable. Intermediate values indicate how much of the neuron’s activity can be accounted for by a specific variable in comparison to the trial-by-trial variability that cannot be explained by any of the variables.

To calculate the effect size, we used an ANOVA model for each neuron, with reward size, direction of visual cue, and the time bin within the trial as variables (see Methods, e.g., values in Fig. 2C–E). The direction effect size estimates the magnitude of the neuron's directional tuning. In the choice task, we define the direction effect size as the side (e.g., left or right) on which the large-reward target appeared. The reward effect size estimates how different the responses were in the large- versus small-reward condition. The time effect size estimates the overall average modulation across all conditions (e.g., Fig. 2E), i.e., the condition-independent modulations.

The results of the effect size analysis confirmed findings from previous studies: (1) The direction of the visual cue and reward effect sizes were significantly different from zero (Fig. 2H–I, p < 10^-6 Wilcoxon signed-rank test) as expected from neurons in the FEF15,17, (2) The effect size was widely distributed, with most of the neurons having small values close to zero but some exhibiting large effect sizes (e.g., Fig. 2G) demonstrating the typical long-tailed coding of task parameters34, (3) The time effect size tended to be the largest (Fig. 2J, and see for example the comparison in Fig. 2G between reward and time effect sizes), consistent with previous studies reporting that condition-independent modulations can account for the largest share of the population activity16,35.

Tagged neurons had the largest time and reward effect sizes during the cue epoch
We compared the effect sizes of the three populations of neurons. On all three tasks, the average time effect size of the tagged neurons was larger than in the untagged and the area-untagged populations (Fig. 2J). In the tasks where we manipulated the upcoming reward size (saccade and pursuit), the reward effect size was larger in the tagged neurons (Fig. 2I). On the tasks where the visual eccentric target provided information about the direction of the upcoming movement (saccade and choice), the direction effect size was not significantly different across populations (Fig. 2H). Thus, the effect size analysis of the cue period indicated that reward and condition-independent modulations were the most strongly observed in inputs to the basal ganglia.

Larger time and reward effect sizes in the tagged neurons persisted into the movement epoch
The FEF demonstrated coding of multiple experimental variables during the movement epoch as well. Figure 3A shows an example neuron that coded multiple experimental variables during the saccade tasks around the time of the saccade. This neuron responded with a burst of activity around the time of the saccade. Interestingly, its response was stronger in the small reward condition (red versus blue lines in Fig. 3B,D). Later we return to an analysis that quantified whether and when reward size was coded by an increase or decrease in activity. The same neuron also responded differently for different directions of movement (Fig. 3C), and the overall modulations were not averaged out across conditions (Fig. 3E). This coding of multiple components was reflected in the positive values of the effect size.

We then compared the effect sizes across populations. As was the case for the cue, the tagged neurons had the largest reward and time effect sizes (Fig. 3G and H). The direction effect size was not significantly different across populations. Thus, analysis of the movement epoch further demonstrated the coding of reward-related information by inputs to the basal ganglia and the larger condition-independent modulations of these inputs. We also calculated the effect sizes during the outcome (Fig. 3I-K) which further supported the larger time and reward effect size by the tagged neurons (Fig. 3K and J).

In addition to the effect size differences between the populations, we also found that the baseline firing rate defined by the pre-cue activity (Fig. 1A pre-cue) was larger for the tagged neurons. Even when taking this firing rate baseline into account, the time and reward effect sizes were larger for the projecting neurons (Fig. S4).

Beyond effect size: taking into account the sign of the modulation and dissociating reward from coding of eye movement
The effect size analysis has the advantage of partitioning activity according to the experimental variables, thereby providing a convenient tool for comparing populations. However, it has some drawbacks that require further analysis to better understand coding in the FEF. The effect size does not indicate whether the modulations are a result of an increase or decrease in rate. For example, the neurons shown in Figure 2A and 3A responded differently to the large and small reward conditions and therefore had a positive reward effect size; however, their responses were qualitatively different since one neuron responded more strongly to the large reward whereas the other responded more strongly to the small reward. Another shortcoming of effect size is that the nature of the interaction between variables can be difficult to interpret. For example, the interaction between reward and direction could result from either reward potentiation or attenuation of directional tuning, or other patterns. Finally, the effect size analysis did not dissociate the effect of reward itself from the effect of reward on eye movements. In particular, we found that saccade latency was shorter for the large reward (Fig. 1C). This raises the question of whether the reward-related modulations observed in the cue and movement epochs reflect differences in latency.

To further study if and how reward was processed in the different populations, we conducted the next analyses by taking the population average of the different populations and studying the distribution of responses across neurons. We aimed to test whether (1) the sign of the reward modulation was consistent across neurons, (2) how reward modulations interacted with the directional tuning curves, and (3) whether the reward modulations could be explained by the effect of reward on eye movements.

Reward potentiated directional tuning in the cue epoch the most strongly for tagged neurons
To study how reward modulated the coding of direction, we first tested whether each neuron was directionally tuned (ANOVA for direction, p < 0.05) and then calculated the preferred direction (PD, see Methods). We then calculated the average activity in the PD and the opposite direction (Null) for the large and small reward conditions. Directional tuning was enhanced by the reward, as indicated by the larger responses in the PD for the large versus small reward condition (Fig. 4A-C). Quantification of reward modulation in the PD at the single-neuron level showed that neurons across all the populations tended to respond more strongly in the large reward condition (Fig. 4E-G). The effect of the reward in the PD was the largest in the tagged neurons (Fig. 4D, solid black line): the difference in rate between the large and small rewards was significantly greater for the tagged neurons than for the other groups (p=0.02 and p=0.002 for tagged versus untagged and tagged versus area-untagged, Wilcoxon rank-sum test). Thus, the directional tuning of the visual responses of inputs to the basal ganglia was already strongly potentiated by reward. This potentiation of visual responses by reward is akin to reports of modulations of spatial attention36,37, suggesting that inputs to the basal ganglia already carry spatial attention information.

Reward did not consistently modulate the sign of activity in neurons that were not directionally tuned. Although reward had a significant effect in many of these neurons (70/365 open dots in Fig. 4H-J), the neurons that increased their activity in the large reward or small reward conditions were overall evenly distributed. As a result, the overall population difference between the large and small rewards did not reach significance (Fig. 4D, dashed line). Similarly, during the cue epoch of the pursuit task when directional information was not available, many neurons coded the reward (174/512), but the sign of the modulation was inconsistent across neurons (Fig. 4D gray line).

The modulations described so far indicated that, in the projection to the basal ganglia, reward information was already integrated. We termed these modulations ‘reward-related’ since reward was the task variable we manipulated. However, it is possible that these modulations reflected effects downstream to the representation of reward expectation. In the extreme case, all reward-related activity could be linked to the motor command. In the current task and recording site in particular, the reward-related modulations could reflect the shorter latency (Fig. 1C). We therefore used trial-by-trial variability in behavior to test whether reward modulations could be explained by saccade latency. Within each direction and reward size condition, we divided the trials into those with short and long saccade latencies. This division had little impact on either the population averages or single-neuron activity (Fig. S5). We then directly compared the short latency trials in the small-reward condition with the long latency trials in the large-reward condition. Selecting this set of trials reversed the behavioral effect (Fig. S5A, purple dots). However, the neural effect persisted, as we found for the full trial set. Specifically, the tuned neurons responded more strongly to large compared to small rewards in their preferred direction (Fig. S5C, p=10^-7 Wilcoxon signed-rank test). In addition, the sign of the modulations in the untuned neurons that coded reward size remained the same (Fig. S5D, 69/70 neurons) indicating that although the behavior flipped in sign (large shorter than small, to small shorter than large) in this control, the neural activity did not.

Similar controls for the amplitude of the saccade, peak velocity showed that these parameters had no detectable impact on FEF activity in the cue (not shown). In addition, removing trials with microsaccades during the cue38 had no impact on the reward modulations (not shown). Thus, as we17 and others 39 have shown in the FEF, reward-related activity could not be explained solely by coding of the eye movement.

During movement, reward modulations averaged out at the population level but remained coded beyond eye movements
The analysis above indicated that in visually tuned neurons, reward potentiated directional tuning. We next tested whether this potentiation extended to movement. In the movement epoch we could control directly for putative differences in the reward effect on saccade latency (Fig. 1C) by aligning the neural activity on the time of the saccade. In all populations, the overall directional tuning was only slightly modulated by reward (Fig. 5A-C), as indicated by the similarity of the population response in the PD at the time of the saccade. Neuron-by-neuron analysis revealed that this lack of modulation was the result of the cancelling out of opposite modulations of reward rather than the elimination of reward modulation by single neurons. This was indicated by the distribution of neurons with significant coding of reward along both sides of the diagonal in Figure 5E-G. Overall, the fraction of neurons that coded reward significantly around the saccade (-100-300 ms) was much greater than chance (142 out of 512 neurons, p ~ 0, chi-square test).

A similar averaging out was observed during pursuit (Fig. S6) and in neurons that were not directionally tuned during saccade and pursuit (Fig. 5D, H). These results extend our previous findings on reward modulation during pursuit17 where we found a general inconsistency in the coding of reward and direction during movement in the FEF. This coding enables commonly assumed readouts such as the population vector, with minimal interference from reward17, thereby permitting simple population readouts that disentangle the multiplexed direction from reward information.

FEF inputs to the basal ganglia code the selected target before movement
Several theories have posited that the basal ganglia implement action selection40. The effect size analysis above indicated that in the choice task, the inputs to the basal ganglia had already differentiated between selection conditions. Specifically, activity was different if the large reward target appeared on the left or right side (dashed line in Fig. 2H). We next performed an analysis that went beyond effect size to better understand how the FEF codes choice-related activity.

For each neuron we defined the preferred choice condition as the choice condition with the larger response, and the condition with the smaller response as the null choice condition. In the choice task we contrasted two directions. As a result, the preferred choice condition could differ from the PD in the tuning task which was based on four directions (see Methods). We averaged the activity across neurons in the preferred and null choice conditions (Fig. 6A-C). We controlled for statistical biases by using half of the data from each neuron to determine the preferred choice condition and the other half to calculate the average activity. We found that in all populations, soon after cue presentation, the FEF activity differentiated between choice conditions (Fig. 6A-C, solid versus dashed black line).

To further study neural representations during the choice task, we ran analyses comparing choice vs. single-target trials16,41. The monkeys almost always selected the larger reward target (Fig. 1E). In addition, saccade latency was very similar (although not identical) in the large-reward and choice trials (Table S1). Given these findings, we next examined how the choice process was reflected in FEF activity and in the information conveyed to the basal ganglia. We tested whether FEF activity and its inputs to the basal ganglia would resemble the responses to the selected target alone, while ignoring the other target. These representations were expected if the FEF is situated near the completion of the choice process and contrast with other representations, which are expected at early or intermediate stages of the choice process (see below).

In all populations, the activity in the choice condition was almost identical to the activity in the corresponding large reward condition; i.e., activity in the choice trials was similar to trials where the monkeys only observed the large reward target. The similarity between choice and large reward is manifested in the similarity of the black and blue lines in the population average in Figure 6A-C. In contrast, activity during choice trials differed from that observed when only the small-reward target was shown. As depicted in Figure 6A–C, responses in the preferred choice condition were stronger than in the single small-reward trials, whether the small-reward target appeared in the same location as the small target in the choice trials (solid black vs. dashed red lines) or when it appeared in location of the large reward target in the choice trials (solid black vs. solid red lines). Quantification using PSTH distances showed that the distance between the choice and the large reward target appearing alone was the smallest (Fig. 6D, E, for preferred and null choice conditions).

This representation in the selection task aligns with expectations associated with the coding of the selected target and its reward value41, but not with other coding schemes typically associated with intermediate processing stages. The difference between choice conditions suggests that the neurons did not code the only the large reward value, regardless of its location42. The results also showed that neurons did not code the reward value in a specific location. If this were the case, we might expect activity in the preferred choice condition to resemble the response to the large reward appearing alone, since the larger reward target lies within the neuron's receptive field. However, this interpretation falls short, because in the null choice condition, where the small reward target is within the receptive field, we would expect activity to resemble the response to the small reward target appearing alone, this was not observed (Fig. 6A-C, dashed black line versus solid red and Fig. 6E). Instead, the activity in the null choice condition more closely matched that of the large reward condition (Fig. 6A-C, dashed black line versus dashed blue line). Finally, the coding of reward in the single-target trials by these same neurons (Fig. 6A–C, blue versus red traces) indicates that they do not merely encode which target was selected, but also incorporate value signals into their activity. Thus, overall, the activity in the selection trials is best interpreted as a post-selection evaluation of the selected target (see Discussion).

In the effect size analysis, we found slightly stronger coding of choice conditions in the tagged neurons, but this effect was not significant (dashed line in Fig. 2H). Therefore, in the analyses above, where we compared the choice and single target conditions, we did not include statistical comparisons between populations. However, as expected, the trends were consistent across analyses, with tagged neurons showing larger modulation.

Methods
Animals and ethics
We collected neural and behavioral data from two female (monkey K and monkey H) Macaca fascicularis monkeys (4-5 kg). All procedures were approved in advance by the Institutional Animal Care and Use Committees of the Hebrew University of Jerusalem.

Surgical procedures
We first implanted head holders to restrain the monkeys' heads in the experiments. We performed a second surgery to place a round recording cylinder with an inner diameter of 19 mm over the FEF. The center of the cylinder was placed above the skull at 19 mm anterior and 15 mm lateral to the stereotaxic zero, and tilted 25°with respect to the medial plane to allow for injections to the caudate nucleus. The localization of the FEF and caudate was based on stereotactic coordinates and then confirmed with post-implant MRI (Figs. S1 and S2) scan and electrophysiological markers (see below).

Experimental setup
After the monkeys recovered from head holder surgery, they were trained to sit calmly in a primate chair (Crist Instruments) and consume liquid food rewards (baby food mixed with water and infant formula) from a tube set in front of them. We trained the monkeys to track spots of light that moved across a video monitor placed in front of them. Visual stimuli were displayed on a CRT monitor with a refresh rate of 85 Hz (55 cm from the eyes of the monkeys). The stimuli appeared on a dark background in a dimly lit room. A computer performed all real-time operations and controlled the sequences of target motions. The position of the eye was measured with a high temporal resolution camera (1 kHz, Eyelink 1000 plus, SR research) and collected for further analysis.

Experimental design
The monkeys were engaged in saccade, pursuit, and choice tasks. In the saccade task each trial started with a bright white circular target that appeared in the center of the screen. After 500 ms of presentation, in which the monkey was required to acquire fixation, a colored target appeared at a position 10° eccentric to the fixation target in one of the four cardinal directions. We defined this time point as the cue onset. The color of the target indicated that upon successful completion of the trial, the monkey would receive either a small or large reward (0.07 or 0.15 ml of food). For monkey K we used blue and red targets for the large and small rewards, and reversed the associations for monkey H. The monkeys were required to maintain fixation on the central target. Moving the eye towards the eccentric target would abort the trial. After a variable delay of 500-700 ms, the fixation target disappeared, which served as the Go signal for the monkeys to move their eyes to the peripheral target. The monkeys had to acquire fixation in 750 ms and then after an additional 200-300 ms they received the reward.

The structure of the pursuit trials was similar to the saccade trials but with the following differences. A colored target replaced the white target in the center of the screen. After the cue epoch, the target jumped 4° in one of the four cardinal directions and then moved in the opposite direction towards and through the center of the screen at 20°/s (step-ramp63). The target moved for 750 ms, then stopped and stayed still for an additional 200-300 ms.

The structure of the choice trials was also similar to the saccade task but instead of a single target, two colored eccentric targets appeared at the cue. The target appeared either along the horizontal or vertical axis 10° eccentric to the fixation target. When the central target disappeared, the monkey was free to select the target to which to saccade. Online we detected the saccade as an eye movement that exceeded 80°/s. The target that was closer to the eye at the end of the saccade remained and the other target disappeared. The monkeys were then required to continue to fixate on this target and received a reward based on the color of the target. In all tasks we enforced a precision window of 3-5°x3-5° around the fixation target with grace periods after the Go signal to allow the monkeys to acquire the target. Trials in which the monkeys failed to meet the fixation requirements were removed from the analysis. In each recording session, we interleaved saccade, pursuit, and choice trials such that they contained 80 pursuit and 80 saccade trials (4 directions X 2 reward conditions X10 repetitions), and 80 selection trials (2 directions X 2 target configuration conditions X 20 repetitions).

Virus injection
We injected AAVretro-hSyn-ChR2-GFP virus (ELSC virus core) into the striatum. We first injected the virus into the striatum of mice and confirmed the expression with histology and recorded LFP and single units in the cortex in response to light stimulation. We then injected the same virus to the monkeys. We made efforts to confirm the injection site without histology, as we aimed to release the monkeys to a sanctuary at the end of the experiments. Before the injections we mapped the recording chamber, and then lowered single electrodes (Thomas recordings Mini matrix) to identify the striatum. Identification was based on well-known physiological and anatomical characteristics, including the depth of the electrode compared to the MRI scan and atlas, the shape of the extracellular spike associated with medium spiny neurons (MSN), the characteristic low firing rate of the MSN, the presence of tonically active neurons (TANs) with their broad extracellular spike, and a gap in the recorded neurons after penetration of the cerebral cortex.

After a few weeks of mapping, we injected the virus. We loaded low-viscosity silicone fluid (polydimethylsiloxane, viscosity: 1 cSt at 25°C, Merck) into a 1 ml syringe and a thick-walled plastic tubing (inner diameter: 0.25 mm, length: 50 cm, Thomas Recordings injection system). The fluid fluoresced in color to allow us to identify the boundary between the silicon and the virus to confirm the injections. After loading the virus, we lowered a recording electrode and an aligned injection micropipette (Thomas Recordings, 35 gauge, lowered with Mini matrix). Based on the extracellular activity we identified the upper edge of the striatum. We then searched for the lower edge of the striatum and then injected 1-2 mm above the lower edge to 1-2 mm before the upper edge of the striatum 1 mm apart. In each site we injected 2-3 \mu l of the virus (titer 2.3*1012) at a speed of 15nl/s. We then waited 5 min before we pulled the electrode 1 mm upwards to the next site. In the upper site of the striatum, we waited 30 min before extracting the electrode from the brain. In each penetration we injected 1-4 sites depending on the thickness of the striatum. We aimed the injections towards the eye movement areas in the caudate (Fig. S1). Overall, we injected in 10 and 9 penetrations in monkeys K and H.

To further verify the injection location, for several injections we added Manganese (Mn+2, 0.01M) to the injected solution. Then, a few hours after the injection, we scanned the monkeys in an MRI using contrasts that make Mn+2 visible (Fig. S1, sequence details: T1, flash, TR=500, TE=3.7, FA=30). In the first monkey we performed the scan in which we injected only Mn+2 to validate our injection system before injecting the virus. After validation in the second monkey, we added Mn+2 to the same solution we used to inject the virus.

Neural recordings and spike sorting
On each recording day, we lowered an optrode (an optical fiber connected to a tetrode, OD=356 \mu m, Thomas Recordings) to the FEF. When we reached the site with the neurons, we applied light stimulation (LED light, wavelength: 470nm) protocol that included 10 repetitions of 10 pulses (5 ms durations) of stimulation at 10, 20 and 50 Hz (overall 10X10X3 = 300 pulses). Online we monitored the activity and searched for responses of single units or local field potential (LFP) to the light stimulation. When we observed a response, we applied the behavioral protocol. Often, we found a relatively thin layer (typically less than 2 mm, Fig. S2) in which neurons or the LFP responded to the stimulation, in contrast to widespread activity that would be expected if the virus had leaked to the recording site. For a more complete comparison we also recorded neural activity in areas where we did not observe responses to the stimulation. We sorted the spikes offline (Plexon offline sorter) and only well-isolated units were included in the analysis.

To confirm that the neurons that responded to light stimulation were the same neurons that were recorded during the task, we calculated the correlation and distance between the spike waveform of all the cells that were recorded during the task session and the light-evoked response waveform (Fig. S3). 86% (93/108) of the neurons identified as connected met previously used criteria for ensuring that light-evoked waveforms were similar to the task waveform (correlation coefficient > 0.9)28.

Quantitative and statistical analyses
To detect responses to the optical stimulation we calculated the spike rate in the 20 ms after the stimulation in 1 ms bins (in units of spikes/ms). We then subtracted the rate before the stimulation from the rate after the stimulation to calculate the rate of increase of the spikes in each ms after the stimulation. We excluded the 50 Hz stimulation condition in this analysis since the inter-stimulus-interval was too short to establish the pre-stimulus activity. Neurons were characterized as responding to the stimulation if the cumulative distribution of the increase in rate exceeded 0.5. This criterion corresponded to an additional spike above baseline within 20 ms of stimulus onset in 50% of the trials, and was much more stringent than a statistical test for an increase in rate. We used this stringent criterion since even a small number of errors in the spike sorting in which a non-responding neuron was contaminated with a responding neuron could lead to a significant increase. Finally, we confirmed that only neurons were included in which the response had a latency of less than 5 ms as determined by an increase in rate of at least 0.2 in the first 5 ms after the stimulation. This additional screening only removed two neurons and was practically negligible. The criterion was conservative in comparison to the classification of a human observer. All the neurons we classified as responding were also classified as responding by a human observer (N=108), but the human observer classified 57 other neurons as responding. Repeating the analysis based on a human observer classification did not alter our conclusions. Overall, 73, 163 and 50 neurons were classified as tagged, untagged and area-untagged in monkey H, and 35, 103 and 88 were classified as tagged, untagged and area-untagged in monkey K.

To calculate the coding of a variable throughout an epoch, we calculated the partial \omega2 in ANOVA models. The ANOVA model could include three variables: 1) Direction, corresponding to the direction of movement or visual cue, 2) Reward size, which was either large or small, and 3) Time, corresponding to the time bin relative to the event, calculated in bins of 250 ms. We included a single time bin before the event and up to four time bins after the event. In the saccade task, we used a 3-way ANOVA in all task epochs (cue, movement, and reward outcome). In the pursuit task, we used only reward and time in the cue epoch, since the direction was not cued in this task, and the full 3-way ANOVA in the movement and reward outcome epochs. In the choice task, we used a 2-way ANOVA model with time and direction, where direction was defined as whether the large reward appeared on one side (e.g., right) or the other (e.g. left).

Note that the variables we used in the effect size analysis were defined by the task design and not by behavior, but since the monkeys were well- trained, there was mostly full correspondence between the two. Specifically, in the saccade and pursuit tasks, the monkeys always moved to the target, so that the target and movement direction were the same experimental condition. A possible deviation could arise during the choice task, but since the monkeys almost always chose the larger reward (Fig. 1E), splitting the task trials by choice direction or by the location of the large reward target was in fact the same. For this reason, we could not calculate the reward effect size in the choice trials since the monkeys very rarely selected the small reward.

To quantify the contribution of each task variable we used \omega2, a common effect size measure employed in ANOVA designs that is often preferred over other effect size measures since it is unbiased. We calculated the number of spikes in 250 ms bins, and then calculated the \omega2 as follows:
\omega2 = (SS_effect + SS_effect x time - df_effect * SS_error) / (N - df_effect - df_effect x time) / (SS_effect + SS_effect x time + df_error * SS_error) (equation 1)
where SS_effect is the ANOVA sum of squares for the effect of a specific variable, SS_effect x time is the ANOVA type II sum of squares for the interaction of a specific variable with time, SS_error is the sum of squares of the errors after accounting for all experimental variables, df_effect, df_effect x time and df_error are the degrees of freedom for a variable, the interaction of the variable with time and the error, respectively. We included the interaction term since it quantifies the time-varying coding of the variable. N is equal to the number of trials x the number of time bins. To calculate the time effect size, we did not include interaction term or the corresponding degree of freedom.

For the cue and outcome epochs we used a time window from 250 before to 500 ms after the event onset. For movement, we used a time window of 0-1000 ms after the Go signal. Changing the bin size or the time window yielded highly correlated effect sizes. We also quantified all possible interactions (Reward x Direction, Reward x Direction x Time) but these did not yield any consistent results and were often small in comparison to other values. We used the partial effect size since it enables a better comparison between neurons that responded or did not respond to other experimental variables. Using the full effect size did not alter any of our conclusions. To test for differences between the average effect size across populations we used a permutation test. We shuffled the group labels, and calculated the difference between the average effect size of the shuffled populations. We repeated this 1000 times and if the actual average difference was larger than 95% of the shuffles, the difference was considered significant.

To determine the preferred direction (PD) of each neuron, we calculated the direction of the center of mass of the responses and assigned the PD to the cardinal direction that was closest to the calculated direction. We used both large and small reward trials to calculate a single PD for each cell in each task and task epoch. We used a window of 50-500 ms after the cue. When aligning the saccade, we used a window from 100 ms before to 300 ms after the saccade. We used a shorter time before the saccade to minimize the effect of the cue on the tuning of the saccade. For pursuit, we used a window of 50-500 ms after motion onset. We confirmed that the results were insensitive to the time windows. We used these same time windows to determine whether a neuron responded significantly to the reward or directions.

For each trial, we counted the number of spikes in the window and performed a 1-way (Direction or Reward) or a 2-way (Direction and Reward) ANOVA to test for significance of the response to a variable. We calculated the peristimulus time histogram in bins of 1ms and smoothed with a 30 ms SD Gaussian. We calculated the population PSTH by averaging the responses from all neurons in the PD or Null direction. Since we were interested in the effect of the reward on tuning and we used both rewards for calculating the PD, the effect of reward on tuning was not biased by this analysis. To avoid bias in the analysis of choice conditions, we used half of the choice trials to test whether a neuron's response was significantly different across the choice conditions and defined the preferred choice direction based on which direction had the larger firing rate. We then used the other half of the trials to plot the population PSTHs and perform comparisons with the single target trials. This procedure insured that the population responses were not statistically biased by the selection of the preferred direction.

All statistical tests were two-sided. The distributions of effect sizes tended to be non-normal and long-tailed. We therefore used permutation methods to compare the mean of two populations. The population labels of the effect sizes were permuted, and the difference between group means was calculated for each permutation. The p-value was defined as the probability of observing a statistic more extreme than that obtained for the real sample. The statistics were recalculated 10,000 times, as we observed that the p-values were stable at this number of repetitions.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In their article, “Enhanced reward coding and condition-independent dynamics in optogenetically identified corticostriatal neurons in monkeys”, the authors seek to test whether cortical input from the frontal eye field (FEF) to the basal ganglia reflect sensorimotor states or reward and choice signals. They use a combination of electrophysiological recordings and optogenetics to specifically identify and record from projection neurons from the FEF to the caudate, while monkeys perform distinct tasks that allowed them to learn the value of different colored targets. Briefly, the authors find that FEF neurons have significant changes in firing rate when the cue is presented and eye movement, and they encode the direction of the colored cue, as well as the reward size it predicts. Moreover, optogenetically-tagged neurons show stronger effect sizes for these conditions than untagged neurons. The authors then show that directional tuning is modulated by the reward encoding in the cue epoch such that firing rates are higher for large rewards than small rewards in the preferred direction. However, during the eye movement firing rates for large and small rewards converge such that the neurons encode direction. Finally, the authors show that FEF neurons encode choice when the monkeys are allowed to choose between large and small reward cues.\nOverall, the technical approach is exciting and the data are generally convincing. However, my enthusiasm is mitigated by overinterpretation of the results, and a limited conceptual advance. Below, I outline my major and minor comments.\nMajor\n1. The authors state in the intro that they are testing whether cortical inputs represent sensorimotor state or reward/choice. They show that reward is represented, but they don’t really show that sensorimotor information is not represented. Based on these limited analyses, they then suggest that we need to rethink RL models of basal ganglia. I am not convinced that their data supports this claim. For instance, Figure 5 indicates directional tuning, independent of reward volume (at the population level)? In that case, isn’t the sensorimotor state being represented, albeit at the level of populations instead of single neurons?\n2. Line 253 - “This response in choice trials resembled a winner-take-all representation, in which the larger reward target is selected and the smaller reward is ignored.” Their own behavior data shows that the monkeys are choosing the large reward on almost every choice trial, so expected value of the reward (or movement vigor based on expected value) could result in neurons exhibiting the same firing rate on choice trials as on large reward single target trials. In other words, the monkeys’ action and expected reward on those trials are identical.\n3. I found myself wondering if the reward modulation of corticostriatal neurons was related to reward-dependent modulation of movement vigor. Notably, only one monkey showed modulation of movement speed in the pursuit task, while the other one did not. Were there different effect sizes for reward in the pursuit task between the two monkeys? For the reward modulation of directional tuning aligned to movement (Figure 5) – did those results differ between the two monkeys?\n4. Line 256 - “If the activity in the choice conditions represented an average of the single trial responses, we would expect the choice-preferred condition to be equidistant from the preferred large reward and the null small reward conditions.” I’m not sure why that would be the case, unless the monkey’s choice probability was at 50% (which it was not, according to Figure 1E).\n5. The authors argue that corticostriatal inputs that represent reward expectations and choice are inconsistent with reinforcement learning theories of the basal ganglia. I fundamentally disagree with this statement, as it makes strong assumptions about the nature of cortical representations of states, which are unknown. In fact, there is such abundant evidence that cortical neurons represent rewards and can predict upcoming choices, that I would argue it is not necessarily surprising that states might be represented as the conjunction of task states, reward expectations, and choices. For instance, if cortical inputs represent sensorimotor state, then that could include the current position/tension of the ocular muscles, and future planned position/tension of those muscles. RPEs would evaluate whether the actions that were executed in those states were better or worse than expected. I don’t know that this is the case, but my point is that the authors are over-interpreting their data and I am not convinced that their data support these strong claims.\n6. Line 302 - “In a system that reduces dimensionality we would expect changes in the way neurons encode information rather than specificity in the coding of experimental variables.” I don’t understand what this sentence is saying. We would expect changes in the way neurons encode information - when? When dimensions are reduced from cortical inputs to basal ganglia outputs? During action selection? The language is imprecise and the meaning is unclear.\n7. Line 325 - Why do the authors assume that effect size for task variables that they have selected is equivalent to signal-to-noise? They don’t actually know what cortical inputs are conveying to the basal ganglia, so it is not clear what is signal and what is noise? I suppose the implicit assumption is that responses to the preferred (or null) reward/direction/choice is signal (or noise) but it is not obvious to me that that would be the case. For instance, since the basal ganglia integrates many cortical inputs, “noise” might be more appropriately defined as reflecting correlated variability (i.e., noise correlations) among corticostriatal neurons, as opposed to discriminability/ROC values for preferred/non-preferred task variables.\nMinor comments:\n1. Figure 4B - it looks like the line in the legend is missing for PD large reward\n2. Figure 2. The text states that the effects sizes are significantly greater than zero, I don’t see where the stats are listed in the text or in the figure.\n3. I don’t understand why direction was not assessed in the pursuit task for Fig 2H. Similarly, why wasn’t Reward included in the ANOVA for the Choice task (Fig 2I). I see that the methods say they were withheld, but I don’t understand why. The choice task still has reward options. Is this because the monkey almost exclusively chose the larger reward? Or is it a task limitation that I don’t understand?", "reviewer_2": "This is a very nice and important study by a very competent and experienced group describing the information carried by identified corticostriatal neurons in the monkey. The experiment is pioneering by recording from neurons with optogenetically identifed projection to caudate nucleus, the perfect use of new methology to advance the knowledge of basal ganglia function. Such work has in the past only been done in rodents that have much less sophisticated and understandable behavior. The study design is straightforward, the data look good, but the text could benefit from improvement that is easy to do. In detail:\nAbstract: there are a few unclear terms:\nLine 13: what is a sensorimotor state? Is that a state comprising all rewards like in reinforcement learning, or a motivational state, or sleep-awake state (probably not)? Please use a more informative term (or define state) for desribing what basal ganglia inputs encode.\nLine 15: ‘being connected’: in which direction: projecting to, or receiving from the basal ganglia?\nLine 16: what does ‘already’ refer to?\nLine 17: what does ‘condition-independent’ mean, conditional on what?\nLine 18: again ‘connected’ to what?\nLine 20: I don’t see how the data allow to distinguish between manipulating rather than generating reward and choice signals.\nIntroduction and Results sections:\nLine 31: the phrase ‘neurons projecting to the basal ganglia may not contain reward signals’ seems mis-stated: how else could the basal ganglia have reward information, plus dopamine neurons with their reward signals are a part of basal ganglia and must receive that information from somewhere.\nLine 32: ‘other signals’ should be specified.\nLine 37: ‘connected’ should be specified as ‘projecting’ to caudate.\nLine 39: ‘characterize the information’ is too unspecific: you are talking about reward information.\nLine 49 and line 83: ‘connected’ should be specified.\nLine 52-53: ‘increase in temporal complexity’ needs explanation to be understandable.\nLine 89-90: the key to the novelty of the experiment, nice and important.\nLine 108: ‘condition-independent components’ needs specification (what do these words mean?).\nLine 111: what is ‘reward conditions’: do you mean small vs large reward; then say so.\nLine 113: ‘dependent on condition’: what ‘condition’? (I guess ‘condition’ is used too often but is uninformative)\nLine 114 and 122: I guess an Anova is appropriate as you test only 2 reward sizes and 2 movements, but otherwise a multiple linear regression would provide more information (a metric) rather than just significance of some difference.\nLine 123: time bin: for simplicity, would one not take reward size and movement direction as the two distinct variables and measure them at particular moments (time bin), rather than making time bin a separate variable (regressor)? Or do you do an Anova running across bins? Please explain your 3-variable approach better.\nLine 129: by ‘direction’ you probably mean ‘movement direction’? If so, please say so.\nLine 580 (legend): ‘conditions’: if you mean reward size, then say so, or else say what you mean by ‘conditions’.\nDiscussion: Ordering of topics: after the appropriate initial conclusions (which could be expanded a bit), I would recommend re-ordering the sequence of topics: first, the methodology about optogenetics, then the own-data interpretations, followed by the general point about reward signals in FEF, and finally the much more general point about the role of the basal ganglia.", "reviewer_3": "In the present manuscript, monkeys performed three different oculomotor tasks while the neural activity in the Frontal Eye Fields was being recorded. The three tasks had in common the fact that two stimuli were shown, which predicted either high or low reward. Behaviorally, large rewards were associated with shorter response latency in the guided saccade task and faster pursuit speed in the smooth pursuit task. Optogenetical tagging was used to identify cortical neurons projecting from the frontal eye field (FEF) to the oculomotor area of the caudate nucleus. The authors identify tagged neurons by delivering light stimulation and looking for changes in the spike rate associated to the light stimulation. After identifying the tagged neurons, analysis of their activity showed that a larger fraction of variance is explained by the reward magnitude for the tagged neurons compared to the untagged neurons. The authors conclude that reward information is already present in FEF neurons, challenging the traditional role of the basal ganglia in reward prediction.\nThis is a well written paper, and the identification of connected neurons, which is a challenging task, has been masterfully executed. I'd like to commend the authors for successfully identifying cortico-striatal projections in macaque monkeys. Not an easy thing to do.\nI have, however, a large concern. The conclusions the authors reach about the role, or lack thereof, of the basal ganglia on reward prediction error are not very well supported by the data, as the experimental design does not actually allow to study this process. Thus, I am afraid that this paper suffers of a fundamental conceptual problem. I explain below:\nFrom my understanding of the work presented, I gather that the animals were already trained in the execution of the tasks at the time the recordings were made. Furthermore, I believe the reward was delivered deterministically (large or small) after the associated stimuli were fixated, pursued or chosen (depending on the task). Even in the choice task, the animals very quickly identified the option associated with the highest reward, even from the beginning, as shown in figure 1E. This makes it impossible to make any interpretation related to reward prediction error. Furthermore, I find it rather unsurprising that FEF neurons projecting to the basal ganglia already encode reward information, since here we are talking about brain areas related to the motor output of a decision. The authors make an argument about the response latencies. This would be a good argument if there was an actual reinforcement learning process happening in the behavioral paradigms used. But again, I do not think this is happening when animals are performing a task in which they have been already trained, stimuli have been extensively associated with a reward amount, and importantly, even if they were naïve to the specific stimuli used during data collection (which I do not think was the case), since reward is given deterministically, we cannot reasonably claim that there is a reward prediction error at any point.\nEven more, the authors determine that there is a behavioral differences that may as well be related to some sort of urgency to move when animals see the more salient stimulus in both the saccade and the pursuit tasks. Sadly, they do not show an equivalent analysis for the choice task. But the effect we can observe here may be more related to that. I have the feeling that the authors are onto something with the data they show in this manuscript. Sadly, for the reasons exposed above I have great doubts their claims about the role of the FEF output to the basal ganglia are supported by the data. The data has to be seen from the perspective of movement, but the experimental design dos not allow to make conclusions about reward coding, or reward prediction. There is a large confound (the response latency) which is difficult to disentangle given the experimental design and the brain areas from which the activity was recorded. In other words, the conclusion that the FEF-basal ganglia circuit does not generate a reward signal, but rather manipulates or does something else with it is unfalsifiable in this study, since there is no contrast.\nA few specific comments:\n1) Lines 50-52. Text reads \"[...] thus suggesting that the integration of reward, choice and sensorimotor occurs already in the inputs to the basal ganglia.\" As I mentioned above, at this point information has already passed through the cognitive cortico-basal ganglia loops. Given that there is no learning happening in the paradigms used, how can we dissociate this activity from other confounds (movement vigor/urgency, stimulus saliency). All this is certainly the result of the previous reward association, but it is no reward information per se.\n2) line 102. It would be good to have the exact fractions of neurons belonging to each category. We get an idea from Figure S2, but I could not find the actual number (did I miss it somewhere?)\n3) Lines 113-117. The explanation of omega-squared was rather confusing, I only understood after looking at the equations in the methods. It may be useful to rewrite for clarity.\n4) Figures 4-6. Population level analysis of firing rates should be done on normalized firing rates, because large differences in FR from individual neurons can both obfuscate effects or lead to spurious results.\n5) lines 284-305. See my main comment above. Yes, the results cannot be explained by theories of Reinforcement Learning and the basal ganglia (RL/BG), but the behavioral paradigms do no allow to study that. My suggestion is to re-frame the whole thing from the standpoint of ocular movements. Because the theories about RL/BG apply to cognitive territories of the BG and the cortex, which are clearly bypassed in over trained tasks. Thus, no claims about that can me made. Reward encoding? Maybe. More likely a derivative of reward. We cannot know that from this data set.\n6) Line 368. Scientific names must be written with only the genus name capitalized, the species epithet must be written in lower case, and the Genus+epithet pair must be italicized."}, "author_response": "We thank the reviewer for the positive assessment. We appreciate the thoughtful feedback and have substantially revised the manuscript to address the concerns regarding potential overinterpretation and to clarify the conceptual contributions of our work. Below, we provide point-by-point responses to all comments\nMajor\n1. The authors state in the intro that they are testing whether cortical inputs represent sensorimotor state or reward/choice. They show that reward is represented, but they don’t really show that sensorimotor information is not represented. Based on these limited analyses, they then suggest that we need to rethink RL models of basal ganglia. I am not convinced that their data supports this claim. For instance, Figure 5 indicates directional tuning, independent of reward volume (at the population level)? In that case, isn’t the sensorimotor state being represented, albeit at the level of populations instead of single neurons?\nWe thank the reviewer for this helpful comment. We fully agree that sensorimotor information is represented in FEF, and we did not intend to suggest otherwise. Indeed, as the reviewer notes, our data (e.g., Figure 5) show strong directional tuning independent of reward, consistent with well-established sensorimotor coding. Our aim was to test the null hypothesis that basal ganglia input from FEF reflects only sensorimotor signals. In the introduction we have explicitly added that the FEF is known to be a central hub for processing visual and movement signals that drive eye movements and further revised the results to clarify that we expected, and indeed found, visual-motor representations alongside reward signals.\n2. Line 253 - “This response in choice trials resembled a winner-take-all representation, in which the larger reward target is selected and the smaller reward is ignored.” Their own behavior data shows that the monkeys are choosing the large reward on almost every choice trial, so expected value of the reward (or movement vigor based on expected value) could result in neurons exhibiting the same firing rate on choice trials as on large reward single target trials. In other words, the monkeys’ action and expected reward on those trials are identical.\nWe thank the reviewer for this comment. Indeed, as we now point out in the revised manuscript, behavior was very similar in the choice and single large reward trials, both in terms of direction of movement (Fig. 1E) and the latency of saccades (we added Table S1). We therefore agree that a process common to both the choice and large reward conditions, such as reward expectation or vigor could result in such representations. We can rule out that modulations were only related to the specifics of eye movement (see below Fig. R1 detailed response to comment 3) but we agree that we cannot assign the modulations to a single cognitive process. Nevertheless, our results show that FEF inputs to the basal ganglia can already clearly distinguish between choice conditions. This would be unexpected if selection were implemented downstream to the FEF in the basal ganglia. We have added a paragraph to the discussion that addresses potential interpretations of the choice results.\n3. I found myself wondering if the reward modulation of corticostriatal neurons was related to reward-dependent modulation of movement vigor. Notably, only one monkey showed modulation of movement speed in the pursuit task, while the other one did not. Were there different effect sizes for reward in the pursuit task between the two monkeys? For the reward modulation of directional tuning aligned to movement (Figure 5) – did those results differ between the two monkeys?\nWe agree with the reviewer that in the original manuscript we did not test sufficiently to dissociate between reward modulations and movement vigor. We have made substantial changes in the revised manuscript to further address this point. In previous published work we1 and others2have shown that reward modulation in the FEF goes beyond coding of the eye movements. Rather than focusing on potential idiosyncratic differences between individual monkeys, our approach was to use trial-by-trial variability in behavior to show that reward modulations cannot be explained by movement parameters. The figure below shows the analysis that controlled for saccade latency. We have added this figure to the manuscript.\nIn each task condition, we divided the trials by the saccade latency. Although the overall saccade latency was shorter for the large reward condition, the short-latency trials in the small reward condition tended to be shorter than the long-latency trials in the large reward condition, as indicated by the position of the purple dots below the equality line in Fig. R1. We used these trials to show that population activity was only weakly modulated by the latency of the upcoming saccade (Fig. R1B). If reward modulations were related to latency, one might expect that activity would reverse sign when comparing behavior with opposite latency modulation. Figures R1C and D show that this was not the case: in tuned neurons, activity remains larger for the large reward condition, and in non-tuned neurons, the sign of the reward modulations persisted even when the behavior was reversed (Fig. R1D).\nFigure R1: Activity in the cue epoch of the saccade task with control over saccade latency. A: Saccade latency in the large and small reward conditions, split into short- and long-latency halves. Each dot represents the average latency for a single recording day. Black and gray symbols indicate long and short latency saccades, respectively. Purple symbols show the saccade latency for large reward trials with long latency and small reward trials with short latency. B: Population average of directionally tuned neurons in their preferred direction. Traces are divided by trial latency (short vs. long; dashed vs. solid) and by reward size (red = small reward, blue = large reward). C,D: Neuron-by-neuron comparison of activity in large reward trials with long latency versus small reward trials with short latency. C: Activity in the preferred direction (PD) of tuned cells. D: Activity of non-tuned cells averaged across all directions. Colors indicate whether activity was significantly larger (green) or smaller (yellow) in the large versus small reward condition. Significance was determined using all trials, and the values shown correspond to the specified half of the trials.\nOther parameters of movement aside from latency such as peak velocity and saccade amplitude were very weakly modulated by the reward, suggesting that they are not the source of reward modulation. To further control for these movement parameters we repeated the analysis above of splitting the behavior into saccade peak velocity or saccade amplitude (Fig. R2). We did not find any modulations in the cue related to these parameters.\nFigure R2: Activity in the cue epoch of the saccade task with control over saccade amplitude and peak velocity. Population average of directionally tuned neurons in their preferred direction. Blue and red traces show large and small reward trials. In A, traces are divided by saccade amplitude (large vs. small; solid vs. dashed). In B traces are divided by peak velocity of the saccade (high vs. low; solid vs. dashed)\nThe reviewers’ comments also alerted us to the possibility of micro-saccades happening during the cue. In a previous study we found that micro-saccades were modulated by reward size3. Figure R3 below shows that removing all the trials with micro-saccades had very little effect on the reward modulations, indicating that micro-saccades were not the source of the reward modulation we found.\nFigure R3: Control for microsaccades in the cue epoch. The traces show the population averages in the preferred direction of the neuron. Blue and red lines show the firing rate for large and small rewards. Solid and dashed lines indicate firing rates on all trials and on trials without micro-saccades.\nIn addition to adding the above analysis to the revised manuscript, we now further emphasize that activity during the saccade itself was modulated by reward (Fig. 5). Since FEF neurons responded strongly to the saccade, many were also influenced by saccade latency. However, in the saccade epoch, we could control for this directly by aligning activity to the saccade. This resulted in an interesting finding which we highlight in the manuscript: although single neurons were found to encode reward (Fig. 5E-G) but at the population-average level, the reward modulations were averaged out (Fig. 5A-C).\n4. Line 256 - “If the activity in the choice conditions represented an average of the single trial responses, we would expect the choice-preferred condition to be equidistant from the preferred large reward and the null small reward conditions.” I’m not sure why that would be the case, unless the monkey’s choice probability was at 50% (which it was not, according to Figure 1E).\nWe thank the reviewer for this comment. We agree that the section interpreting the choice results was unclear. We have therefore completely rewritten this section. We now focus on the contrast between the output of the selection process and the intermediate stages, such as the coding of offer value. We also recognize that the interpretation the reviewer found unclear did not contribute much to the paper, and we have removed it. In short, we were aiming to compare the choice response to averaging that has been observed in sensory areas, and in eye movement behavior such as vector averaging during pursuit4 or rapid saccades to dual targets5, but the lack of such behavior in the current task made this interpretation irrelevant.\n5. The authors argue that corticostriatal inputs that represent reward expectations and choice are inconsistent with reinforcement learning (RL) theories of the basal ganglia. I fundamentally disagree with this statement, as it makes strong assumptions about the nature of cortical representations of states, which are unknown. In fact, there is such abundant evidence that cortical neurons represent rewards and can predict upcoming choices, that I would argue it is not necessarily surprising that states might be represented as the conjunction of task states, reward expectations, and choices. For instance, if cortical inputs represent sensorimotor state, then that could include the current position/tension of the ocular muscles, and future planned position/tension of those muscles. RPEs would evaluate whether the actions that were executed in those states were better or worse than expected. I don’t know that this is the case, but my point is that the authors are over-interpreting their data and I am not convinced that their data support these strong claims.\nWe agree with the reviewer that our findings do not completely rule out reinforcement learning (RL) theories. This was acknowledged in the original manuscript, but perhaps not with sufficient detail and depth. We have made a major revision to the manuscript to avoid over-interpretation of our results. Specifically, we have removed RL-related interpretations from the introduction and now present a toned-down view at the end of the discussion.\n6. Line 302 - “In a system that reduces dimensionality we would expect changes in the way neurons encode information rather than specificity in the coding of experimental variables.” I don’t understand what this sentence is saying. We would expect changes in the way neurons encode information - when? When dimensions are reduced from cortical inputs to basal ganglia outputs? During action selection? The language is imprecise and the meaning is unclear.\nWe thank the reviewer for pointing out the lack of clarity in our argument. We wanted to link the current results to our previous findings, but based on the reviewer’s comments, we now realize that this connection would require a type of discussion that goes beyond the scope of the current work. We have removed the unclear statements from the manuscript.\n7. Line 325 - Why do the authors assume that effect size for task variables that they have selected is equivalent to signal-to-noise? They don’t actually know what cortical inputs are conveying to the basal ganglia, so it is not clear what is signal and what is noise? I suppose the implicit assumption is that responses to the preferred (or null) reward/direction/choice is signal (or noise) but it is not obvious to me that that would be the case. For instance, since the basal ganglia integrates many cortical inputs, “noise” might be more appropriately defined as reflecting correlated variability (i.e., noise correlations) among corticostriatal neurons, as opposed to discriminability/ROC values for preferred/non-preferred task variables.\nWe adhered to terminology used in the literature that refers to the within-condition trial-by-trial variability as noise. This is precisely the definition of noise in the term noise correlation. As many studies, including ours6,7, have clearly noted, trial-by-trial variability can contain meaningful information. We agree that a more detailed characterization of noise is an important research question, but is beyond the scope of the current study since we do not have sufficient data from neurons that were recorded simultaneously or many repeats that would make it possible to correlate noise with behavior. In the revised manuscript, we rephrased the section in which we present the effect size analysis accordingly. We feel that the term 'signal-to-noise' is intuitive, and therefore we prefer to retain it while presenting it more precisely.\nMinor comments:\n1. Figure 4B - it looks like the line in the legend is missing for PD large reward\nFixed\n2. Figure 2. The text states that the effects sizes are significantly greater than zero, I don’t see where the stats are listed in the text or in the figure.\nWe added the statistics to the text.\n3. I don’t understand why direction was not assessed in the pursuit task for Fig 2H. Similarly, why wasn’t Reward included in the ANOVA for the Choice task (Fig 2I). I see that the methods say they were withheld, but I don’t understand why. The choice task still has reward options. Is this because the monkey almost exclusively chose the larger reward? Or is it a task limitation that I don’t understand?\nIn the revised manuscript we added more explanations to the results section on the characteristics of each parameter on the different tasks and which epochs they were used in. In the pursuit condition we did not indicate the direction of motion before the movement epoch, so we do not have the data to test for directional tuning during the cue. This is now stated in the results section and we also added this to the section in the method that was unclear to the reviewer.\nAs for choice, since the monkey almost exclusively chose the larger reward target, we could not split the trials by the eventual reward. Therefore, calculating the reward effect size would not have been meaningful. We have added this point to the revised manuscript.\nReviewer #2 (Remarks to the Author):\nThis is a very nice and important study by a very competent and experienced group describing the information carried by identified corticostriatal neurons in the monkey. The experiment is pioneering by recording from neurons with optogenetically identified projection to caudate nucleus, the perfect use of new mythology to advance the knowledge of basal ganglia function. Such work has in the past only been done in rodents that have much less sophisticated and understandable behavior. The study design is straightforward, the data look good, but the text could benefit from improvement that is easy to do.\nWe thank the reviewer for the thoughtful comments and valuable suggestions to improve the manuscript. We have carefully reviewed and revised the manuscript to use more precise terminology.\nAbstract: there are a few unclear terms:\nLine 13: what is a sensorimotor state? Is that a state comprising all rewards like in reinforcement learning, or a motivational state, or sleep-awake state (probably not)? Please use a more informative term (or define state) for describing what basal ganglia inputs encode.\nWe rephrased. Based on these comments and the comments of the other reviewers we have made major changes to the way we present the paper. We do not use the term ‘state’ anymore.\nLine 15: ‘being connected’: in which direction: projecting to, or receiving from the basal ganglia?\nRephrased to projecting.\nLine 16: what does ‘already’ refer to?\nRemoved\nLine 17: what does ‘condition-independent’ mean, conditional on what?\nThese are modulations in firing rate that do not depend on task condition; i.e., modulations in activity that do not distinguish between task conditions. We use the terminology implemented for example in Hoback et al. Elife 2016. We have added further explanations in the results of the revised manuscript.\nLine 18: again ‘connected’ to what?\nRephrased to projecting\nLine 20: I don’t see how the data allow to distinguish between manipulating rather than generating reward and choice signals.\nWe agree that our terminology and the discussion about these points was not sufficiently clear. We have revised the text and removed this point from the manuscript.\nIntroduction and Results sections:\nLine 31: the phrase ‘neurons projecting to the basal ganglia may not contain reward signals’ seems mis-stated: how else could the basal ganglia have reward information, plus dopamine neurons with their reward signals are a part of basal ganglia and must receive that information from somewhere.\nRephrased to neurons projecting from the cortex to the basal ganglia.\nLine 32: ‘other signals’ should be specified.\nFixed\nLine 37: ‘connected’ should be specified as ‘projecting’ to caudate.\nFixed\nLine 39: ‘characterize the information’ is too unspecific: you are talking about reward information.\nFixed.\nLine 49 and line 83: ‘connected’ should be specified.\nWe specified that these neurons were opto-tagged or projecting.\nLine 52-53: ‘increase in temporal complexity’ needs explanation to be understandable.\nIn the revision these sentences were removed.\nLine 89-90: the key to the novelty of the experiment, nice and important.\nWe thank the reviewer for acknowledging the novelty.\nLine 108: ‘condition-independent components’ needs specification (what do these words mean?).\nWe explained this more extensively in the revised manuscript.\nLine 111: what is ‘reward conditions’: do you mean small vs large reward; then say so.\nFixed\nLine 113: ‘dependent on condition’: what ‘condition’? (I guess ‘condition’ is used too often but is uninformative)\nWe specified that we mean reward and direction.\nLine 114 and 122: I guess an Anova is appropriate as you test only 2 reward sizes and 2 movements, but otherwise a multiple linear regression would provide more information (a metric) rather than just significance of some difference.\nNote that we did not use ANOVA solely to test for significance; rather, we extracted the effect size from the ANOVA model and used it as a metric to compare populations. We agree that linear regression could have been used as an alternative for some of the computations. In fact, the ANOVA model in MATLAB is implemented with a linear regression of the categorial variables. We prefer to use ANOVAs and quantify the effect sizes for several reasons:\n1. It allows us to flexibly add categorical variables, even when we do not expect a monotonic relationship between variables and firing rate. This is especially important for characterizing the average modulation over time (time effect size).\n2. Isolating the contribution of each variable with the partial effect size is simple.\n3. In some of the tasks we had four movement directions, which made the application of regression slightly more complicated.\n4. The omega effect size which we extracted from the ANOVA is unbiased. This makes it simple to test whether the effect size in a population of neurons is significantly different than zero.\nNote that although not widely used in neuroscience, we are not the first to use this approach: see for example E.K. Miller and colleagues8.\nLine 123: time bin: for simplicity, would one not take reward size and movement direction as the two distinct variables and measure them at particular moments (time bin), rather than making time bin a separate variable (regressor)? Or do you do an Anova running across bins? Please explain your 3-variable approach better.\nWe did not measure the effect size separately for each bin; rather, we used time as one of the parameters. We split the time into non-overlapping bins of 250 ms. Together with reward size and direction of movement these were the three variables that defined the task. We used a 2- or 3-way ANOVA (time x direction x reward or time x direction), and then extracted the effect size from the results of the ANOVA table as specified in the manuscript. We added this information to the methods of the revised manuscript.\nLine 129: by ‘direction’ you probably mean ‘movement direction’? If so, please say so.\nIn the cue epoch, direction corresponds to the direction of the visual cue.\nLine 580 (legend): ‘conditions’: if you mean reward size, then say so, or else say what you mean by ‘conditions’.\nFixed\nDiscussion: Ordering of topics: after the appropriate initial conclusions (which could be expanded a bit), I would recommend re-ordering the sequence of topics: first, the methodology about optogenetics, then the own-data interpretations, followed by the general point about reward signals in FEF, and finally the much more general point about the role of the basal ganglia.\nWe agree that the order proposed by the reviewer substantially improves the clarity of the discussion. We implemented the proposed order in the revised manuscript.\nReviewer #3 (Remarks to the Author):\nIn the present manuscript, monkeys performed three different oculomotor tasks while the neural activity in the Frontal Eye Fields was being recorded. The three tasks had in common the fact that two stimuli were shown, which predicted either high or low reward. Behaviorally, large rewards were associated with shorter response latency in the guided saccade task and faster pursuit speed in the smooth pursuit task. Optogenetical tagging was used to identify cortical neurons projecting from the frontal eye field (FEF) to the oculomotor area of the caudate nucleus. The authors identify tagged neurons by delivering light stimulation and looking for changes in the spike rate associated to the light stimulation. After identifying the tagged neurons, analysis of their activity showed that a larger fraction of variance is explained by the reward magnitude for the tagged neurons compared to the untagged neurons. The authors conclude that reward information is already present in FEF neurons, challenging the traditional role of the basal ganglia in reward prediction.\nThis is a well written paper, and the identification of connected neurons, which is a challenging task, has been masterfully executed. I'd like to commend the authors for successfully identifying cortico-striatal projections in macaque monkeys. Not an easy thing to do.\nI have, however, a large concern. The conclusions the authors reach about the role, or lack thereof, of the basal ganglia on reward prediction error are not very well supported by the data, as the experimental design does not actually allow to study this process. Thus, I am afraid that this paper suffers of a fundamental conceptual problem. I explain below:\nWe thank the reviewer for the positive assessment, and hope that we have adequately addressed the conceptual concerns in the revision. It appears that our results may have been interpreted in ways we did not intend. Crucially, we did not aim to claim that the basal ganglia or the FEF play no role in reward prediction error (RPE). This misunderstanding may stem from our initial emphasis on reinforcement learning in the introduction. In response to this comment, as well as to related points raised by the other reviewers, we have revised the introduction to emphasize the convergence of reward signals from multiple sources, rather than suggesting or challenging a specific model.\nFrom my understanding of the work presented, I gather that the animals were already trained in the execution of the tasks at the time the recordings were made. Furthermore, I believe the reward was delivered deterministically (large or small) after the associated stimuli were fixated, pursued or chosen (depending on the task). Even in the choice task, the animals very quickly identified the option associated with the highest reward, even from the beginning, as shown in figure 1E. This makes it impossible to make any interpretation related to reward prediction error.\nWe indeed recorded activity only after the monkeys were well trained on the task. We mention this explicitly in the second paragraph of the revised manuscript. We also agree that the usage of deterministic rewards limits our ability to draw conclusions about reward prediction error (RPE). However, in this work we did not aim to dissociate reward responses from reward prediction errors that would have more specific predictions in a probabilistic or learning context. We discuss this limitation in the revised manuscript. It is possible that emphasizing RPE coding by dopamine neurons in the abstract and introduction gave this erroneous impression. To address this, we have revised the manuscript to present dopamine coding in the broader sense of reward, rather than focusing solely on RPE.\nFurthermore, I find it rather unsurprising that FEF neurons projecting to the basal ganglia already encode reward information, since here we are talking about brain areas related to the motor output of a decision. The authors make an argument about the response latencies. This would be a good argument if there was an actual reinforcement learning process happening in the behavioral paradigms used. But again, I do not think this is happening when animals are performing a task in which they have been already trained, stimuli have been extensively associated with a reward amount, and importantly, even if they were naïve to the specific stimuli used during data collection (which I do not think was the case), since reward is given deterministically, we cannot reasonably claim that there is a reward prediction error at any point.\nBased on this comment and those of the other reviewers, we have made major changes to the paper and largely toned down the reinforcement learning interpretations. Instead, we now frame it in the context of converging reward signals from several sources. We also ran further analyses to demonstrate that changes in eye movements could not account for the observed reward modulations (see response below and our similar response to Reviewer 1 and Fig. S5)\nEven more, the authors determine that there is a behavioral differences that may as well be related to some sort of urgency to move when animals see the more salient stimulus in both the saccade and the pursuit tasks. Sadly, they do not show an equivalent analysis for the choice task. But the effect we can observe here may be more related to that.\nWe thank the reviewer for this comment. In the choice trials, the behavioral effect of reward was reflected in the monkeys’ clear preference for the larger reward, since the smaller reward was rarely chosen. We ran further analyses to compare the latency of the saccades in the choice and single target tasks. Since the monkeys almost always selected the large reward target, we expected that the latency in the choice trials would be more similar to the large reward trials. We found this was indeed the case. The table below shows these results and was added as a supplementary table to the revised manuscript.\nMonkey Large reward Small reward Choice\nH 188.7 23.6 273.4  30.3 191.8  28.0\nK 227.5  30.9 305.9  16.8 218.7  29.2\nTable R1: Average latency of saccades in the single target and choice tasks. Values show the averages and S.D. across directions of movement and sessions for the two monkeys.\nIn both monkeys, the difference between the small reward and choice latency was highly significant (p < 10^-5, Wilcoxon signed-rank test). In monkey H, the difference between choice and large reward was not significant (p = 0.18), whereas in monkey K, the faster latency in choice compared to large reward (9 ms) was significant (p = 0.0002). We found this faster latency in the choice condition interesting, although we are not sure what might account for this idiosyncratic result. Note, however, that the sensory stimuli in the tasks were different, because the choice task involved two targets. Nevertheless, it is clear that the latency in the the choice condition was substantially shorter than in the small reward condition. We incorporated this information into the Results section of the paper.\nI have the feeling that the authors are onto something with the data they show in this manuscript. Sadly, for the reasons exposed above I have great doubts their claims about the role of the FEF output to the basal ganglia are supported by the data. The data has to be seen from the perspective of movement, but the experimental design dos not allow to make conclusions about reward coding, or reward prediction. There is a large confound (the response latency) which is difficult to disentangle given the experimental design and the brain areas from which the activity was recorded. In other words, the conclusion that the FEF-basal ganglia circuit does not generate a reward signal, but rather manipulates or does something else with it is unfalsifiable in this study, since there is no contrast.\nWe thank the reviewer for this important comment. Work from our lab9 and others2 has demonstrated that in tasks with tight control over movement parameters, the reward potentiates FEF activity beyond the effects it has on eye movements. Here we ran further analyses to control for saccade latency by splitting the trials into fast and slow saccades groups and testing how this affected the neural responses to large and small rewards. Overall, we found that even when controlling for latency, reward strongly modulated neural activity. Reviewer 1 made the same point. Below we repeat our response to this shared concern.\nIn each task condition, we divided the trials by saccade latency. Although the overall saccade latency was shorter for the large reward condition, the short-latency trials in the small reward condition tended to be shorter than the long-latency trials in the large reward condition, as indicated by the position of the purple dots below the equality line in Fig. R1. We used these trials to show that population activity was only weakly modulated by the latency of the upcoming saccade (Fig. R1B). If reward modulations were related to latency, one might expect that activity would reverse sign when comparing behavior with opposite latency modulation. Figures R1C and D show that this was not the case: in tuned neurons, activity remained larger for the large reward condition, and in non-tuned neurons, the sign of reward modulations persisted even when the behavior is reversed (Fig. R1D).\nFigure R1: Activity in the cue epoch of the saccade task with control over saccade latency. A: Saccade latency in the large and small reward conditions, split into short- and long-latency halves. Each dot represents the average latency for a single recording day. Black and gray symbols indicate long and short latency saccades, respectively. Purple symbols show the saccade latency for large reward trials with long latency and small reward trials with short latency. B: Population average of directionally tuned neurons in their preferred direction. Traces are divided by trial latency (short vs. long; dashed vs. solid) and by reward size (red = small reward, blue = large reward). C,D: Neuron-by-neuron comparison of activity in large reward trials with long latency versus small reward trials with short latency. C: Activity in the preferred direction (PD) of tuned cells. D: Activity of non-tuned cells averaged across all directions. Colors indicate whether activity was significantly larger (green) or smaller (yellow) in the large versus small reward condition. Significance was determined using all trials and values shown correspond to the specified half of the trials.\nOther parameters of movement aside from latency such as peak velocity and saccade amplitude were very weakly modulated by the reward, suggesting that they were not the source of reward modulation. To further control for these movement parameters we repeated the analysis above of splitting the behavior by saccade peak velocity or saccade amplitude (Fig. R2). We did not find any modulations in the cue related to these parameters.\nFigure R2: Activity in the cue epoch of the saccade task with control over saccade amplitude and peak velocity. Population average of directionally-tuned neurons in their preferred direction. Blue and red traces show large and small reward trials. In A, traces are divided by saccade amplitude (large vs. small; solid vs. dashed). In B, traces are divided by the peak velocity of the saccade (high vs. low; solid vs. dashed)\nThe reviewers’ comments also alerted us to the possibility of micro-saccades happening during the cue. In a previous study we found that micro-saccades are modulated by reward size3. Figure R3 below shows that removing all the trials with micro-saccades had very little effect on the reward modulations, indicating that micro-saccades were not the source of the reward modulation we found.\nFigure R3: Control for microsaccades in the cue epoch. The traces show the population average in the preferred direction of the neuron. Blue and red lines show the firing rate for large and small rewards. Solid and dashed lines indicate firing rates on all trials and on trials without microsaccades.\nA few specific comments:\n1) Lines 50-52. Text reads \"[...] thus suggesting that the integration of reward, choice and sensorimotor occurs already in the inputs to the basal ganglia.\" As I mentioned above, at this point information has already passed through the cognitive cortico-basal ganglia loops. Given that there is no learning happening in the paradigms used, how can we dissociate this activity from other confounds (movement vigor/urgency, stimulus saliency). All this is certainly the result of the previous reward association, but it is no reward information per se.\nWe acknowledge that our results do not provide definitive evidence that the activity is related to reward per se. Simple sources, such as the motor command or sensory information, could however be ruled out by our findings. However, we agree that we do not know which cognitive processes are represented in the activity that differentiates between small and large reward conditions. We have added a paragraph to the discussion specifically addressing this point.\n2) line 102. It would be good to have the exact fractions of neurons belonging to each category. We get an idea from Figure S2, but I could not find the actual number (did I miss it somewhere?)\nThe actual numbers are: tagged =108, untagged =266 and area-untagged =138. We added this information to the results in the revised manuscript.\n3) Lines 113-117. The explanation of omega-squared was rather confusing, I only understood after looking at the equations in the methods. It may be useful to rewrite for clarity.\nWe have rewritten the paragraph where we introduce omega-squared. We also added more references to neuroscience studies that have used it; for example, E.K. Miller and colleagues8.\n4) Figures 4-6. Population level analysis of firing rates should be done on normalized firing rates, because large differences in FR from individual neurons can both obfuscate effects or lead to spurious results.\nWe thank the reviewer for this comment. Our approach in all our papers is to show firing rate, since it is the simplest descriptive statistic and the closest to the raw data. As a complement we show the distribution of firing rates (Fig. 4E–J, 5E–G, Supplementary 5D–F) and ran tests that accounted for the non-normality of the firing rate effects. This dual approach (showing the average firing rate and the distributions) helps depict the data in an intuitive way and rules out spurious results.\n5) lines 284-305. See my main comment above. Yes, the results cannot be explained by theories of Reinforcement Learning and the basal ganglia (RL/BG), but the behavioral paradigms do no allow to study that. My suggestion is to re-frame the whole thing from the standpoint of ocular movements. Because the theories about RL/BG apply to cognitive territories of the BG and the cortex, which are clearly bypassed in over trained tasks. Thus, no claims about that can me made. Reward encoding? Maybe. More likely a derivative of reward. We cannot know that from this data set.\nWe agree, we have toned down the RL interpretation and focused more on the integration of reward- related signals from several sources. We now also provide more controls for eye movement (see above) and place greater emphasis on the relationship between modulation and movement.\n6) Line 368. Scientific names must be written with only the genus name capitalized, the species epithet must be written in lower case, and the Genus+epithet pair must be italicized.\nFixed."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have generally done a good job of addressing my previous comments. The new manuscript reads better and the claims more closely reflect the data.\nHowever, there are two outstanding issues, where claims are made that are not supported by the data. These claims need to be removed or adjusted.\n(1) With regard to figure 6 - “This winner-take-all representation aligns with expectations associated with the output of a selection process but not with other coding schemes such as coding of the offer value.“\nThis claim is not supported by the data. The authors find that the response to the large reward target is identical regardless of whether the animal gets to choose it. That seems entirely consistent with coding of the offer value, since the offer value of the large reward target is identical across the different tasks. Moreover, the fact that neural firing rates for the preferred direction are different depending on large or small rewards is not consistent with the activity simply reflecting the output of a selection process. None of this has any bearing on a “winner-take-all” mechanism. The data do not support these claims. Figure 6 does not rule out encoding of offer value.\n(2) Despite the authors claim that they addressed my major comment #5, the discussion still includes an interpretation that somehow these results are at odds with or undermine classic reinforcement learning theories of the basal ganglia. This is not true, and not supported by their data. As I said in my previous review, the cue associated with the large or small reward could be a part of the cortical representation of state. No one knows how animals represent states of a task or environment cognitively, or in neural dynamics. Why must it be the case that the state representation would not include information about rewards? The authors seem to misunderstand reinforcement learning. Their interpretation is not accurate and not supported by their data.", "reviewer_2": "Thank you for the careful revision. The paper is fine from my point of view. As a note: I still think a regression is more informative and intuitive compared to an Anova as it indicates numerically the probability of different populations (the partial r-square), although a numeric comparison exists also with the chosen Anova method as the authors indicate. And maybe a little note to the authors: it would help the referees if the authors indicated exactly where the changes are found (e.g. which line, or which subsection) so that we don't need to search for them (given the naturally changed line numbers). Anyway, congratulations on a nice and important paper.", "reviewer_3": "The authors have addressed my concerns, and have done a good job reframing some of their results in a way that is better supported by the data they present.\nI specially appreciate the addition of supplementary figure S5 and table S1.\nAt this point, I have no further concerns or comments."}, "author_response": "We thank the reviewer for the useful comments and the positive assessment. We have removed the claim about reinforcement learning and revised the claim regarding the selection task.\nWith regard to figure 6 - “This winner-take-all representation aligns with expectations associated with the output of a selection process but not with other coding schemes such as coding of the offer value.“ This claim is not supported by the data. The authors find that the response to the large reward target is identical regardless of whether the animal gets to choose it. That seems entirely consistent with coding of the offer value, since the offer value of the large reward target is identical across the different tasks.\nWe understand the reviewer’s point. In the original version, we interpreted the lack of coding of the small reward during selection as evidence against offer-value coding at the location of the small reward target. We now recognize that this interpretation, and the terminology we used, was inaccurate. We have therefore removed any mention of offer-value coding from the revised manuscript.\nMoreover, the fact that neural firing rates for the preferred direction are different depending on large or small rewards is not consistent with the activity simply reflecting the output of a selection process. None of this has any bearing on a “winner-take-all” mechanism. The data do not support these claims. Figure 6 does not rule out encoding of offer value.\nWe thank the reviewer for this comment and agree that this is indeed an important point that we overlooked in the previous version. We believe that the previous version fell short in integrating both value coding in the single target task and selection representations. As pointed out by the reviewer, value coding when targets appear alone indicates that the FEF does not encode only the output of the selection process.\nWe now acknowledge and discuss this point in the revised manuscript. We have added this to the paragraph in the discussion in which we deal with the FEF responses across selection and single-target tasks (lines 338-341 and 419–428 in the revised manuscript). Briefly, we now propose that the FEF encodes which target was selected target and the reward value associated with it. We have also revised the result section describing the selection takes (Fig. 6) to remove any mention of a winner-take-all.\n(2) Despite the authors claim that they addressed my major comment #5, the discussion still includes an interpretation that somehow these results are at odds with or undermine classic reinforcement learning theories of the basal ganglia. This is not true, and not supported by their data. As I said in my previous review, the cue associated with the large or small reward could be a part of the cortical representation of state. No one knows how animals represent states of a task or environment cognitively, or in neural dynamics. Why must it be the case that the state representation would not include information about rewards? The authors seem to misunderstand reinforcement learning. Their interpretation is not accurate and not supported by their data.\nWe have removed the paragraph discussing our results in relation to reinforcement learning models.\nReviewer #2 (Remarks to the Author):\nThank you for the careful revision. The paper is fine from my point of view. As a note: I still think a regression is more informative and intuitive compared to an Anova as it indicates numerically the probability of different populations (the partial r-square), although a numeric comparison exists also with the chosen Anova method as the authors indicate. And maybe a little note to the authors: it would help the referees if the authors indicated exactly where the changes are found (e.g. which line, or which subsection) so that we don't need to search for them (given the naturally changed line numbers). Anyway, congratulations on a nice and important paper.\nWe thank the reviewer for the useful comments and positive assessment. To better understand the relationship between effect size and the regression analysis suggested by the reviewer, we compared the results obtained from the partial 𝑅2 of the regression model with the ANOVA effect size. The results of this comparison are shown in the figure below.\nFigure R1A shows a very high correlation between the partial 𝑅2 and the main effect size (excluding interactions) of the parameters (reward, time, and direction). The ANOVA model allows the calculation of interaction terms, and we defined the effect size as the sum of the main effect and its interaction with time (see line 611 in the Methods of the revised manuscript). Even when these interactions are included in the model, we still observe a high correlation between partial 𝑅2 and effect size.\nAn additional point demonstrated by these plots is that partial 𝑅2 is bounded below by zero, whereas the effect size can take negative values. This illustrates that, unlike partial 𝑅2, the effect size is not biased by a non-negativity constraint. Thus, this analysis clarifies the relationship between the approach suggested by the reviewer and our analysis, and highlights why we chose to use effect size.\nFigure R1. Comparison of linear regression and effect size analyses. The regression model was computed between firing rate and categorical variables for direction, reward, and time. Each dot represents the effect size for a single neuron and a single variable. The horizontal and vertical axes show the effect size and the partial 𝑅2 of the regression model. In A, the effect size was calculated using only the main effect, without interactions. In B, for direction and reward, the effect size was defined as the sum of the main effect and the interaction between the variable and time."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have addressed my concerns, I have no further comments.", "reviewer_2": "Thanks for the clarifications regarding regression vs. Anova. I stand corrected, your explanations are convincing (and sorry to having misstated 'probability of different populations' instead of 'variance explained'). Congratulations on an important paper."}, "author_response": ""}]

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