Neural correlates of trial outcome monitoring during long-term learning in primate posterior parietal cortex

Domain: physics
Published: 2025-11-27
Editorial outcome: major_revision

Abstract
Monitoring behavioral outcomes is essential for optimizing behavior and learning new tasks. However, how the brain monitors outcome information during long-term learning remains largely unknown. Using two-photon calcium imaging in behaving monkeys, we tracked neuronal activity in area 7a of the posterior parietal cortex (PPC), an important associative cortex that has not been typically implicated in associative learning (AL), while the macaque monkeys learned new visuomotor associations across multiple days. We found robust neuronal representation of salient behavioral outcomes (correct vs. incorrect) which closely correlated with the monkeys’ learning behavior. Furthermore, these outcome representations significantly reorganized when monkeys transitioned to learn novel associations and gradually evolved over subsequent learning days, a process constrained by the functional connectivity among neurons within local network. These results suggest a substantive role for primate PPC in long-term AL through monitoring trial outcome, and indicate a principle for long-term learning: network connectivity constrains the evolution of neural encodings.

Introduction
The ability to learn appropriate motor responses to diverse environmental stimuli is essential for an animal’s survival in complex environment. During AL, the effectiveness of establishing new sensorimotor associations relies on the brain’s ability to monitor the positive and negative outcomes of our action choices during individual learning trials1–3. This outcome monitoring allows brain to evaluate the action consequences, reinforce relevant associations, and guide adaptive changes. Theoretically, brain regions central to outcome monitoring during AL should fulfill several criteria. First, they should exhibit significant encoding of trial outcomes (correct vs. incorrect) following the completion of each trial, with outcome signals reflecting higher-order cognitive processing rather than mere reward receipt. Second, the strength or pattern of outcome encoding should correlate with the animal’s learning performance or strategy and adapt dynamically to the learning context. Third, these outcome representations should contribute causally or functionally to animal’s subsequent behavior during the learning process.

The prefrontal cortex, cingulate cortex, and other subcortical areas have been implicated in representing trial outcomes during sensorimotor AL3–10, as their neural activity reflects the correctness of animals’ action choice during short-term AL. These outcome representations were not merely reflective of reward reception; some were correlated with the animals’ behavior performance during leaning6. However, the mechanisms by which the brain monitors behavioral outcomes remains elusive, as the previous studies only partially satisfied one or two criteria. Furthermore, acquiring complex sensorimotor skills often requires gradual learning over periods spanning days or longer, but previous primate studies have primarily focused on the changes of neuronal activity during relative short period of minutes to hours during the learning process6,11–17. Since no study has tracked the evolution of neuronal encoding of the same population of neurons in the primate brain over different learning days, our understanding of the mechanisms underlying long-term AL remains limited. Key questions regarding outcome monitoring remain untested, particularly during long-term AL: How does the neuronal representation of trial outcomes correlate with the animals’ behavior to influence learning? How does the outcome representation change throughout the learning process and what factors govern these changes?

Previous studies have highlighted the fronto-striatal circuit as the central hub for sensorimotor AL15,18–23, with less emphasis placed on the roles of other brain areas. Nevertheless, changes in neuronal activity were observed in other brain areas both during and after sensorimotor AL12,24–26, suggesting the engagement of a potentially broader brain network. The PPC, a crucial association area positioned at the intermediate stage of sensorimotor transformation, has been implicated in various cognitive functions27–34, but was usually not implicated in AL. The PPC has been shown to play important roles in representing stimulus-stimulus association and sensorimotor association after long-term learning24,35,36, implying potential involvement in sensorimotor AL. Specifically, neuronal activity in the lateral parietal cortex has been reported to reflect outcome information (rewarded vs. non-rewarded) during a decision task37. However, the precise role of PPC in long-term AL remains largely unexplored.

Area 7a, a subregion of the PPC, occupies the highest level of the visual processing hierarchy in the parietal lobe38. Traditionally, it has been implicated in diverse cognitive functions, including spatial attention, multisensory integration, motor planning, coordinate transformations, and other higher-order visuospatial processes30,31,39–45. However, its core functional roles remain debated. Notably, area 7a is directly connected with regions involved in learning and memory, such as prefrontal cortex and medial temporal lobe38,46,47, as well as with structures engaged in sensorimotor transformation, such as oculomotor areas48. These anatomical connections suggest a potential role in sensorimotor AL. Despite this, direct empirical evidence for its involvement in AL—and the mechanisms through which it may contribute—remains unexplored.

In this study, we investigated the role of primate PPC in long-term AL by performing two-photon calcium imaging to record large neuronal populations in area 7a while monkeys were learning image-saccade associations. Approximately half of these 7a neurons exhibited distinct representations of trial outcome and its recent history, which closely correlated with the monkeys’ learning behaviors. Notably, outcome representations in 7a were primarily sensitive to outcome switches and appeared to drive the exploration behavior in the subsequent trial. Remarkably, by tracking these neurons over the course of long-term learning, we found that the neuronal representations of trial outcomes substantially reorganized when monkeys transitioned from exploiting familiar association to exploring novel association, followed by gradual evolution dependent on the specific learning content. Critically, this evolution of outcome representation was constrained by the local network architecture, as reflected by functional connectivity between neurons. Together, our findings provide new insights into the neural mechanisms of outcome monitoring during long-term learning, highlighting a previously underappreciated role for primate PPC in AL.

Results
Representation of trial outcomes in area 7a
We trained two Macaque monkeys to perform an image-saccade association learning (ISAL) task (Fig. 1a), in which monkeys had to choose one of two saccade targets based on a presented visual image. Monkeys learned to associate different visual images with corresponding saccade directions based on the trial outcome–receiving juice reward or a brief time-out punishment after correct or incorrect saccade choices, respectively. The reward was delivered immediately after disappearance of the saccade target incorrect trials, ensuring that the trial outcome was swiftly apparent in both correct and incorrect trials. Each time the monkeys mastered two image-saccade associations (accuracy 0.89 ± 0.05, mean ± STD), they were given two novel associations in subsequent days (Fig. 1b), with the familiar associations no longer being tested at this stage of learning. While the monkeys learned new image-saccade associations, the saccade targets themselves remained constant. The monkeys’ performance accuracy substantially decreased on the first day when a new association was introduced (0.61 ± 0.12, Fig. 1c, d), and then gradually rose to a high level within a few days (3.9 ± 1.3 days). We employed a dynamic model, PsyTrack49, to fit the monkeys’ performance accuracy across learning each pair of associations (Fig. 1c, e and f). This model describes choice behavior at the resolution of single trials, allowing us to quantify the evolution of animal’s decision strategies (represented by different weights) across the learning course. It revealed a progressive increase in the weight attributed to visual stimuli, coupled with a gradual decrease in choice bias when learning new associations, consistent with the monkeys gradually learned the new associations.

To monitor the neuronal activity during sensorimotor AL, we injected AAV to induce GCaMP6f expression in neurons at a depth of ~400 μm within PPC area 7a, allowing us to simultaneously record the activity of hundreds of neurons over multiple days (Fig. 1b, g and h). The recordings spanned depths of 200–300 μm beneath the arachnoid membrane, primarily corresponding to layers 2–3. We recorded activity from a total of 27,511 single neurons with a good signal-to-noise ratio from two monkeys (see Methods). We found that these 7a neurons encoded trial outcome after the saccade target disappeared (ranksum test, P < 0.01, Bonferroni corrected, see Methods): some neurons exhibited stronger responses following incorrect saccade choices (error neurons, ENs; Fig. 2a); others did the opposite (correct neurons, CNs: Fig. 2b). To quantify the strength of neuronal encoding related to trial outcomes, we used one-way ANOVA to calculate the unbiased fraction of explained variance (FEV) in the neuron’s activity attributable to the trial outcome (see Methods). Figure 2c, d show an example of field-of-view (FOV) within which most neurons (92%) showed significant outcome selectivity. Furthermore, ENs outnumbered the CNs (92 vs. 51), and there was no obvious spatial clustering for either type of outcome-selective neuron.

Population results from both monkeys were well represented by the phenomenon observed in the example FOV (Fig. 2e–i, Supplementary Figs. 1 and 2). About half of the identified neurons exhibited significant encoding of trial outcome, with greater prevalence to negative outcomes (14.8% CNs, 34.8% ENs, Fig. 2e, f). Even though both CNs and ENs showed persistent outcome encoding on the population level, ENs exhibited significantly greater outcome selectivity than CNs (P = 5.168e-16, paired t-test, Fig. 2g). Most outcome-selective neurons (63%) exhibited sustained outcome encoding beyond 1 second (Fig. 2h). We did not detect spatial clustering for the outcome encoding in the majority of the FOVs (Supplementary Fig. 3). Moreover, although the outcome encoding in 7a spans a wide-ranging latency throughout the inter-trial interval (Fig. 2i), it cannot be attributed to differences in monkeys’ eye positions or eye movement-related behaviors between correct and error trials during this period. First, there is no significant correlation between the strength of outcome encoding and the monkeys’ eye positions during the inter-trial interval (Supplementary Fig. 4a, b). Second, both monkeys exhibited similar saccade behaviors between correct and error trials during the inter-trial interval for the majority of recording sessions, including direction (82.91%), frequency (62.39%), and amplitude (56.41%), and the difference of saccade behavior between correct and error trials did not significantly contribute to 7a neuronal activity (Supplementary Fig. 4c). Third, although the monkeys’ blink frequencies during the inter-trial interval differed between correct and error trials in more than half of the recording sessions (64.1%), 7a neural activity did not significantly correlated with the frequency of eye blinks during the inter-trial interval (Supplementary Fig. 4c). These results suggested a stable and sustained outcome encoding in the primate PPC, predominantly representing the negative outcomes.

We next tested whether 7a neuronal activity reflected the outcome history of the previous trials. For this analysis, we focused solely on data from one monkey due to the high frequency of fixation breaks (fix-break ratio = 0.57 ± 0.08) during performing the task exhibited by the other monkey. This resulted in an excessive number of trials with no choice interspersed between completed trials. Using a linear regression model, we found that 7a neurons most strongly encoded the outcome of the immediately preceding trial, rather than those coming earlier (Supplementary Fig. 5). We therefore focused on analyzing the encoding of the outcome of the preceding trial. As illustrated by the examples shown in Fig. 2j, k, 7a neurons could show significant encoding of outcome-history during the fixation, sample and delay period of the current trial. In an example FOV (Fig. 2l and m) and 57.8% of neurons exhibited significant encoding of the outcome history. Among these, ENs significantly outnumbered CNs (P < 0.001, Chi-square test) and neither neuron type showed significant spatial clustering. Population results were similar (Fig. 2n–r and Supplementary Fig. 6), with 52.9% the identified neurons showing significant encoding of the outcome history (Fig. 2n–o, 10.4% CNs, 42.5% ENs). The EN signal significantly surpassed that of CNs (P = 6.54e-30, Wilcoxon test) and peaked during the fixation period (Fig. 2p). The outcome-history encoding of the majority 7a neurons predominantly emerged and localized within the fixation period of the ISAL task (Fig. 2q, r). Data from the other monkey also exhibited significant encoding of the outcome history (Supplementary Fig. 7). Notably, the outcome-history encoding was not merely hysteresis (Supplementary Fig. 8): 39% of neurons exhibited inconsistent encoding of the trial outcome and outcome history, either showing significant encoding of only one of them or displaying significantly opposite preferences (7.3%).

We also quantified the activity of 7a neurons during other task epochs in the ISAL task (Supplementary Fig. 9). Consistent with previous findings, a substantial proportion of neurons encoded saccade direction, particularly during the post-saccade epoch (delay period: 20%, peri-saccadic period: 31%, post-saccadic period: 64%). The average strengths of saccade direction encoding and outcome encoding were positively correlated both within individual learning days and across learning days (Supplementary Fig. 10a, b). In contrast, very few neurons responded robustly to the visual cue (0.2%) or exhibited significant encoding on the image identity during the cue presentation period (<4%). Notably, the saccade direction selectivity during the delay period—which may reflect image-saccade associations—was notably modest (Supplementary Fig. 10c). Furthermore, pre-saccadic encoding in area 7a did not exhibit systematic changes across learning days that aligned with the monkeys’ learning performance (Supplementary Fig. 10d). These results suggest that area 7a is unlikely to be a central region for encoding and acquiring sensorimotor associations.

To further quantify the strength of neural encoding of current outcome, outcome history and saccade direction, we applied demixed principal component analysis to decompose 7a population activity into individual components corresponding to neural representations of different task variables (see Methods). This revealed significant encoding of all variables at the population level (Supplementary Fig. 11). Trial outcome and outcome history together explained significantly more variance of the population activity than the saccade direction did (P = 3.53e-16, t(32) = 15.18, paired t-test; history: 0.173 ± 0.022, outcome: 0.114 ± 0.010, saccade: 0.108 ± 0.011).

Relevance for outcome monitoring
Upon detecting prominent outcome encoding in area 7a, we proceeded to assess whether it satisfies the three criteria for outcome monitoring.

Initially, we tested whether outcome signal in 7a just reflected reward reception. To test this, we first compared outcome encoding for the same neurons between in the ISAL task and a separate visual saccade task (Supplementary Fig. 12). In this task, monkeys performed saccades like those in the ISAL task but without learning associations, with rewards doubled or canceled in a quarter of randomly chosen trials (see Methods). If outcome selectivity primarily correlated with reward reception, we expected similar levels of outcome selectivity between the tasks. Instead, both the proportion of significantly outcome-selective neurons and the population-level strength of the outcome encoding dramatically diminished during the visual saccade task (Fig. 3a–d, P = 2.07e-11, F = 46.92, repeated ANOVA), suggesting that the outcome encoding in 7a during ISAL task correlated with sensorimotor AL.

Second, we examined whether outcome encoding in 7a was modulated by punishment. During some of the ISAL recording sessions, we removed the time-out punishment after incorrect trials. This manipulation significantly attenuated the strength of outcome encoding in 7a neurons (Fig. 3e, f, P = 7.7e-23, t(832) = -10.13, paired t-test). Notably, the deletion of punishment changed the distribution of outcome-selective neurons: the proportion of CNs diminished significantly whereas that of ENs increased notably (Fig. 3g, P = 0.011, P = 1.4e-8, chi-square test). Some ENs (12.73%) even switched to encode correctness upon adding punishment.

Third, we investigated whether outcome encoding in area 7a was modulated by outcome history, specifically the outcome of the preceding trial. We quantified the encoding strength separately for trials following outcome switches (correct→error or error→correct transitions) versus outcome repetitions (correct→correct or error→error). Notably, outcome encoding was much stronger following outcome switches compared to outcome repetitions (Fig. 3h, P = 6.7e-14, t(116) = -8.5, paired t-test). This effect was consistent across both error-preferring and correct-preferring neurons for both monkeys (Supplementary Fig. 13). Together, these results demonstrate that outcome encoding in area 7a extends beyond simple reward reception. It reflects the evaluation of both positive and negative choice consequences, and dynamically incorporates recent behavioral history. These findings align with the established role of the primate posterior parietal cortex (PPC) in salience representation, suggesting that area 7a preferentially encodes behaviorally salient outcomes during AL.

Next, we investigated the relationship between outcome encoding in area 7a and the monkeys’ learning behavior during AL. If outcome encoding in 7a supports outcome monitoring and contributes to sensorimotor AL, we would expect that the strength of population-level outcome encoding correlates with the monkeys’ choice strategies and behavioral performance over the course of learning. To test this, we first assessed whether outcome encoding strength was associated with the extent to which monkeys relied on an outcome-guided strategy (win–stay or lose–shift) versus an outcome-ignored strategy (win–shift or lose–stay) during learning. We modeled the monkeys’ saccade choices for each image-saccade association using the PsyTrack model, which estimates a trial-by-trial weight for outcome-guided strategies. Across different learning days, we found a significant positive correlation between the outcome-guided strategy weight and the strength of outcome encoding in area 7a (Fig. 4a, b; Pearson’s r = 0.54; Spearman’s r = 0.49). This result indicates that outcome encoding in 7a tracked the monkeys’ use of outcome-driven strategies, supporting its role in trial-by-trial outcome monitoring during AL.

Second, we examined whether outcome encoding in area 7a was associated with the monkeys’ learning performance. For each learning day, we categorized the two tested associations as either better-learned (higher accuracy) or worse-learned (lower accuracy), based on the monkeys’ performance accuracy. In sessions with a notable performance difference between the two associations, 7a neurons exhibited significantly stronger outcome encoding for the better-learned association compared to the worse-learned one (Fig. 4c; P = 1.56e-7, t(17) = -8.5, paired t-test), regardless of whether the better-learned association was linked to contralateral or ipsilateral saccades (Supplementary Fig. 14). This difference in outcome selectivity is unlikely to be attributable to saccade direction encoding alone, as saccade choices were made at least one second prior to target offset. In contrast, in sessions where behavioral performance was similar between the two associations, outcome encoding remained comparable across conditions (Fig. 4d; P = 0.68, t(8) = 0.42, paired t-test). Together, these results indicate that outcome encoding in area 7a is closely linked to both the monkeys’ behavioral strategies and their learning performance, reinforcing its proposed role in outcome monitoring during AL.

Furthermore, we investigated whether outcome selectivity in area 7a directly influenced the monkeys’ learning of associations. Trials were categorized into two conditions: exploration, where monkeys switched saccade direction relative to the previous trial for the same image–saccade association, and exploitation, where they repeated the same saccade direction. For each condition, we assessed outcome selectivity in the preceding trial. Outcome selectivity was much stronger prior to exploratory trials than exploitative ones (Fig. 4e, P = 6.94e-13, t(115) = -8.1, paired t-test), suggesting that enhanced outcome encoding in area 7a predicted subsequent exploratory behavior. This effect was observed in both correct-preferring and error-preferring neurons across both monkeys (Supplementary Fig. 15a, b). In contrast, 7a neurons did not exhibit elevated outcome selectivity prior to trials where monkeys switched saccade direction independently of the image–saccade association (Fig. 4f and Supplementary Fig. 15c, d). These findings suggest that outcome representations in area 7a may contribute specifically to strategy shifts during learning sensorimotor associations, rather than reflecting general motor variability. Additionally, we found that pre-saccadic selectivity of 7a neurons was significantly, though very modestly, stronger when the monkey’s saccade choice was correct in the preceding trial compared to when it was incorrect (Supplementary Fig. 16), suggesting that outcome signals may modulate the strength of association encoding in area 7a in the subsequent trial. Together, these findings indicate that outcome encoding in area 7a fulfills all three proposed criteria for outcome monitoring, suggesting that this region plays an active role in monitoring trial outcomes and contributes functionally to sensorimotor AL.

Evolution of outcome representation during associative learning
Building on the above findings that establish a correlation between outcome encoding in 7a and outcome monitoring during AL, we next delved into the dynamics of outcome encoding across different learning days during long-term AL. We tracked the activity of neurons in 12 FOVs spanning the sensorimotor AL course (Supplementary Fig. 17 and Supplementary Table 1). The longitudinal recordings for each FOV spanned from the initial day when monkeys performed the ISAL task with familiar associations to the day when monkeys mastered a new pair of associations (Figs. 1b, 5 and Supplementary Fig. 17). We assessed the changes in preference and strength of outcome encoding between familiar days (when monkeys were tested with familiar associations) and learning days (when monkeys were learning novel associations). Figure 5a–j illustrates the progression of outcome encoding in neurons from two representative FOVs. For the FOV in Fig. 5a–e, the number of neurons showing outcome selectivity gradually increased after the monkey transitioned to learn novel associations. Within this example FOV, most neurons (76%), initially devoid of outcome encoding during familiar day, developed significant outcome encoding during learning days; while some of the remaining neurons (13.3%) switched outcome encoding upon acquiring new associations (Fig. 5c–e). At population level, outcome encoding in this FOV progressively increased during the learning days (Fig. 5d). Conversely, the other FOV exhibited a distinct evolution of outcome selectivity (Fig. 5f–j). Following an initial surge on the first learning day, outcome selectivity gradually waned over subsequent learning days. This decline is evident from the diminishing outcome selectivity in individual neurons and a reduction in the number of neurons significantly selective for outcomes (Fig. 5h–j). We also identified FOVs in which outcome selectivity was strongest during the familiar day, and dramatically diminished after transitioned to learn novel associations (Supplementary Fig. 18).

We found changes in outcome selectivity during AL in most of the tested FOVs (Supplementary Fig. 19), although the patterns of change were not unitary. Across all the FOVs, 12% of neurons gained or lost outcome selectivity after monkeys learned novel associations (Fig. 5k), 62% of neurons changed their outcome selectivity during the learning course (decrease, increase, or switch encoding preference, see Methods), while only 12% of neurons maintained stable outcome encoding across the learning course. Notably, changes in outcome encoding were more prevalent than the changes in saccade direction encoding in 7a, as significantly fewer neurons displayed significant changes in post-saccadic selectivity over the learning course (Fig. 5k, P << 0.001, chi-square test).

Subsequently, we investigated the stability and evolving patterns of outcome encoding at the population level throughout each learning course through two distinct approaches. First, we calculated the correlations in the patterns of outcome selectivity for all identified neurons in each FOV between successive days (see Methods). This revealed generally low correlation values for most FOVs (r = 0.345 ± 0.146). The inter-day correlations found for outcome encoding were significantly lower than those for saccade direction encoding (Fig. 6a, P = 3.4e-4, t(11) = -5.12, paired t-test), indicating that outcome encoding changed faster than the saccade direction encoding during AL. The patterns of neural encodings on trial outcome and saccade direction were distinct, with modest population-level correlation in only 1 of 12 FOVs (r = 0.015, P = 0.008). Importantly, we found lower inter-day correlations for outcome selectivity between the familiar day and the first learning day than that between successive learning days (Fig. 6a, P = 0.035, t(11) = -2.24, paired t-test). Conversely, neural encoding of saccade direction did not exhibit such divergent patterns of changes across different learning periods. This suggests that the neural pattern of outcome encoding reorganized in 7a when monkeys began to learn novel associations. The remapping of outcome encoding was notable after learning new associations, as evidenced by significantly lower correlations of outcome encoding between the familiar day and the final learning day (when the monkey had learned a novel pair of associations), compared to the inter-day correlations between successive learning days (Fig. 6a).

Second, we investigated how the population neural codes for trial outcome changed throughout learning. Specifically, we quantified the degree of alignment (or overlap) between the neural subspaces of the population activity from different days within each learning course, and tested how the neural subspaces encoding trial outcome or saccade direction changed throughout the learning course. This approach compares the percentage of variance explained when projecting population activity from one day onto neural axes defined by population activity from another day, producing an alignment index (AI) ranging from 0 (perfect orthogonality between the two subspaces) and 1 (perfect alignment; Fig. 6b and Supplementary Fig. 20; see Methods). We found significantly lower AIs for trial outcome compared to saccade direction in most FOVs (Fig. 6c), indicating faster changes in neural encoding of trial outcome than that of saccade direction. We then split the AIs into two groups (Fig. 6b): (1) AIs from projecting neural activity on learning days onto neural axes defined by neural activity from familiar days (novel-familiar group); and (2) AIs calculated using neural activity in different learning days (novel-novel group). Remarkably, AIs for outcome encoding in the novel-familiar group were significantly smaller than that in the novel-novel groups (Fig. 6d). This difference resulted from neither the difference in monkeys’ behavior performance nor the difference in the temporal gaps between familiar day and learning days (Supplementary Fig. 21a, b). Furthermore, the AIs from projecting neural activity on the final learning day onto neural axes defined by activity from familiar days were significantly lower compared to those in the novel-novel condition. Together, these results demonstrate that outcome encoding in 7a was contingent on learning content, and evolved gradually during learning novel associations.

In order to excess the extent of outcome encoding remapping caused by learning novel associations, we further calculated the percentage of neurons whose outcome selectivity significantly changed after the monkeys transitioned to learning novel associations. For each learning course, we compared the outcome encoding on the first or last learning day with the outcome encoding on the familiar day. We found that the majority of neurons (60%) exhibited significant changes in outcome selectivity after transitioning to learn novel associations (Fig. 6e), while only a small proportion of neurons (17–18%) maintained stable outcome encoding. To further quantify the extent of such reorganization in population-level outcome encoding, we defined a remapping index as the ratio of inter-day change in population-level outcome encoding (measured by the alignment index) for the novel-familiar group compared to the novel-novel group (see Methods). A higher remapping index indicates a greater degree of change in outcome encoding resulting from the transition to novel associations, relative to the evolution of outcome encoding during the learning of the same associations. Values greater than 1 suggest a trend of remapping, with higher values reflecting more significant changes. We found that the remapping index for all the FOVs was greater than 1, with an average value close to 1.5 (Fig. 6f), suggest that the reorganization of outcome encoding due to the transition to novel associations was substantial.

Moreover, changes in outcome encoding during AL were not primarily due to variations in the signal to noise ratio of the recorded neurons across different days. First, AIs for outcome encoding calculated using data from two successive days showed no significant difference from the AIs calculated using half-session data within each day (Supplementary Fig. 21c). Second, changes in outcome encoding across different days did not correlate with changes in baseline activity of each recorded neuron (Supplementary Fig. 21d).

Network connectivity constrained the evolution of outcome encoding
We hypothesized that the evolution of outcome representations during AL is constrained by network structure. One functional measure of network connectivity is ‘noise correlation’50,51, the covariation in activity across repeated trials. We calculated the noise correlation between each pair of recorded neurons for each FOV. This reveled that noise correlation was greater for within-type pairs than cross-type pairs for the outcome-selective neurons (ENs/CNs, Fig. 7a), indicating that neurons that showed similar neural encodings were more likely to coactivate. We therefore tested whether the strength of noise correlation could predict the changes of outcome selectivity across different days during AL. If the evolution of outcome encodings in 7a was governed by the network connectivity, we expected that neurons that coactivated more closely (more noise correlation) on one (henceforth ‘current’) day would demonstrate similar neural encodings (higher signal correlations) on the next day. Indeed, we found significant correlations between the noise correlation patterns on the current day and the signal correlation patterns on the next day for all successive days. (Fig. 7b, maximum P = 6.9e-64). Additionally, changes in signal correlations were significantly correlated with changes in noise correlations for all successive learning days (Fig. 7c, maximum P = 5.5e-19). These correlations suggest that the network connectivity potentially constrained the changes in the outcome encoding during long-term AL.

To further investigate how the evolution of outcome encoding was governed by the local network connectivity, we examined whether the noise correlation could predict changes in neuron types during AL. We focused on two major groups of neurons whose outcome encodings changed across two concessive days. First, we tested neurons belonging to the same type (CN, EN, or non-selective) on the current day but transitioning to different types on the next day. We categorized these neurons into different subgroups based on their outcome-selectivity on the next day (Fig. 7d–g), and calculated the noise correlations (within the same neuron type) on the current day for both within-future-type pairs (neurons belonging to the same type on the next day) and cross-future-type pairs (neurons transitioning to the different types on the next day). This revealed that noise correlation was predictive of the changes of neuron types during AL: noise correlation on the current day was significantly higher for within-future-type pairs than cross-future-type pairs (Fig. 7e–g). This trend was specifically observed in neurons exhibiting significant outcome selectivity (ENs and CNs), but dramatically diminished in neurons that were non selective on the next day (Fig. 7e–g, P = 3.1e-5, t(11) = -6.76, paired t-test). Remarkably, the prediction power for the changes in neuron types significantly decreased when signal correlation was quantified instead of noise correlation (Fig. 7g, P = 7.7e-5, t(19) = 5.01, paired t-test). These results indicate that the functional connectivity between the same type of neurons could predict the changes in outcome encodings across learning days.

Second, we examined neurons that were initially of different types on the current day but merged to the same type on the following day. Similarly, we quantified noise correlations between these neurons (across different neuron types) on the current day and sorted them into within-future-type pairs and cross-future-type pairs, categorized by the outcome selectivity of these neurons on the next day (Fig. 7h–k). This also disclosed significantly higher noise correlation on the current day for within-future-type pairs than cross-future-type pairs (Fig. 7i–k), with this pattern evident only among neurons exhibiting significant outcome selectivity on the next day (Fig. 7i, k, P = 5.3e-5, t(7) = -8.71, paired t-test). Furthermore, noise correlation exhibited better predictive ability for changes of neuron types than the signal correlation between these neurons (Fig. 7k, P = 7.1e-5, t(24) = 4.79, paired t-test). These results suggest that neurons exhibiting closer coactivation on the current day were more likely to develop similar neuronal encodings on the next day, even though they were currently of different types. Together, our results indicate that network connectivity shaped the evolution of outcome encoding in 7a during long-term AL.

Methods
Animal preparation and behavioral training
We trained two male Rhesus Macaques, aged 4–5 years and weighing 5–6 kg, on two different behavioral tasks. Before behavior training, we implanted three head-posts in an inverted Y-shape configuration on the monkeys’ skulls. A T-shaped steel frame was affixed to these head-posts to enhance the stability of the head fixing. Our surgical and behavioral approaches have been described in detail previously75,76. The monkeys were individually housed in cages, subjected to a 12-h light/dark cycle. Behavioral training and experimental recordings were exclusively conducted during the light phase. Monkeys were comfortably seated and head-fixed in a custom-made primate chair inside a dark experiment rig. Task stimuli were displayed on a 27-inch color LCD monitor (1920 × 1080 resolution, 75 Hz refresh rate, 45 cm viewing distance). A solenoid-operated reward system was used to deliver juice reward to the monkeys. Monkeys’ eye positions were monitored by an optical eye tracker (ISCAN ETL-200) at a sampling rate of 240 Hz and stored for offline analysis. Stimulus presentation, task events, rewards, and behavioral data acquisition were accomplished using an Intel-based PC equipped with MonkeyLogic software running in MATLAB77 (http://www.monkeylogic.net). All experimental and surgical procedures were in accordance with Peking University Animal Care and Use Committee.

Behavioral tasks
Image saccade association learning task (ISAL). Monkeys had to learn the associations between different visual images and two saccade directions in this task. On each trial, monkeys were required to saccade to one of the two targets based on a presented visual image (Fig. 1a). Each trial started with a central fixation point, and monkeys needed to maintain their gaze within a 3° radius of this point until it disappeared. 1500 ms after monkeys acquired fixation, a visual image, randomly selected from the internet and characterized by full contrast and a 6.5° diameter, appeared on the screen for 500 ms. Importantly, the image was always presented on the contralateral side of the screen relative to the recorded brain hemisphere (7° eccentricities, orientated at 135°). Following a 1 s delay, the fixation point disappeared and two saccade targets (white, 0.5° diameter) appeared simultaneously on the left and right side of the screen, equidistant from the center at 7° eccentricities. These targets were positioned orthogonally to the visual image’s orientation (oriented at 45°). The spatial configurations of task stimuli remained constant throughout the study. Monkeys had to saccade to the direction associated with the presented visual image within a 400 ms time window, and maintain fixation on the chosen target for 1 s to get juice reward. The juice reward was promptly delivered upon the target’s disappearance. As such, the trial outcome, whether correct or erroneous, became quickly evident, even though explicit feedback regarding the outcome of each trial was absent. Once they had fully learned a pair of image-saccade associations, the monkeys were then instructed to learn two novel associations in subsequent days.

Visual saccade task. A visual saccade task was used as a control task to test whether the outcome encoding in PPC solely reflected getting reward. Trials were initiated by the monkeys acquiring central fixation. Following a 1500 ms fixation period, the fixation point disappeared, and a visual target was presented at one of four possible locations, evenly spaced at 90° angular intervals with a 7° eccentricity. Two of the target locations corresponded precisely to those in the ISAL task. Monkeys had to make a single saccade to the target within 400 ms, and fixate at target for 1000 ms in order to receive a juice reward. At the behavioral training phase, monkeys received a standard-size juice reward after completing every trial. However, during neural recording sessions, the reward probability was manipulated: in one-quarter of randomly selected trials, monkeys were rewarded with double the usual amount, while the juice reward was omitted entirely in another quarter of randomly selected trials.

Task training. Both monkeys were trained with the visual saccade task before neural recording. However, there were slight differences in the training procedures for the ISAL task between the two monkeys. Monkey H received training in the ISAL task for 61 days but had not fully acquired proficiency in the first pair of image-saccade associations before the commencement of neural recording. Consequently, Monkey H continued to learn these initial associations after the neural recording began, spending almost one month to master them. In contrast, Monkey X received more extended training in the ISAL task and had already fully learned the first pair of associations before neural recording commenced. Both monkeys learned new sets of image-saccade associations more quickly as they gained experience with each new set. Notably, they took significantly longer to learn the first pair of associations, as they had to familiarize themselves with the task rules and stimulus sequence during this initial stage. Over the course of neural recording, two monkeys learned a total of 12 pairs of novel associations (Monkey H: 2 pairs, Monkey X: 10 pairs). This allowed us to consistently track the same neurons across all 12 subregions within 7a throughout the entire learning courses. During each learning course’s neural recording, monkeys were initially tested with the ISAL task using the previously learned (familiar) associations on the first day. Subsequently, they were introduced to a new pair of associations (novel) on the following days until full proficiency was achieved. At this stage of learning, only the novel associations were tested, while the familiar associations were not. Importantly, within each recording session/day, the image-saccade associations remained constant.

While Monkey H was in the process of learning the initial pair of image-saccade associations during the neural recording phase, its performance accuracy stabilized at approximately 80% for a period of several days. During this phase, we removed the time-out punishment following error trials in the ISAL task for four randomly selected sessions/days. This was designed to investigate the impact of punishment on outcome encoding in the PPC. Importantly, all task variables remained consistent within each recording session/day.

Neural recording
Virus injection and chamber implanting. In order to record the activity of a large population of neurons and monitor their activity changes across different days, we implemented two-photon calcium imaging recording on behavioral monkeys. Detailed procedures for the surgical and recording processes are elaborated upon in the previous studies75. In brief, we conducted two surgeries on each animal to inject the virus for transferring the calcium indicator into cortex and to implant the optical recording chamber respectively. During the first surgery, a 14-mm diameter craniotomy was executed on the skull over 7a region in the right hemisphere of each monkey. The localization of 7a was determined in accordance with the monkey brain atlas, specifically situated as the posterior part of the inferior parietal gyrus, located beneath the intraparietal sulcus and above the superior temporal sulcus (Fig. 1g, h). Following the dura mater’s exposure, we performed a pressure injection of 50–100 nL AAV9-Syn-GCaMP6f-WPRE-SV40 (titer 2.6e13 (GC/ml), Addgene) into cortex at a depth of approximately 400 μm. To ensure effective transfection, the virus solution was injected to many sites widely spread in 7a. Approximately 45 days after AAV injection, a second surgery was conducted to implant the recording chamber. A glass coverslip (diameter 8 mm and thickness 0.17 mm) glued in a titanium ring with silicone adhesive (KN-300X, Kanglibang, China) was used to gently cover the cortex. The titanium ring was sealed on the skull with dental acrylic to form an imaging chamber. In both monkeys, tissue regeneration occurred within the imaging chamber several weeks after the chamber implantation surgery. This dramatically degraded the quality of optical signal. We therefore surgically removed the regenerated tissues and replaced the glass coverslip with a new one.

Two photon calcium imaging recording. Approximately one week following the implantation of the recording chamber, we initiated the two-photon calcium imaging recording experiment using a Thorlabs two-photon microscope and a Ti:Sapphire laser (Mai Tai eHP, Spectra Physics). In each recording session, a cortical area of 800 μm × 800 μm was imaged at 30 Hz using a 16× objective lens (0.8 N.A., Nikon). The recording depth ranged from 200 to 300 μm beneath the arachnoid membrane. A standard slow galvo scan was used to obtain static images of neurons with high resolution (1024 × 1024). For imaging neuronal activity, we employed a fast resonant scan capable of capturing up to 30 frames per second (averaging each 4 frames resulted in 7.5 frames/s). Each recording session lasted for a duration of 4–6 h, generating an imaged dataset consisting of 80–120 k frames. Approximately every 5–10 min, we manually corrected slow drifts in the field of view (FOV) by comparing them to a reference image. In total, we conducted 117 recording sessions across 25 imaged FOVs distributed in 7a from two monkeys (7 FOVs and 36 recording sessions for Monkey H and 18 FOVs and 81 recording sessions for Monkey X). Within 12 of the 25 FOVs, we consistently monitored the activities of the same neurons throughout a full learning course spanning a period of 3 to 7 days. To ensure alignment with the same imaging plane on consecutive days, we initially performed coarse alignment based on the recording coordinates, blood vessels, pia mater, and arachnoid, followed by precise alignment to reference images.

Data analysis
Behavioral performance. Only recording sessions where the monkeys completed a substantial number of trials (n >= 800 for Monkey H and n >= 700 for Monkey X) were included in our subsequent analyses. On average, monkey H completed 1156 trials per session, and monkey X completed 965 trials per session. We assessed each monkey’s performance accuracy by calculating the ratio of correct trials to the total number of completed trials, encompassing both correct and error trials. In Fig. 1c, we depicted the real-time accuracy dynamics using a sliding time window approach (width: 100 trials, step size: 20 trials). To determine whether monkeys had fully acquired a pair of image-saccade associations, we applied two criteria: (1) the monkey’s average accuracy throughout the session exceeded 0.8, and (2) the monkey’s average accuracy reached 0.9 over a continuous period of at least 200 trials. Additionally, we assessed whether monkeys exhibited a significant bias in saccade direction within a given session by examining the disparity in their performance accuracies between two different saccade directions. If this performance accuracy difference reached 0.2 within a specific section, we classified that section as a biased session.

Dynamic model fitting. In order to capture the dynamics of monkeys’ behavior strategies during sensorimotor AL, we fitted monkeys’ performance accuracy with a dynamic Bernoulli generalized linear model, characterized by a set of smoothly evolving psychophysical weights. Specifically, we used ‘PsyTrack’ package, which was described in detail in a previous study and proven suitable for parametrizing the time-varying behavior in learning task49 (https://github.com/nicholas-roy/PsyTrack). This model aims to fit modeling weights that depict the decision-making strategy of the animals on a trial-by-trial basis, as a linear combination of available task variables. Therefore, this model describes choice behavior at the resolution of single trials, allowing us to quantify the evolution of animal’s decision strategies (represented by different weights) across the learning course. Notably, the magnitude of a particular weight directly correlates with the extent to which the animal relies on the corresponding task variable in its decision-making process. Specifically, we utilized the task condition (Cue identity) as input for the model, denoting each single trial as either -1 or 1, while the monkeys’ choice (Saccade direction) served as the model’s output, labeled as 0 or 1 for each single trial. We determined three behavioral weights, namely the bias weight, previous answer weight and stimulus weight, through model fitting.

Neuronal data analysis
Identify single neurons within each session. We used an image processing software (suite2p, https://github.com/MouseLand/suite2p), for motion correction, definition of putative cell bodies, and extraction of fluorescence traces. We extracted the single neuron activity within each FOV based on three primary steps. First, the raw data streaming from the microscope system was converted into ome.tif format using Fiji software (version 2.13, https://imagej.net/software/fiji/). Subsequently, we transferred this data to ‘suite2p’ for offline processing, which included motion correction, extraction of neuronal signals, and denoising. Second, we manually selected the resulting cell bodies (ROIs) candidates from the original ROI panels. We then employed a machine learning approach to optimize parameters tailored to the data recorded from different subregions of the 7a cortex in each monkey. These refined parameters were subsequently applied to ‘suite2p’ to reprocess the data again, generating new ROIs for each data session. Lastly, we manually inspected and selected ROIs once more to ensure that all identified ROIs exhibited satisfactory neuron morphology and met the quality criteria.

Identify the same group of neurons across sessions. Following the selection of ROIs for each recording session, we proceeded to align the neurons across all the recording sessions/days throughout the learning course for each tested 7a FOVs. This involved the following steps. (1) We utilized ‘suite2p’ to generate a cross-day classifier by leveraging data from all sessions/days within each learning course for each 7a subregion. (2) Subsequently, we imported the cross-day classifier back into ‘suite2p’ to generate ROIs within each FOV for each individual day within the learning course. Using these steps, the neurons (ROIs) within each FOV were identified for each recording day through the ‘suite2p’. (3) We then manually cross-referenced and matched the neurons (ROIs) among different recording sessions within each learning course, based on their spatial location in the reference plane and their morphology. If a neuron could not be confidently identified in one of the sessions within the learning course, it was excluded from consideration for all recording sessions within that learning course. Consequently, our approach yielded an incomplete map of all neurons across all recording days for each FOV.

Nonetheless, this method offers advantages over other commonly used approaches. Other approaches often use a single map of ROI masks for all days, such that this map is transformed on each day to best fit that day’s imaging alignment. Slight deviations in the axial plane of the image or other sources of in-plane distortion could lead to slight offsets in masks from day-to-day relative to the ideal alignment. Such slight offsets could result in contamination from activity in other cells, dendrites, and axons. In contrast, our approach identifies signal sources on each day, circumventing the risk of contamination from signal sources on different days. Furthermore, our procedure necessitates the selection of neurons that consistently exhibit a high signal-to-noise ratio throughout the entire learning course. Consequently, we have confidence that the observed changes in neural encoding for each individual neuron during the learning course are unlikely to be attributable to fluctuations in the recording quality across different recording sessions.

Neuronal pre-screening, denoise and normalized activity. Since our recording sessions typically extended for a considerable duration (4–6 h each), various factors such as slow drifts in the imaging plane’s focus and light bleaching occurred within each session. These factors resulted in gradual alterations in the baseline fluorescence levels of individual neurons during each session. To mitigate these potential sources of interference, we implemented a high-pass filter on the fluorescence trace of each neuron, effectively eliminating very slow dynamics within each recording session (Highpass filter, 0.005 Hz). We plotted the relative changes in fluorescence, denoted as F(t)/F0, for all the examples of single neuron. Here, F0 represents the mean activity of the region of interest (ROI) over the entire session, while F(t) signifies the activity of each individual neuron.

Quantify the outcome encodings
Identify outcome selectivity. To identify neurons with significant encoding of trial outcome, we applied a Wilcoxon ranksum test to assess activity between the correct and error trials in the task period following target offset (1–16 frames (0–2.2 s) after target offset). We further divided this period into four smaller windows, each comprising 4 frames. If statistically significant differential activity between correct and error trials was observed within at least one of these windows (P < 0.01, Bonferroni corrected), we classified the neuron as outcome-selective. Similarly, to identify whether the 7a neurons exhibited significant selectivity for the outcome of the previous trial (outcome-history), we compared activity between the trials in which the previous trial was a correct vs. incorrect trial. This comparison included neural activity during the fixation, cue and delay periods of the ISA task (12 frames before to 12 frames after cue onset). We partitioned this time period into six evenly spaced time windows and designated a neuron as outcome-history selective if a statistically significant difference emerged during this comparison (P < 0.01, Bonferroni corrected).

Identify saccade direction selectivity. To identify neurons exhibiting significant saccade direction encoding, we employed a Wilcoxon ranksum test to compare neural activities around saccade onset between trials featuring different saccade directions. Specifically, we assessed saccade direction selectivity during both the pre-saccadic period (1–4 frames before saccade onset) and the post-saccadic phase (1–8 frames after saccade onset). While the pre-saccadic epoch was analyzed as a single time window, we divided the post-saccadic epoch into two smaller time windows (4 frames each). Neurons were classified as pre- or post-saccade selective if there was statistically significant differential activity observed between trials with distinct saccade directions in at least one time window within the pre- or post-saccadic epoch (P < 0.01, Bonferroni corrected).

Identify visual responsive neurons. To determine whether 7a neurons exhibited a significant visual response to the visual cue in the ISAL task, we conducted a comparison between each neuron’s activity during cue presentation (1–4 frames after cue onset) and its baseline activity (defined as the fixation period, spanning from 1 to 5 frames before cue onset). Neurons were categorized as ‘visual responsive’ if their activity during cue presentation exceeded their baseline activity by at least one standard deviation.

Unbiased fraction of explained variance (FEV). We performed one-way ANOVA on the neuron’s activity within each time frame to quantify the amount of information that each neuron encoded about trial outcome. Similar as in the previous studies78, We calculated the unbiased FEV in the neuron’s activity that could be attributed to the trial outcome with the following:

FEV(factor) = (SS(factor) - (k-1)MSE) / (SS(total) + MSE) (1)

where SS indicates the sum of squares, MSE indicates mean square error, and k indicates number of conditions.

We observed variations in the magnitudes of outcome selectivity across trials in which monkeys execute saccades in different directions. Moreover, monkeys exhibited biased choices toward different saccade directions on different days during AL. Additionally, we recognized that the imbalance in trial numbers between correct and incorrect trials on different days could have varying effects on the FEV value of outcome selectivity. To mitigate potential confounding factors stemming from these phenomena when comparing outcome selectivity across different days, we employed a two-step approach to calculate the FEV value of outcome selectivity for each neuron. (1) Initially, we separately computed the FEV value of outcome selectivity for each of the two saccade directions and subsequently averaged these values. (2) For each saccade direction, we randomly selected an equal number of correct and error trials, matching the smaller of the two trial numbers. We repeated this process 500 times, calculating FEV values each time, and then averaged the resulting 500 FEV values.

Determining the latency of outcome selectivity. For each neuron, a Wilcoxon ranksum test was applied to the distributions of activity to the both correct and error trials within each frame to determine the latency of outcome selectivity. The starting point of outcome selectivity was defined as the first of three consecutive frames in which a significant difference (P < 0.01) was observed in neuronal responses between the two trial types.

Determining the duration of outcome selectivity. For each neuron displaying outcome selectivity, we divided the task epoch (which was used for identifying outcome selectivity) into multiple time windows, each spanning 4 frames (~0.54 s), to assess the duration of outcome selectivity. Within each time window, we performed a Wilcoxon ranksum test to compare neuronal activity between correct and error trials. We then determined the number of time windows in which a statistically significant difference (P < 0.05, Bonferroni corrected) was observed.

Linear regression model. We examined whether the outcome selectivity of 7a neurons can be simply attributed to encodings relative to differences in monkeys’ eye positions between correct and error trials during the inter-trial interval. Specifically, we employed a multiple linear regression model to quantify the dependence of 7a neuronal activity on monkeys’ eye positions in both horizontal and vertical directions, saccade choice direction, as well as trial outcome:

y_i(t) = b_0(t) + b_1(t)c_i + b_2(t)h_i(t) + b_3(t)v_i(t) + b_4(t)s_i (2)

Here, y_i(t) represents the neuron’s activity at the t-th frame after the target offset in the i-th trial, c_i is the trial outcome of trial i (1 and -1 denotes correct and incorrect, respectively), h_i and v_i represent the horizontal and vertical eye positions relative to the fixation center, and s_i is the saccade choice direction of trial i. The coefficients b_0 through b_4 denote the regression coefficients. The linear regression was applied to neuronal activity for each time frame after target offset in the inter-trial interval.

Furthermore, we tested whether the outcome selectivity of 7a neurons was primarily driven by differences in the monkeys’ saccade behavior or blinks between correct and error trials during the inter-trial interval. Saccades and blinks were identified using the following steps: First, we calculated eye movement speed v(t) during the inter-trial interval after each trial. We marked speed peaks (dv/dt = 0, d^2v/dt^2 < 0) as potential blinks or saccades. Peaks with a maximum speed v_max below 100°/s are excluded, whereas those peaks with a speed v_max exceeding 900°/s are identified as blinks, yielding the blink count or frequency f_b for each trial.

Next, we identified potential saccade starts (dv/dt > 0, v(t) >= 30°/s) and stops (dv/dt < 0, v(t) >= 0.1 v_max) around the remaining peaks. If overlapping saccades were detected, only the one with the higher peak speed was retained. We then verified that all saccades had a magnitude m_s greater than 2° by examining eye position at the start and stop points. Additionally, we checked for consistent saccade direction (cos^-1(V_start · V_stop) < 15°) within each saccade. Only movements that passed both criteria were classified as saccades, providing the saccade frequency f_s and saccade magnitudes m_s for each trial.

After identifying saccades and blinks during the inter-trial interval, we used the following linear regression model to assess the influence of differences in saccades and blinks between correct and error trials on 7a neural activity:

y_i(t) = b_0(t) + b_1(t)c_i + b_2(t)s_i + b_3(t)f_bi + b_4(t)f_si + b_5(t)m_si (3)

Here, y_i(t) represents the neuron’s activity at the t-th frame after the target offset in the i-th trial, c_i is the trial outcome of trial i (1 and -1 denotes correct and incorrect, respectively), s_i is the saccade choice direction of trial i, f_bi and f_si represent blink frequency and saccade frequency during the inter-trial interval, and the m_si is the average saccade magnitude of those saccades. The coefficients b_0 through b_5 denote the regression coefficients. The linear regression was applied to neuronal activity for each time frame after target offset in the inter-trial interval.

Furthermore, in order to test whether 7a neural activity reflected the outcome history of the previous trials, we employed a multiple linear regression model to quantify the relationship between 7a neuronal activity and the outcome history of previous trials using the following equation:

y_i(t) = b_0(t) + b_1(t)c_{i-1} + b_2(t)c_{i-2} + b_3(t)c_{i-3} (4)

Here, y_i(t) represents the neuron’s activity at t-th frame after fixation onset in the i-th trial, and c_i is the trial outcome of trial i, and the coefficients b_0 through b_3 denote the regression coefficients. For this analysis, we focused solely on data from one monkey.

Spatial tuning index. We employed two different methods to assess whether the outcome-selective neurons exhibited pronounced spatial clustering.

First, we calculated the Euclidean distances between neurons to assess the presence of clustering among different neuron types within each FOV. Utilizing the suite2p software, we obtained positional coordinates for each neuron within the recording plane. Distances between pairs of neurons were measured based on their positional coordinates within the recording plane, with no regard for their depth. We then examined whether the mean distance between neurons of the same cell type (within-type distance) differed significantly from the mean distance between neurons of different cell types (across-type distance). If the within-type distance was significantly smaller than the across-type distance (P < 0.01, Wilcoxon Ranksun test) in a given FOV, we considered the outcome-selective neurons to be spatially clustered within that 7a subregion.

Second, we calculated a spatial turning index based on both the neural selectivity and spatial position of each neuron. The spatial tuning index is defined as:

C_tuning(d) = E(1-ρ_actual) / E(1-ρ_resampled) (5)

Where ρ is the tuning similarity between neuron pairs, and d is the spatial distance between neuron pairs. The ρ we used equals to the Pearson correlation coefficient of outcome-related activity, calculated using the activity matrix S2R_{N×ct} of each neuron. Where N denotes the recorded cell number of certain FOV, c denotes two outcome conditions (correct and error), and t denotes time (1–16 imaging frame after the target offset). The distance d was discretized into 50 μm bins from 0 to 800 μm. The ρ_actual was computed for pairs of neurons whose distances fell within each distance bin. The ρ_resampled was the average tuning similarity between randomly selected cell pairs, calculated using bootstrap (1000 times).

Spatial tuning index values greater than 1 indicate that the outcome selectivity of neurons was more similar than chance level, whereas values smaller than 1 indicate that the outcome selectivity of neurons was more different than chance level. The significance of spatial tuning index is determined by comparing the observed result to a shuffled distribution (bootstrap size = 1000). A result is deemed statistically significant in two circumstances: When the minimum actual C exceeded the values found in 99.9% of the shuffled results, the C_tuning values were regarded as significantly higher than chance; when the maximum actual C fell below 99.9% of the shuffled results, the C_tuning values were regarded as significantly lower than chance.

Demixed principle component analysis (dPCA). We conducted demixed principal component analysis using the methodology and the code from a previous study79 (http://github.com/machenslab/dPCA) to reduce the dimensionality of the population activity as the standard PCA and demixes all task variables. Specifically, we tested how much each task variable (trial outcome, outcome-history, saccade direction, history-saccade interaction, history-outcome interaction, outcome-saccade interaction, timing) contributes to the 7a population activity during the ISAL task.

As demonstrated in the previous study, the dPCA finds separate decoder (F) and encoder (D) matrices for each task variable (Φ) by minimizing the loss function:

L_dPCA = ||X_Φ - F_Φ D_Φ X||^2 (6)

where X is a linear decomposition of the data matrix, which contains the instantaneous activity of the recorded neurons, into variable-specific averages:

X = Σ X_Φ + X_noise (7)

Here, we decomposed the neural activities into seven parts: history dependent (the trial outcome in previous trial), current outcome dependent, saccade-dependent (two saccade directions), dependent on the history-saccade interaction, dependent on the history-outcome interaction, dependent on the outcome-saccade interaction, and the noise. The decoder and encoder axes permit us to reduce the data into a few components capturing the majority of the variance of the data dependent on each task variable. Only data from Monkey H were included in this analysis.

Quantify the evolution of outcome selectivity during AL
Quantify the proportion of neurons that changed selectivity. To evaluate the stability of neural encodings (either outcome encoding or saccade direction encoding) over the AL course, we calculated the proportion of neurons whose selectivity changed from one day to another within the learning course. To achieve this, we first assessed whether each neuron exhibited significant saccade or outcome selectivity. We employed FEV analysis to quantify the strength of neural selectivity across various time windows (using the same time windows used for identifying selective neurons). We determined a neuron’s selectivity preference for a particular day based on the time window with the highest FEV value.

Subsequently, we categorized the neurons in each learning course into several groups. (1) Neurons that exhibited significant selectivity on the familiar day but lost this selectivity across all learning days were classified as ‘lost selectivity’. (2) Neurons that lacked significant selectivity on the familiar day but developed significant selectivity on all learning days were designated as ‘gain selectivity’. (3) Neurons that consistently displayed significant selectivity on every recording day within a learning session without altering their preference were termed ‘persistent selectivity’. (4) Neurons that did not exhibit significant selectivity on any recording day during that learning session were categorized as ‘no selectivity’. (5) The remaining neurons, which encompassed those that altered their outcome selectivity across different learning days, were grouped as ‘change selectivity’.

Correlation of neural encoding across different days. In order to quantify the stability of the neural encoding across each learning course, we calculated the correlation of outcome/saccade encoding between two consecutive days within the learning course for each FOV. This process involved two steps. (1) For each individual neuron within the FOV, we quantified the strength of the outcome/saccade encoding using FEV analysis and determined their preferences for each time point. Consequently, for each time point, we constructed an encoding vector that represented the neural encoding within each FOV. This vector incorporated both the strength and preference of neural encodings for all identified neurons within this FOV. (2) At each time point, we calculated the Pearson correlation between the encoding vectors for two consecutive days. This resulted in a correlation value that indicated the similarity of the neural encodings between those two days for each of the given time point. We repeated the procedures for all time points included in the analysis. In Fig. 6a, neural encoding correlations were computed separately for two conditions: (1) between the familiar day and the first learning day (familiar-to-novel) and (2) between successive learning days (novel-to-novel). For the novel-to-novel condition, we first calculated pairwise correlations between each pair of successive learning days (while monkeys were acquiring the same novel associations) and then averaged these correlation values.

Alignment index. To quantify the relationship between the population codes for trial outcome across different days, we calculated the alignment index (AI), similar as in the previous studies80. We quantified the degree of alignment/overlap between the neural subspaces of the population activity from different days within each learning course, and tested how these neural subspaces, responsible for encoding trial outcomes or saccade direction, evolved throughout the learning course.

To calculate AI, we first constructed the population activity matrix P_dayi ∈ R^{N×CT} for each day, where N is the total number of neurons, C is the numbers of conditions and T is the numbers of time points. Subsequently, to calculate the AI between day i and day j (A(i,j)), we conducted principal component analysis (PCA) on activity matrix P_dayi to obtain the PCs of day i, considering each neuron as a variable. We selected the top 10 PCs to form a 10-dimensonal orthogonal axis in N-dimensional neural space. The degree of overlap between neural subspaces can be estimated by projecting the activity matrix from day j onto the PCs of day i and quantifying the proportion of variance explained relative to the total variance of population activity on day j. Thus, we defined the alignment index A(i,j) as:

A(i,j) = Tr(D_dayi^T C_dayj D_dayi) / Σ_{k=1}^{10} σ_dayj(k) (8)

Where D_dayi is the top 10 eigenvectors obtained by PCA. C_dayj is the covariance of matrix P_dayj. σ_dayj(k) is the k-th singular value of C_dayj. The Tr() function denotes the matrix trace, which sums along the diagonal entry. The reverse, A(j,i), can be calculated in similar ways. The numerator quantifies the proportion of variance in population activity from day j that is captured by the top ten PCs of day i. The denominator normalizes the alignment index with respect to the maximum amount of variance in population activity from day j that can be explained by a ten-dimensional subspace (i.e., the variance captured by the top ten PCs of day j).

As a result, the AI ranged from 0, indicating perfect orthogonality, to 1, signifying perfect alignment.

The detailed parameters used to construct the activity matrix are as follows:
I. To create the activity matrix for saccade encoding (P_saccade), we used the activity within a time window spanning from one to 16 imaging frames after saccade onset for both saccade directions (left and right).
II. The activity matrix for outcome encoding (P_outcome) is constructed using neural activity within the time window spanning from one to 16 imaging frames after target offset for both outcome conditions (correct and error).
III. The baseline activity matrix (P_baseline) is assembled using neural activity within the time window spanning from 4 imaging frames before fixation onset to 12 imaging frames after fixation onset, under the same conditions as those used for P_outcome.
IV. In order to estimate the rate of change in neural encoding within a day/session, we calculate the AIs using data within each day (Supplementary Fig. 21). We separated the neural data into two equal-sized portions based on the trial sequence (i.e., the first/second half of all trials, or the odd/even trials). We then constructed the activity matrix for each part of the data.
V. The activity matrix representing changes in outcome or baseline activity across two consecutive days is defined as the difference between the activity matrices for the two days.

In order to quantify the relationship between the outcome encodings on familiar day and learning days, we calculated the A(familar, novel) using the activity matrix from familiar day and that from each of the learning days for every learning course (Fig. 6b). The AI values was calculated by projecting the activity matrix from each of the learning days onto the top PCs of the activity matrix from the familiar day. Subsequently, we averaged these AI values to estimate the similarity of neural encodings between familiar day and learning days. Similarly, we calculated the A(novel, novel) by employing the activity matrix from different learning days for every learning course to quantify the relationship between the outcome encodings on different learning days. The resulting AI values, obtained by projecting the activity matrix from the later learning days onto the top PCs of the activity matrix from the earlier learning days, were averaged to measure the neural encodings similarity between different learning days.

Remapping index
We calculated the remapping index as the ratio of inter-day changes in population-level outcome encoding between familiar days and learning days (novel-familiar group) compared to inter-day changes between different learning days (novel-novel group), shown as follows:

Remapping index = (1 - A(novel-familiar group)) / (1 - A(novel-novel group)) (9)

where A indicates the alignment index quantifying the overlap of the population-level outcome encoding between two days.

Quantify the potential mechanisms that constrained the evolution of outcome selectivity during AL
Signal correlation. To quantify the similarity of outcome selectivity between different neurons, we calculated the signal correlation for each pair of neurons. In contrast to classical methods, we incorporated time with outcome conditions to calculate the signal correlation. To achieve this, we first constructed the activity vector A ∈ R^{vct}, where c denotes two outcome conditions (correct and error), and t denotes time (1–16 imaging frame after the target offset). The signal correlation between each pair of neurons was defined as the Pearson correlation between their respective activity vectors.

Noise correlation. We calculated the noise correlation between different neurons to quantify the connectivity between them following these steps: (1) Single-trial responses were estimated using the averaged activity within two time windows (1–4 frames or 5–8 frames after target offset) in the ISAL task. Alternatively, another time window (1–4 frames or 5–8 frames before cue onset) was used to calculate noise correlation, which yielded quantitatively similar results. (2) Pearson correlations were calculated separately for correct trials and error trials using the pairwise trial-by-trial activity. (3) We calculated a single correlation value by averaging the two correlation values for correct and error trials, using the trial numbers of each condition as weights. For each data session, we obtained two correlation matrixes C_noise, C_signal ∈ R^{N×N}, within N representing the number of neurons within each FOV. In Fig. 7b, in order to quantify the correlation between noise correlation and signal correlation, we transformed the upper-triangles of those matrixes into 1×N(N-1)/2 vectors, and subsequently calculated the correlations between these vectors from different days. In Fig. 7c, we defined changes in correlations between two days as the difference between the vectors from those different days. In Fig. 7d–g, we calculated the within-type correlations in the current day, and tested whether these noise correlations could predict changes in outcome selectivity on the following day. Specifically, this analysis focused on neurons belonging to the same type (CN, EN, or non-selective) on the current day but transitioning to different types on the next day. We categorized these neurons into ‘within-future-type’ pairs (neurons belonging to the same type on the next day) and ‘cross-future-type’ pairs (neurons transitioning to the different types on the next day) based on their outcome-selectivity on the next day (Fig. 7d). In Fig. 7h–k, we tested whether the noise correlation among cross-type neuron pairs could also predict changes in outcome selectivity during AL. Here, we focused on the neurons that were initially belonged to different types on the current day but converged to the same type on the following day. Similar to the previous analysis, we categorized these neurons into ‘within-future-type’ pairs and ‘cross-future-type’ pairs, based on their outcome selectivity on the next day (Fig. 7i). For both analyses, only the data sessions in which there were sufficient number of neurons (n >= 10) in each neuron type were included. Due to limited data sessions meeting this neuron number requirement for all cell types, we independently selected data sessions for outcome-selective and non-selective neurons for the analysis. In Fig. 7g–k, we also employed the signal correlation to predict changes in outcome selectivity during AL. The calculation steps were identical to those used for noise correlation, with the distinction that we utilized signal correlation between neurons to predict changes in neural ensembles.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Liu et al. investigate neuronal activity in the posterior parietal cortex (PPC) of Macaque monkeys during a learning task using two-photon calcium imaging longitudinally. While the authors demonstrate the feasibility of two-photon microscopy in the primate posterior parietal cortex (PPC) across days, it does not present a sufficiently significant scientific advancement and remains too preliminary, requiring additional data and analysis. The technology itself—although very crucial for studying individual neuronal activity—has already been established by the same group in the prefrontal cortex. Consequently, this study provides little advancement in imaging methods and in elucidating the role of the PPC beyond what has previously been demonstrated.\n\nMajor comments\nThe potential significance of this study in elucidating the specific role of the PPC in learning is unclear. Prior studies have addressed the PPC function in learning and memory, and the authors do not convincingly demonstrate how their data substantially deepen or challenge existing knowledge. The listed studies—Morcos & Harvey (2016, Nature Neuroscience), Pho et al. (2018, Nature Communications), and Hanks et al. (2015, Nature), and more studies (Buneo and Andersen, 2006 Neuropsychologia; Erlich et al., 2016 eLIFE; Goard et al., 2015 eLIFE; Harvey et al., 2012 Nature; Raposo et al., 2014 Nature Neuroscience; Shadlen and Newsome, 2001 Journal of Neurophysiology; Zhang et al., 2022 Nature Communications)—have demonstrated the role of the posterior parietal cortex (PPC) in outcome history-dependent decision-making across various tasks. In addition, numerous studies support the role of the PPC in learning and memory, including its function in evidence accumulation. However, the authors only focus on a narrow niche of sensorimotor association within the broader domain of associative learning. It is uncertain whether this study on sensorimotor association provides new insights or meaningful scientific advancements beyond the existing body of knowledge. In this sense, the authors primarily emphasize the proof-of-concept aspect in long-term imaging rather than providing robust new insights into neuronal processing during learning tasks.\n\nAnother potential significance of this study lies in tracing the dynamic changes in single-neuron activity to reveal how learning progresses over days. However, the study does not make clear which representational components undergo change across days. In their task, termed the Image-Saccade Association Learning (ISAL) task, the authors propose that remapping occurs during learning. Yet, it remains unclear what this remapping represents, as novel objects were introduced from the beginning of learning. If PPC neurons merely encode object identity, remapping could occur independently of sensorimotor association, making it difficult to determine whether neural representation changes are linked to learning. The task structure, therefore, complicates the interpretation of how neuronal activity reflects the process of sensorimotor association formation. To make clear what this remapping represents, another task, such as the reversal version of Image-Saccade Association learning task, is necessary. A simple change in neuronal activity during the learning period does not necessarily constitute an important finding, especially since previous single-neuron studies in macaque monkeys have already demonstrated similar representational changes (McMahon et al., 2014 Journal of Neurophysiology; Yasuda et al., 2012 Journal of Neuroscience)\n\nThe introduction requires revision, as the discussion of technology and scientific concepts is intermingled, making it difficult to grasp the main focus of the study. From a scientific perspective, associative learning encompasses various forms, yet the importance of specifically studying sensorimotor association remains unclear. The authors argue that this function has been ‘traditionally less appreciated,’ but it is necessary to clarify why this aspect warrants investigation. Given the substantial body of research on both the PPC and sensorimotor association learning, the introduction should reference relevant studies to justify the need for examining the PPC’s role in this specific learning process. Strengthening the conceptual framework and clearly distinguishing the technological contribution from the scientific inquiry would improve the clarity and impact of the introduction.\n\nThe authors posed two questions as follows: ‘How does the neuronal representation of trial outcomes change throughout the learning process to facilitate learning? And what governs these changes in neuronal representation of trial outcomes?’\nHowever, the questions in the introduction are not sufficiently addressed by the results, as they do not clarify which neuronal representations facilitate learning or what governs these changes. To address these questions, a behavioural causality study would be necessary—such as investigating how inhibiting neural representation changes affects learning performance. In other words, the scope of the questions exceeds the range of the conclusions that can be drawn from the results, highlighting a gap between the study’s initial inquiries and its empirical findings.\n\nFurther analyses are necessary to characterize the function of the primate PPC. The authors have primarily focused on neuronal activity following the target-off period, yet their task includes cue-on, delay, and target-on periods, which could provide valuable insights into the PPC’s role in associative memory, working memory, and action value. To draw a robust conclusion about whether the PPC is selective for history-based choice, both single-neuron and population-level analyses are needed. Expanding the analyses to these task phases would provide a more comprehensive understanding of PPC function in learning and decision-making.\n\nLastly, what do the authors mean by ‘monitoring’? The study does not clearly define this term, making it difficult to interpret its relevance to their findings. To draw a meaningful conclusion, the authors must first provide a clear definition of monitoring and then evaluate whether the neuronal representation in the PPC aligns with this definition. At this stage, the observed neuronal activity in the PPC appears to be a simple outcome representation that persists into the next trial, rather than an explicit monitoring mechanism.\n\nMinor comments\nDid you confirm whether AAV expression is selectively restricted to layers 2 and 3? The explanation on page 3 regarding AAV expression is unclear and may confuse readers. If not, please revise the sentence.\n\nThe figures require revision to improve clarity: For example, FOV figures lack scale bars. Figures such as 4E and 4J are missing X and Y axis labels. Additionally, the ordering of panels within each figure is difficult to follow. Overall, the figures should be revised to include all necessary information for proper interpretation.\n\nThe behavioural paradigm in Figure 1 should specify the inter-trial interval (ITI) to provide a complete description of the task structure.\n\nLine 98: multiples days → multiple days\nLine 128: during inter-trial interval → during the inter-trial interval\nLine 244: notable after learned → notable after learning\nLine 746: difference in monkeys' eye positions → differences in monkeys' eye position\np.3 Figure S2 (A): significant different → significant difference\nThere are additional grammatical errors in the manuscript that need to be addressed. The manuscript needs to be revised by a native English speaker.\n\nLine 133: What does 0.57 ± 0.08 represent in relation to fixation breaks? Please clarify.\n• Line 134: Figure S4 should be incorporated into Figure 2 in the main text.\n• Line 174: What does WN type n refer to? Should this be EN type n instead?\n• Line 250: I cannot find the data for outcome encoding correlation in the referenced figure. Please indicate where this data is presented.\n• Line 568: Is 400 mm the correct value? Please verify.\nAdditionally, how was N.-N. calculated in Figure 5A?", "reviewer_2": "The study \"Neural correlates of trial outcome monitoring during long-term learning in primate posterior parietal cortex\" by Liu, Jiang, she, Fang, Tang and Zhou shows a unique and important data set tracking the responses in parietal area 7a during associative learning. The study shows robust representation of decision outcome in a subset of these neurons. This is the case both at the end of the trial during reward as well as - for the outcome of the previous trial - during the current trial. Their data also indicate changes in the representation during learning and postulates a role for network connectivity in constraining this. However, - as currently presented - the direct link between these changes in neuronal representations and learning could be shown more clearly.\n\nThe major strength of the study lies in the technically challenging tracking of individual neurons in parietal cortex during several sequences of associative learning for a image-saccade pair. The data showing robust representations of behavioural outcome in area 7a are compelling.\n\n1) Some critical aspects of the presentation make it difficult for the reader as well as the reviewer and additional information/clarifications are needed with regard to the following aspects:\n- in the abstract/title: the monkey species and the area recorded from (7a) must be named. Actually the species seems to be missing from the entire paper. It should also be mentioned at the beginning of the methods.\n-introduction: PPC in the primate is large with divers areas and response properties. I would have expected a paragraph discussion the role and response properties previously found in area 7a, so the reader has a chance to situate this study and its importance better. In this context, a brief sketch of the response properties in 7a in relations to what is known about sensorimotor processing and trial outcome would be essential. The only area referred to in the introduction seems to be lateral intraparietal area (line 60, ref 37), which is wrongly referred to as \"lateral parietal cortex\", which needs correcting. As it is, it is not clear why 7a is explored at all, other than being on the surface of the (macaque??) brain and therefore more accessible.\n- Figures, Results: as the two monkeys differed in their learning , all results should also be reported for each monkey separately and in all figures the examples should be clearly associated with the animal they come from - as as is custom in the field.\n- Results: a critical piece of evidence relies on assigning neurons to a specific type of representation (e.g. lines 101-109). An Anova on FEV seems to be used. In the results, the exact statistical criterion and what the correction for multiple comparisons was should be included.\n- Results, Figure 2H, line 115-116: text and figure appear to contradict each other: Figure shows about 63% of neurons below one 1s, text states it the other way round.\n- Results, all Figures, timing labels \"Time to target off\": these are generally positive numbers, so I presume it is time \"after\" or \"from\" target off that is meant. This is highly confusing and needs to be corrected. Please also check the \"time to cue on\" and revise, so the language is unambiguous and consistent.\n- Results, Figures 2A, B, J, K: the meaning of the dashed lines in relation to the task is unclear. Please label appropriately, perhaps with arrows and names on top.\n- Results 2C, 2L are not very clear. What colour is which type? It seems the yellow cells might only be present in smaller subparts of the FOV, which could be considered as clustering?\n-Results, lines 133 in brackets: what is being measured (trials?) in what units (%?).\n-Results Figure 3D, what is suddenly \"WN\"?\n-Results Figure 4D and 4I: the abbreviations the legends N., D1 .... are not explained.\n-Results Figures 4B,C,G,H: what is the x-axis?\n\n2) Interpretation of the results.\nThe section \"relevance of outcome encoding in 7a for associative learning shows that it is not reward and punishment alone. The link between outcome encoding and weight of previous choice is does not directly show a contribution to learning. the same applies for the saccade bias. Also, Figure 3H seems to rely heavily on data points that show neither a sizeable weight of the previous answer nor sizeable encoding strength.\nWhile the data presented are suggestive of a link between behavioural outcome representation and associative learning, this is at present rather descriptive rather than compelling. What is needed is a quantitative, statistical link between changes in representation and changes in behavioural performance either day-to-day or trial-by-trial.\nSince outcome encoding of the previous choice is ongoing when the animal makes a decision, so interacts directly with incoming activity (rather than what is measured at the end of the trial). Any correlation with actually learning of the association might be clearer for this epoch.\nAt present l201-203 only the first part is clearly supported by the data, \"this encoding is closed correlated with AL\" should be removed until some robust direct link can be made statistically to learning outcome. The same applies for the first sentence of the next paragraph.\nl235-252: the lack of a link between outcome encoding and saccade direction encoding is also problematic in this context. From Figure 5 and results text, it is not clearly presented whether or how the reorganisation trial outcome representations is linked to learning. This part of the results is crucial for understanding and impact of these results. At the moment, this could do with a clearer structure reporting the key results.\n\n3) Results, Figure 2I and 2R: The latency data (negative numbers) seems to suggest that trial outcome is encoded BEFORE the reward is given. So the animal had no feedback yet. This should change your interpretation of what is encoded or at least taken into account?\n\n4) Relationship of outcome representation to neuronal tuning for saccade localisation and relationship of previous trial to current trial outcome representations. If the outcome representations contribute to learning the appropriate association. The tuning for stimuli and saccade of neurons should matter. Doe the authors find any evidence whether different subgroups of neurons related to their tuning show different outcome representations and that the link between the two properties affect learning success?\n\n5) Network connectivity lines 296ff and Figure 6: Potentially an interesting results. But between what neurons (tuning, entry criterion) was the noise correlation measured?\nWhich time window did you use? As you have effects of previous trial outcome represented in some of the time windows, you would expect some correlation due to this signal. How did you take this into account and investigate/eliminate this effect on correlations?\nFigure 6F,G,J,K need clearers description in the figure legends. What data am I looking at and what is the critical piece of data shown.\n\nMinor:\nAbbreviations: the many abbreviations make the text not very readable, e.g. AL, ISAL could be written out as they are not standardly abbreviated terms.\nIntroduction: l51: sentence is unclear and grammatically incorrect.\nFigure 1D. Legends states data are from each monkey. What part is from what monkey?\nFigure: the labelling by letter seems haphazard in most figures and does not follow a clear pattern. Some panels are not really visible, like e.g. the bottom of 1G\nFigurelegends: it would be helpful if the main outcome for each panel is summarised as well as only describing what is plotted.\nP-values for stats are sometimes \"p\" (Methods) and sometimes \"P\" (Results). Very confusing as you also look at proportions. I suggest to standardise to \"p =\" and always write directly after naming the test.\nLabelled neurons in Figure 4A and 4F are not easily discernible.", "reviewer_3": "This study investigates the changes in the activity of cells in the primate area 7a, measured with two-photon calcium imaging, during an associative learning task that demands new visual-motor associations over multiple days. The results showed a strong neural representation of the trial outcome, which was closely correlated with monkeys’ learning. This neural outcome signal was reorganized as monkeys transitioned from exploitation to exploration and showed a gradual evolution while the animals learned new visual-motor associations. In addition, the encoding of trail outcome was not closely related to the cell encoding of the output saccadic movements. Finally, the modulation in outcome encoding was constrained by the local network connectivity.\n\nThis is a very interesting and well performed study, providing novel information about how the PPC incorporates local outcome signals to promote visual-motor associations along multiple days. An important strength of this manuscript is the use of calcium imaging with two-photon that allows the measurement of the activity of the same population of neurons during periods of learning that last days. Another strength in the study is the use of a simple task where two images are associated with two saccade directions over a period of days, which permits to measure the reconfiguration of the cell and network responses during the process. Nevertheless, a have some concerns. First, it is difficult to know whether the data of the figures are from the monkey with stable saccadic behaviors or some of them are from the monkey with high frequency of fixation breaks. A direct comparison of the functional properties of 7a neurons between monkeys is in place. The heterogeneity in the outcome signal modulation across FOVs is large, that should be clear in the paper and something needs to be say about what is the roles of such heterogeneity in AL. In addition, it is important to know where the FOV where within area 7a, which is a large structure and limits with 7ab and 7m that have different connectivity and functional properties. The present results describe how outcome encoding changes during long-term AL, not how the outcome signals strengths the visuomotor associations during learning. The same question is valid for the observation that neurons coactivated more closely were more likely to develop strong connections over time, leading to similar outcome encoding, but the question remains of how this encoding signal directly affects the association between image-saccade direction across days. Finally, for Figure 6 I highly recommend computing the mutual information of the neural activity during the task period between cells to generate robust graphical models of functional connectivity across cell types and learning epochs.\n\nMinor comments\nPlease describe what the PsyTrack parameters are and why this model is better than others to describe AL.\nThe change of outcome signaling in the population showed in Figure 4 is astonishing. Please add the color code for outcome encoding of Figure 2C in Panels 4A and F.\nIs the outcome signal in PPC used locally for AL or is also used in other areas?\nHow the complex remapping of outcome encoding linked to new associations is used to generate the AL. How the previous and actual trial outcome signal affects the image-saccade association, when the mapping of outcome encoding remapping is to complex.\nWhat is the outcome signal doing to generate the association between the image and the saccade direction? The outcome-history encoding of the majority 7a neurons predominantly emerged and localized within the fixation period of the ISAL task, so these signals have an effect on how the cue images are progressed or kept in memory? Or is a signal that changes the reaction time of the saccades or the saccade direction?\nWhat is the functional meaning of having outcome signals that are different between the actual and the previous trial?\nWhat is the effect of the speed of learning (e.i. 3 vs 7 days) on the magnitude of the encoding of outcome by neurons and on dynamics across days.\nWhat is the proportion of cells that encode the stimulus onset, the image identity, the saccade movement preparation and execution and the outcome. How many neurons encoded more than one parameter, and how the outcome signal changes during AL modified the encoding of the image and the saccade direction in the same cells or in other populations.\nHow many neurons encode both the previous and the actual outcome as a function of learning.\nPlease provide Figures of the FOV with pronounced outcome selectivity for the second monkey as in Figure S1 and S9.\nTypos.\nThere are many typos in the manuscript such as:\nfrom two monkeys. (see Methods).\nFigure.2C"}, "author_response": "Authors’ Response to Reviewers\nWe sincerely thank all reviewers for their thoughtful and detailed feedback on the\nearlier version of our manuscript. The comments and questions were very helpful,\nand we have addressed each of them in the responses below and incorporated the\ncorresponding revisions into the manuscript. As a result, the manuscript has been\nsubstantially improved. The revised version includes updates to all sections of the\ntext, revised Figures 1–6 (including a new Figure 4), and 5 new supplementary\nfigures. In brief, we have added multiple lines of evidence showing that outcome\nencoding in area 7a was closely linked to the monkeys’ learning behavior during\nassociative learning (AL). Notably, 7a neurons primarily encoded salient outcomes\nand may directly contribute to exploration behavior during AL. In the response below,\nreviewers’ comments are shown in black, with our point-by-point replies in blue.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nLiu et al. investigate neuronal activity in the posterior parietal cortex (PPC) of Macaque\nmonkeys during a learning task using two-photon calcium imaging longitudinally. While the\nauthors demonstrate the feasibility of two-photon microscopy in the primate posterior\nparietal cortex (PPC) across days, it does not present a sufficiently significant scientific\nadvancement and remains too preliminary, requiring additional data and analysis. The\ntechnology itself—although very crucial for studying individual neuronal activity—has\nalready been established by the same group in the prefrontal cortex. Consequently, this\nstudy provides little advancement in imaging methods and in elucidating the role of the\nPPC beyond what has previously been demonstrated.\nResponse: We acknowledge that our original manuscript did not sufficiently highlight the\nnovelty of our findings or fully clarify the interpretation of the results. In the revised version,\nwe have added new analyses and substantially refined our introduction and discussion to\naddress these points. Please refer to our detailed point-by-point responses below.\nAdditionally, while our study does not focus on two-photon calcium imaging in\nbehaving monkeys as a technical advancement (as this method has been well-established\nin our lab for years), it does represent an important methodological improvement relative\nto our prior PFC study. For the first time in primates, we tracked a large population of\nneurons across long-term learning, enabling us to observe how neuronal encoding evolves\nover learning days.\nMajor comments\nThe potential significance of this study in elucidating the specific role of the PPC in learning\nis unclear. Prior studies have addressed the PPC function in learning and memory, and the\nauthors do not convincingly demonstrate how their data substantially deepen or challenge\nexisting knowledge. The listed studies—Morcos & Harvey (2016, Nature Neuroscience),\nPho et al. (2018, Nature Communications), and Hanks et al. (2015, Nature), and more\nstudies (Buneo and Andersen, 2006 Neuropsychologia; Erlich et al., 2016 eLIFE; Goard et\nal., 2015 eLIFE; Harvey et al., 2012 Nature; Raposo et al., 2014 Nature Neuroscience;\nShadlen and Newsome, 2001 Journal of Neurophysiology; Zhang et al., 2022 Nature\nCommunications)—have demonstrated the role of the posterior parietal cortex (PPC) in\noutcome history-dependent decision-making across various tasks. In addition, numerous\nstudies support the role of the PPC in learning and memory, including its function in\nevidence accumulation. However, the authors only focus on a narrow niche of sensorimotor\nassociation within the broader domain of associative learning. It is uncertain whether this\nstudy on sensorimotor association provides new insights or meaningful scientific\nadvancements beyond the existing body of knowledge. In this sense, the authors primarily\nemphasize the proof-of-concept aspect in long-term imaging rather than providing robust\nnew insights into neuronal processing during learning tasks.\nResponse: We agree with the reviewer that the original version of the manuscript did not\nsufficiently clarify the primate posterior parietal cortex (PPC)’s specific role in associative\nlearning (AL). As noted, the PPC’s involvement in decision-making (e.g., evidence\naccumulation in perceptual/reward-based tasks) and spatial memory is well-documented\nin primates and rodents. In contrast, our work focuses on the PPC’s contribution to\nsensorimotor AL—a process critical for adaptive actions, decision-making, and motor\ncontrol, enabling organisms to respond effectively to changing environments.\nStudying sensorimotor AL offers critical insights into how the brain links sensory inputs\nto motor outputs based on experience to guide behavior. A key component of this process\nis behavioral outcome monitoring, which enables the brain to evaluate the consequences\nof actions, strengthen relevant associations, and adapt behavior accordingly. Therefore,\nunderstanding how the brain represents behavioral outcomes—and how these\nrepresentations support learning—is essential for uncovering the neural mechanisms\nunderlying AL. While regions such as the prefrontal cortex (PFC), cingulate cortex, and\nsubcortical structures have been shown to represent trial outcomes during AL, the neural\nmechanisms underlying outcome monitoring remains poorly understood. Although\nprevious studies in rodents have implicated the PPC in processing stimulus history and in\nbiasing action selection based on that history, it remains an open question whether and\nhow the primate PPC similarly monitors outcomes to support learning. In this study, we\naddressed this gap by proposing three key criteria that a brain region should meet to be\nconsidered central to outcome monitoring during AL. While previous studies only partially\nmet one or two criteria, our findings demonstrate that area 7a of the primate PPC satisfies\nall three criteria, suggesting that this region plays an active role in monitoring trial outcomes\nand contributes functionally to sensorimotor AL.\nTo date, only one prior nonhuman primate study has reported outcome encoding\n(rewarded/unrewarded) in the PPC (LIP, a subregion of PPC) during a matching pennies\ngame task, where monkeys made free choices against a computer opponent based on\nreward history. In contrast, our study focused on a different and less-characterized\nsubregion of the PPC—area 7a. We found that outcome encoding in area 7a is not merely\na response to reward delivery, but reflects outcome monitoring during AL, whereas the\nprevious LIP study did not control for potential sensory feedback confounds in outcome\nencoding.\nCritically, we found that outcome encoding in area 7a was closely linked to the\nmonkeys' learning behavior in several key ways: (1) Encoding strength predicted trial-\nby-trial learning strategies, such as win-stay/lose-shift patterns, across learning days\n(revised Figure 4A-4B); (2) Stronger encoding was associated with better learning\nperformance, showing enhanced representation for associations with higher performance\naccuracy (revised Figure 4C-4D and Figure S14); (3) Outcome representations were\ndramatically influenced by the outcome history, as it was significantly stronger after\noutcome switches (correct→error or error→correct transitions) than after outcome\nrepetitions (correct→correct or error→error) (new results, new Figure 3H and new Figure\nS13), suggesting area 7a primarily represents salient outcome; (4) The strength of\noutcome encoding significantly predicted the monkeys’ learning strategy in subsequent\ntrials, with higher outcome selectivity linked to exploratory behavior(switching saccade\nchoice direction for the same association), suggesting that outcome encoding in area 7a\nmight directly contribute to exploration behavior during learning (new results, new Figure\n4E-4F and new Figure S15); (5) Encoding strength markedly decreased when the monkeys\nwere not engaged in learning (Figure 3A-3D); and (6) Outcome representation was\ndynamically linked to learning content, with population-level encoding patterns remapping\nwhen the monkeys shifted from exploiting familiar associations to exploring novel ones\n(Figure 6). Together, these findings reveal potential mechanisms for outcome monitoring\nin primate area 7a and substantially extend current understanding of decision-making and\nlearning processes in the PPC.\nIn the revised manuscript, we expanded our analyses to clarify how outcome encoding\nin area 7a contributes to sensorimotor AL through outcome monitoring. First, we examined\nwhether and how this encoding relates to the monkeys’ learning strategies. For each\nlearning day, we fitted monkeys’ behavior to a ‘Psytrack’ model to generate a weight of\nprevious answer, quantifying the extent to which the monkeys’ saccade choice was\nfollowed an outcome-guided strategy (win–stay or lose–shift) or an outcome-ignored\nstrategy (win–shift or lose–stay). We found a positive correlation between the averaged\nstrength of outcome encoding and the extent to which the monkeys relied on win–\nstay/lose–shift strategies across different learning days (revised Figure 4A-4B, shown in\nbellow). This result indicates that outcome encoding in area 7a is closely linked to the\nexpression of adaptive choice strategies during learning. We have revised the\ninterpretation of this result to clarify this point, shown as follows:\nWe have revised the corresponding results section as follows:\n“Next, we investigated the relationship between outcome encoding in area 7a and the\nmonkeys’ learning behavior during AL. If outcome encoding in 7a supports outcome\nmonitoring and contributes to sensorimotor AL, we would expect that the strength of\npopulation-level outcome encoding correlates with the monkeys’ choice strategies and\nbehavioral performance over the course of learning. To test this, we first assessed whether\noutcome encoding strength was associated with the extent to which monkeys relied on an\noutcome-guided strategy (win–stay or lose–shift) versus an outcome-ignored strategy\n(win–shift or lose–stay) during learning. We modeled the monkeys’ saccade choices for\neach image-saccade association pair using the PsyTrack model, which estimates a trial-\nby-trial weight for outcome-guided strategies. Across different learning days, we found a\nsignificant positive correlation between the outcome-guided strategy weight and the\nstrength of outcome encoding in area 7a (Figure 4A-4B; Pearson’s r = 0.54; Spearman’\ns r = 0.49). This result indicates that outcome encoding in 7a tracked the monkeys’ use of\noutcome-driven strategies, supporting its role in trial-by-trial outcome monitoring during AL.”\nSecond, we investigated whether outcome encoding in area 7a was associated with\nthe monkeys’ learning performance (revised Figure 4C-4D and Figure S14). For each\nlearning day, we categorized the two tested associations as either better-learned or worse-\nlearned based on the monkeys’ behavioral accuracy. We found that outcome encoding\nwas significantly stronger for the better-learned association compared to the worse-learned\none. In contrast, when the monkeys’ performance was similar across the two associations,\noutcome encoding strength did not differ significantly. These findings suggest that stronger\noutcome encoding in area 7a may contribute to more effective AL. We have revised the\ninterpretation of this result and added it into the main figures, shown as follows:\nWe have revised the corresponding results section as follows:\n“Second, we examined whether outcome encoding in area 7a was associated with the\nmonkeys’ learning performance. For each learning day, we categorized the two tested\nassociations as either better-learned (higher accuracy) or worse-learned (lower accuracy),\nbased on the monkeys’ performance accuracy. In sessions with a notable performance\ndifference between the two associations, 7a neurons exhibited significantly stronger\noutcome encoding for the better-learned association compared to the worse-learned one\n(Figure 4C; P = 1.56e-7, t(17) = -8.5, paired t-test), regardless of whether the better-learned\nassociation was linked to contralateral or ipsilateral saccades (Figure S14). This difference\nin outcome selectivity is unlikely to be attributable to saccade direction encoding alone, as\nsaccade choices were made at least one second prior to target offset. In contrast, in\nsessions where behavioral performance did not differ significantly between the two\nassociations, outcome encoding remained comparable across conditions (Figure 4D; P =\n0.68, t(8) = 0.42, paired t-test). Together, these results indicate that outcome encoding in\narea 7a is closely linked to both the monkeys’ behavioral strategies and their learning\nperformance, reinforcing its proposed role in outcome monitoring during AL.”\nThird, we have included new analysis to investigate whether outcome encoding in\narea 7a was modulated by outcome history, specifically the outcome of the preceding trial.\nWe quantified the encoding strength separately for trials following outcome switches\n(correct→error or error→correct transitions) versus outcome repetitions (correct→correct\nor error→error). Notably, outcome encoding was much stronger following outcome\nswitches compared to outcome repetitions (Figure 3H, P = 6.7e-14, t(116) = -8.5, paired t-test). This effect was consistent\nacross both error-preferring and correct-preferring neurons for both monkeys (Figure S13).\nTogether, these results demonstrate that outcome encoding in area 7a extends beyond\nsimple reward reception. It reflects the evaluation of both positive and negative choice\nconsequences, and dynamically incorporates recent behavioral history. These findings\nalign with the well-established role of the primate posterior parietal cortex (PPC) in salience\nrepresentation. Detailed results are shown in bellow:\nWe have added one paragraph in the revised Results section to describe this result\nas follows:\n“Third, we investigated whether outcome encoding in area 7a was modulated by\noutcome history, specifically the outcome of the preceding trial. We quantified the encoding\nstrength separately for trials following outcome switches (correct→error or error→correct\ntransitions) versus outcome repetitions (correct→correct or error→error). Notably, outcome\nencoding was much stronger following outcome switches compared to outcome\nrepetitions (Figure 3H, P = 6.7e-14, t(116) = -8.5, paired t-test). This effect was consistent\nacross both error-preferring and correct-preferring neurons for both monkeys (Figure S13).\nTogether, these results demonstrate that outcome encoding in area 7a extends beyond\nsimple reward reception. It reflects the evaluation of both positive and negative choice\nconsequences, and dynamically incorporates recent behavioral history. These findings\nalign with the established role of the primate posterior parietal cortex (PPC) in salience\nrepresentation, suggesting that area 7a preferentially encodes behaviorally salient\noutcomes during AL.”\nFourth, we conducted new analyses to test whether outcome selectivity in area 7a\ndirectly influenced the monkeys’ learning behavior. Trials were categorized into two\nconditions: exploration, in which monkeys switched saccade direction relative to the\nprevious trial with the same image-saccade association, and exploitation, in which they\nrepeated the same saccade direction for the same association. For each condition, we\nmeasured outcome selectivity in the preceding trial. Outcome selectivity was significantly\nstronger prior to exploratory trials compared to exploitative ones (new Figure 4E),\nsuggesting that enhanced outcome encoding in area 7a predicted subsequent exploratory\nbehavior. The phenomenon was found for both correct-preferring and error-preferring\nneurons across two monkeys (Figure S15A to S15B). In contrast, area 7a neurons did not\nshow increased outcome selectivity prior to trials in which the monkeys switched saccade\ndirection independently of the image-saccade association (new Figure 4F and Figure S15C to S15D). These findings suggest that outcome representations in area 7a may contribute specifically\nto strategy shifts during sensorimotor association learning, rather than reflecting general\nswitching behavior. The detailed results were shown as follows:\nThese figures plot the strength of outcome encoding (quantified by FEV) for trials prior to the exploration\nand exploitation trials respectively.\nThese figures plot the strength of outcome encoding (quantified by FEV) for trials prior to saccade direction\nswitching and repeating trials independently of the image-saccade association. The left panel shows the\naveraged results from two monkeys.\nWe have added one paragraph in the revised Results section to describe this result\nas follows:\n‘Furthermore, we investigated whether outcome selectivity in area 7a directly\ninfluenced the monkeys’ learning of associations. Trials were categorized into two\nconditions: exploration, where monkeys switched saccade direction relative to the previous\ntrial for the same image–saccade association, and exploitation, where they repeated the\nsame saccade direction. For each condition, we assessed outcome selectivity in the\npreceding trial. Outcome selectivity was much stronger prior to exploratory trials than\nexploitative ones (Figure 4E, P = 6.94e-13 , t(115) = -8.1, paired t-test), suggesting that\nenhanced outcome encoding in area 7a predicted subsequent exploratory behavior. This\neffect was observed in both correct-preferring and error-preferring neurons across both\nmonkeys (Figure S15A to S15B). In contrast, 7a neurons did not exhibit elevated outcome\nselectivity prior to trials where monkeys switched saccade direction independently of the\nimage–saccade association (Figure 4F and Figure S15C to S15D). These findings suggest\nthat outcome representations in area 7a may contribute specifically to strategy shifts during\nlearning sensorimotor associations, rather than reflecting general motor variability. ”\nAdditionally, we substantially revised the Discussion sections to better clarify the\nadvancements our study makes beyond the existing body of knowledge, as detailed below:\n“The outcome signal observed in area 7a exhibited several notable features that\nsupport its important role in outcome monitoring during sensorimotor AL. First, the outcome\nencoding in 7a dramatically diminished in the simple saccade task that involved no learning.\nSecond, the strength of outcome encoding significantly decreased when the time-out\npunishment was removed for the incorrect trials. These results suggest that the outcome\nrepresentation in 7a transcends simple physical reinforcement feedback, implying\ninvolvement of cognitive processing. Third, the strength of outcome encoding in area 7a\ncorrelated with both the extent to which the monkeys' saccade choices followed a win-stay,\nlose-shift strategy and their performance accuracy during AL. Fourth, the outcome\nencoding in 7a was contingent on the specific learning content and gradually evolved\nduring AL. This strong reliance of outcome representation on monkeys' learning behavior\nimplies a functional contribution of the outcome signal in 7a to AL. Fifth, outcome\nrepresentations in area 7a were particularly sensitive to outcome switches. This aligns with\nthe established role of the primate PPC in salience representation. Finally, stronger\noutcome encoding in area 7a predicted exploratory choices in the following trial but did not\npredict improved learning performance. This indicates that while outcome signals in 7a\nmay not directly facilitate the formation of sensorimotor associations, they may instead\npromote exploratory behavior during the learning process. Together, these results suggest\nthat the primate PPC satisfy the three criteria for outcome monitoring. It contributes to\nsensorimotor AL by monitoring behavioral outcomes to facilitate exploratory behavior,\nextending its recognized role in reward-based decision-making.\nPrevious studies have identified outcome representation in brain areas outside PPC,\nemphasizing the significance of the frontal-striatum network in evaluating reward\nreinforcement. Outcome signal has been proposed to serves as a teaching signal,\nreinforcing correct associations while weakening incorrect ones. However, the observed\noutcome representation in PPC differs markedly from these earlier findings, instead\nhighlighting a predominant sensitivity to salient, negative outcomes that may drive\nexploratory choices during AL. This suggests a potential complementary role of PPC in\nmonitoring trial outcomes during sensorimotor AL, relative to the role of the frontal-striatum\nnetwork. While the frontal-striatum circuitry primarily evaluates reward reinforcement and\nsupports the acquisition and exploitation of learned associations during AL, PPC may\ninstead play a predominant role in detecting salient ‘error signals’ and facilitating\nexploratory behavior throughout the learning process. The outcome signal in area 7a may\nbe primarily relayed to brain regions such as the frontal pole, which are known to drive\nexploratory behavior during learning. This is also in line with opponent-process theories,\nwhich propose that emotional experiences and motivated behavior are regulated by pairs\nof opposing processes. ’.\nAnother potential significance of this study lies in tracing the dynamic changes in single-\nneuron activity to reveal how learning progresses over days. However, the study does not\nmake clear which representational components undergo change across days. In their task,\ntermed the Image-Saccade Association Learning (ISAL) task, the authors propose that\nremapping occurs during learning. Yet, it remains unclear what this remapping represents,\nas novel objects were introduced from the beginning of learning. If PPC neurons merely\nencode object identity, remapping could occur independently of sensorimotor association,\nmaking it difficult to determine whether neural representation changes are linked to\nlearning. The task structure, therefore, complicates the interpretation of how neuronal\nactivity reflects the process of sensorimotor association formation. To make clear what this\nremapping represents, another task, such as the reversal version of Image-Saccade\nAssociation learning task, is necessary. A simple change in neuronal activity during the\nlearning period does not necessarily constitute an important finding, especially since\nprevious single-neuron studies in macaque monkeys have already demonstrated similar\nrepresentational changes (McMahon et al., 2014 Journal of Neurophysiology; Yasuda et\nal., 2012 Journal of Neuroscience)\nResponse: We agree with the reviewer that a simple change in neuronal activity during\nlearning does not, in itself, constitute a major finding. We also acknowledge that our original\ndescription of tracking dynamic changes in single-neuron activity across learning days was\nunclear. Our study represents, to our knowledge, one of the first in primates to\nsystematically examine how neural encoding across a large neuronal population evolves\nover multiple learning days—extending beyond prior studies that tracked only a small\nnumber of neurons longitudinally (as the suggested references).\nWe acknowledge that our original explanation of the experimental design spanning\nthe learning course could have been clearer, which may have led to reviewer\nmisunderstanding. For each neural population (FOV), recordings began on the familiar day\n(when monkeys performed well-learned associations) and continued until they had\nsuccessfully acquired a novel pair of associations. Thus, the learning phase (novel day)\nspanned from the second recording day through the final day. This design allowed us to\nanalyze how neural encoding changed both during the transition from familiar to novel\nassociations, and throughout the process of acquiring the new associations. We have\nrevised the results section to clarify this point in more details, shown as follows:\n‘The longitudinal recordings for each FOV spanned from the initial day when monkeys\nperformed the ISAL task with familiar associations to the day when monkeys mastered a\nnew pair of associations.”\nDuring data analysis, we primarily focused on the changing dynamics of outcome\nrepresentation across days. As shown in revised Figure 5 and 6 (original Figure 4 and 5),\nwe found that the outcome encoding remapped when monkeys transitioned from exploiting\nfamiliar associations to learning novel ones, and then gradually evolved during the process\nof learning new associations over multiple days. In contrast, the saccade direction\nencoding (including both pre- and post-saccadic intervals) did not exhibit such remapping\nphenomenon, and evolved significantly slower across days. These results suggest that\noutcome encoding in area 7a is linked to the content of learning, rather than reflecting\nhighly generalized reinforcement signal.\nFurthermore, our revised analysis (summarized in the previous response) showed that\nthe strength of outcome encoding was correlated with the monkeys’ choice strategy across\nlearning days (revised Figure 4A-4B), indicating that the dynamics of outcome encoding\nclosely tracked learning behavior.\nImportantly, we believe that the remapping of outcome encoding in area 7a is not\nsimply a reflection of visual stimulus encoding, for two main reasons. First, the 7a neurons\nwe recorded did not appear to be involved in cue identity encoding, as fewer than 4%\nshowed significant selectivity for cue identity during the cue presentation period. This\ncontrasts with prior electrophysiological studies in area 7a that reported robust encoding\nof task-relevant visual stimuli. Second, outcome encoding in our data emerged more than\ntwo seconds after the visual stimuli had disappeared. Together, these suggest that the\noutcome encoding we observed is not driven by residual visual information. Instead, we\npropose that the learning content–dependent outcome signals in area 7a may reflect the\nformation of a content-specific \"engram\" representing accumulated learning experiences.\nFinally, we agree with the reviewer that additional experiments—such as reversing the\nlearned associations—could provide deeper insights into the functional significance of\nneural remapping of outcome representations during learning. Unfortunately, we were\nunable to conduct such follow-up experiments, as the monkeys used in this study have\nsince been retired. Nonetheless, we recognize the importance of this question and have\nhighlighted it as a direction for future research in the revised Discussion section, as follows:\n“Further studies—such as reversing the sensorimotor associations and causally\nmanipulating activity dynamics—are needed to clarify the potential role of encoding\nremapping during AL.”\nThe introduction requires revision, as the discussion of technology and scientific concepts\nis intermingled, making it difficult to grasp the main focus of the study. From a scientific\nperspective, associative learning encompasses various forms, yet the importance of specifically studying sensorimotor association remains unclear. The authors argue that this\nfunction has been ‘traditionally less appreciated,’ but it is necessary to clarify why this\naspect warrants investigation. Given the substantial body of research on both the PPC and\nsensorimotor association learning, the introduction should reference relevant studies to\njustify the need for examining the PPC’s role in this specific learning process.\nStrengthening the conceptual framework and clearly distinguishing the technological\ncontribution from the scientific inquiry would improve the clarity and impact of the\nintroduction.\nResponse: We thank the reviewer for raising this important point. We acknowledge that\nthe original introduction did not clearly articulate the conceptual framework or adequately\ndistinguish the study’s technological contributions from its scientific questions. In response\nto the reviewer’s suggestion, we have substantially revised the Introduction section to\nbetter clarify these aspects, shown as in below:\n“The ability to learn appropriate motor responses to different environmental stimuli is\nessential for an animal's survival in complex environment. During AL, the effectiveness of\nestablishing new sensorimotor associations relies on the brain's ability to monitor the\npositive and negative outcomes of our action choices during individual learning trials. This\noutcome monitoring allows brain to evaluate the action consequences, reinforce relevant\nassociations, and guide adaptive changes. Theoretically, brain regions central to outcome\nmonitoring during AL should fulfill several criteria. First, they should exhibit significant\nencoding of trial outcomes (correct vs. incorrect) following the completion of each trial, with\noutcome signals that go beyond mere reward reception to reflect higher-level cognitive\nprocessing. Second, the strength or pattern of outcome encoding should correlate with the\nanimal’s learning performance or strategy and adapt dynamically to the learning context.\nThird, such outcome representations should contribute causally or functionally to animal’s\nsubsequent behavior during the learning process.\nThe prefrontal cortex (PFC), cingulate cortex, and other subcortical areas have been\nimplicated in representing trial outcomes during sensorimotor AL, as their neural activity\nreflects the correctness of animals' action choice during short-term AL. These outcome\nrepresentations were not merely reflective of reward reception; some were correlated with\nthe animals’ behavior performance during leaning. However, how the brain monitors\nbehavioral outcomes remains elusive, as the previous studies only partially met one or two\ncriteria. Furthermore, acquiring complex sensorimotor skills often requires gradual learning\nover periods spanning days or longer, but previous primate studies have primarily focused\non the changes of neuronal activity during relative short period of minutes to hours during\nthe learning process. Since no study has tracked the evolution of neuronal encoding of the\nsame population of neurons in the primate brain over different learning days, our\nunderstanding of the mechanisms underlying long-term AL is highly limited. Key questions\nregarding outcome monitoring remain untested, particularly during long-term AL: How does\nthe neuronal representation of trial outcomes correlate with the animals’ behavior to\npotentially influence learning? How does the outcome representation change throughout\nthe learning process and what factors govern these changes?\nPrevious studies have highlighted the fronto-striatal circuit as the central hub for\nsensorimotor AL, with less emphasis placed on the roles of other brain areas. However,\nchanges in neuronal activity were observed in other brain areas both during and after\nsensorimotor AL, suggesting the engagement of a potentially broader brain network.\nThe PPC, a crucial association cortex positioned at the intermediate stage of sensorimotor\ntransformation, has been implicated in various cognitive functions, but was usually not\nimplicated in AL. The PPC has been shown to play important roles in representing stimulus-\nstimulus association and sensorimotor association after long-term learning, implying\npotential involvement in sensorimotor AL. Specifically, neuronal activity in the lateral\nparietal cortex has been reported to reflect outcome information (rewarded vs. non-\nrewarded) during a decision task. Nonetheless, the exact role of PPC in long-term AL\nremains largely unexplored.”\nThe authors posed two questions as follows: ‘How does the neuronal representation of trial\noutcomes change throughout the learning process to facilitate learning? And what governs\nthese changes in neuronal representation of trial outcomes?’\nHowever, the questions in the introduction are not sufficiently addressed by the results, as\nthey do not clarify which neuronal representations facilitate learning or what governs these\nchanges. To address these questions, a behavioural causality study would be necessary—\nsuch as investigating how inhibiting neural representation changes affects learning\nperformance. In other words, the scope of the questions exceeds the range of the\nconclusions that can be drawn from the results, highlighting a gap between the study’s\ninitial inquiries and its empirical findings.\nResponse: We thank the reviewer for raising this important issue. We agree that our study\ndoes not establish a causal link between changes in outcome representation and the\nmonkeys’ learning behavior. Although we have considered causally manipulating neural\nactivity in area 7a to directly test its role in sensorimotor AL, we found that demonstrating\nsuch a causal relationship in the primate brain remains highly challenging with currently\navailable techniques, for two main reasons.\nFirst, the primate PPC is implicated in a broad range of cognitive functions. To\nestablish a causal role for outcome encoding in area 7a, any manipulation would need to\ntarget outcome-related signals specifically during the inter-trial interval. This would require\nboth high temporal and spatial precision, which is difficult to achieve in nonhuman primates.\nTechniques such as reversible inactivation lack the necessary temporal resolution, while\nmicrostimulation faces limitations due to the spatial overlap of different neuron types in\narea 7a—such as outcome-selective neurons (both correct- and error-preferring) and\nsaccade-selective neurons—making selective targeting of outcome encoding unfeasible.\nSecond, it is technically difficult to selectively suppress or modify the evolution of neural\nrepresentations related to learning without also disrupting ongoing task-relevant activity.\nCurrent tools do not allow for precise interference with the trajectory of representational\nchanges over time without broadly affecting neural processing.\nHowever, in the revised manuscript, we present six lines of evidence demonstrating\nthat outcome encoding in area 7a is closely associated with the monkeys’ learning behavior\nduring AL: 1) The strength of outcome encoding predicted the monkeys’ trial-by-trial\nlearning strategy (win-stay/lose-shift) across different learning days (revised Figure 4A-4B);\n2) Encoding strength correlated with learning performance, showing stronger outcome\nrepresentations for associations with higher behavioral accuracy (revised Figure 4C-4D\nand Figure S14); 3) 7a neurons primarily represented the salient outcome, as it exhibited\nsignificantly stronger outcome selectivity after outcome switches than after outcome\nrepetitions (new Figure 3H and Figure S13); 4) The strength of outcome encoding\nsignificantly predicted the monkeys’ learning strategy in subsequent trials, with higher\noutcome selectivity linked to exploratory behavior (new Figure 4E-4F and Figure S 15).\nThis suggests that the outcome encoding in area 7a may directly influence the monkeys’\nsubsequent learning strategy; 5) Encoding strength markedly decreased when the\nmonkeys were not engaged in learning; 6) Outcome representations were linked to\nlearning content, with population-level encoding undergoing significant remapping as\nmonkeys transitioned from familiar to novel associations. Together, these findings suggest\nthat outcome encoding in primate area 7a plays a meaningful role in supporting AL\nBased on the findings list above, we have changed our questions in the revised\nmanuscript as: “How does the neuronal representation of trial outcomes correlate with the\nanimals’ behavior to potentially influence learning? How does the outcome representation\nchange throughout the learning process and what govern these changes? ”.\nFurthermore, we have addressed the second question in original Figure 6 (revised\nFigure 7). We demonstrated that the functional connectivity between neurons (measure by\nnoise correlation) within local network significantly predicted the changes in outcome\nencoding of these neurons across learning days. Such prediction power was significantly\ngreater than the functional similarity (measured by signal correlation) between neurons.\nThese results suggest that functional connectivity between neurons was a notable factor\nwhich constrained the evolution of outcome encoding of individual 7a neurons across\nlearning days.\nAdditionally, we recognize the importance of casual experiment to test the roles of\nPPC played during AL and have highlighted it as a direction for future research in the\nrevised Discussion section, as follows:\n‘Nonetheless, the role of the primate PPC in outcome monitoring and its potential\ncontribution to exploration-driven learning processes warranting further investigation with\ncausal manipulations.”.\nFurther analyses are necessary to characterize the function of the primate PPC. The\nauthors have primarily focused on neuronal activity following the target-off period, yet their\ntask includes cue-on, delay, and target-on periods, which could provide valuable insights\ninto the PPC’s role in associative memory, working memory, and action value. To draw a\nrobust conclusion about whether the PPC is selective for history-based choice, both single-\nneuron and population-level analyses are needed. Expanding the analyses to these task\nphases would provide a more comprehensive understanding of PPC function in learning\nand decision-making.\nResponse: We agree with the reviewer that additional analyses of neuronal activity during\nthe cue-on, delay, and target-on periods could provide a more robust understanding of the\nPPC’s role in history-based decision-making. In the original manuscript, we reported that\nfewer than 1% of area 7a neurons exhibited significant visual responses during the cue-on\nperiod (original Figure S8). Approximately 20% of 7a neurons showed significant encoding\nof saccade direction—which reflects image-saccade associations—during the delay period,\nwhile around 65% exhibited significant saccade direction encoding during the target-on\nperiod. These results were included as revised Figure S9, and shown in below.\nWe have also demonstrated that the saccade direction encoding (combining both pre- and post-saccadic intervals) evolved significantly slower than that of outcome encoding across days (revised Figure 6C).\nThe proportion of neurons exhibited different selectivity in the ISAL task.\nFollowing the reviewer’s suggestion, we have included additional analyses of saccade\ndirection encoding during the delay period in the revised manuscript, as this activity could\npotentially reflect the formation of image-saccade associations. Specifically, we examined\nhow saccade direction encoding during the delay period evolved across different learning\ndays. We did not find systematic relationship between the averaged strength of saccade\ndirection encoding during the delay interval and the monkeys’ performance accuracy\nduring learning. This result was included in new Figure S10 in the revised manuscript. This\nindicates that the saccade direction encoding in area 7a might not directly contribute to\nacquiring the image-saccade associations. The detailed result is shown as follows:\nFurthermore, we have added new analysis to investigate whether the saccade\ndirection encoding was modulated by the trial outcome in the preceding trials. Specifically,\nwe found that saccade direction encoding of 7a neurons—which likely reflects image-\nsaccade associations—was very modestly, though significantly, stronger when the\nmonkey’s saccade choice was correct in the preceding trial compared to when it was\nincorrect. This effect was observed in both left-preferring and right-preferring neurons.\nThese findings suggest that outcome signals may modulate saccade direction encoding in\nthe subsequent trial in area 7a. These results have been included as new Figure S16, and\nshown in bellow:\nHowever, the current study specifically focused on the role of the area 7a of primate\nPPC in AL, particularly in outcome monitoring, for two reasons. First, prior studies—\nincluding our own causal works—have demonstrated that the primate PPC is critical for\nperceptual and categorical decision-making (Zhou & Freedman, 2019; Zhou et. al., 2023),\nyet its role in AL remains unclear. Second, as listed in bellow, we believe area 7a may not\nbe central to acquiring or storing sensorimotor associations.\nWe propose that neural activity in area 7a may not primarily reflect the formation of\nsensorimotor associations, based on several observations. First, very few 7a neurons (<\n4%) exhibited significant visual responses encoding cue identity or specific cue–saccade\nassociations. Second, only a small subset of neurons (approximately 20%, much less than\nthe outcome selective neurons) showed significant pre-saccadic activity during the delay\nperiod indicative of association encoding. And the averaged encoding strength of saccade\ndirection was very weak during the delay period. Third, as detailed in the above response,\nthis pre-saccadic encoding did not display systematic changes across learning days that\ncorrelated with the monkeys’ learning behavior (new Figure S10). These findings suggest\nthat area 7a may not play a central role in forming or encoding sensorimotor associations\ndirectly. Accordingly, the current study focuses on elucidating the role of area 7a in\nmonitoring trial outcomes during AL.\nTo clarify these points, we have expanded the discussion on the role of area 7a in AL\nin the revised manuscript, as shown below:\n‘The outcome signal observed in area 7a exhibited several notable features that\nsupport its important role in outcome monitoring during sensorimotor AL. First, the outcome\nencoding in 7a dramatically diminished in the simple saccade task that involved no learning.\nSecond, the strength of outcome encoding significantly decreased when the time-out\npunishment was removed for the incorrect trials. These results suggest that the outcome\nrepresentation in 7a transcends simple physical reinforcement feedback, implying\ninvolvement of cognitive processing. Third, the strength of outcome encoding in area 7a\ncorrelated with both the extent to which the monkeys' saccade choices followed a win-stay,\nlose-shift strategy and their performance accuracy during AL. Fourth, the outcome\nencoding in 7a was contingent on the specific learning content and gradually evolved\nduring AL. This strong reliance of outcome representation on monkeys' learning behavior\nimplies a functional contribution of the outcome signal in 7a to AL. Fifth, outcome\nrepresentations in area 7a were particularly sensitive to outcome switches. This aligns with\nthe established role of the primate PPC in salience representation. Finally, stronger\noutcome encoding in area 7a predicted exploratory choices in the following trial but did not\npredict improved learning performance. This indicates that while outcome signals in 7a\nmay not directly facilitate the formation of sensorimotor associations, they may instead\npromote exploratory behavior during the learning process. Together, these results suggest\nthat the primate PPC satisfy the three criteria for outcome monitoring. It contributes to\nsensorimotor AL by monitoring behavioral outcomes to facilitate exploratory behavior,\nextending its recognized role in reward-based decision-making.\nPrevious studies have identified outcome representation in brain areas outside PPC,\nemphasizing the significance of the frontal-striatum network in evaluating reward\nreinforcement. Outcome signal has been proposed to serves as a teaching signal,\nreinforcing correct associations while weakening incorrect ones. However, the observed\noutcome representation in PPC differs markedly from these earlier findings, instead\nhighlighting a predominant sensitivity to salient, negative outcomes that may drive\nexploratory choices during AL. This suggests a potential complementary role of PPC in\nmonitoring trial outcomes during sensorimotor AL, relative to the role of the frontal-striatum\nnetwork. While the frontal-striatum circuitry primarily evaluates reward reinforcement and\nsupports the acquisition and exploitation of learned associations during AL, PPC may\ninstead play a predominant role in detecting salient ‘error signals’ and facilitating\nexploratory behavior throughout the learning process. The outcome signal in area 7a may\nbe primarily relayed to brain regions such as the frontal pole, which are known to drive\nexploratory behavior during learning. This is also in line with opponent-process theories,\nwhich propose that emotional experiences and motivated behavior are regulated by pairs\nof opposing processes.\nThe critical roles of the fronto-striatal network in AL have been demonstrated in\nprevious studies using causal approaches, such lesion studies. However, the causal\ninvolvement of the primate PPC in long-term AL remains to be tested in future studies. Our\nfindings suggest that area 7a may not play a central role in forming or encoding\nsensorimotor associations during AL. First, only a small fraction of 7a neurons showed\nsignificant encoding of the visual cue during either the cue presentation (<4%) or delay\n(20%) periods of the ISA task. Second, encoding of saccade direction prior to the monkeys’\nchoices was notably weak. Third, this pre-saccadic encoding did not show systematic\nchanges across learning days that aligned with the monkeys’ learning performance. The\nrelative lack of attention to the primate PPC in earlier AL studies aligns with our observation\nthat area 7a may not be directly involved in acquiring new associations. Instead, it may\ncontribute to outcome monitoring—specifically by encoding salient outcome signals that\npromote exploratory behavior during AL. Nonetheless, the role of the primate PPC in\noutcome monitoring and its potential contribution to exploration-driven learning processes\nwarranting further investigation with causal manipulations. Previous rodent studies have\nreported that PPC was causally involved in processing sensory-stimulus history and\nhistory-based action selection bias to guide decision behavior. Moreover, PPC was also\nshown to represent discrepancies between the current model and observed state\ntransitions in reinforcement learning, as well as reward-related belief updating during\nflexible decision-making. Collectively, these results indicate that PPC likely plays an\nimportant role in updating the estimation of the current state by incorporating past\nexperience, to guide decision-making and learning.’.\nTherefore, the revised manuscript remains focusing on investigating the PPC’s\npotential role in monitoring trial outcomes during sensorimotor AL. Outcome signals can\nserve as teaching signals that guide learning, so we centered our analyses on the post-\ntarget period, when monkeys could evaluate trial outcomes. We found that area 7a\nexhibited robust encoding of trial outcomes, which went beyond mere reward reception\nand was closely linked to the monkeys' learning behavior. Notably, 7a neurons primarily\nrepresented the salient outcome during leaning, and the strength of this encoding\npotentially drive the monkeys’ learning strategy shifts. These findings highlight a key role\nfor primate PPC in monitoring outcomes during sensorimotor AL. In contrast, because the\nmonkeys could not determine the trial outcome during the cue-on, delay, and target-on\nperiods, we did not prioritize those epochs in our analysis.\nLastly, what do the authors mean by ‘monitoring’? The study does not clearly define this\nterm, making it difficult to interpret its relevance to their findings. To draw a meaningful\nconclusion, the authors must first provide a clear definition of monitoring and then evaluate\nwhether the neuronal representation in the PPC aligns with this definition. At this stage, the\nobserved neuronal activity in the PPC appears to be a simple outcome representation that\npersists into the next trial, rather than an explicit monitoring mechanism.\nResponse: We thank the reviewer for this valuable suggestion, which motivated us to\nreorganize both the introduction and results sections. In theory, outcome signals can\nfunction as teaching signals to drive AL. In the revised manuscript, we propose that\nneurons involved in outcome monitoring during AL should meet three criteria: First, they\nshould exhibit significant encoding of trial outcomes (correct vs. incorrect) following the\ncompletion of each trial, with outcome signals that go beyond mere reward reception to\nreflect higher-level cognitive processing. Second, the strength or pattern of outcome\nencoding should correlate with the animal’s learning performance or strategy and adapt\ndynamically to the learning context. Third, such outcome representations should contribute\ncausally or functionally to animal’s subsequent behavior during the learning process.\nIn the revised introduction, we clearly articulate these criteria for identifying outcome-\nmonitoring neurons. We also revised the results section to demonstrate that outcome\nencoding in area 7a meets these criteria. The updated introduction is provided below:\n‘Theoretically, brain regions central to outcome monitoring during AL should fulfill\nseveral criteria. First, they should exhibit significant encoding of trial outcomes (correct vs.\nincorrect) following the completion of each trial, with outcome signals that go beyond mere\nreward reception to reflect higher-level cognitive processing. Second, the strength or\npattern of outcome encoding should correlate with the animal’s learning performance or\nstrategy and adapt dynamically to the learning context. Third, such outcome\nrepresentations should contribute causally or functionally to animal’s subsequent behavior\nduring the learning process.’\nMinor comments\nDid you confirm whether AAV expression is selectively restricted to layers 2 and 3? The\nexplanation on page 3 regarding AAV expression is unclear and may confuse readers. If\nnot, please revise the sentence.\nResponse: We realize that our original statement may have been unclear and potentially\nconfuse readers. The details of our protocol for virus expression in the macaque brain has\nbeen demonstrated previously (Li, M., Liu, F., Jiang, H., Lee, T. S., & Tang, S. (2017).). The\nvirus was injected a depth about 400 μm in the cortex. We are not sure that the AAV virus\nexpression was only restricted to layers 2-3. Because of the technical limitation of two-\nphoton calcium imaging in monkeys, we could only neuronal activity in layers 2-3 with good\nsignal to noise ratio. We have revised this sentence to make it clear in the revised results\nsection, shown as follows:\n‘To monitor the neuronal activity during sensorimotor AL, we injected AAV to induce\nGCaMP6f expression in neurons at a depth of ~400 μm within area 7a, allowing us to\nsimultaneously record the activity of hundreds of neurons over multiples days (Figure 1B,\n1G and 1H). The recordings spanned depths of 200–300 μm beneath the arachnoid\nmembrane, primarily corresponding to layers 2–3.’\nThe figures require revision to improve clarity: For example, FOV figures lack scale bars.\nFigures such as 4E and 4J are missing X and Y axis labels. Additionally, the ordering of\npanels within each figure is difficult to follow. Overall, the figures should be revised to\ninclude all necessary information for proper interpretation.\nResponse: Thank you for the helpful suggestion. In the revised manuscript, we have added\nmore details to the figures to improve clarity and readability. For example, we included\nscale bars in the FOV images and added X and Y axis labels in the Sankey diagrams (e.g.,\nFigures 4E and 4J). In addition, we reorganized the layout of most main figure panels to\nenhance visual coherence. Some of the revised figures are shown below:\nThe behavioural paradigm in Figure 1 should specify the inter-trial interval (ITI) to provide\na complete description of the task structure.\nResponse: Thank for the suggestion. We have revised the Figure 1A and added a time line,\nshown as follows:\nLine 98: multiples days → multiple days\nLine 128: during inter-trial interval → during the inter-trial interval\nLine 244: notable after learned → notable after learning\nLine 746: difference in monkeys' eye positions → differences in monkeys' eye position\np.3 Figure S2 (A): significant different → significant difference\nThere are additional grammatical errors in the manuscript that need to be addressed. The\nmanuscript needs to be revised by a native English speaker.\nResponse: Thanks for the corrections. We have corrected them all in the revised\nmanuscript.\nLine 133: What does 0.57 ± 0.08 represent in relation to fixation breaks? Please clarify.\nResponse: We realized that our description in the original manuscript might be unclear.\n0.57 ± 0.08 represents fix-break ration of this monkey during performing the task. We have\nrevised this sentence to make it clear as follows:\n‘For this analysis, we focused solely on data from one monkey due to the high\nfrequency of fixation breaks (fix-break ratio = 0.57±0.08) during performing the task\nexhibited by the other monkey.’\n• Line 134: Figure S4 should be incorporated into Figure 2 in the main text.\nResponse: We thank the reviewer for the valuable suggestion and agree that the results in\nthe original Figure S4 are important and align with those in Figure 2. However, Figure 2 is\nalready quite large, and adding Figure S4 would make it difficult to organize all the panels\neffectively. Therefore, we have decided to keep Figure S4 as a supplementary figure. That\nsaid, if the reviewer feels it is necessary, we are happy to incorporate Figure S4 into the\nrevised Figure 2.\n• Line 174: What does WN type n refer to? Should this be EN type n instead?\nResponse: Thanks for pointing out this typo. We have corrected it in the revised Figure3D,\nshown as follows:\n• Line 250: I cannot find the data for outcome encoding correlation in the referenced figure.\nPlease indicate where this data is presented.\nResponse: We thank the reviewer to point this out. We have added this results in the\nrevised Figure 6 A, shown as follows:\n• Line 568: Is 400 mm the correct value? Please verify.\nResponse: Thanks for pointing out this typo. This should be 400 μm. We have corrected it\nin the revised manuscript.\nAdditionally, how was N.-N. calculated in Figure 5A?\nResponse: We realized that this part might not be very clear. In the original Methods section,\nwe described the detailed method for plotting this figure:\n“In order to quantify the stability of the neural encoding across each learning course,\nwe calculated the correlation of outcome/saccade encoding between two consecutive days\nwithin the learning course for each FOV. This process involved two steps. 1) For each\nindividual neuron within the FOV, we quantified the strength of the outcome/saccade\nencoding using FEV analysis and determined their preferences for each time point.\nConsequently, for each time point, we constructed an encoding vector that represented the\nneural encoding within each FOV. This vector incorporated both the strength and\npreference of neural encodings for all identified neurons within this FOV. 2) At each time\npoint, we calculated the Pearson correlation between the encoding vectors for two\nconsecutive days. This resulted in a correlation value that indicated the similarity of the\nneural encodings between those two days for each of the given tine point. We repeated\nthe procedures for all time points included in the analysis.”\nTo improve clarity, we have added a paragraph in the revised Methods section to\nfurther detail the analysis:\n‘In Figure 6A, neural encoding correlations were computed separately for two\nconditions: (1) between the familiar day and the first learning day (familiar-to-novel) and (2)\nbetween successive learning days (novel-to-novel). For the novel-to-novel condition, we\nfirst calculated pairwise correlations between each pair of successive learning days (while\nmonkeys were acquiring the same novel associations) and then averaged these correlation\nvalues.’\nReviewer #2 (Remarks to the Author):\nThe study \"Neural correlates of trial outcome monitoring during long-term learning in\nprimate posterior parietal cortex\" by Liu, Jiang, she, Fang, Tang and Zhou shows a unique\nand important data set tracking the responses in parietal area 7a during associative\nlearning. The study shows robust representation of decision outcome in a subset of these\nneurons. This is the case both at the end of the trial during reward as well as - for the\noutcome of the previous trial - during the current trial. Their data also indicate changes in\nthe representation during learning and postulates a role for network connectivity in\nconstraining this. However, - as currently presented - the direct link between these changes\nin neuronal representations and learning could be shown more clearly.\nThe major strength of the study lies in the technically challenging tracking of individual\nneurons in parietal cortex during several sequences of associative learning for a image-\nsaccade pair. The data showing robust representations of behavioural outcome in area 7a\nare compelling.\nResponse: We thank the reviewer for the positive comments on our work. We have\naddressed all the comments as described below, which were very helpful in improving the\nmanuscript.\n1) Some critical aspects of the presentation make it difficult for the reader as well as the\nreviewer and additional information/clarifications are needed with regard to the following\naspects:\n- in the abstract/title: the monkey species and the area recorded from (7a) must be named.\nActually the species seems to be missing from the entire paper. It should also be mentioned\nat the beginning of the methods.\nResponse: Thanks for this suggestion! We used Rhesus Macaque in the current study. We\nhave added the monkey species in the Abstract, Results, and Methods sections of the\nrevised manuscript. We have also added area 7a in the revised abstract, shown as follows:\n“Using two-photon calcium imaging in behaving macaque monkeys, we tracked the\nneuronal activity in area 7a of the posterior parietal cortex (PPC), an important associative\ncortex that has not been typically implicated in associative learning (AL), while the monkeys\nlearned new visual-motor associations over multiple days.”\n-introduction: PPC in the primate is large with divers areas and response properties. I would\nhave expected a paragraph discussion the role and response properties previously found\nin area 7a, so the reader has a chance to situate this study and its importance better. In\nthis context, a brief sketch of the response properties in 7a in relations to what is known\nabout sensorimotor processing and trial outcome would be essential. The only area\nreferred to in the introduction seems to be lateral intraparietal area (line 60, ref 37), which\nis wrongly referred to as \"lateral parietal cortex\", which needs correcting. As it is, it is not\nclear why 7a is explored at all, other than being on the surface of the (macaque??) brain\nand therefore more accessible.\nResponse: This is a good suggestion! We have added a paragraph in the Introduction\nsection to summarize the known role of area 7a in sensorimotor processing and its\npreviously reported response properties, as shown below:\n“Area 7a, a subregion of the PPC, occupies the highest level of the visual processing\nhierarchy in the parietal lobe. Traditionally, it has been implicated in diverse cognitive\nfunctions, including spatial attention, multisensory integration, motor planning, coordinate\ntransformations, and other higher-order visuospatial processes. However, its core\ncomputational roles remain debated. Notably, Area 7a exhibited direct connections with\nregions involved in learning and memory, such as PFC and medial temporal lobe, as well\nas with structures engaged in sensorimotor transformation, such as oculomotor areas,\nsuggesting a potential role in sensorimotor AL. Despite this, direct empirical evidence for\nits involvement in AL—and the mechanisms through which it may contribute—remains\nunexplored”\nFurthermore, LIP (lateral intraparietal area) is a subregion of the PPC in primates and\nhas been studied more extensively than area 7a.\n- Figures, Results: as the two monkeys differed in their learning, all results should also be\nreported for each monkey separately and in all figures the examples should be clearly\nassociated with the animal they come from - as is custom in the field.\nResponse: Thank you for the suggestion! In the revised manuscript, we have added\nsupplementary figures presenting the results for each monkey separately for most\nanalyses, except for Figures 5, 6 and 7 which investigate the evolution of outcome\nencoding across learning course. This exception is due to the fact that we only recorded a\ncomplete learning course from two FOVs in one monkey, and the data from this animal\nalone is insufficient for meaningful statistical analysis. Comparisons of neuronal encoding\nbetween the two monkeys are provided in the revised Figures S1, S2, S6, S11, S13, and\nS15.\nFurthermore, we have revised several figures and indicated the data from the two\nmonkeys in the figure legends throughout the manuscript. For example, in the revised\nFigure 1D–F, we used different colors to distinguish between the monkeys (green for\nMonkey H and black for Monkey X), as shown below:\nAdditionally, we have included the monkey’s name in the example FOVs, such as the\nrevised Figure 2C and 2L.\n- Results: a critical piece of evidence relies on assigning neurons to a specific type of\nrepresentation (e.g. lines 101-109). An Anova on FEV seems to be used. In the results, the\nexact statistical criterion and what the correction for multiple comparisons was should be\nincluded.\nResponse: Thanks for pointing this out. We realized that we missed the important method\ninformation for identifying outcome-selective neurons in the original results section. We\nclassified neurons into different types of outcome-selective neurons based on the following\nmethod as stated in the original methods section:\n“To identify neurons with significant encoding of trial outcome, we applied a Wilcoxon\nranksum test to assess activity between the correct and error trials in the task period\nfollowing target offset (1-16 frames (0-2.2s) after target offset). We further divided this\nperiod into four smaller windows, each comprising 4 frames. If statistically significant\ndifferential activity between correct and error trials was observed within at least one of\nthese windows (P < 0.01, Bonferroni corrected), we classified the neuron as outcome-\nselective. If the neurons exhibited significant greater activity on correct trials, we classified\nthem as correct neurons, and classified them as error neurons vice verses.’\nIn the revised manuscript, we have included a new sentence to clarify the statistical\nmethod used for identifying the outcome selective neurons in the results section, which is\nshown as follows:\n“We found that some 7a neurons significantly encoded trial outcome after the saccade\ntarget disappeared (ranksum test, P < 0.01, Bonferroni corrected, see Methods).”\n- Results, Figure 2H, line 115-116: text and figure appear to contradict each other: Figure\nshows about 63% of neurons below one 1s, text states it the other way round.\nResponse: We realized our original figure was unclear and caused confusion to the\nreviewer. As described in our previous response, we examined the outcome selectivity in\na time period consist of 16 recoding time frames, and separated the time periods into four\nsmall time windows within each consist of four time frames. Since the temporal resolution\nof our two-photon calcium imaging recoding was 7.4 HZ, neuron exhibited significant\noutcome selectivity within two time windows indicating that its outcome encoding sustained\nmore than 1s. We have changed the corresponding X tick in Figure 2H and added one\nsentence in the revised figure legend to clarify this point, which is shown as follows:\n‘(H) Duration of outcome encoding for all outcome-selective neurons. Each time\nwindow spans 0.54s. 63% neurons exhibited significant outcome encoding two or more\ntime windows”\n- Results, all Figures, timing labels \"Time to target off\": these are generally positive\nnumbers, so I presume it is time \"after\" or \"from\" target off that is meant. This is highly\nconfusing and needs to be corrected. Please also check the \"time to cue on\" and revise,\nso the language is unambiguous and consistent.\nResponse: Thank you for the suggestion! We have updated the revised figures to use 'Time\nfrom' accordingly.\n- Results, Figures 2A, B, J, K: the meaning of the dashed lines in relation to the task is\nunclear. Please label appropriately, perhaps with arrows and names on top.\nResponse: Thanks for this suggestion! We have revised the figures following the reviewer’s\nsuggestion and added arrows and names on the top of these figures, shown as follows:\n- Results 2C, 2L are not very clear. What colour is which type? It seems the yellow cells\nmight only be present in smaller subparts of the FOV, which could be considered as\nclustering\nThe color bar (ranging from blue to yellow) represents the average strength (quantified by\nFEV values) and the preference of neuronal encoding for trial outcomes in the current trial\n(Figure 2C) or the previous trial (Figure 2L). Yellow indicates neurons that prefer correct\noutcomes, while blue indicates neurons that prefer error outcomes. We have included this\ninformation in the revised figures (shown in below).\nThese yellow-labeled neurons are not clustered within a small subregion but are\ninstead distributed across both FOVs. Furthermore, we calculated Euclidean Distance to\nquantify the spatial clustering of different neuron types, and the analysis did not reveal\nsignificant clustering of outcome-encoding neurons within most FOVs. The results are\nshown below:\nIn the above figures, we quantified the mean distances between neurons within each\ntype (correct-preferring neurons [CN], error-preferring neurons [EN], and non-selective\nneurons) as well as between different types. Data from Figure 2C are highlighted in red.\nAlthough there was a trend suggesting that within-type distances were smaller than\nbetween-type distances, this difference did not reach statistical significance.\n-Results, lines 133 in brackets: what is being measured (trials?) in what units (%?).\nResponse: We realize that the original statement may have been unclear. The value\n“0.57 ± 0.08” refers to the proportion of fixation-break trials during task performance. We\nhave revised the sentence accordingly, as follows:\n“For this analysis, we focused solely on data from one monkey due to the high\nfrequency of fixation breaks (fix-break ratio = 0.57±0.08) during performing the task\nexhibited by the other monkey. This resulted in an excessive number of trials with no choice\ninterspersed between completed trials.”.\n-Results Figure 3D, what is suddenly \"WN\"?\nResponse: Thanks for pointing this type out. We have corrected it in the revised Figure3D\nshown in bellow:\n-Results Figure 4D and 4I: the abbreviations the legends N., D1 .... are not explained.\nResponse: In the revised figure legend, we have added detailed annotation for these\nabbreviations, shown as follows:\n“Fa.: familiar day; N., D1: novel stimuli, day 1; N., D2: novel stimuli, day2; and so\non”.\n-Results Figures 4B,C,G,H: what is the x-axis?\nResponse: In Figures 4B,C,G,H (revised Figure 5), the x-axis denotes the time period after\ntarget off. We have added the labels of the x-axis in these figures in the revised manuscript,\nshown as follows:\n2) Interpretation of the results.\nThe section \"relevance of outcome encoding in 7a for associative learning shows that it is\nnot reward and punishment alone. The link between outcome encoding and weight of\nprevious choice is does not directly show a contribution to learning. the same applies for\nthe saccade bias. Also, Figure 3H seems to rely heavily on data points that show neither a\nsizeable weight of the previous answer nor sizeable encoding strength.\nResponse: We acknowledge that the original manuscript did not fully clarify the\ninterpretation of our results. We also agree with the reviewer that the link between outcome\nencoding, the weight of the previous choice, and the relationship between outcome\nencoding and choice bias did not directly demonstrate the contribution of outcome signals\nto learning, but rather reflected a correlation between outcome encoding and the monkeys'\nlearning behavior. In the revised manuscript, we have reinterpreted these results and\nadded further analysis to demonstrate that outcome encoding in area 7a is closely linked\nto the monkeys’ learning strategy and is correlated with their performance during learning.\nFirst, we have revised the interpretation of the results regarding the positive\ncorrelation between the strength of outcome encoding and the weight of previous choices.\nThe \"weight of previous choice\" refers to how much the monkeys' saccade choices rely on\nthe win-stay and lose-shift strategy, a common strategy used by animals to learn novel\nassociations. Outcome information is expected to support the application of this strategy.\nWe found a positive correlation between the daily strength of outcome encoding and how\nmuch the monkeys' saccade choices relied on this win-stay/lose-shift strategy across\ndifferent learning days. This indicate that the fluctuation of the outcome encoding strength\nacross learning days correlated with monkeys’ daily learning strategy. Regarding Figure\n3H, we have added two additional analyses to confirm the findings. On one hand, we found\na significant rank correlation (r = 0.49) between the strength of outcome encoding and the\nweight of the previous choice on the monkeys' decisions, in addition to significant Pearson\ncorrelation reported in the original Figure 3H (now revised Figure 4A). Second, we\ndemonstrated that this correlation value was substantially higher than the correlation\nvalues from a shuffle analysis. These results indicate that the observed correlation is not\njust driven by data points with very week encoding strength. Therefore, we are confident\nthat the positive correlation in Figure 3H reflects a genuine relationship. The revised results\nare shown below:\nSecond, we revised the analysis to explore the relationship between the strength of\noutcome encoding and the monkeys' choice bias. In fact, the monkeys' performance\naccuracy was generally higher in the biased condition than in the opposite condition.\nTherefore, we separated the two image-saccade association conditions into \"better-\nlearned\" and \"worse-learned\" conditions based on the monkeys' average performance\naccuracy for each learning day. We found that outcome encoding was stronger for the\nbetter-learned association than for the worse-learned association, particularly when there\nwas a notable difference in performance accuracy between the two associations. However,\nwhen the performance accuracies were similar between the two associations, the strength\nof outcome encoding was comparable. These results are presented in the revised Figure\n4C-4D, as shown below:\nThird, we added an analysis to show that outcome encoding dramatically influenced\nby the outcome history (outcome in the preceding trial). We separately quantified encoding\nstrength for trials following outcome switches (correct→error or error→correct transitions)\nversus outcome repetitions (correct→correct or error→error). Notably, outcome encoding\nwas much stronger after outcome switches than after outcome repetitions (new Figure 3H,\nshown in below). This effect was observed in both correct-preferring and error-preferring\nneurons across both monkeys (Figure S13). This indicates that outcome encoding strength\nin area 7a tracked the monkeys’ trial-by-trial behavioral performance during learning, and\nmay support associative learning through heightened sensitivity to salient outcomes.\nFourth, we conducted new analyses to test whether outcome selectivity in area 7a\ndirectly influenced the monkeys’ learning behavior in the subsequent trial. We categorized\ntrials into two conditions: exploration (monkeys switched saccade direction relative to the\nprevious trial with the same image-saccade association) and exploitation (monkeys\nrepeated the same saccade direction for the same association). For each condition, we\nquantified outcome selectivity in the preceding trial. We found significantly higher outcome\nselectivity prior to the exploration condition compared to the exploitation condition,\nsuggesting that stronger outcome encoding in area 7a predicted exploratory behavior in\nthe subsequent trial (new Figure 4E). The phenomenon was consistent for both correct-\npreferring and error-preferring neurons across two monkeys (new Figure S15). In contrast,\narea 7a neurons did not show increased outcome selectivity prior to trials in which the\nmonkeys switched saccade direction independently of the image-saccade association\n(new Figure 4F and Figure S15). These results imply that area 7a’s outcome\nrepresentations may drive learning strategy shifts during learning sensorimotor\nassociations, rather than reflecting general motor variability. The detailed results were\nshown as follows:\nThese figures plot the strength of outcome encoding (quantified by FEV) for trials prior to the exploration\nand exploitation trials respectively.\nThese figures plot the strength of outcome encoding (quantified by FEV) for trials prior to saccade direction\nswitching and repeating trials independently of the image-saccade association. The left panel shows the\naveraged results from two monkeys.\nIn summary, we believe we have provided strong evidence demonstrating that outcome\nencoding in area 7a is closely correlated with the monkeys’ learning behavior, and\npotentially contrite to their learning strategy. We also acknowledge that a causal\nexperiment would be more suitable for establishing a direct link between outcome encoding\nand associative learning. The necessity of such a causal experiment for future studies has\nbeen discussed in the revised discussion section, shown in below:\n‘Nonetheless, the role of the primate PPC in outcome monitoring and its potential\ncontribution to exploration-driven learning processes warranting further investigation with\ncausal manipulations.’.\nWhile the data presented are suggestive of a link between behavioural outcome\nrepresentation and associative learning, this is at present rather descriptive rather than\ncompelling. What is needed is a quantitative, statistical link between changes in\nrepresentation and changes in behavioural performance either day-to-day or trial-by-trial.\nResponse: We agree with the reviewer that a quantitative, statistical link between changes\nin neural representation and behavioral performance provides strong evidence that\noutcome signals may contribute to associative learning. In response to this suggestion, we\nhave conducted four additional analyses in the revised manuscript.\nFirst, we found that the averaged outcome encoding was stronger for the better-\nlearned association compared to the worse-learned association for each learning day\n(revised Figure 4C-4D). This result was detailed in a previous response.\nSecond, as described earlier, we demonstrated that outcome encoding dramatically\ninfluenced by the outcome history, as it was significantly stronger after outcome switches\nthan after outcome repetitions (new Figure 3H and Figure S13). This finding further\nsuggests that outcome encoding is closely correlated with the monkeys’ trial-by-trial\nbehavior during learning, potentially facilitating adaptive learning through emphasizing the\nsalient outcome signal.\nThird, as described in the earlier response, we conducted new analyses to show that\noutcome selectivity in area 7a may directly influence the monkeys’ learning behavior in\nthe subsequent trials, as the stronger outcome selectivity lead to exploration behavior during\nlearning (new Figure 4E-4F and Figure S15).\nIn summary, we have revised our original analysis and added more data to strengthen the\nlink between outcome encoding in area 7a and the monkeys’ learning behavior. As a result,\nwe believe the connection between outcome representation in area 7a and AL is now more\ncompelling than in the original version.\nSince outcome encoding of the previous choice is ongoing when the animal makes a\ndecision, so interacts directly with incoming activity (rather than what is measured at the\nend of the trial). Any correlation with actually learning of the association might be clearer\nfor this epoch.\nResponse: We thank the reviewer for this inspiring suggestion. In the revised manuscript,\nwe have added analysis examining whether outcome history influenced the neural\nencoding of trial encoding in the current trial. As detailed in our previous responses, we\nfound that the outcome encoding in area 7a was significantly stronger after outcome\nswitches than after outcome repetitions (new Figure 3H and Figure S13).\nFurthermore, we examined whether the encoding of outcome history influences the\nencoding of image-saccade associations during the cue presentation and delay periods of\nthe learning task—prior to the monkeys' saccade choice. We performed this analysis using\ntwo complementary approaches. First, we examined whether outcome history influenced\nthe encoding of image-saccade associations in neurons that significantly represented\nsaccade direction /these associations. Our analysis revealed that saccade direction\nencoding—which likely reflects image-saccade associations—was very modestly, but\nsignificantly, stronger when the monkey’s saccade choice was correct in the preceding trial\ncompared to when it was incorrect. This effect was observed in both left-preferring and\nright-preferring neurons. These findings suggest that outcome signals may modulate\nsaccade direction encoding in the subsequent trial, potentially contributing to the learning\nof new associations. These findings have been incorporated into the revised manuscript\nas Figure S16 (shown below):\nSecond, we focused specifically on neurons that showed significant encoding of\noutcome history. These neurons were categorized into correct-preferring neurons (those\nshowing greater activity following correct trials) and error-preferring neurons (those\nshowing greater activity following error trials). Both types of neurons exhibited a trend of\ngreater saccade direction encoding when the preceding trial was correct compared to when\nit was incorrect (shown in below). Since this result is similar as the one described above,\nwe did not include it into the revised manuscript.\nIn summary, we found that outcome history affected both outcome encoding and saccade\ndirection encoding in area 7a neurons during subsequent trials. However, these two types\nof encoding were modulated by outcome history in distinct ways. Notably, the influence of\noutcome history was substantially stronger on outcome encoding than on saccade\ndirection encoding in the following trial. Both sets of results have been included in the\nrevised manuscript.\nAt present l201-203 only the first part is clearly supported by the data, \"this encoding is\nclosed correlated with AL\" should be removed until some robust direct link can be made\nstatistically to learning outcome. The same applies for the first sentence of the next\nparagraph.\nResponse: We recognize that the original statement may have been overly conclusive. In\nthe revised manuscript, we have added further analyses and carefully revised the\ninterpretation of some original results to more accurately demonstrate the relationship\nbetween outcome encoding and the monkeys’ learning behavior.\nAs detailed in our previous responses, the revised manuscript presents six lines of\nevidence showing that outcome encoding in area 7a is closely associated with learning\nbehavior during AL: 1) The strength of outcome encoding predicted the monkeys’ trial-by-\ntrial learning strategy (win-stay/lose-shift) across different learning days (revised Figure\n4A-4B); 2) Encoding strength correlated with learning performance, showing stronger\noutcome representations for associations with higher behavioral accuracy (revised Figure\n4C-4D); 3) Outcome encoding was significantly stronger following outcome switches\ncompared to following outcome repetitions (new Figure 3H and Figure S13); 4) The\nstrength of outcome encoding significantly predicted the monkeys’ learning strategy in\nsubsequent trials, with higher outcome selectivity linked to exploratory behavior (new\nFigure 4E-4F and Figure S15); 5) Encoding strength markedly decreased when the\nmonkeys were not engaged in learning (Figure 3A-3D); 6) Outcome representations were\nlinked to learning content, with population-level encoding undergoing significant remapping\nas monkeys transitioned from familiar to novel associations (Figure 6). Together, these\nfindings strengthen our confidence in concluding that outcome encoding in primate area\n7a is closely correlated with the monkeys’ behavior during AL.\nAccordingly, we have substantially revised this part in the revised result section, and\nconcluded this part as follows:\n“Together, these findings indicate that outcome encoding in area 7a fulfills all three\nproposed criteria for outcome monitoring, suggesting that this region plays an active role\nin monitoring trial outcomes and contributes functionally to sensorimotor AL.”.\nl235-252: the lack of a link between outcome encoding and saccade direction encoding is\nalso problematic in this context. From Figure 5 and results text, it is not clearly presented\nwhether or how the reorganisation trial outcome representations is linked to learning. This\npart of the results is crucial for understanding and impact of these results. At the moment,\nthis could do with a clearer structure reporting the key results.\nResponse: Thank you for the suggestion. We realized that the original manuscript did not\nsystematically address the relationship between outcome encoding and saccade direction\nencoding. To fill this gap, we performed two analyses on saccade direction encoding during\nthe delay interval (potentially encode the image-saccade associations) in the revised\nmanuscript.\nFirst, we tested whether the strength of saccade direction encoding differed between\ntrials with distinct outcome histories. This analysis revealed that saccade direction\nencoding was slightly stronger when the monkey’s saccade choice was correct in the\npreceding trial compared to when it was incorrect, suggesting that outcome signals may\nmodulate saccade direction encoding in the subsequent trial, potentially influencing the\nlearning of new associations. We have included this results as new Figure S16. Please\nrefer to our previous response.\nSecond, we examined the relationship between outcome encoding and saccade\ndirection encoding. At the population level, we found that the average strength of outcome\nencoding was positively correlated with the average strength of saccade direction encoding\n(during the delay period) across learning days (R = 0.48). A similar positive correlation\nbetween outcome encoding and saccade direction encoding was observed in the majority\nof recording sessions. These results are now presented in the new Figure S10A-S10B,\nshown below:\nNotably, although the above results demonstrate correlations between the outcome\nsignal and the saccade direction encoding, our results do not support a critical role for\nprimate area 7a in representing or acquiring sensorimotor associations. First, only a very\nsmall proportion of 7a neurons (<4%) exhibited significant visual responses encoding cue\nidentity or specific cue–saccade associations. Second, only a limited subset of neurons\n(approximately 20%)—substantially fewer than outcome-selective neurons—showed\nsignificant pre-saccadic activity during the delay period that could reflect association\nencoding, and even this pre-saccadic activity was markedly weaker than outcome\nencoding. Third, the strength of this pre-saccadic activity did not systematically change\nacross learning days in a manner that correlated with behavioral performance (new Figure\nS10). These findings suggest that area 7a is unlikely to play a central role in directly forming\nor encoding sensorimotor associations. Rather, the outcome-related signals observed in\narea 7a may be relayed to other brain regions more directly involved in acquiring and\nstoring these associations. We have added on paragraph in the revised discussion section\nto discuss this point:\n“Our findings suggest that area 7a may not play a central role in forming or encoding\nsensorimotor associations during AL. First, only a small fraction of 7a neurons showed\nsignificant encoding of the visual cue during either the cue presentation (<4%) or delay\n(20%) periods of the ISA task. Second, encoding of saccade direction prior to the monkeys’\nchoices was notably weak. Third, this pre-saccadic encoding did not show systematic\nchanges across learning days that aligned with the monkeys’ learning performance. The\nrelative lack of attention to the primate PPC in earlier AL studies aligns with our observation\nthat area 7a may not be directly involved in acquiring new associations. Instead, it may\ncontribute to outcome monitoring—specifically by encoding salient outcome signals that\npromote exploratory behavior during AL.”. \n3) Results, Figure 2I and 2R: The latency data (negative numbers) seems to suggest that\ntrial outcome is encoded BEFORE the reward is given. So the animal had no feedback yet.\nThis should change your interpretation of what is encoded or at least taken into account?\nResponse: We realized that the original Figure 2I and 2R may have been unclear and\npotentially caused confusion for the reviewer. Specifically, Figure 2I illustrates the latency\nof outcome encoding in the current trial relative to target offset, with no negative values on\nthe x-axis. In contrast, Figure 2R depicts the latency of outcome history encoding from the\nprevious trial relative to visual cue onset. In the revised manuscript, we have updated these\nfigures and added clear labeling on the x-axis to indicate the corresponding time windows\nin each panel, as shown below:\n4) Relationship of outcome representation to neuronal tuning for saccade localization and\nrelationship of previous trial to current trial outcome representations. If the outcome\nrepresentations contribute to learning the appropriate association. The tuning for stimuli\nand saccade of neurons should matter. Doe the authors find any evidence whether different\nsubgroups of neurons related to their tuning show different outcome representations and\nthat the link between the two properties affect learning success?\nResponse: This is a valuable suggestion. In the revised manuscript, we have added\nanalysis examining whether outcome history influenced the neural encoding of outcome\nencoding in the current trial. As detailed in our previous responses, we found that the\noutcome encoding in area 7a was significantly stronger after outcome switches than after\noutcome repetitions (new Figure 3H and Figure S13).\nFurthermore, we have included two new analysis to address the link between the\nsaccade direction encoding and outcome encoding in area 7a. First, we tested whether\nthere were differences in outcome encoding between neurons that preferred to encode\ndifferent saccade directions. Specifically, we separated neurons that exhibited significant\nsaccade direction encoding into left-preferring neurons and right-preferring neurons, and\nthen tested whether there were differences in the strength and preference in their outcome\nencoding. Since there are very few 7a neurons significantly encoded the cue-identify during\nthe cue-presentation period, we focused this analysis during the delay and target-on\nperiods. This revealed different patterns of outcome encoding for left-preferring and right-\npreferring neurons: the left-preferring neurons exhibited significantly greater outcome\nencoding in both monkeys when they are ENs than when they are CNs; while the right-\npreferring neurons exhibited opposite results between two monkeys. We believe that these\nresults do not indicate a direct contribution of outcome encoding to the association\nformation. The detailed results are shown in below:\nSecond, we examined whether there was relationship between the strength of outcome\nencoding and the strength of saccade direction encoding for each 7a neurons. We found\nsignificant positive correlations between the strength of outcome encoding and the strength\nof saccade direction encoding for the majority of the recording sessions. However, such\ncorrelations did not exhibit systematic change as the learning progressed, shown as\nfollows:\nThe figure above shows the correlation between the strength of outcome encoding and saccade encoding\nacross different learning epochs, with each dashed line representing data from a single FOV.\nNotably, as described earlier, we conducted new analyses demonstrating that outcome\nselectivity in area 7a may drive the monkeys’ learning behavior in subsequent trials.\nSpecifically, stronger outcome selectivity led to increased exploration behavior (i.e.,\nswitching choice direction for the same association), as shown in new Figure 4E-4F and\nFigure S15. This effect was observed in both correct-preferring and error-preferring\nneurons across monkeys. These results suggest that outcome signals in area 7a may not\ndirectly contribute to forming sensorimotor associations during learning but instead likely\nfacilitate exploratory behavior.\n5) Network connectivity lines 296ff and Figure 6: Potentially an interesting results. But\nbetween what neurons (tuning, entry criterion) was the noise correlation measured?\nResponse: We realized that the description in the original manuscript might not be clear.\nIn the original Figure 6A-6C, we calculated the noise correlation between each pair of\nrecorded neurons for each FOV. We have added this information in the revised manuscript\nas follows:\n“We calculated the noise correlation between each pair of recorded neurons for each\nFOV. This reveled that noise correlation was greater for within-type pairs than cross-type\npairs for the outcome-selective neurons (ENs /CNs, Figure 6A), indicating that neurons\nthat showed similar neural encodings were more likely to coactivate”\nIn the original Figure 6D-K, we only included data from specific group of neurons for\neach of the analysis. We defined different groups of neurons based on their outcome\nselectivity in two successive learning days. We have specified the neuron data in both the\nresult section and the figure legend.\nWhich time window did you use? As you have effects of previous trial outcome represented\nin some of the time windows, you would expect some correlation due to this signal. How\ndid you take this into account and investigate/eliminate this effect on correlations?\nResponse: We calculated noise correlation using neural activity from frames 1–8 (1.1 s)\nfollowing reward delivery. To minimize potential confounds from outcome encoding, noise\ncorrelations were computed separately for correct and error trials, and a weighted average\nwas used as the estimated noise correlation. Since most neurons did not exhibit significant\nencoding of outcome history during this period, we believe that previous outcome encoding\ndid not substantially influence the findings presented in Figure 6. As a control, we also\ncalculated noise correlation using neural activity during the fixation period (8 frames prior\nto cue onset), applying a weighted average of noise correlations across trials with different\noutcome histories. This analysis produced similar results.\nFigure 6F,G,J,K need clearers description in the figure legends. What data am I looking at\nand what is the critical piece of data shown.\nResponse: We realized that the description in the original manuscript might not be clear.\nIn the revised figure legend, we have added more detailed description of the results, shown\nas follows:\n“(F) An ROC analysis was used for quantify the difference in noise correlation between\nwithin-future-type pairs and across-future-type pairs. The area under curve (AUC) value\nquantifies the predictive power of noise correlation for neuron ensemble evolution. The red\nand gray lines indicate the ROC curves for outcome-selective and outcome-non-selective\nneurons, respectively. Data are from the same example session shown in (E). (G).\nComparison of the predictive power for neuron ensemble evolution between noise\ncorrelation and signal correlation. Each dashed line connects data from the same day. Red\nand gray dots represent results from outcome-selective and outcome-non-selective\nneurons, respectively. The red horizontal line represents the average value. The green\ndots denote data from the example session. Noise correlation was significantly different\nbetween within-future-type and across-future-type pairs for neurons which were outcome-\nselective on the second day, but no such difference was observed for neurons which were\noutcome-non-selective on the second day.”\n“Data in (J), (K) were shown in the same format as in (F), (G).”\nMinor:\nAbbreviations: the many abbreviations make the text not very readable, e.g. AL, ISAL could\nbe written out as they are not standardly abbreviated terms.\nResponse: We thank the reviewer for pointing this out and agree that the original\nmanuscript contained an excessive number of abbreviations, which may have led to\nconfusion. In the revised version, we have substantially reduced the use of abbreviations\nto improve readability. However, we have retained a few key abbreviations—such as AL\n(associative learning), ISAL (image-saccade associative learning), and PPC (posterior\nparietal cortex)—as they are frequently used throughout the manuscript and help maintain\nconciseness.\nIntroduction: l51: sentence is unclear and grammatically incorrect.\nResponse: Thanks for pointing this out. In the revised manuscript, we have corrected it as\nfollows:\n\"And what factors govern these changes?\"\nFigure 1D. Legends states data are from each monkey. What part is from what monkey?\nResponse: We realized that the description in the original manuscript might not be clear.\nFigure 1D-1E included data from both monkeys. In the revised manuscript, we have\ndistinguished the data from the two monkeys in different colors, shown as follows:\nFigure: the labelling by letter seems haphazard in most figures and does not follow a clear\npattern. Some panels are not really visible, like e.g. the bottom of 1G\nResponse: We have reorganized the panels in each main figure to improve clarity, and we\nhope the lettering is now more intuitive in the revised versions.\nFigure legends: it would be helpful if the main outcome for each panel is summarised as\nwell as only describing what is plotted.\nResponse: We thank the reviewer for pointing this out. Following the reviewer’ suggestion,\nwe have added short summaries for most of the important findings in the revised figure\nlegend.\nP-values for stats are sometimes \"p\" (Methods) and sometimes \"P\" (Results). Very\nconfusing as you also look at proportions. I suggest to standardise to \"p =\" and always\nwrite directly after naming the test.\nResponse: Thanks for the correction. We have changed them all to ‘P’ in the revised\nmanuscript.\nLabelled neurons in Figure 4A and 4F are not easily discernible.\nResponse: We realize that our original description may have been unclear and may have\nled to confusion for the reviewer. In the original Figures 4A and 4F, we presented the\naveraged images of two example FOVs without labeling individual neurons. The purpose\nof these images was to illustrate the stability of recordings across days. In the revised\nfigures, we have now labeled individual neurons and added a color code indicating\noutcome encoding to improve clarity, as shown below:\nReviewer #3 (Remarks to the Author):\nThis study investigates the changes in the activity of cells in the primate area 7a, measured\nwith two-photon calcium imaging, during an associative learning task that demands new\nvisual-motor associations over multiple days. The results showed a strong neural\nrepresentation of the trial outcome, which was closely correlated with monkeys’ learning.\nThis neural outcome signal was reorganized as monkeys transitioned from exploitation to\nexploration and showed a gradual evolution while the animals learned new visual-motor\nassociations. In addition, the encoding of trail outcome was not closely related to the cell\nencoding of the output saccadic movements. Finally, the modulation in outcome encoding\nwas constrained by the local network connectivity.\nThis is a very interesting and well performed study, providing novel information about how\nthe PPC incorporates local outcome signals to promote visual-motor associations along\nmultiple days. An important strength of this manuscript is the use of calcium imaging with\ntwo-photon that allows the measurement of the activity of the same population of neurons\nduring periods of learning that last days. Another strength in the study is the use of a simple\ntask where two images are associated with two saccade directions over a period of days,\nwhich permits to measure the reconfiguration of the cell and network responses during the\nprocess. Nevertheless, a have some concerns.\nResponse: We appreciate the reviewer’s positive feedback on our work. As detailed below,\nwe have addressed all comments, which have been very helpful in improving the\nmanuscript.\nFirst, it is difficult to know whether the data of the figures are from the monkey with stable\nsaccadic behaviors or some of them are from the monkey with high frequency of fixation\nbreaks. A direct comparison of the functional properties of 7a neurons between monkeys\nis in place.\nResponse: We realize that our original description may have been unclear and may have\ncaused confusion for the reviewer. We also agree that a direct comparison of the functional\nproperties of area 7a neurons between the two monkeys could provide valuable insights\nto the manuscript. In the original version, most of the population-level results presented in\nthe main figures included data from both monkeys, with a few exceptions noted below.\nFirst, the original Figures 2N to 2R included data only from the monkey that exhibited\nstable saccadic behavior. In these panels, we demonstrated that PPC neuronal activity\nencoded outcome history from the previous trial. The other monkey showed a high\nfrequency of fixation breaks (fix-break ratio = 0.57 ± 0.08) during task performance,\nresulting in an excessive number of trials with no choice responses interspersed between\ncompleted trials. Consequently, data from this monkey were excluded from the main\nfigures (2N to 2R) but presented separately in a supplementary figure (Figure S6) in the\noriginal manuscript. These supplementary data also revealed significant encoding of\nprevious-trial outcomes in area 7a neurons, although the encoding strength was weaker\ncompared to the monkey with stable saccadic behavior.\nSecond, the original Figures 3A to 3D included data only from the monkey with a high\nfrequency of fixation breaks. This is because we were able to record both the learning task\nand the control saccade task exclusively in this monkey. Conversely, the original Figures\n3E to 3G included data only from the monkey with stable saccadic behavior, as only this\nmonkey experienced learning of associations both with and without punishment for error\ntrials across different recording sessions. These details have been clarified in both the\nResults section and the figure legends in the revised manuscript.\nFollowing the reviewer’s suggestion, we now present data from both monkeys separately\nfor most results in the supplementary figures, except for the analysis of the evolution of\noutcome encoding during learning. This exception is because the monkey with stable\nsaccadic behavior was successfully recorded across a complete learning course in only\ntwo FOVs. Comparisons of neuronal encoding between the two monkeys are provided in\nthe revised Figures S1, S2, S6, S11, S13, and S15.\nFurthermore, we have clearly specified the data corresponding to each monkey in the\nlegends of all relevant figures.\nThe heterogeneity in the outcome signal modulation across FOVs is large, that should be\nclear in the paper and something needs to be say about what is the roles of such\nheterogeneity in AL. In addition, it is important to know where the FOV where within area\n7a, which is a large structure and limits with 7ab and 7m that have different connectivity\nand functional properties.\nResponse: We agree with the reviewer that the heterogeneity of outcome representation\nacross FOVs is worth discussing. We speculate that two main factors may contribute to\nthis variability. First, our findings suggest that outcome encoding in area 7a is linked to\nlearning content. It is therefore possible that some 7a neurons exhibit stronger outcome\nencoding when the monkeys learn certain associations, but show weaker encoding when\nlearning different ones. This heterogeneity in outcome representation may reflect the\nstorage of distinct learning experiences or facilitate the learning of different content.\nSecond, previous studies have shown that neurons with similar functions tend to be\nclustered in higher cortical areas. Thus, outcome-encoding neurons in area 7a may not be\nuniformly distributed but rather spatially clustered in subregions within area 7a. In the\nrevised manuscript, we have added a sentence in the Discussion section to address this\npotential heterogeneity of outcome representation across FOVs, as shown below:\n‘This content-dependent outcome encoding may also explain the heterogeneous\nevolution of outcome signals across different 7a subregions (FOVs) throughout the\nlearning process. Beyond potential clustering of outcome-encoding neurons, such\nheterogeneity might reflect the storage of distinct learning experiences or support the\nacquisition of different types of content.’.\nFurthermore, we have labeled all recorded FOVs in the revised Figure 1 (shown in\nbelow), clearly showing that they are all located within area 7a. However, it is important to\nnote that our recordings did not cover the entire extent of area 7a in either monkey, due to\ntechnical limitations inherent to two-photon calcium imaging in behaving monkeys.\nThe present results describe how outcome encoding changes during long-term AL, not\nhow the outcome signals strengths the visuomotor associations during learning. The same\nquestion is valid for the observation that neurons coactivated more closely were more likely\nto develop strong connections over time, leading to similar outcome encoding, but the\nquestion remains of how this encoding signal directly affects the association between\nimage-saccade direction across days.\nResponse: The reviewer raised a good point which did not clearly address in the original\nmanuscript. To fill this gap, we have included two new analysis on the saccade direction\nencoding during the delay period-which likely represent the image-saccade associations-\nin the revised manuscript.\nFirst, we examined whether the saccade direction encoding during the delay interval\n(which potentially encoded the image-saccade association) was influenced by the outcome\nhistory (outcome in the preceding trial). We found that saccade direction encoding—which\nlikely reflects image-saccade associations—was very modestly, but significantly, stronger\nwhen the monkey’s saccade choice was correct in the preceding trial compared to when it\nwas incorrect. This finding suggests that outcome signals itself may modulate saccade\ndirection encoding in the subsequent trial, potentially contributing to the learning of new\nassociations. These results are included in the revised manuscript as Figure S16, and\nshown in below:\nSecond, we examined whether the average strength of outcome encoding correlated\nwith the average strength of saccade direction encoding across different learning days for\nall the neurons within each FOV. On the population level, we found a significant positive\ncorrelation between strength of outcome encoding correlated and strength of saccade\ndirection encoding across different learning days. This result is now included as revised\nFigure S10, shown as bellow:\nHowever, although the encoding of image-saccade association (saccade direction\nselectivity during the delay period) within area 7a was influenced by the outcome history,\nthe saccade direction encoding in area 7a did not exhibited systematic relationship with\nthe monkeys’ behavior performance across learning days (shown in below).\nNotably, as described earlier, we conducted new analyses demonstrating that\noutcome selectivity in area 7a may directly influence the monkeys’ learning behavior in\nsubsequent trials. Specifically, stronger outcome selectivity led to increased exploration\nbehavior (i.e., switching choice direction for the same association), as shown in new Figure\n4E-4F and Figure S15. This effect was observed in both correct-preferring and error-\npreferring neurons across monkeys. These results suggest that outcome signals in area\n7a may not directly contribute to forming sensorimotor associations during learning but\ninstead primarily facilitate exploratory behavior. We have substantially revised the\ndiscussion section to discuss the potential roles of outcome representations in area 7a\nplayed during AL, which is shown in below:\n“Previous studies have identified outcome representation in brain areas outside PPC,\nemphasizing the significance of the frontal-striatum network in evaluating reward\nreinforcement. Outcome signal has been proposed to serves as a teaching signal,\nreinforcing correct associations while weakening incorrect ones. However, the observed\noutcome representation in PPC differs markedly from these earlier findings, instead\nhighlighting a predominant sensitivity to salient, negative outcomes that may drive\nexploratory choices during AL. This suggests a potential complementary role of PPC in\nmonitoring trial outcomes during sensorimotor AL, relative to the role of the frontal-striatum\nnetwork. While the frontal-striatum circuitry primarily evaluates reward reinforcement and\nsupports the acquisition and exploitation of learned associations during AL, PPC may\ninstead play a predominant role in detecting salient ‘error signals’ and facilitating\nexploratory behavior throughout the learning process. The outcome signal in area 7a may\nbe primarily relayed to brain regions such as the frontal pole, which are known to drive\nexploratory behavior during learning. This is also in line with opponent-process theories,\nwhich propose that emotional experiences and motivated behavior are regulated by pairs\nof opposing processes.\nThe critical roles of the fronto-striatal network in AL have been demonstrated in\nprevious studies using causal approaches, such lesion studies. However, the causal\ninvolvement of the primate PPC in long-term AL remains to be tested in future studies. Our\nfindings suggest that area 7a may not play a central role in forming or encoding\nsensorimotor associations during AL. First, only a small fraction of 7a neurons showed\nsignificant encoding of the visual cue during either the cue presentation (<4%) or delay\n(20%) periods of the ISA task. Second, encoding of saccade direction prior to the monkeys’\nchoices was notably weak. Third, this pre-saccadic encoding did not show systematic\nchanges across learning days that aligned with the monkeys’ learning performance. The\nrelative lack of attention to the primate PPC in earlier AL studies aligns with our observation\nthat area 7a may not be directly involved in acquiring new associations.\nInstead, it may contribute to outcome monitoring—specifically by encoding salient outcome\nsignals that promote exploratory behavior during AL. Nonetheless, the role of the primate\nPPC in outcome monitoring and its potential contribution to exploration-driven learning\nprocesses warranting further investigation with causal manipulations. Previous rodent studies have\nreported that PPC was causally involved in processing sensory-stimulus history and\nhistory-based action selection bias to guide decision behavior. Moreover, PPC was also\nshown to represent discrepancies between the current model and observed state\ntransitions in reinforcement learning, as well as reward-related belief updating during\nflexible decision-making. Collectively, these results indicate that PPC likely plays an\nimportant role in updating the estimation of the current state by incorporating past\nexperience, to guide decision-making and learning.”\nFinally, for Figure 6 I highly recommend computing the mutual information of the neural\nactivity during the task period between cells to generate robust graphical models of\nfunctional connectivity across cell types and learning epochs.\nResponse: We agree with the reviewer that applying graph theoretical analysis to the\nfunctional neuronal network could yield valuable insights. In a separate manuscript, we\nconstructed functional neuron networks using partial noise correlation and applied graph\ntheoretical analysis to the 7a neuron network. This approach provided novel insights into\nhow network topology constrains both the functional roles and the evolution of neuronal\nencoding across learning days.\nIn response to the reviewer’s suggestion, we also performed the analysis in Figure 6\nusing mutual information instead of noise correlation. However, this approach did not\nproduce satisfactory results compared to the original analysis, as functional connectivity\nbased on mutual information failed to predict the evolution of neuronal encoding during\nlearning. This result is shown below:\nAs shown in the figures above, while functional activity measured by mutual\ninformation could barely predict changes in neural encoding across learning days (left\npanel, same group of neurons in day 1 but different groups of neuros in day2, mean AUC\n= 0.55; right panel, different group of neurons in day 1 but same group of neurons in day2,\nmean AUC = 0.51), its predictive power was not greater than that of signal correlation. Most\nnotably, this predictive power was substantially weaker than that derived from noise\ncorrelation (Figure 7G, mean AUC = 0.75; Figure 7K, mean AUC =0.76).\nWe believe this difference arises primarily because mutual information lacks a critical\nfeature necessary for our analysis—its inability to distinguish between positive and\nnegative connectivity. Although mutual information and noise correlation values are highly\ncorrelated (as shown in the figure below), mutual information only yields positive values,\nwhereas noise correlation captures both positive and negative values, allowing us to\ndifferentiate between excitatory and inhibitory (or anti-correlated) relationships. In our data,\nneurons with opposite encoding preferences tended to exhibit negative functional\nconnectivity. Therefore, mutual information may not be an appropriate metric to fully\nrepresent the connectivity between neurons in this study.\nMinor comments\nPlease describe what the PsyTrack parameters are and why this model is better than\nothers to describe AL.\nResponse: To capture the dynamics of the monkeys’ behavioral strategies during learning,\nwe fitted their performance accuracy using the ‘PsyTrack’ model. This model estimates\ntime-varying weights that characterize the animals' decision-making strategy on a trial-by-\ntrial basis, modeled as a linear combination of available task variables. As such, it allows\nfor trial-resolution tracking of how decision strategies (reflected by different weights) evolve\nthroughout the learning process. Specifically, we used the task condition (cue identity) as\nthe model input, coding each trial as either -1 or 1, while the monkeys’ saccade choices\nserved as the output, coded as 0 or 1 per trial.\nFor model fitting, we applied three hyperparameters: ‘sigInit’ = 2^4 (controlling the prior\nvariance of initial weights), ‘sigma’ = 2^-4 (determining the smoothness of weight evolution\nacross trials), and ‘sigDay’ = 2^-1 (capturing potential day-to-day shifts in weights). The\nmodel yielded three behavioral weights: bias weight, previous answer weight, and stimulus\nweight, reflecting the degree to which the monkeys' saccade choices depended on internal\nbias, the previous trial’s outcome (the extent to which the monkeys’ saccade choice was\ndependent on the win-stay, lose-shift strategy), and the visual cue, respectively. These\ndetails were provided in the Methods section; we have now also added one sentence in\nthe Results section to describe the PsyTrack model fitting procedure and its advantages,\nas shown below:\n‘This model describes choice behavior at the resolution of single trials, allowing us to\nquantify the evolution of animal’s decision strategies (represented by different weights)\nacross the learning course.’.\nThe change of outcome signaling in the population showed in Figure 4 is astonishing.\nPlease add the color code for outcome encoding of Figure 2C in Panels 4A and F.\nResponse: Following the reviewer’s suggestion, we have added the color code for outcome\nencoding in the revised Figure 5A and F, shown as follows:\nIs the outcome signal in PPC used locally for AL or is also used in other areas?\nResponse: This is an interesting and important question; however, the current study cannot\ndirectly address it. As we detailed in the previous response, area 7a might not be the key\nbrain region for acquiring and representing the sensorimotor associations, and outcome\nencoding in area 7a may primarily drive exploration behavior during learning. Therefore,\nwe believe it is more plausible that the outcome signals are transmitted to other subregions\nsuch as the frontal pole, where they may directly contribute to drive exploratory choices.\nHowever, because our recordings were primarily limited to cortical layers 2/3, without\naccess to deeper layers, it remains possible that outcome signals in deeper cortical layers\nof area 7a could participate in learning and presenting sensorimotor associations. We have\ndiscussed this possibility in the revised manuscript as follows:\n‘While the frontal-striatum circuitry primarily evaluates reward reinforcement and\nsupports the acquisition and exploitation of learned associations during AL, PPC may\ninstead play a predominant role in detecting salient ‘error signals’ and facilitating\nexploratory behavior throughout the learning process. The outcome signal in area 7a may\nbe primarily relayed to brain regions such as the frontal pole, which are known to drive\nexploratory behavior during learning.‘\nHow the complex remapping of outcome encoding linked to new associations is used to\ngenerate the AL. How the previous and actual trial outcome signal affects the image-\nsaccade association, when the mapping of outcome encoding remapping is to complex.\nWhat is the outcome signal doing to generate the association between the image and the\nsaccade direction?\nResponse: We thank the reviewer for these insightful questions. In this study, we found\nthat outcome encoding in area 7a underwent significant remapping as the monkeys\ntransitioned from familiar to novel associations. This suggests that outcome encoding in\narea 7a is not abstract but closely tied to the specific content of learning. Consequently,\ndistinct neuronal ensembles may contribute to representing trial outcomes depending on\nthe specific associations being learned. We speculate that PPC neurons exhibiting strong\noutcome encoding during the learning of specific associations may become functionally\nlinked to neurons representing these associations, thereby influencing the formation of\nsensorimotor associations during learning. Such content-dependent outcome\nrepresentations may provide an advantage over abstract outcome signals by enabling\noutcome-encoding neurons to directly influence the neuronal ensembles representing\nspecific associations, without requiring more complex targeting mechanisms. Consistent\nwith the notion that the outcome encoding in area 7a was related to learning content, we\nfound that stronger outcome selectivity was specifically linked to switching saccade choice\ndirection for the same imaging-association rather than simply switching saccade directions\nirrespective to the associations. We have added several sentences in the revised\nDiscussion section to discuss this point as follows:\n“Outcome signal has been proposed to serves as a teaching signal, reinforcing correct\nassociations while weakening incorrect ones.”\n“We speculate that PPC neurons exhibiting strong outcome encoding during the\nlearning of specific associations may become functionally coupled with neurons\nrepresenting these associations, thereby influencing the learning of sensorimotor\nassociations. Such content-dependent outcome representations may provide a\ncomputational advantage over abstract, content-independent outcome signals by enabling\noutcome-encoding neurons to directly influence the neuronal ensembles representing\nspecific associations, without requiring more complex targeting mechanisms.”\nAn intriguing question concerns the precise role of the outcome signal during the\nlearning of novel associations. Previous studies have proposed that the outcome signal\nserves as a teaching signal, reinforcing correct associations while weakening incorrect\nones. This mechanism enables the brain to determine, through trial-and-error learning,\nwhich saccade direction is correctly linked to a given visual image. As noted in our previous\nresponse, we speculate that this learning process may primarily occur in other brain\nregions that are directly responsible for acquiring and storing sensorimotor associations.\nInstead, the outcome encoding in area 7a might primarily facilitate the exploration choice\nduring learning, as we demonstrated that greater outcome encoding in area 7a lead to\nsaccade choice switches during learning.\nThe outcome-history encoding of the majority 7a neurons predominantly emerged and\nlocalized within the fixation period of the ISAL task, so these signals have an effect on how\nthe cue images are progressed or kept in memory? Or is a signal that changes the reaction\ntime of the saccades or the saccade direction?\nResponse: This is a good question. We conducted five analyses to address it. First, as\ndescribed in previous response, we examined whether outcome history influenced the\nencoding of visual cues during the cue presentation and delay periods of the learning task.\nOur analysis revealed that saccade direction encoding—which likely reflects image-\nsaccade associations—was slightly stronger when the monkey’s saccade choice was\ncorrect in the preceding trial compared to when it was incorrect. This effect was observed\nin both left-preferring and right-preferring neurons. These findings suggest that outcome\nsignals may modulate saccade direction encoding in the subsequently trial, potentially\ncontributing to the learning of new associations.\nSecond, we tested whether the outcome history of the previous trial influenced the\nmonkeys’ reaction time (RT) in the current trial. This analysis revealed no significant\ndifference in RT during most recording sessions(46/59, the averaged result is shown below）\nbetween trials preceded by a positive outcome and those preceded by an negative\noutcome. This result is not surprising, given that our task design included a fixed delay\nperiod between stimulus offset and target onset, which likely minimized the influence of\nprior outcomes on reaction time.\nThird, we examined whether outcome history in the previous trial influenced the\nmonkeys’ saccade direction in the current trial in two different ways. First, we tested\nwhether the monkeys exhibited saccade direction bias when previous trial was correct or\nincorrect. This revealed no significant bias in the majority of learning sessions (40/59).\nSecond, we tested whether the outcome history effected the monkeys saccade choice\nstrategy, i.e.’ win-stay, lose-shift. Our results showed that the outcome history significantly\ninfluenced the monkeys’ choice strategy in a minority of the learning sessions (21/59). The\naveraged results are shown in bellow:\nLeft two panels: the percentages of trial that the monkeys chose different saccade directions followed\ncorrect or incorrect outcomes; right two panels: the percentages of trials using different choice strategies\nfollowed correct or incorrect outcomes.\nFourth, we have included new analysis to investigate whether outcome encoding in\narea 7a was modulated by outcome history, specifically the outcome of the preceding trial.\nWe quantified the encoding strength separately for trials following outcome switches\n(correct→error or error→correct transitions) versus outcome repetitions (correct→correct\nor error→error). Notably, outcome encoding was significantly stronger following outcome\nswitches compared to outcome repetitions (new Figure 3H). This effect was observed for\nboth error-preferring and correct-preferring neurons across two monkeys (new Figure S13).\nThese findings suggest that outcome encoding strength in area 7a was remarkably\ninfluenced by the outcome history during learning, and that area 7a is particularly sensitive\nto outcome changes. This observation aligns with the well-established role of the primate\nPPC in salience representation. Detailed results are shown in bellow:\nLastly, we conducted new analyses to test whether outcome selectivity in area 7a\ndirectly influenced the monkeys’ learning behavior in the subsequent trial. Trials were\ncategorized into two conditions: exploration, in which monkeys switched saccade direction\nrelative to the previous trial for a given image-saccade association, and exploitation, in\nwhich they repeated the same saccade direction for the same association. For each\ncondition, we measured outcome selectivity in the preceding trial. Outcome selectivity was\nsignificantly stronger prior to exploratory trials compared to exploitative ones (new Figure\n4E and Figure S15), suggesting that enhanced outcome encoding in area 7a predicted\nsubsequent exploratory behavior. The phenomenon was found for both correct-preferring\nand error-preferring neurons across two monkeys. In contrast, area 7a neurons did not\nshow increased outcome selectivity prior to trials in which the monkeys switched saccade\ndirection independently of the image-saccade association (new Figure 4F and Figure S15).\nThese findings suggest that outcome representations in area 7a may contribute specifically\nto strategy shifts during sensorimotor association learning, rather than reflecting general\nswitching behavior. The detailed results were shown as follows:\nThese figures plot the strength of outcome encoding (quantified by FEV) for trials prior to the exploration\nand exploitation trials respectively.\nThese figures plot the strength of outcome encoding (quantified by FEV) for trials prior to saccade direction\nswitching and repeating trials independently of the image-saccade association. The left panel shows the\naveraged results from two monkeys.\nWhat is the functional meaning of having outcome signals that are different between the\nactual and the previous trial?\nResponse: As the reviewer noted, a substantial fraction of neurons exhibited inconsistent\nencoding of trial outcomes or outcome history. We speculate that this variability may not\nreflect specific functional roles, but rather the dynamic nature of outcome encoding within\nthe network, either within area 7a or across interconnected brain regions. The maintenance\nof outcome information may rely not only on persistent firing during the inter-trial interval\nbut also on the overall population activity state and potentially short-term synaptic plasticity.\nSimilar dynamic encoding of information during delay periods has been reported at both\nthe single-neuron and population levels in previous studies (Spaak et al. ,2017; Stokes，\n2015).\nWhat is the effect of the speed of learning (e.i. 3 vs 7 days) on the magnitude of the\nencoding of outcome by neurons and on dynamics across days.\nResponse: This is an excellent point. Following the reviewer’s suggestion, we calculated\nthe correlation between the magnitude of outcome encoding and the monkeys’ learning\nspeed. Given the heterogeneity of outcome representation across different FOVs, we\napplied a multiple-class linear regression using the average strength of outcome encoding\nfor each recording session, with the corresponding average learning speed as a regressor\nwhile controlling for FOV differences. We calculated the daily learning speed for each\nimage-saccade association separately for each learning session. This analysis revealed\nno systematic correlation patterns between learning speed and the strength of outcome\nencoding across different 7a subregions (FOVs). The detailed results are shown in below:\nHowever, we believe that the absence of a clear correlation does not rule out a\nfunctional role for outcome encoding in associative learning. This is because outcome\nencoding in area 7a appears to be content-dependent—that is, the recorded neurons in\neach session may have contributed to real-time learning to varying degrees (fully,\npartially, or minimally). Such variability could account for the inconsistent relationship observed\nbetween outcome encoding strength and learning speed. Moreover, as outlined in our\nprevious response, outcome encoding in area 7a may primarily drive exploratory behavior\nduring learning. Since exploratory behavior may not consistently influence learning speed\nacross different learning stages, this could further obscure a systematic relationship\nbetween outcome encoding strength and behavioral improvement.\nWhat is the proportion of cells that encode the stimulus onset, the image identity, the\nsaccade movement preparation and execution and the outcome. How many neurons\nencoded more than one parameter, and how the outcome signal changes during AL\nmodified the encoding of the image and the saccade direction in the same cells or in other\npopulations.\nHow many neurons encode both the previous and the actual outcome as a function of\nlearning.\nResponse: In the revised manuscript, we have included additional analyses to illustrate\nthese results, summarized as follows. First, beyond reporting the proportion of neurons\nshowing significant responses to stimulus onset (visual response), saccade preparation\n(pre-saccadic response), and post-saccadic response, we have now added the proportion\nof neurons that exhibited significant encoding of image identity and saccade execution\n(peri-saccadic activity) in the revised Figure S9. Second, we quantified the proportion of\nneurons exhibiting various forms of mixed selectivity, and these results are also presented\nin the revised Figure S9. Third, we conducted a new analysis to examine how changes in\noutcome signals during AL influenced the encoding of image identity and saccade direction.\nSpecifically, we assessed the correlation between the strength of outcome encoding and\nthe strength of saccade direction encoding during the delay period across different learning\ndays. This analysis revealed a positive correlation at the population level (r = 0.48), as\nshown in the new Figure S10. Fourth, we quantified the proportion of neurons encoding\nboth the previous and current trial outcomes as a function of learning progress. This\nrevealed a decreasing trend of neuron encoding both trial outcome and outcome history\nafter the monkeys fully learned the associations.\nAll of these revised and additional results are presented in the figures below.\nThese figures show the proportion of neurons exhibiting different selectivity.\nThis figure plots the proportion of neurons exhibiting different levels of mixed encoding.\nThis figure plots the correlation between outcome encoding and saccade direction encoding during the\ndelay period.\nThese figures show the proportion of neurons exhibiting significant encoding on current and previous\noutcomes across different learning epochs..\nPlease provide Figures of the FOV with pronounced outcome selectivity for the second\nmonkey as in Figure S1 and S9.\nResponse: We apologize for the unclear description in the original manuscript. In the\nrevised version, we have provided more detailed annotations distinguishing data from the\ntwo monkeys in the figures. Actually, the upper and lower panels in Figure S1 show data\nfrom the two monkeys respectively. Additionally, we have included the results for the\nsecond monkey in the revised Figure S11 (original Figure S9). The updated figures are\nshown below.\nTypos.\nThere are many typos in the manuscript such as:\nfrom two monkeys. (see Methods).\nFigure.2C\nResponse: Thanks for the correction, we have corrected them all.\nAuthors’ Response to Reviewers\nWe sincerely thank all reviewers for their thoughtful and detailed feedback on the earlier\nversion of our manuscript. Their comments and questions have greatly helped us to\nimprove the quality and clarity of our work. We also appreciate the reviewers’ positive\nrecognition of our previous revisions and responses. In this new version of the manuscript,\nwe have carefully addressed the latest comments from the reviewers.\nSpecifically, Reviewer 1 suggested us to invite a native English speaker to improve the\nwriting. Following the suggestion, we invited a native English speaker to polish the\nmanuscript language to enhance the clarity.\nReviewer 2 expressed no further concerns or questions.\nReviewer 3 recommended enlarging and clarifying the black stars denoting significant\neffects in the figures and suggested adding new references. Accordingly, we have updated\nthe corresponding figures and incorporated the suggested references in the introduction\nsection of the revised manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have carefully reviewed the authors’ revised manuscript and their detailed responses to the reviewers’ comments, and I greatly appreciate the substantial effort the authors have invested in addressing the concerns raised in the initial review. The revised manuscript now presents a much clearer and stronger narrative. The authors have provided additional analyses linking outcome encoding in PPC area 7a to behavioral strategies, performance accuracy, and outcome history. They have also added new figures, including revised Fig. 3H, Fig. 4A–F, and Fig. S13–S15, which convincingly demonstrate the robustness of their findings. In addition, they clarified the conceptual framework in the Introduction and Discussion by distinguishing methodological contributions from the scientific insights, explicitly acknowledged the correlational nature of their findings while highlighting the importance of future causal approaches, and improved the clarity of their description of the experimental design and the interpretation of neural remapping across learning days.\n\nI find that the emphasis on outcome monitoring is well presented. I also find it very important that the authors highlighted that these effects were observed in the primate PPC, which significantly strengthens the impact of the study. While it is somewhat disappointing that the authors were not able to perform reversing of the learned associations, the revised manuscript clearly focuses on how the PPC monitors outcomes during sensorimotor associative learning. The inclusion of multiple independent lines of evidence linking neural activity to learning and behavior is a substantial improvement, and this revision convincingly underscores the significance of the study.\n\nI also found the authors’ interpretation that “We propose that neural activity in area 7a may not primarily reflect the formation of sensorimotor associations, based on several observations” to be particularly interesting. This provides a new and valuable perspective on PPC function, and the deeper analyses presented in the revision convincingly strengthen the manuscript. Taken together, these points show that the authors have not only addressed the previous concerns but have also added depth and clarity to their central argument.\n\nI commend the authors for their thorough and thoughtful revisions. I am generally satisfied with their responses, and I support acceptance of this manuscript for publication. As a final recommendation, I strongly encourage the authors to seek a round of native English proofreading to ensure clarity and polish throughout the manuscript.", "reviewer_2": "The authors' extensive revisions have addressed my main concerns and I have no further comments.\n\nIn summary, the study \"Neural correlates of trial outcome monitoring during long-term learning in primate posterior parietal cortex\" by Liu, Jiang, Shi, Fang, Tang and Zhou shows a unique and important data set tracking the responses in parietal area 7a during associative learning. Their manuscript makes an important contribution to our understanding of the cortical encoding of association learning.", "reviewer_3": "The authors answered properly all the reviewers´ comments. The new version of the paper is quite better, making a strong and congruent scientific story. I recommend the authors to make the black start of significant effect larger and clearer. Also please consider referring to the following papers on spatial cognition signals in area 7a:\n\nCrowe, D. A., Chafee, M. V., Averbeck, B. B., & Georgopoulos, A. P. (2004). Neural activity in primate parietal area 7a related to spatial analysis of visual mazes. Cerebral Cortex, 14(1), 23-34.\nCrowe, D. A., Goodwin, S. J., Blackman, R. K., Sakellaridi, S., Sponheim, S. R., MacDonald III, A. W., & Chafee, M. V. (2013). Prefrontal neurons transmit signals to parietal neurons that reflect executive control of cognition. Nature neuroscience, 16(10), 1484-1491.\nMerchant, H., Battaglia-Mayer, A., & Georgopoulos, A. P. (2004). Neural responses during interception of real and apparent circularly moving stimuli in motor cortex and area 7a. Cerebral Cortex, 14(3), 314-331."}, "author_response": "Authors’ Response to Reviewers\nWe sincerely thank all reviewers for their thoughtful and detailed feedback on the earlier\nversion of our manuscript. Their comments and questions have greatly helped us to\nimprove the quality and clarity of our work. We also appreciate the reviewers’ positive\nrecognition of our previous revisions and responses. In this new version of the manuscript,\nwe have carefully addressed the latest comments from the reviewers.\nSpecifically, Reviewer 1 suggested us to invite a native English speaker to improve the\nwriting. Following the suggestion, we invited a native English speaker to polish the\nmanuscript language to enhance the clarity.\nReviewer 2 expressed no further concerns or questions.\nReviewer 3 recommended enlarging and clarifying the black stars denoting significant\neffects in the figures and suggested adding new references. Accordingly, we have updated\nthe corresponding figures and incorporated the suggested references in the introduction\nsection of the revised manuscript."}]

Decision letter