Psilocybin asymmetrically modulates outcome-based choice and cortical processing under uncertainty
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eLife Assessment
This valuable manuscript contains exciting data that has the potential to advances understanding of how psilocybin acutely alters decision making by modulating the impact of outcomes on future choices and altering how prefrontal cortex neurons encode outcomes. While the experimental paradigm, behavioral and neural analysis are elegant, the strength of evidence is, at present, incomplete given the lack of a saline treated comparison. This work will be of interest to behavioral and computational neuroscientists studying decision making, flexibility, or psychedelics.
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Abstract
Emerging clinical research with psilocybin highlights the value of the psychedelic experience on successful treatment of a multitude of psychiatric disorders. While the psychedelic trip or clinical support procedures cannot be modeled in rodents, neural processes critical to meta-learning and flexible modification of previously learned strategies can be quantified during psilocybin exposure. Here we applied computational modeling and single unit recordings from medial prefrontal cortex to characterize the effect of psilocybin using a value-based probabilistic choice task. Psilocybin improved choice selection in uncertain contexts and shifted reinforcement learning rate bidirectionally, enhancing updating from rewarded, while reducing updating from unrewarded, actions. These behavioral changes coincided with selective shifts in the neural encoding of task features including enhanced neural representation of rewarded, and diminished representation of unrewarded, outcomes. Collectively these data indicate that psilocybin improves choice selection in uncertain contexts by reweighing cortical coding in a way that favors learning from positive new information over prospective actions.
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Psilocybin asymmetrically modulates outcome-based choice and cortical processing under uncertainty elifesciences.org/reviewed-pre...
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eLife Assessment
This valuable manuscript contains exciting data that has the potential to advances understanding of how psilocybin acutely alters decision making by modulating the impact of outcomes on future choices and altering how prefrontal cortex neurons encode outcomes. While the experimental paradigm, behavioral and neural analysis are elegant, the strength of evidence is, at present, incomplete given the lack of a saline treated comparison. This work will be of interest to behavioral and computational neuroscientists studying decision making, flexibility, or psychedelics.
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Reviewer #1 (Public review):
Summary:
Jacobs et al. report how acute psilocybin influences value-based decision-making and associated neural correlates in the medial prefrontal cortex (mPFC) of rats performing a probabilistic two-choice task with reward probability varying in blocks. The authors highlight that acute psilocybin does not influence basic outcome-sensitive choice strategies (i.e., win-stay/lose-shift), but find that psilocybin increases choice variability and optimal choices in low-uncertainty contexts. Using RL modeling, the authors chose one of five models examined because it represented the behavior around transitions. This recapitulates their behavioral results, showing changes in choice variability (entropy) specifically under high-uncertainty conditions and improving asymptotic performance under low uncertainty. …
Reviewer #1 (Public review):
Summary:
Jacobs et al. report how acute psilocybin influences value-based decision-making and associated neural correlates in the medial prefrontal cortex (mPFC) of rats performing a probabilistic two-choice task with reward probability varying in blocks. The authors highlight that acute psilocybin does not influence basic outcome-sensitive choice strategies (i.e., win-stay/lose-shift), but find that psilocybin increases choice variability and optimal choices in low-uncertainty contexts. Using RL modeling, the authors chose one of five models examined because it represented the behavior around transitions. This recapitulates their behavioral results, showing changes in choice variability (entropy) specifically under high-uncertainty conditions and improving asymptotic performance under low uncertainty. Single-unit recording from mPFC during the task revealed that psilocybin may enhance the representation of reward-preferring neurons and suppress non-reward-preferring neurons, with no changes to outcome encoding and total/relative value signals largely intact. The authors interpret these findings as evidence that psilocybin rebalances mPFC processing to favour learning from positive outcomes, potentially offering a candidate neural coding mechanism for psilocybin's purported ability to increase cognitive flexibility acutely. While the research question is highly significant, the experimental design, analysis, and interpretations have critical shortcomings.
Strengths:
Understanding how psilocybin influences behavioral effects like reward learning and cognitive flexibility is critical to understanding its potential use in the clinic. The behavioral, neural, and modeling studies use a powerful set of approaches that are useful for understanding this complex problem.
Weaknesses:
(1) Design and sample size were major concerns. Only 8 rats were used, and the primary comparison is within-subject psilocybin vs. the immediately preceding "baseline" day rather than a fully counterbalanced or randomized drug/vehicle design. Since psilocybin sessions always followed baseline sessions (never the reverse), order effects, practice effects, or day-to-day drift cannot be fully excluded. Additionally, a saline control was only available for 5 of 8 animals, on a haphazard schedule ("due to unanticipated technical issues"), so the claim that effects are pharmacologically specific to psilocybin rather than to injection/novelty stress rests on a small, incomplete control dataset.
(2) There are a number of major concerns regarding behavioral confounds. Results about the number of trials performed in each condition seem buried in the methods instead of being provided with the rest of the behavioral data. Critically, the results show a 40% reduction in the number of trials completed. This is important to highlight and consider in the context of reduced motivation/hunger, and also in terms of the associated neural activity. Additionally, the percentage of rewarded trials is not reported - this is important for the interpretation of the behavioral results. For example, is the increased choice variability following psilocybin treatment measured by entropy advantageous? Does this result in more or fewer rewarded trials?
(3) Any differences in neural data are greatly diminished by the behavioral differences. Given the extraordinary long latency (averages of up to 20s) to reward receipt, it is unclear how the neural data in Figure 3 referencing reward is meaningful given the time frame is a maximum of 1.5s post-response. How can neurons be labeled non-reward-preferring based on their activity well before they retrieved the reward? Further, it is unclear how the post-response neural activity should be interpreted in the context of this long and variable latency to retrieve reward. The abnormally long latency to retrieve a reward is not discussed, especially in the context of response latencies on the order of 2 to 4s. Furthermore, the difference between groups (7 versus 20 s) should be discussed because this may represent a difference in motivation/hunger/response drive between groups and influence the interpretation of both behavioral and neural data.
Additionally, it is unclear how to interpret the neural data given the offset timing between groups because of the large behavioral differences. For example, given the decrease in latency to respond following the psilocybin treatment, the neural data shown (aligned to the response; e.g., Figure 6A, B) is largely offset from the time of trial initiation. There is no discussion of what is occurring behaviorally during the latency, which may be the cause of the different neural signatures.
(4) For the RL Modeling, there is circularity in the author's approach to choosing a model - namely, based on the "posterior predictive ability to recover psilocybin effects". The author's process for arriving at Model 3 as the specific "preferred" model over the intermediate options (particularly Model 4 with stickiness) is not well-described. The validation step (of recovering the key psilocybin behavioral transition signatures) is circular and not sound evidence for model adequacy rather than independent confirmation. I would want to see the actual PSIS-LOO comparison table (with SEs and Pareto k diagnostics) from Supplemental Figure 2A before fully accepting that Model 3 was clearly the best-justified choice rather than simply the most convenient one given convergence constraints. There was no prior predictive check with the parameters being chosen from the literature. Additionally, the Bayesian parameter effects rely on a relatively lenient 80% HDI threshold (rather than 95%).
(5) Some minor concerns:
a) The order of the Type I and II neurons appears to be reversed in this sentence based on the rest of the results/figures. "These neurons had distinct temporal patterns, showing reward-related increases immediately at action or later in the action/outcome epoch and are depicted as Type I and Type II, respectively".
b) Connecting results from Figures 3 and 4 would be interesting; in other words, showing how Type I/II neurons look in Figure 4 plots.
c) In Figure 1H, are the post-transition trials collapsed across high and low next blocks, since it seems from the depicted trial order that low uncertainty blocks can be followed by either a high uncertainty block or another (reversed) low uncertainty block.
d) The methods by which the authors arrived at the binned value neurons are not well-described, and more clarity on how authors arrived at relative and total value estimates would be helpful.
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Reviewer #2 (Public review):
Summary:
The authors set out to examine how psilocybin acutely alters learning in rats in a reward-based probabilistic choice task. Additionally, they asked how psilocybin acutely alters encoding of decision variables in the prefrontal cortex (PFC).
Strengths:
The behavioral paradigm is robust. Thorough and rigorous computational analyses suggest that psilocybin may differentially modulate the impact of rewarded and unrewarded outcomes and enhance exploration under uncertainty. Single-unit neural activity in PFC suggests a potential mechanism for these effects, with psilocybin inducing opposing effects on encoding of rewarded and unrewarded actions. These findings are potentially exciting and important.
Weaknesses:
The major weakness of this work is the lack of a saline-treated control. All conclusions rest …
Reviewer #2 (Public review):
Summary:
The authors set out to examine how psilocybin acutely alters learning in rats in a reward-based probabilistic choice task. Additionally, they asked how psilocybin acutely alters encoding of decision variables in the prefrontal cortex (PFC).
Strengths:
The behavioral paradigm is robust. Thorough and rigorous computational analyses suggest that psilocybin may differentially modulate the impact of rewarded and unrewarded outcomes and enhance exploration under uncertainty. Single-unit neural activity in PFC suggests a potential mechanism for these effects, with psilocybin inducing opposing effects on encoding of rewarded and unrewarded actions. These findings are potentially exciting and important.
Weaknesses:
The major weakness of this work is the lack of a saline-treated control. All conclusions rest on analyses that are limited to within-animal comparisons between a baseline session and a later session in which rats were injected with psilocybin prior to session start. There is no control for the injection stress or passage of time. The manuscript mentions that 5 of 8 mice were also tested in a session following a saline injection, but because data were lost for some animals, this was not pursued. Figure S1G reports very limited analysis of this data to suggest that there is no effect. At a minimum, this must be analyzed in full (behavioral and neural data) and included in the manuscript to support the central claim that the effects reported are indeed attributable to psilocybin.
Beyond this, it would be of interest to examine if the effect of psilocybin is restricted to the acute session or if any effects persist.
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Reviewer #3 (Public review):
Summary:
Current theories suggest psilocybin promotes meta-learning, modifies learned associations, and promotes flexibility, contributing to its transdiagnostic clinical relevance. However, the effects of psilocybin on neural state dynamics during learning and decision-making are unclear. Jacobs et al. use a rat model to examine how psilocybin changes prefrontal cortex encoding during a value-based probabilistic choice task. The authors found psilocybin increased high-value choices when uncertainty was low and increased choice variability when uncertainty was high, compared to baseline performance. A reinforcement learning model found psilocybin decreased side bias and increased reward learning while decreasing the unrewarded learning rate. Encoding of rewarded actions was enhanced in the mPFC while …
Reviewer #3 (Public review):
Summary:
Current theories suggest psilocybin promotes meta-learning, modifies learned associations, and promotes flexibility, contributing to its transdiagnostic clinical relevance. However, the effects of psilocybin on neural state dynamics during learning and decision-making are unclear. Jacobs et al. use a rat model to examine how psilocybin changes prefrontal cortex encoding during a value-based probabilistic choice task. The authors found psilocybin increased high-value choices when uncertainty was low and increased choice variability when uncertainty was high, compared to baseline performance. A reinforcement learning model found psilocybin decreased side bias and increased reward learning while decreasing the unrewarded learning rate. Encoding of rewarded actions was enhanced in the mPFC while unrewarded actions were attenuated.
Strengths:
The neural correlates and computations underlying the impacts of psychedelics are poorly understood, and this study examines these through well-established decision-making approaches. These data show changes in mPFC activity during a dynamic task, allowing for analysis across different contexts and events. The data presented provide some support for leading theories on psilocybin's therapeutic use in promoting meta-learning and flexibility. The combination of techniques, including decision-making, computational modeling, and neural recordings, is appropriate, powerful, and needed to make sense of the underlying change in experience triggered by psilocybin.
Weaknesses:
An unfortunate weakness in the design that limits the interpretability of the findings is a lack of control data under saline administration, and a lack of comparison of psilocybin to saline rather than baseline. Psilocybin performance is compared to a single baseline session, which lacks the effect of an injection that would be present in a saline control. There is some evidence that mild stress can itself impact outcome encoding. Supplemental data in this manuscript suggest there is no significant difference in probability of selecting the optimal choice on low uncertainty trials between baseline and saline; however, the direction of impact of the saline injection is in the same direction as psilocybin in the main figures. This means that the mild stress of simply receiving an injection of saline may not be totally neutral with regard to these measures, and may contribute to the effects attributed to psilocybin at both a behavioral and neural level.
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Author response:
I am writing to provide a description of our planned revisions. We thank the reviewers for their thoughtful comments on the manuscript. All three reviewers had the primary concern of “lack of saline-treated control.” We had, in fact, included saline-treated control in our design, and shown some of this data in the original submission (Supplemental data Fig 1G). We agree with the Editor’s note that more analysis for saline sessions should be provided. The planned revisions will include additional saline data to compare saline injections on behavior (as shown for psilocybin on figure 1), on reward-preferring and non-preferring cluster analysis (as shown for psilocybin on figure 3), and on action decoding and encoding (as shown for psilocybin on figure 6).
It should be emphasized that the work involved outbred rats …
Author response:
I am writing to provide a description of our planned revisions. We thank the reviewers for their thoughtful comments on the manuscript. All three reviewers had the primary concern of “lack of saline-treated control.” We had, in fact, included saline-treated control in our design, and shown some of this data in the original submission (Supplemental data Fig 1G). We agree with the Editor’s note that more analysis for saline sessions should be provided. The planned revisions will include additional saline data to compare saline injections on behavior (as shown for psilocybin on figure 1), on reward-preferring and non-preferring cluster analysis (as shown for psilocybin on figure 3), and on action decoding and encoding (as shown for psilocybin on figure 6).
It should be emphasized that the work involved outbred rats performing a complex probability-shifting decision-making task while freely moving in an operant box. Some of the comments by reviewer 1 (such as considering long/changing latencies to retrieve reward as “extraordinarily” or “abnormally” long) suggest that there may have been a mix-up with what is common in freely-moving rats versus simpler and head-fixed mouse paradigms. This distinction is relevant to some of the concerns raised by reviewer 1. First, our n is well within the range seen in electrophysiological studies in behaving rats as most these studies utilize n=5-9 (e.g., Sias et al., Nature Neuro, 2024; Maor et al., Nature Comm, 2026). Second, our analysis dealt with shifting latencies accordingly (e.g. time-locking to action) and focused on within subject performance because in complex cognitive tasks such as ours, there is a considerable amount of baseline performance variability in outbred rats. It is, therefore, common, and clinically relevant, to focus on within subject analysis of drug or vehicle effects. This has been the case with previous studies on the effect of psilocybin on cognitive flexibility (e.g. Torrado Pacheco et al, NPP 2023) and was our approach here. The additional saline data will hopefully convince the reviewers and interested readers that the reported effects of psilocybin were not an artifact of i.p. injections
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Nice review AND we have actual data to support (or refute!) some of the theories here on how psychedelics may influence reward learning and behavioral flexibility www.biorxiv.org/content/10.6...
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This is a really nice review! And we have actual data in support of some of your prediction looking at the effect of psilocybin on the exact task shown on Figure 3 www.biorxiv.org/content/10.6...
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www.biorxiv.org/content/10.6...
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Psilocybin asymmetrically modulates outcome-based choice and cortical processing under uncertainty https://www.biorxiv.org/content/10.64898/2026.07.16.733837v1
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Psilocybin asymmetrically modulates outcome-based choice and cortical processing under uncertainty https://www.biorxiv.org/content/10.64898/2026.07.16.733837v1
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