Biology subjects

Piray, P.

Publications and source records attributed to Piray, P..

13 recordsLinked to original sources

bioRxiv2026

Hippocampal replay repairs the cognitive map for flexible planning

Hippocampal replay is highly structured: prospective replay precedes retrospective replay within a stopping period, each aligns with the animal's future and past paths, with replay sometimes being directed towards goals the animal has abandoned. These regularities have resisted a single explanation. Replay seems sometimes to serve the animal's next decision and sometimes to work against it, and its prospective-then-retrospective ordering, present even in animals with little experience, runs opposite to what the leading normative account predicts. We propose that replay repairs the cognitive map. A map that supports planning must already be built around where the animal is going since that is where its plans end. Every change of goal leaves the map wrong in a specific way and in need of two corrections: one for the goal being adopted and another for the goal being left behind. Their relative size and reach account for replay's ordering, its direction, and its apparently paradoxical content. The account also makes a prediction about a specific condition, when the animal's past and future paths converge, under which the usual ordering should invert and this is confirmed from recordings of rats foraging in an open arena.

neuroscience↗

bioRxiv2026

Dissociating volatility and stochasticity reveals transdiagnostic computational signatures of psychopathology

Adaptive learning requires distinguishing volatility, changes in the latent state of the environment, from moment-to-moment stochasticity of observations. The two demand opposite adjustments to the learning rate: volatility calls for faster updating, stochasticity for slower. Disentangling them is computationally difficult because both inflate experienced variance, leaving the inference prone to systematic individual differences with potential consequences for psychopathology. Three computational phenotypes capture this variation: intact learners; stochasticity-blind learners, who over-update by treating noise as change; and volatility-blind learners, who under-update by treating change as noise. In two large online samples and across three tasks, we found a double dissociation between these phenotypes and transdiagnostic psychiatric dimensions: stochasticity-blind learners scored higher on Internalizing (anxiety, depression), volatility-blind learners on Externalizing (behavioral addiction, compulsivity). Distinct symptom dimensions thus correspond to distinct failures of inference about uncertainty, supporting a selective rather than generalized account of learning-under-uncertainty deficits in psychopathology.

neuroscience↗

bioRxiv2026

Categorical Bayes Filtering for Computational Phenotyping in Adaptive Learning

Adaptive learning requires distinguishing environmental volatility from observation stochasticity, two sources of uncertainty that demand opposite adjustments to the learning rate but inflate experienced variance similarly. Disentangling them is computationally difficult with no tractable closed-form solution. Particle-filter methods are the natural tool for this kind of joint inference, but their stochastic likelihoods and non-differentiable objectives force derivative-free fitting protocols and discourage the individual-difference analyses central to cognitive modeling, where small effect sizes leave little room for additional estimator noise. We introduce the Categorical Bayes Filter (CBF), a deterministic alternative that preserves the conditional structure of recent particle-filter accounts but replaces the stochastic outer layer with a categorical distribution on a quantile grid parameterized through differentiable Beta quantile functions. The procedure performs evidence maximization with an exact, deterministic marginal likelihood that is fully differentiable in the grid parameters. In a volatility-stochasticity task with N = 643 participants, fitted CBF dispersion parameters reveal a cross-over phenotyping pattern between volatility-blind and stochasticity-blind subjects that is not recoverable from particle-filter parameters fit to the same data under a state-of-the-art protocol. The deterministic structure also yields a trial-by-trial ambiguity signal that predicts response times not used in fitting. More broadly, the approach opens individual-level analyses in cognitive modeling and computational psychiatry that stochastic methods have effectively foreclosed.

neuroscience↗

bioRxiv2026

Default Feature Representations of the Cognitive Map

Updating a predictive cognitive map when the environment changes is a central problem for both biological agents and reinforcement learning, yet existing approaches either depend on explicit model knowledge or learn the full state-indexed map from samples. We propose Default Feature Representations (DFR), a featurized parameterization of predictive cognitive maps in which a fixed feature basis is composed with an operator that encodes the current environment. We provide two forms for the operator: a model-based closed form when the structural change between environments is known, and a model-free temporal-difference learning rule that recovers the operator from sampled transitions, with provable convergence to the model-based solution. The model-free DFR reconstructs the perturbed map from samples alone, achieves planning performance comparable to the model-based solution, and substantially outperforms successor-representation baselines on replanning tasks. We also show that DFR captures the local remapping of grid cells observed under local environmental change. By separating the cognitive map into a stable feature basis and a fast-adapting operator, DFR offers a sample-based account of how a predictive map can be updated from local experience, mirroring the stability of entorhinal grid fields across environments.

neuroscience↗

bioRxiv2026

Inferring the causes of noise from binary outcomes: A normative theory of learning under uncertainty

Inferring the true cause of noise--distinguishing between volatility (environmental change) and stochasticity (outcome randomness)--is essential for learning in noisy environments. While most studies rely on binary outcomes, previous models are designed for continuous outcome and use ad hoc approximations to handle binary data, introducing theoretical inconsistencies and interpretational issues. Here, we develop a normative framework for inferring the causes of noise from binary feedback that remains faithful to the discrete nature of the generative process and underlying statistical structure. First, we establish a generative model using a state space approach tailored for binary outcomes and derive the corresponding hidden Markov model inference procedure. Second, we introduce a computational model combining the hidden Markov model with particle filtering to simultaneously infer volatility and stochasticity from binary outcomes. Third, we validate predictions through a 2x2 probabilistic reversal learning task with human participants, systematically manipulating both noise parameters. Results show that participants adjust their learning rates consistent with model predictions, increasing learning rates under volatile conditions and decreasing them under high stochasticity. Our theoretical and experimental results offer a principled approach for dissociating volatility and stochasticity from binary outcomes, providing insights into learning processes relevant to typical cognition and psychiatric conditions.

neuroscience↗

bioRxiv2026

Efficient Learning of Predictive Maps for Flexible Planning

Cognitive maps enable flexible behavior by providing reusable internal representations of task structure. The successor representation, a predictive map that encodes expected future state occupancy, has been proposed as one way such maps might be computed in the brain, but its policy dependence severely limits flexible planning. We introduce a new model, the successor representation with importance sampling (SR-IS), which combines temporal-difference learning with importance sampling to construct policy-independent predictive maps. SR-IS learns the structure of the environment without being constrained by the agents current decision policy. These representations can be efficiently updated when the environment changes, enabling rapid behavioral adaptation. We show that SR-IS outperforms existing models in planning tasks and provides a better account of the graded biases in human replanning that previous models could not explain. This work bridges theories of predictive maps with observed planning behavior and offers new insights into flexible decision-making in the brain.

animal behavior and cognition↗

bioRxiv2024

Reconciling Flexibility and Efficiency: Medial Entorhinal Cortex Represents a Compositional Cognitive Map

The influential concept of a cognitive map envisions that the brain builds mental representations of objects, barriers, and goals. This idea has been formalized in a range of computational models that show how such representations can be useful for guiding goal-directed behavior, for instance by planning novel routes to maximize long-run rewards. One key feature of flexible cognitive representations generally is that they exploit compositionality - the ability to build complex structures by recombining simpler parts. However, how this principle plays out in neural representations of cognitive maps and map-based planning remains largely unexplored. Indeed, as we show here, compositionality can be difficult to reconcile with efficient planning: because reuse tends to oppose flexibility, it is challenging to construct a compositional representation of the environment which is also organized in a way that enables generalizability and efficient planning. Here, we propose a novel model for efficiently creating and planning with compositional predictive maps, and further show that it successfully simulates various aspects of response fields in the medial entorhinal cortex, particularly object vector cells and grid cells. The model treats each object as an alteration to a baseline map linked to open space, creating complete predictive maps by combining object-related representations compositionally. Overall, this work provides a comprehensive and realistic model for efficient model learning and model-based planning in animals, and offers insights into the brain processes supporting efficient, flexible planning using compositional predictive maps.

neuroscience↗

bioRxiv2023

Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction

When an important event occurs, the observer should identify which features of the environment might have caused it. This is the latent cause inference problem, and it must be solved if observers are to understand their environments. The problem is acute in social settings where individuals may not make equal contributions to the outcomes they achieve together. Here, we designed a novel task in which participants inferred which of two characters was the more likely to have been responsible for outcomes achieved by working together. Using computational modelling, univariate and multivariate analysis of human fMRI, and continuous theta burst stimulation we identified two brain regions that solved the task. Notably, as each outcome occurred, it was possible to decode inference of its cause (the responsible character) from activity in hippocampus. Activity in dorsomedial prefrontal cortex updated estimates of association between cause -- the responsible character - and the outcome.

neuroscience↗

bioRxiv2021

Social framing effects in decision making

Human decisions are known to be strongly influenced by the manner in which options are presented, the "framing effect". Here, we ask whether decision-makers are also influenced by how advice from other knowledgeable agents are framed, a "social framing effect". For example, do students learn better from a teacher who often frames advice by emphasizing appetitive outcomes, or do they learn better from another teacher who usually emphasizes avoiding options that can be harmful to their progress? We study the computational and neural mechanisms by which framing of advice affect decision-making, social learning, and trust. We found that human participants are more likely to trust and follow an adviser who often uses an appetitive frame for advice compared with another one who often uses an aversive frame. This social framing effect is implemented through a modulation of the integrative abilities of the ventromedial prefrontal cortex. At the time of choice, this region combines information learned via personal experiences of reward with social information, but the combination differs depending on the social framing of advice. Personally-acquired information is weighted more strongly when dealing with an adviser who uses an aversive frame. The findings suggest that social advice is systematically incorporated into our decisions, while being affected by biases similar to those influencing individual value-based learning.

neuroscience↗

bioRxiv2020

Unpredictability vs. volatility and the control of learning

Previous research has stressed the importance of uncertainty for controlling the speed of learning, and of how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a new learning model, in which both factors are learned simultaneously from experience. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence both complicates and enriches the interpretation of previous results, such as those thought to implicate volatility in psychiatric conditions. This provides a novel mechanism for understanding pathological learning in amygdala damage and anxiety disorders.

neuroscience↗

bioRxiv2019

A common model explaining flexible decision making, grid fields and cognitive control

It is thought that the brains judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, we introduce a new model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of specific, quantifiable biases. It replaces the classic nonlinear, model-based optimization with a linear approximation that softly maximizes around (and is weakly biased toward) a default policy. This solution exposes connections between seemingly disparate phenomena across behavioral neuroscience, notably flexible replanning with biases and cognitive control. It also gives new insight into how the brain can represent maps of long-distance contingencies stably and componentially, as in entorhinal response fields, and exploit them to guide choice even under changing goals.

neuroscience↗

bioRxiv2019

A transparent model for learning in volatile environments

Sound principles of statistical inference dictate that uncertainty shapes learning. In this work, we revisit the question of learning in volatile environments, in which both the first and second-order statistics of observations dynamically evolve over time. We propose a new model, the volatile Kalman filter (VKF), which is based on a tractable state-space model of uncertainty and extends the Kalman filter algorithm to volatile environments. The proposed model is algorithmically simple and encompasses the Kalman filter as a special case. Specifically, in addition to the error-correcting rule of Kalman filter for learning observations, the VKF learns volatility according to a second error-correcting rule. These dual updates echo and contextualize classical psychological models of learning, in particular hybrid accounts of Pearce-Hall and Rescorla-Wagner. At the computational level, compared with existing models, the VKF gives up some flexibility in the generative model to enable a more faithful approximation to exact inference. When fit to empirical data, the VKF is better behaved than alternatives and better captures human choice data in two independent datasets of probabilistic learning tasks. The proposed model provides a coherent account of learning in stable or volatile environments and has implications for decision neuroscience research.

neuroscience↗

bioRxiv2018

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of HBI enables researchers to quantify uncertainty in group parameter estimates, for each candidate model separately, and to perform statistical tests on parameters of a population.

neuroscience↗