Payam Piray

This paper proposes that hippocampal replay repairs the cognitive map by updating it for new goals and maintaining abandoned ones, supported by rat foraging experiments.

The paper develops a framework to infer noise causes from binary outcomes, showing participants adjust learning rates based on volatility and stochasticity.

The paper identifies computational signatures of psychopathology by dissociating volatility and stochasticity in learning tasks, finding distinct associations with internalizing and externalizing symptoms.

This paper introduces SR-IS, a model that learns policy-independent predictive maps for flexible planning, outperforming existing models in planning tasks and explaining human replanning biases.

The paper introduces a deterministic Bayesian filtering method to disentangle environmental volatility and observation stochasticity in adaptive learning, revealing distinct phenotyping patterns.

This paper introduces Default Feature Representations (DFR) for updating predictive cognitive maps, showing it outperforms baselines in replanning tasks and captures grid cell remapping.

This paper introduces a model for efficient model learning and planning using compositional predictive maps, showing it simulates response fields in the medial entorhinal cortex.

The paper identifies hippocampus and dorsomedial prefrontal cortex as regions involved in inferring and updating latent causes during social interaction using fMRI and computational modeling.

This paper introduces a learning model that jointly estimates volatility and stochasticity to better understand learning dynamics and their implications for psychiatric conditions.

The paper investigates how the framing of social advice influences decision-making, finding that appetitive framing increases trust and learning compared to aversive framing.

This paper introduces a linear reinforcement learning model that explains flexible planning, cognitive control, and biases in decision-making by reusing temporally abstracted future event maps.

The paper introduces a new model, the volatile Kalman filter, for learning in volatile environments and shows it better captures human choice data than existing models.

This paper introduces a hierarchical Bayesian inference framework for simultaneous model comparison and parameter estimation in group studies, showing improved accuracy and robustness.