Saurabh Bedi

The paper explains nonlinearities in multi-attribute decision making through resource-rational compression of value differences, supported by empirical tests on estimation tasks and existing datasets.

The paper investigates how efficient coding operates in perception and valuation stages of goal-directed behavior, finding that both stages use separate efficient codes that interact to shape behavior.

The paper identifies distinct neurocomputational mechanisms for multisensory learning using a novel task and fMRI, showing dissociable networks for statistical learning, reinforcement learning, and outcome surprise.

This paper shows acute stress reduces risk-aversion by altering magnitude perception, as evidenced by behavioral and neural changes in payoff valuation.

This study examines age-related changes in multisensory learning using behavioral, neuroimaging, and computational methods in children, finding faster processing and stronger neural engagement in older children.

This study shows that human parietal cortex dynamically recalibrates number representations based on context, improving behavioral accuracy through distributed range adaptation.

The paper shows probability weighting arises from cognitive noise repelled by probability boundaries, confirmed through experiments on risky choice and perception.