Policy compression is a computational framework that describes how capacity-limited
agents trade reward for simpler action policies to reduce cognitive cost. In this study, we …

Despite being unpredictable and uncertain, reward environments often exhibit certain
regularities, and animals navigating these environments try to detect and utilize such …

All biological and artificial agents must act given limits on their ability to acquire and process
information. As such, a general theory of adaptive behavior should be able to account for the …

Throughout the cognitive-science literature, there is widespread agreement that decision-
making agents operating in the real world do so under limited information-processing …

Psychopathology is vast and diverse. Across distinct disease states, individuals exhibit
symptoms that appear counter to the standard view of rationality (expected utility …

Active reinforcement learning enables dynamic prediction and control, where one should not
only maximize rewards but also minimize costs such as of inference, decisions, actions, and …

A default assumption in the design of reinforcement-learning algorithms is that a decision-
making agent always explores to learn optimal behavior. In sufficiently complex …

Anhedonia, the loss of pleasure, is prevalent and impairing. Parsing its computational basis
promises to explain its transdiagnostic character. We argue that one manifestation of …

The frontal cortex plays a critical role in decision-making. One specific frontal area, the
anterior cingulate cortex, has been identified as crucial for setting a threshold for how much …

In daily life, decisions are often biased by Pavlovian (eg, reward-associated) cues present in
the environment. Here, we propose that Pavlovian bias can instigate suboptimal choices …