When develo** models in cognitive science, researchers typically start with their own
intuitions about human behavior in a given task and then build in mechanisms that explain …

Abstract The Rescorla–Wagner rule remains the most popular tool to describe human
behavior in reinforcement learning tasks. Nevertheless, it cannot fit human learning in …

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 …

Different brain systems have been hypothesized to subserve multiple “experts” that compete
to generate behavior. In reinforcement learning, two general processes, one model-free …

Computational and systems neuroscience: The next 20 years | PLOS Biology Skip to main
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This paper presents a novel machine learning approach for inferring interpretable human
reinforcement learning models from behavioral data. By combining recurrent neural …

Perseveration–repeating one choice when others would generate larger rewards–is a
common behavior, but neither its purpose nor neuronal mechanisms are understood. Here …

Extracting time-varying latent variables from computational cognitive models is a key step in
model-based neural analysis, which aims to understand the neural correlates of cognitive …

Cognitive and computational modeling has been used as a method to understand the
processes underlying behavior in humans and other animals. A common approach in this …

Symbolic models play a key role in cognitive science, expressing computationally precise
hypotheses about how the brain implements a cognitive process. Identifying an appropriate …