Reinforcement learning models of error-driven learning and sequential-sampling models of
decision making have provided significant insight into the neural basis of a variety of …

Evidence accumulation models (EAMs) have been the dominant models of speeded
decision-making for several decades. These models propose that evidence accumulates for …

Classical dynamic theories of decision making assume that responses are triggered by
accumulating a threshold amount of information. Recently, there has been a growing …

Computational modeling has become a central aspect of research in the cognitive
neurosciences. As the field matures, it is increasingly important to move beyond standard …

Diffusion models of evidence accumulation have successfully accounted for the distributions
of response times and choice probabilities from many experimental tasks, but recently their …

Over the last decade, there has been a robust debate in decision neuroscience and
psychology about what mechanism governs the time course of decision-making. Historically …

Despite advances in techniques for exploring reciprocity in brain-behavior relations, few
studies focus on building neurocognitive models that describe both human EEG and …

In many domains of psychological research, decisions are subject to a speed-accuracy trade-
off: faster responses are more often incorrect. This trade-off makes it difficult to focus on one …

Learning to make adaptive decisions involves making choices, assessing their
consequence, and leveraging this assessment to attain higher rewarding states. Despite …

The drift-diffusion model (DDM) is widely used and broadly accepted for its ability to account
for binary choices (in both the perceptual and preferential domains) and response times …