Computational modeling of behavior has revolutionized psychology and neuroscience. By
fitting models to experimental data we can probe the algorithms underlying behavior, find …

Mental-tasks based brain–computer interfaces (MT-BCIs) allow their users to interact with an
external device solely by using brain signals produced through mental tasks. While MT-BCIs …

Many of the computing systems programmed using Machine Learning are opaque: it is
difficult to know why they do what they do or how they work. Explainable Artificial …

Leveraging a scientific infrastructure for exploring how students learn, we have developed
cognitive and statistical models of skill acquisition and used them to understand …

Much of our understanding of human thinking is based on probabilistic models. This
innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the …

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and
computational theories with which we can disentangle psychiatric conditions into the basic …

According to the principle of parsimony, model selection methods should value both
descriptive accuracy and simplicity. Here we focus primarily on Bayes factors and minimum …

Abstract [Correction Notice: An erratum for this article was reported in Vol 118 (1) of
Psychological Review (see record 2011-00732-004). The name of the philosopher Charles …

This study extends the U&G theoretical perspective to account for the situated, adaptive, and
dynamic nature of mediated cognition and behavior. It specifies dynamic uses and …

Meaning is a fundamental component of nearly all aspects of human cognition, but formal
models of semantic memory have classically lagged behind many other areas of cognition …