Deep reinforcement learning (RL) methods have driven impressive advances in artificial
intelligence in recent years, exceeding human performance in domains ranging from Atari to …

Human action control relies on representations that integrate perception and action, but the
relevant research is scattered over various experimental paradigms and the theorizing is …

Cognitive linguists and psychologists have often argued that language is best understood as
an association network; however while the network view of language has had a significant …

Contemporary theories of clinical reasoning espouse a dual processing model, which
consists of a rapid, intuitive component (Type 1) and a slower, logical and analytical …

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the
brain deal successfully with the continual deluge of linguistic input? We argue that, to deal …

Uncertainty and affect are fundamental and interrelated aspects of the human condition.
Uncertainty is often associated with negative affect, but in some circumstances, it is …

A work that reveals the profound links between the evolution, acquisition, and processing of
language, and proposes a new integrative framework for the language sciences. Language …

Prominent models of attentional control assert a dichotomy between top-down and bottom-
up control, with the former determined by current selection goals and the latter determined …

Distributed networks of brain areas interact with one another in a time-varying fashion to
enable complex cognitive and sensorimotor functions. Here we used new network-analysis …

Bayes factors have been advocated as superior to p-values for assessing statistical
evidence in data. Despite the advantages of Bayes factors and the drawbacks of p-values …