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

Why has computational psychiatry yet to influence routine clinical practice? One reason may
be that it has neglected context and temporal dynamics in the models of certain mental …

The recent years have witnessed a dramatic increase in the use of reinforcement learning
(RL) models in social, cognitive and affective neuroscience. This approach, in combination …

When observing the outcome of a choice, people are sensitive to the choice's context, such
that the experienced value of an option depends on the alternatives: getting $1 when the …

Humans are autotelic agents who learn by setting and pursuing their own goals. However,
the precise mechanisms guiding human goal selection remain unclear. Learning progress …

Across the lifespan, individuals frequently choose between exploiting known rewarding
options or exploring unknown alternatives. A large body of work has suggested that children …

Both novelty and uncertainty are potent features guiding exploration; however, they are often
experimentally conflated, and an understanding of how they interact to regulate the balance …

Sound principles of statistical inference dictate that uncertainty shapes learning. In this work,
we revisit the question of learning in volatile environments, in which both the first and …

How does race influence the impressions we form through direct interaction? In two
preregistered experiments (N= 239/179), White American participants played a money …

Converging evidence from both human neuroimaging and animal studies has supported a
model of mesolimbic processing underlying reward learning behaviors, based on the …