When facing uncertainty, adaptive behavioral strategies demand that the brain performs
probabilistic computations. In this probabilistic framework, the notion of certainty and …

Adaptive behavior in a complex, dynamic, and multisensory world poses some of the most
fundamental computational challenges for the brain, notably inference, decision-making …

Human perceptual decisions are often described as optimal. Critics of this view have argued
that claims of optimality are overly flexible and lack explanatory power. Meanwhile …

For decades now, normative theories of perceptual decisions, and their implementation as
drift diffusion models, have driven and significantly improved our understanding of human …

Our confidence in a choice and the evidence pertaining to a choice appear to be
inseparable. However, an emerging computational consensus holds that the brain should …

The drift-diffusion model (DDM) is an important decision-making model in cognitive
neuroscience. However, innovations in model form have been limited by methodological …

Neural mechanisms of perceptual decision making have been extensively studied in
experimental settings that mimic stable environments with repeating stimuli, fixed rules, and …

In our dynamic world, decisions about noisy stimuli can require temporal accumulation of
evidence to identify steady signals, differentiation to detect unpredictable changes in those …

Making optimal decisions in the face of noise requires balancing short-term speed and
accuracy. But a theory of optimality should account for the fact that short-term speed can …

The processing of multisensory signals is crucial for effective interaction with the
environment, but our ability to perform this vital function changes as we age. In the first part …