Sequential sampling models assume that people make speeded decisions by gradually
accumulating noisy information until a threshold of evidence is reached. In cognitive …

When evaluating cognitive models based on fits to observed data (or, really, any model that
has free parameters), parameter estimation is critically important. Traditional techniques like …

The runjags package provides a set of interface functions to facilitate running Markov chain
Monte Carlo models in JAGS from within R. Automated calculation of appropriate …

Current reinforcement-learning models often assume simplified decision processes that do
not fully reflect the dynamic complexities of choice processes. Conversely, sequential …

Trained monkeys performed a two-choice perceptual decision-making task in which they
reported the perceived orientation of a dynamic Glass pattern, before and after unilateral …

Most data analyses rely on models. To complement statistical models, psychologists have
developed cognitive models, which translate observed variables into psychologically …

Abstract Diffusion models (Ratcliff, 1978) make it possible to identify and separate different
cognitive processes underlying responses in binary decision tasks (eg, the speed of …

Methods of fitting the diffusion model were examined with a focus on what the model can tell
us about individual differences. Diffusion model parameters were obtained from the fits to …

Dopamine fundamentally contributes to reinforcement learning, but recent accounts also
suggest a contribution to specific action selection mechanisms and the regulation of …

Perceptual decision making can be accounted for by drift-diffusion models, a class of
decision-making models that assume a stochastic accumulation of evidence on each trial …