Researchers have benefited from characterizing evidence-based decision making as a
process involving sequential sampling. More recently, sequential sampling models have …

Recent advancements in Bayesian modeling have allowed for likelihood-free posterior
estimation. Such estimation techniques are crucial to the understanding of simulation-based …

Abstract Markov Chain Monte–Carlo (MCMC) is an increasingly popular method for
obtaining information about distributions, especially for estimating posterior distributions in …

Parameter estimation in evidence-accumulation models of choice response times is
demanding of both the data and the user. We outline how to fit evidence-accumulation …

Our understanding of cognition has been advanced by two traditionally non-overlapping and
non-interacting groups. Mathematical psychologists rely on behavioral data to evaluate …

Mind wandering is an ubiquitous phenomenon in everyday life. In the cognitive
neurosciences, mind wandering has been associated with several distinct neural processes …

Dual-process models of altruistic choice assume that automatic responses give way to
deliberation over time, and are a popular way to conceptualize how people make generous …

In accounting for phenomena present in preferential choice experiments, modern models
assume a wide array of different mechanisms such as lateral inhibition, leakage, loss …

Trial-to-trial fluctuations in an observer's state of mind have a direct influence on their
behavior. However, characterizing an observer's state of mind is difficult to do with …

For many years the Diffusion Decision Model (DDM) has successfully accounted for
behavioral data from a wide range of domains. Important contributors to the DDM's success …