RT Journal Article
SR Electronic
T1 CHaRTr: An R toolbox for modeling Choices and Response Times in decision-making tasks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 570184
DO 10.1101/570184
A1 Chandramouli Chandrasekaran
A1 Guy E. Hawkins
YR 2019
UL http://biorxiv.org/content/early/2019/03/06/570184.abstract
AB Decision-making is the process of choosing and performing actions in response to sensory cues so as to achieve behavioral goals. A sophisticated research effort has led to the development of many mathematical models to describe the response time (RT) distributions and choice behavior of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques. Although some of these problems have been surmounted in existing software packages, the packages have often focused on the classical decision-making model, the diffusion decision model. Recent theoretical advances in decision-making that posit roles for “urgency”, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence, have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluations among these competing classes of decision-making models. Here, we present a toolbox — Choices and Response Times in R, or CHaRTr — that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries. Earlier versions of CHaRTr have been instrumental in a number of recent studies of humans and monkeys performing perceptual decision-making tasks. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.