We present a cognitive process model of response choice and response time performance data that has excellent psychometric properties and may be used in a wide variety of contexts. In the model there is an accumulator associated with each response option. These accumulators have bounds, and the first accumulator to reach its bound determines the response time and response choice. The times at which accumulator reaches its bound is assumed to be lognormally distributed, hence the model is race or minima process among lognormal variables. A key property of the model is that it is relatively straightforward to place a wide variety of models on the logarithm of these finishing times including linear models, structural equation models, autoregressive models, growth-curve models, etc. Consequently, the model has excellent statistical and psychometric properties and can be used in a wide range of contexts, from laboratory experiments to high-stakes testing, to assess performance. We provide a Bayesian hierarchical analysis of the model, and illustrate its flexibility with an application in testing and one in lexical decision making, a reading skill.

Item Type:	Refereed Article
Research Division:	Psychology and Cognitive Sciences
Research Group:	Cognitive Sciences
Research Field:	Decision Making
Objective Division:	Expanding Knowledge
Objective Group:	Expanding Knowledge
Objective Field:	Expanding Knowledge in Psychology and Cognitive Sciences
Author:	Heathcote, A (Professor Andrew Heathcote)
ID Code:	98931
Year Published:	2015 (online first 2014)
Web of Science® Times Cited:	11
Deposited By:	Medicine (Discipline)
Deposited On:	2015-03-10
Last Modified:	2017-10-31
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