Using diffusion models to understand clinical disorders

Abstract

Sequential sampling models provide an alternative to traditional analyses of reaction times and accuracy in two-choice tasks. These models are reviewed with particular focus on the diffusion model (Ratcliff, 1978) and how its application can aide research on clinical disorders. The advantages of a diffusion model analysis over traditional comparisons are shown through simulations and a simple lexical decision experiment. Application of the diffusion model to a clinically-relevant topic is demonstrated through an analysis of data from nonclinical participants with high-and low-trait anxiety in a recognition memory task. The model showed that after committing an error, participants with high trait anxiety responded more cautiously by increasing their boundary separation, whereas participants with low trait anxiety did not. The article concludes with suggestions for ways to improve and broaden the application of these models to studies of clinical disorders.Techniques and models from cognitive psychology are being used with increasing frequency in investigations of psychopathology and clinical disorders (e.g., McFall, Treat, & Viken, 1997;McNally & Reese, 2009;Treat & Dirks, 2007). These methods and models play a significant role in elucidating the abnormal cognitive processes that are associated with such disorders. In this article we demonstrate how a theory of cognitive processing can enhance cognitive-clinical interactions and lead to a better understanding of the cognitive effects of psychopathologies like depression and anxiety. The focus is on the use of sequential sampling models to analyze data from two-choice response time (RT) tasks. The article is structured as follows: We briefly review some areas in which two-choice tasks have been employed to investigate cognitive processing in anxiety and depression. We then show through simulations and a simple lexical decision experiment how a sequential sampling model, Ratcliff's diffusion model (Ratcliff, 1978;Ratcliff, Van Zandt, & McKoon, 1999), can improve analyses of twochoice tasks by decomposing accuracy and RT distributions into distinct components of processing. Application of the diffusion model to a clinically-relevant topic is then demonstrated through analysis of recognition memory data from subclinical participants with high-and low-trait anxiety to assess changes in decision criteria that result from committing an error. The article concludes with discussion of areas of potential improvement for the application of sampling models like the diffusion model to inform clinical research. Two-Choice Tasks and Clinical ResearchThe focus of this article is two-choice tasks to which sequential sampling models can be applied. These tasks involve a fast (typically less than two seconds), one-process decision and

Perceptual Criteria in the Human Brain

“…Note that line activation had more activation in the right than left hemisphere because the line stimuli were presented to the left of the central fixation cross. Activation of visual cortex in relation to object size is consistent with the role of this region in processing relevant visual information (Murray et al, 2006), as visual stimuli were presented in the task.…”

“…The DDM (Ratcliff and Smith, 2004;Ratcliff and McKoon, 2008) was fit to the behavioral data for each participant individually, and separate fits were conducted for line and dot trials. The DDM included the following parameters which were held constant across condition in the fitting process: boundary separation (a), nondecision time (Ter), starting point (z/a), across-trial variability in drift rate (), across-trial variability in starting point (s z ), and across-trial variability in nondecision time (s t ).…”

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

“…One interesting approach to addressing the issue of trial numbers by way of experimental design has been proposed by White, Ratcliff, Vasey, and McKoon (2009), who used filler trials (see also White et al, 2010b) to achieve higher accuracy in parameter estimation. Some parameters (response criteria and nondecisional processes) were estimated on the basis of both target and filler trials.…”

“…Examples indicating the wide range of applications for the diffusion model include analyses of cognitive processes in such typical experimental paradigms as the lexical decision task (e.g., Yap, Balota, & Tan, 2013), sequential priming paradigms (e.g., Voss, Rothermund, Gast, & Wentura, 2013), task switching (Schmitz & Voss, 2012, or prospective memory paradigms (e.g., Boywitt & Rummel, 2012). Other applications encompass social cognitive research (e.g., Germar, Schlemmer, Krug, Voss, & Mojzisch, 2014;Klauer, Voss, Schmitz, & Teige-Mocigemba, 2007;Voss, Rothermund, & Brandtstädter, 2008), cognitive aging (e.g., McKoon & Ratcliff, 2013;Spaniol, Madden, & Voss, 2006), cognitive processes related to psychological disorders (e.g., Metin et al, 2013;Pe, Vandekerckhove, & Kuppens, 2013;White, Ratcliff, Vasey, & McKoon, 2010b), and other fields of psychology.…”

Understanding perceptual judgment in autism spectrum disorder using the drift diffusion model.

“…However, there are limitations to the conclusions that can be drawn by analysing response times and accuracy rates, as there are a number of processes that underlie a behavioural response in addition to the particular perceptual domain or cognitive skill that the task is designed to measure. These include response caution, response bias, and non-decision time (see White, Ratcliff, Vasey & McKoon, 2010). This is especially true in psychophysics tasks, where accuracy of response is usually the only dependent variable considered.…”