Literature DB >> 23895923

Diffusion models in experimental psychology: a practical introduction.

Andreas Voss1, Markus Nagler, Veronika Lerche.   

Abstract

Stochastic diffusion models (Ratcliff, 1978) can be used to analyze response time data from binary decision tasks. They provide detailed information about cognitive processes underlying the performance in such tasks. Most importantly, different parameters are estimated from the response time distributions of correct responses and errors that map (1) the speed of information uptake, (2) the amount of information used to make a decision, (3) possible decision biases, and (4) the duration of nondecisional processes. Although this kind of model can be applied to many experimental paradigms and provides much more insight than the analysis of mean response times can, it is still rarely used in cognitive psychology. In the present paper, we provide comprehensive information on the theory of the diffusion model, as well as on practical issues that have to be considered for implementing the model.

Entities:  

Keywords:  DMAT; EZ diffusion; diffusion model; fast-dm; mathematical model; response times

Mesh:

Year:  2013        PMID: 23895923     DOI: 10.1027/1618-3169/a000218

Source DB:  PubMed          Journal:  Exp Psychol        ISSN: 1618-3169


  105 in total

1.  Individual differences in emotion word processing: A diffusion model analysis.

Authors:  Christina J Mueller; Lars Kuchinke
Journal:  Cogn Affect Behav Neurosci       Date:  2016-06       Impact factor: 3.282

2.  A diffusion model analysis of sustained attention in children with attention deficit hyperactivity disorder.

Authors:  Cynthia Huang-Pollock; Roger Ratcliff; Gail McKoon; Alexandra Roule; Tyler Warner; Jason Feldman; Shane Wise
Journal:  Neuropsychology       Date:  2020-04-23       Impact factor: 3.295

3.  A Systematic Investigation of Accuracy and Response Time Based Measures Used to Index ANS Acuity.

Authors:  Julia Felicitas Dietrich; Stefan Huber; Elise Klein; Klaus Willmes; Silvia Pixner; Korbinian Moeller
Journal:  PLoS One       Date:  2016-09-16       Impact factor: 3.240

4.  Testing the primary and convergent retrieval model of recall: Recall practice produces faster recall success but also faster recall failure.

Authors:  William J Hopper; David E Huber
Journal:  Mem Cognit       Date:  2019-05

5.  Electrophysiological correlates of the drift diffusion model in visual word recognition.

Authors:  Christina J Mueller; Corey N White; Lars Kuchinke
Journal:  Hum Brain Mapp       Date:  2017-07-31       Impact factor: 5.038

6.  Semantic incongruity influences response caution in audio-visual integration.

Authors:  Benjamin Steinweg; Fred W Mast
Journal:  Exp Brain Res       Date:  2016-10-12       Impact factor: 1.972

7.  Functional modular architecture underlying attentional control in aging.

Authors:  Zachary A Monge; Benjamin R Geib; Rachel E Siciliano; Lauren E Packard; Catherine W Tallman; David J Madden
Journal:  Neuroimage       Date:  2017-05-02       Impact factor: 6.556

8.  On the Relation Between the (Censored) Shifted Wald and the Wiener Distribution as Measurement Models for Choice Response Times.

Authors:  Robert Miller; Stefan Scherbaum; Daniel W Heck; Thomas Goschke; Sören Enge
Journal:  Appl Psychol Meas       Date:  2017-05-30

9.  Valence and ownership: object desirability influences self-prioritization.

Authors:  Marius Golubickis; Nerissa S P Ho; Johanna K Falbén; Carlotta L Schwertel; Alessia Maiuri; Dagmara Dublas; William A Cunningham; C Neil Macrae
Journal:  Psychol Res       Date:  2019-08-01

Review 10.  Diffusion Decision Model: Current Issues and History.

Authors:  Roger Ratcliff; Philip L Smith; Scott D Brown; Gail McKoon
Journal:  Trends Cogn Sci       Date:  2016-03-05       Impact factor: 20.229

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