Trait Characteristics of Diffusion Model Parameters

Cognitive modeling of response time distributions has seen a huge rise in popularity in individual differences research. In particular, several studies have shown that individual differences in the drift rate parameter of the diffusion model, which reflects the speed of information uptake, are substantially related to individual differences in intelligence. However, if diffusion model parameters are to reflect trait-like properties of cognitive processes, they have to qualify as trait-like variables themselves, i.e., they have to be stable across time and consistent over different situations. To assess their trait characteristics, we conducted a latent state-trait analysis of diffusion model parameters estimated from three response time tasks that 114 participants completed at two laboratory sessions eight months apart. Drift rate, boundary separation, and non-decision time parameters showed a great temporal stability over a period of eight months. However, the coefficients of consistency and reliability were only low to moderate and highest for drift rate parameters. These results show that the consistent variance of diffusion model parameters across tasks can be regarded as temporally stable ability parameters. Moreover, they illustrate the need for using broader batteries of response time tasks in future studies on the relationship between diffusion model parameters and intelligence.

[1]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[2]  Michael C. Pyryt Human cognitive abilities: A survey of factor analytic studies , 1998 .

[3]  Roger Ratcliff,et al.  Individual differences, aging, and IQ in two-choice tasks , 2010, Cognitive Psychology.

[4]  Andreas Voss,et al.  Fast-dm: A free program for efficient diffusion model analysis , 2007, Behavior research methods.

[5]  A. Ribaupierre,et al.  Generalization of the worst performance rule across the lifespan , 2014 .

[6]  Philip A. Vernon,et al.  Intelligence and speed of information-processing: A review of 50 years of research , 2008 .

[7]  A. Jensen,et al.  The nature of psychometric g: Unitary process or a number of independent processes? , 1991 .

[8]  Thomas R. Coyle,et al.  A review of the worst performance rule: Evidence, theory, and alternative hypotheses , 2003 .

[9]  Roger Ratcliff,et al.  Effects of aging and IQ on item and associative memory. , 2011, Journal of experimental psychology. General.

[10]  T. Doll,et al.  Motivation, reaction time, and the contents of active verbal memory. , 1971, Journal of experimental psychology.

[11]  Denny Borsboom,et al.  Cognitive psychology meets psychometric theory: on the relation between process models for decision making and latent variable models for individual differences. , 2011, Psychological review.

[12]  Michael Eid,et al.  Latent state–trait theory and research in personality and individual differences , 1999 .

[13]  Linda T. Miller,et al.  Intelligence, reaction time, and working memory in 4- to 6-year-old children , 1996 .

[14]  H. Nyborg,et al.  The stability of general intelligence from early adulthood to middle-age , 2008 .

[15]  M I Posner,et al.  Chronometric analysis of classification. , 1967, Psychological review.

[16]  I. McNish Clocking the Mind: Mental Chronometry and Individual Differences , 2007 .

[17]  W. E. Hick Quarterly Journal of Experimental Psychology , 1948, Nature.

[18]  A. Voss,et al.  Retest reliability of the parameters of the Ratcliff diffusion model , 2017, Psychological research.

[19]  R. Ratcliff,et al.  Modeling reaction time and accuracy of multiple-alternative decisions , 2010, Attention, perception & psychophysics.

[20]  D. Mewhort,et al.  Analysis of Response Time Distributions: An Example Using the Stroop Task , 1991 .

[21]  D. Balota,et al.  Levels of selective attention revealed through analyses of response time distributions. , 2000, Journal of experimental psychology. Human perception and performance.

[22]  R. Steyer,et al.  States and traits in psychological assessment. , 1992 .

[23]  A. Voss,et al.  Interpreting the parameters of the diffusion model: An empirical validation , 2004, Memory & cognition.

[24]  Francis Tuerlinckx,et al.  Diffusion model analysis with MATLAB: A DMAT primer , 2008, Behavior research methods.

[25]  R. Ratcliff,et al.  Explicitly modeling the effects of aging on response time , 2000, Psychonomic bulletin & review.

[26]  Andrea Schankin,et al.  Decomposing the relationship between mental speed and mental abilities , 2015 .

[27]  Francis Tuerlinckx,et al.  Fitting the ratcliff diffusion model to experimental data , 2007, Psychonomic bulletin & review.

[28]  A. Chatterjee,et al.  Mental fatigue and temporal preparation in simple reaction-time performance. , 2010, Acta psychologica.

[29]  Scott D. Brown,et al.  The simplest complete model of choice response time: Linear ballistic accumulation , 2008, Cognitive Psychology.

[30]  Axel Mayer,et al.  A theory of states and traits--revised. , 2015, Annual review of clinical psychology.

[31]  Roger Ratcliff,et al.  The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.

[32]  Joachim Vandekerckhove,et al.  A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. , 2014 .

[33]  Han L. J. van der Maas,et al.  Fitting diffusion item response theory models for responses and response times using the R package diffIRT , 2015 .

[34]  Eric-Jan Wagenmakers,et al.  An EZ-diffusion model for response time and accuracy , 2007, Psychonomic bulletin & review.

[35]  Clarissa A. Thompson,et al.  Modeling individual differences in response time and accuracy in numeracy , 2015, Cognition.

[36]  Ramesh Srinivasan,et al.  Individual differences in attention influence perceptual decision making , 2015, Front. Psychol..

[37]  Glen A. Smith,et al.  Decision Time Unmasked: Individuals Adopt Different Strategies , 1987 .

[38]  A. S. Levine Personality: A systematic theoretical and factual study. , 1952 .

[39]  Daragh E. Sibley,et al.  Individual Differences in Visual Word Recognition: Insights from the English Lexicon Project , 2012 .

[40]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[41]  J. Funke,et al.  Beyond IQ: A Latent State-Trait Analysis of General Intelligence, Dynamic Decision Making, and Implicit Learning , 2011 .

[42]  H. Hollingworth Personality a psychological interpretation. , 1938 .

[43]  Klaus Oberauer,et al.  Individual differences in components of reaction time distributions and their relations to working memory and intelligence. , 2007, Journal of experimental psychology. General.

[44]  Francis Tuerlinckx,et al.  Two interpretations of the discrimination parameter , 2005 .

[45]  Christoph Stahl,et al.  Behavioral components of impulsivity. , 2014, Journal of experimental psychology. General.

[46]  A. Neubauer,et al.  Situational effects in trait assessment: The FPI, NEOFFI, and EPI questionnaires , 1995 .

[47]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[48]  A. Voss,et al.  Diffusion models in experimental psychology: a practical introduction. , 2013, Experimental psychology.

[49]  S. Sternberg Memory-scanning: mental processes revealed by reaction-time experiments. , 1969, American scientist.

[50]  D. Saccuzzo,et al.  Speed of Information Processing and Individual Differences in Intelligence. , 1986 .

[51]  Philipp Doebler,et al.  The relationship of choice reaction time variability and intelligence: A meta-analysis , 2016 .

[52]  A. Jensen,et al.  THE IMPORTANCE OF INTRAINDIVIDUAL VARIATION IN REACTION TIME , 1992 .

[53]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[54]  Gerald E. Larson,et al.  Reaction time variability and intelligence: A “worst performance” analysis of individual differences , 1990 .