Parameter recovery, bias and standard errors in the linear ballistic accumulator model. (May 2017)

• Record Type: Journal Article
• Authors: Visser, Ingmar; Poessé, Rens
• Other Names: Visser Ingmar guestEditor.; Molenaar Dylan guestEditor.
• Is Part Of: British journal of mathematical & statistical psychology. Volume 70:Part 2(2017:May)
• Page Start: 280
• Page End: 296
• Publication Date: 2017-05
• Subjects: linear ballistic accumulator model -- parameter recovery -- parameter bias -- standard error -- maximum likelihood -- implicit learning -- bootstrap -- response times -- R‐package -- glba; Psychometrics -- Periodicals; Psychology -- Mathematical models -- Periodicals; Psychology -- Statistical methods -- Periodicals; 150.727
• DOI: 10.1111/bmsp.12100
• Languages: English
• ISSNs: 0007-1102

Abstract

Abstract : The linear ballistic accumulator (LBA) model (Brown & Heathcote, 2008, Cogn. Psychol ., 57, 153) is increasingly popular in modelling response times from experimental data. An R package, glba, has been developed to fit the LBA model using maximum likelihood estimation which is validated by means of a parameter recovery study. At sufficient sample sizes parameter recovery is good, whereas at smaller sample sizes there can be large bias in parameters. In a second simulation study, two methods for computing parameter standard errors are compared. The Hessian‐based method is found to be adequate and is (much) faster than the alternative bootstrap method. The use of parameter standard errors in model selection and inference is illustrated in an example using data from an implicit learning experiment (Visser et al ., 2007, Mem. Cogn ., 35, 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model.