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Parameter recovery, bias and standard errors in the linear ballistic accumulator model
Author(s) -
Visser Ingmar,
Poessé Rens
Publication year - 2017
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/bmsp.12100
Subject(s) - hessian matrix , accumulator (cryptography) , inference , computer science , model selection , estimation theory , sample size determination , algorithm , linear model , statistics , standard error , mathematics , artificial intelligence
The linear ballistic accumulator (LBA) model (Brown & Heathcote, [Brown, S., 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 ., [Visser, I., 2007], Mem. Cogn ., 35 , 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model.