Academic journal article Canadian Journal of Experimental Psychology

Diffusion versus Linear Ballistic Accumulation: Different Models for Response Time with Different Conclusions about Psychological Mechanisms?

Academic journal article Canadian Journal of Experimental Psychology

Diffusion versus Linear Ballistic Accumulation: Different Models for Response Time with Different Conclusions about Psychological Mechanisms?

Article excerpt

Two similar classes of evidence-accumulation model have dominated theorizing about rapid binary choice: diffusion models and racing accumulator pairs. Donkin, Brown, Heathcote, and Wagenmakers (2011) examined mimicry between the Ratcliff diffusion (RD; Ratcliff & Smith, 2004) and the linear ballistic accumulator (LBA; Brown & Heathcote, 2008), the 2 least similar models from each class that provide a comprehensive account of a set benchmark phenomena in rapid binary choice. Where conditions differed only in the rate of evidence accumulation (the most common case in past research), simulations showed the models supported equivalent psychological inferences. In contrast, differences in 2 other parameters of key psychological interest, response caution (the amount of information required for a decision), and nondecision time, traded-off when fitting 1 model to data simulated from the other, implying the potential for divergent inferences about latent cognitive processes. However, Donkin, Brown, Heathcote, and Wagenmakers did not find such inconsistencies between fits of the RD and LBA models in a survey of data sets from paradigms using a range of experimental manipulations. We examined a further data set, collected by Dutilh, Vandekerckhove, Tuerlinckx, and Wagenmakers (2009), which used a manipulation not surveyed by Donkin, Brown, Heathcote, and Wagenmakers's practice. Dutilh et al.'s RD model fits indicated that practice had large effects on all three types of parameters. We show that in this case the LBA provides a different and simpler account of practice effects. Implications for evidence accumulation modelling are discussed.

Keywords: response time, mathematical psychology, practice, lexical decision

Measurements related to performance in choice tasks underpin many psychological investigations. When choices are rapid (i.e., made in a few seconds or less), not only the response chosen, but also response time (reaction time [RT]), is of interest. This is particularly the case in which choices are scored for accuracy, as inferences about psychological processes based on either accuracy or RT alone are confounded when participants engage in a speedaccuracy trade-off (e.g., increasing accuracy by increasing RT). The most successful method of addressing such confounding is to fit the data with an evidence-accumulation model, as inferences based on model-parameter estimates address potential speedaccuracy trade-off. A second advantage of this approach is that it titrates the time required to complete decision and nondecision processes.

However, there are several different evidence accumulation models that provide apparently equally comprehensive accounts of the benchmark phenomena common to a range of rapid choice paradigms (Brown & Heathcote, 2005a, 2008; Ratcliff & Smith, 2004). This led Donkin, Brown, Heathcote, and Wagenmakers (2011) to question whether there was potential for different models to support conflicting inferences about psychological processes. Simulation results showed there was indeed room for such ambiguity, but reassuringly, they failed to find any evidence that it occurred in a survey of empirical data sets. However, we show here that ambiguity does occur in data collected by Dutilh, Vandekerckhove, Tuerlinckx, and Wagenmakers (2009) on the effect of practice on binary choice in the lexical-decision task (i.e., deciding if a string of letters forms a word) and discuss the implications.

Evidence Accumulation Models

Until relatively recently, successful evidence accumulation models assumed that choice errors are mainly caused by random fluctuations in evidence from moment-to-moment during a choice trial (Ratcliff & Smith, 2004). Accumulating (i.e., summing) evidence over time averages out this stochastic noise, increasing accuracy. Speed-accuracy trade-off is explained by changes in response caution, which determines the evidence boundary required to trigger a response. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.