The simplest complete model of choice response time: linear ballistic accumulation.

By Scott D. Brown, Andrew Heathcote. Cognitive Psychology, 2008

1,140 citations recorded by the source corpus.

We propose a linear ballistic accumulator (LBA) model of decision making and reaction time. The LBA is simpler than other models of choice response time, with independent accumulators that race towards a common response threshold. Activity in the accumulators increases in a linear and deterministic manner. The simplicity of the model allows complete analytic solutions for choices between any number of alternatives. These solutions (and freely-available computer code) make the model easy to apply to both binary and multiple choice situations. Using data from five previously published experiments, we demonstrate that the LBA model successfully accommodates empirical phenomena from binary and multiple choice tasks that have proven difficult for other theoretical accounts. Our results are encouraging in a field beset by the tradeoff between complexity and completeness.

Subfields: Cognitive Neuroscience, General Decision Sciences, Safety Research

View Paper: https://www.semanticscholar.org/paper/ed63f501f204e79d92b61d1cb0cde1480d25e1b9

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Citations in the corpus

One and Done? Optimal Decisions From Very Few Samples (2014, methodology): “…sampling models (Ratcliff & Smith, 2004) based on the SPRT (Wald, 1947), drift diffusion (Gold & Shadlen, 2000; Ratcliff, 1978), or accumulators (Brown & Heathcote, 2008; Vickers, 1979) have been applied not only to sampling data from the environment but also to more internal processes, such as…”
A computational model of inhibitory control in frontal cortex and basal ganglia. (2011, methodology): “ogical study reported delayed onset of SC activity following (inhibited) stop-signal trials (Pouget et al., 2011). The authors corroborated this nding by tting the Linear Ballistic Accumulator model (Brown and Heathcote, 2008) to the behavioral saccade data, showing a delay in the onset of evidence ”
Diffusion Decision Model: Current Issues and History (2016, background): “The assumption that model parameters vary from trial to trial is made by most current models that successfully account for experimental data in psychological applications [1,40].”
Two-stage dynamic signal detection: a theory of choice, decision time, and confidence. (2010, methodology): “There are other race models that might be better candidates for the two-stage balance of evidence hypothesis, including Brown and Heathcote’s (2008) linear ballistic accumulator and Usher and McClelland’s (2001) leaky accumulator model.”
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python (2013, methodology): “Currently, HDDM includes two of the most commonly used SSMs: the drift diffusion model (DDM) (Ratcliff and Rouder, 1998; Ratcliff and McKoon, 2008) belonging to the class of diffusion models and the linear ballistic accumulator (LBA) (Brown and Heathcote, 2008) belonging to the class of race models.”
To transform or not to transform: using generalized linear mixed models to analyse reaction time data (2015, neutral): “…rates (e.g., Carpenter and Williams, 1995), the implications associated with the reciprocal nature of this transformation on RT appears to be limited because psychological models assuming linearity are able to closely predict responses in observed data (Ratcliﬀ, 1978; Brown and Heathcote, 2008).”
A dual-stage two-phase model of selective attention. (2010, methodology): “…accuracy) are also highly informative for the present objective, we depict our data by using so-called defective cumulative distributions (S. D. Brown & Heathcote, 2008; Feller, 1968), which refer to cumulative distributions weighted by the relative proportion of correct and incorrect…”
Safe models for risky decisions (2017, methodology): “Originally proposed by Busemeyer and Stout (2002), the EV model is arguably the most popular model for the IGT (for references see Steingroever, Wetzels, &Wagenmakers, 2013a, and for alternative IGT models see Ahn et al.”
How to use the diffusion model: Parameter recovery of three methods: EZ, fast-dm, and DMAT (2009, methodology): “The three methods were applied to simulated data generated by the diffusionmodel, by the leaky, competing accumulator (LCA)model (Usher &McClelland, 2001) and by the linear ballistic accumulator (LBA) model (Brown & Heathcote, 2008).”