Abstract
We present new findings that distinguish drift diffusion models (DDMs) from the linear ballistic accumulator (LBA) model as descriptions of human behavior in a two-alternative forced-choice reward maximization (Rmax) task. Previous comparisons have not considered Rmax tasks, and differences identified between the models' predictions have centered on practice effects. Unlike the parameter-free optimal performance curves of the pure DDM, the extended DDM and LBA predict families of curves depending on their additional parameters, and those of the LBA show significant differences from the DDMs, especially for poorly discriminable stimuli that incur high error rates. Moreover, fits to behavior reveal that the LBA and DDM provide different interpretations of behavior as stimulus discriminability increases. Trends for threshold setting (caution) in the DDMs are consistent between fits, while in the corresponding LBA fits, thresholds interact with distributions of starting points in a complex manner that depends upon parameter constraints. Our results suggest that reinterpretation of LBA parameters may be necessary in modeling the Rmax paradigm.
Highlights
Among the many models proposed to describe decision tasks, leaky competing accumulators (LCAs) (Usher and McClelland, 2001) and drift diffusion models (DDMs) e.g., Ratcliff and Rouder (1998) have been especially prominent
All have been validated against particular behavioral data, but since they differ in structure, number of parameters, and the manner in which variability enters, they may suggest different processing mechanisms [ the DDM can be derived from the LCA under certain conditions (Bogacz et al, 2006)]
In this paper we compare accounts of behavior provided by fitting DDM and linear ballistic accumulator (LBA) models to behavioral data from a 2AFC reward maximization (Rmax) task
Summary
Among the many models proposed to describe decision tasks, leaky competing accumulators (LCAs) (Usher and McClelland, 2001) and drift diffusion models (DDMs) e.g., Ratcliff and Rouder (1998) have been especially prominent. More recently the linear ballistic accumulator (LBA) (Brown and Heathcote, 2008) was introduced as a conceptually simpler alternative to DDMs. More recently the linear ballistic accumulator (LBA) (Brown and Heathcote, 2008) was introduced as a conceptually simpler alternative to DDMs All these models employ drift terms that describe mean rates of evidence accumulation, thresholds that signal decision times when crossed, and sources of variability, either within or across trials. All have been validated against particular behavioral data, but since they differ in structure, number of parameters, and the manner in which variability enters, they may suggest different processing mechanisms [ the DDM can be derived from the LCA under certain conditions (Bogacz et al, 2006)]. Differences in drift rates, non-decision times and caution parameters were found in many-parameter fits of practice effects (Heathcote and Hayes, 2012), but these differences were not connected to optimal theories of performance in perceptual choice tasks. Nor have the LBA and DDMs been compared for reward maximization (Rmax) tasks in which participants have learned strategies and apply task-based knowledge to optimize performance
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