Cognitive model decomposition of the BART: Assessment and application
Section snippets
The BART models
The BART models by Wallsten et al. (2005) are cognitive decision models inspired by the Expectancy–Valence model (Busemeyer & Stout, 2002) for the famous Iowa Gambling Task (Bechara, Damasio, Tranel, & Damasio, 1997). In their article, Wallsten et al. presented a total of 10 models that make different assumptions about the details of the decision process (for an overview see Wallsten et al. (2005), p. 870, Table 2). As a basis of our discussion we use Wallsten et al. ’s “Model 3”, a
Bayesian parameter estimation
In previous work, parameter estimation for the 4-parameter BART model was carried out by means of individual subject maximum likelihood (Wallsten et al., 2005).2 This means that the model was applied to each participant’s data separately, and that inference concerned the parameter point values that make the observed data most likely.
Here we estimate the parameters of the BART model in a Bayesian way. In Bayesian inference, the researcher
Parameter recovery simulations
In this section we examine parameter recovery of the 2-parameter simplification of the BART model. In this model, is fixed to the value of . This way, the only parameters left to estimate are and . We generated data for a grid of values for parameters and . We did so by plugging in the parameter values into Eqs. (3), (4), to calculate the probability that DM will pump on trial for pump opportunity , . We generated pumps and cashes based on these probabilities,
Experiment
In this section we will present an application of a hierarchical version of the 2-parameter model to empirical BART data. In a within-subjects design, we administered three different doses of alcohol to every participant, each measured in blood alcohol content, or BAC, in grams per liter: a placebo condition (BAC = 0), a tipsy condition (BAC = 0.5) and a drunk condition (BAC = 1). After consumption, each participant completed a 20 min version of the BART.
We expected that a higher dose of
Concluding comments
The first goal of this paper was to increase our knowledge about how the BART models can be applied to empirical data. In order to do so, we have assessed parameter recovery for a simplified version of the BART model by Wallsten et al. (2005) with 4, 3, and 2 parameters, of which the results for the 4- and 3-parameter versions of the model are reported online. Our second goal was to test the effects of alcohol on performance on the BART task in an experimental setting. We have created a
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