Abstract
Decision-making deficits in clinical populations are often assessed with the Iowa gambling task (IGT). Performance on this task is driven by latent psychological processes, the assessment of which requires an analysis using cognitive models. Two popular examples of such models are the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models. These models have recently been subjected to sophisticated procedures of model checking, spawning a hybrid version of the EV and PVL models—the PVL-Delta model. In order to test the validity of the PVL-Delta model we present a parameter space partitioning (PSP) study and a test of selective influence. The PSP study allows one to assess the choice patterns that the PVL-Delta model generates across its entire parameter space. The PSP study revealed that the model accounts for empirical choice patterns featuring a preference for the good decks or the decks with infrequent losses; however, the model fails to account for empirical choice patterns featuring a preference for the bad decks. The test of selective influence investigates the effectiveness of experimental manipulations designed to target only a single model parameter. This test showed that the manipulations were successful for all but one parameter. To conclude, despite a few shortcomings, the PVL-Delta model seems to be a better IGT model than the popular EV and PVL models.
Highlights
The Iowa gambling task (IGT; Bechara et al, 1994) is arguably the most popular neuropsychological paradigm to assess decisionmaking deficits in clinical populations
It is evident that choice patterns that are typically shown by healthy participants—the choice patterns “good decks over bad decks” and “infrequent losses over frequent losses”
Using the broad definition of choice patterns, the parameter space partitioning (PSP) study revealed that the Prospect Valence Learning model (PVL)-Delta model can generate all typical empirical choice patterns
Summary
The Iowa gambling task (IGT; Bechara et al, 1994) is arguably the most popular neuropsychological paradigm to assess decisionmaking deficits in clinical populations. Two of the most frequently used representatives include the Expectancy Valence model (EV; see Steingroever et al, 2013b, for references), and the Prospect Valence Learning model (PVL; see Steingroever et al, 2013b, for references and a detailed description of the models). The parameters of these models correspond to distinct psychological processes such as motivation, learning/memory, and response consistency (Busemeyer et al, in press)
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