Iowa gambling task: there is more to consider than long-term outcome. Using a linear equation model to disentangle the impact of outcome and frequency of gains and losses.

Horstmann A, Villringer A, Neumann J - Front Neurosci (2012)

Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3350871&req=5

Figure 1: Development of the difference score between the sum of cards drawn from advantageous decks C and D and disadvantageous decks A and B over five consecutive blocks of 20 cards of the Iowa Gambling Task. Bars represent SE of the mean (SE), asterisks indicate the level of significance: *p < 0.05, ***p < 0.005 (Student’s t-test against zero).
Mentions: According to Bechara et al. (1994) healthy subjects should gradually learn to choose an approximately equal number of cards from decks C and D and avoid cards from decks A and B, assuming that they focus on the long-term outcome of the decks and ignore all other features. This behavior would result in a difference score between advantageous and disadvantageous decks that develops from around zero at the beginning of the experiment toward a clear positive value at later stages. In contrast to this prediction, in a large sample of healthy young adults we observed only a moderately positive difference score between decks C/D (advantageous) and decks A/B (disadvantageous) at the end of the task (see Figure 1). Although average difference scores increased from negative to positive values over the course of the experiment (significant effect of block; F4.469, = 3.02, p = 0.018), and differed significantly from zero on all but the second block, subjects on average chose only 2.8 more cards from the advantageous than from disadvantageous decks in the last block of the experiment.

Bottom Line: Subjects preferred choices associated with high-frequency gains to those with low-frequency gains, regardless of long-term outcome.However, subjects in general do not learn to solely base their preference for particular decks on expected long-term outcome.In sum, our model facilitates a more focused conclusion about the factors guiding decision-making in the IGT.

View Article: PubMed Central - PubMed

Affiliation: Department Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.

ABSTRACT
The Iowa Gambling Task (IGT) has been widely used to assess differences in decision-making under uncertainty. Recently, several studies have shown that healthy subjects do not meet the basic predictions of the task (i.e., prefer options with positive long-term outcome), hence questioning its basic assumptions. Since choice options are characterized by gain and net loss frequency in addition to long-term outcome, we hypothesized that a combination of features rather than a single feature would influence participants' choices. Offering an alternative way of modeling IGT data, we propose to use a system of linear equations to estimate weights that quantify the influence of each individual feature on decision-making in the IGT. With our proposed model it is possible to disentangle and quantify the impact of each of these features. Results from 119 healthy young subjects suggest that choice behavior is predominantly influenced by gain and loss frequency. Subjects preferred choices associated with high-frequency gains to those with low-frequency gains, regardless of long-term outcome. However, among options with low-frequency gains, subjects learned to distinguish between choices that led to advantageous and disadvantageous long-term consequences. This is reflected in the relationship between the weights for gain frequency (highest), loss frequency (intermediate), and long-term outcome (lowest). Further, cluster analysis of estimated feature weights revealed sub-groups of participants with distinct weight patterns and associated advantageous decision behavior. However, subjects in general do not learn to solely base their preference for particular decks on expected long-term outcome. Consequently, long-term outcome alone is not able to drive choice behavior on the IGT. In sum, our model facilitates a more focused conclusion about the factors guiding decision-making in the IGT. In addition, differences between clinical groups can be assessed for each factor individually.

No MeSH data available.

Related in: MedlinePlus