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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)

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

Pattern of card selection for each cluster identified in block 5 (trials 81–100). Bars represent the median number of cards selected from each deck (with 95% confidence intervals).
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Figure 6: Pattern of card selection for each cluster identified in block 5 (trials 81–100). Bars represent the median number of cards selected from each deck (with 95% confidence intervals).

Mentions: Finally, we related cluster membership in the final block back to subjects’ choice behavior and the initially proposed difference score to measure task performance. In Figure 5, cluster membership is plotted against difference score on the last block of trials. While performance of subjects in the largest cluster resulted in a difference score close to zero, subjects in cluster 1 (high weight for loss frequency, low weight for gain frequency, and a weight close to zero for outcome), and subjects in cluster 3 (low weight for loss frequency, high weight for gain frequency, and high weight for outcome) both had a high positive difference score. However, this high difference score was driven either by a clear preference for deck C (cluster 1) or deck D (cluster 3), as shown in Figure 6. Subjects in cluster 2 exhibited a more distributed choice behavior with small preferences for decks B and D.


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)

Pattern of card selection for each cluster identified in block 5 (trials 81–100). Bars represent the median number of cards selected from each deck (with 95% confidence intervals).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Pattern of card selection for each cluster identified in block 5 (trials 81–100). Bars represent the median number of cards selected from each deck (with 95% confidence intervals).
Mentions: Finally, we related cluster membership in the final block back to subjects’ choice behavior and the initially proposed difference score to measure task performance. In Figure 5, cluster membership is plotted against difference score on the last block of trials. While performance of subjects in the largest cluster resulted in a difference score close to zero, subjects in cluster 1 (high weight for loss frequency, low weight for gain frequency, and a weight close to zero for outcome), and subjects in cluster 3 (low weight for loss frequency, high weight for gain frequency, and high weight for outcome) both had a high positive difference score. However, this high difference score was driven either by a clear preference for deck C (cluster 1) or deck D (cluster 3), as shown in Figure 6. Subjects in cluster 2 exhibited a more distributed choice behavior with small preferences for decks B and D.

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