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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

(A) Distribution of weight values for clusters obtained with a two-step clustering algorithm on block one of the IGT. Columns correspond to the three task features and rows correspond to different clusters. (B) Distribution of weight values for clusters obtained with a two-step clustering algorithm on the last block of the IGT.
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Figure 4: (A) Distribution of weight values for clusters obtained with a two-step clustering algorithm on block one of the IGT. Columns correspond to the three task features and rows correspond to different clusters. (B) Distribution of weight values for clusters obtained with a two-step clustering algorithm on the last block of the IGT.

Mentions: In the first block of the task, the predictor importance was 1 for outcome, 0.93 for loss frequency, and 0.61 for gain frequency. About 78.2% of subjects belonged to cluster 1 (median weight for outcome −0.09, loss frequency 0.04, and gain frequency 0.04) and the remaining 21.8% belonged to cluster 2 (median weight for outcome 0.20, loss frequency −0.18, and gain frequency 0.20). The distribution of weights for both clusters and each feature can be seen in Figure 4A.


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)

(A) Distribution of weight values for clusters obtained with a two-step clustering algorithm on block one of the IGT. Columns correspond to the three task features and rows correspond to different clusters. (B) Distribution of weight values for clusters obtained with a two-step clustering algorithm on the last block of the IGT.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: (A) Distribution of weight values for clusters obtained with a two-step clustering algorithm on block one of the IGT. Columns correspond to the three task features and rows correspond to different clusters. (B) Distribution of weight values for clusters obtained with a two-step clustering algorithm on the last block of the IGT.
Mentions: In the first block of the task, the predictor importance was 1 for outcome, 0.93 for loss frequency, and 0.61 for gain frequency. About 78.2% of subjects belonged to cluster 1 (median weight for outcome −0.09, loss frequency 0.04, and gain frequency 0.04) and the remaining 21.8% belonged to cluster 2 (median weight for outcome 0.20, loss frequency −0.18, and gain frequency 0.20). The distribution of weights for both clusters and each feature can be seen in Figure 4A.

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