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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

Mean number of cards drawn from each deck of the Iowa Gambling Task over five consecutive blocks of 20 choices. Red lines indicate decks identified as disadvantageous in the original publication of the task. Solid lines identify decks with high gain frequency, broken lines those with low gain frequency. Bars represent SE of the mean (SE).
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Figure 2: Mean number of cards drawn from each deck of the Iowa Gambling Task over five consecutive blocks of 20 choices. Red lines indicate decks identified as disadvantageous in the original publication of the task. Solid lines identify decks with high gain frequency, broken lines those with low gain frequency. Bars represent SE of the mean (SE).

Mentions: A separate analysis of subjects’ choice behavior for all four decks revealed a clear preference for decks with frequent gains (decks B and D) over decks with infrequent gains (decks A and C) throughout the task (see Table 3; Figure 2, Friedman test, all p < 0.05). Furthermore, we observed that healthy subjects learned to differentiate between disadvantageous deck A and advantageous deck C from the third block on (Friedman test, all p < 0.05) but not between disadvantageous deck B and advantageous deck D (see Figure 2).


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)

Mean number of cards drawn from each deck of the Iowa Gambling Task over five consecutive blocks of 20 choices. Red lines indicate decks identified as disadvantageous in the original publication of the task. Solid lines identify decks with high gain frequency, broken lines those with low gain frequency. Bars represent SE of the mean (SE).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Mean number of cards drawn from each deck of the Iowa Gambling Task over five consecutive blocks of 20 choices. Red lines indicate decks identified as disadvantageous in the original publication of the task. Solid lines identify decks with high gain frequency, broken lines those with low gain frequency. Bars represent SE of the mean (SE).
Mentions: A separate analysis of subjects’ choice behavior for all four decks revealed a clear preference for decks with frequent gains (decks B and D) over decks with infrequent gains (decks A and C) throughout the task (see Table 3; Figure 2, Friedman test, all p < 0.05). Furthermore, we observed that healthy subjects learned to differentiate between disadvantageous deck A and advantageous deck C from the third block on (Friedman test, all p < 0.05) but not between disadvantageous deck B and advantageous deck D (see Figure 2).

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