Suboptimal foraging behavior: a new perspective on gambling

Abstract

Why do people gamble? Conventional views hold that gambling may be motivated by irrational beliefs, risk-seeking, impulsive temperament, or dysfunction within the same reward circuitry affected by drugs of abuse. An alternate, unexplored perspective is that gambling is an extension of natural foraging behavior to a financial environment. However, when these foraging algorithms are applied to stochastic gambling outcomes, undesirable results may occur. To test this hypothesis, we recruited participants based on their frequency of gambling-yearly (or less), monthly, and weekly-and investigated how gambling frequency related to irrational beliefs, risk-taking/impulsivity, and foraging behavior. We found that increased gambling frequency corresponded to greater gambling-related beliefs, more exploratory choices on an explore/exploit foraging task, and fewer points earned on a Patchy Foraging Task. Gambling-related beliefs negatively related to performance on the Patchy Foraging Task, indicating that individuals with more gambling-related cognitions tended to leave a patch too quickly. This indicates that frequent gamblers have reduced foraging ability to maximize rewards; however, gambling frequency -and by extension, poor foraging ability- was not related to risk-taking or impulsive behavior. These results suggest that gambling reflects the application of a dysfunctional foraging process to financial outcomes.

Figure 1

Average behavioral performance and total points earned on the 4-Armed Bandit, Patchy Foraging, Balloon Analogue Risk, and Risky Preference Tasks across the three gambling-frequency groups. Error bars are S.E.M. *p < 0.05, **p < 0.005.

Figure 2

Average patch leave time (i.e., number of choices to remain at current patch) for each delay (in seconds) to the next patch across the three gambling-frequency groups. The yearly and monthly gamblers had significantly longer patch leave times in the 20 sec delay condition compared to the 5 sec delay condition (p’s < 0.005). The weekly gamblers had similar patch leave times across the delay conditions. Error bars are S.E.M.

Figure 3

Correlation between Gambling-related Cognition Scores and Patchy Foraging Task performance. Performance is represented as the difference between individual’s average leave time (measured in the number of selections to stay at the current bush) and the optimal leave time derived using the marginal value theorem. Spearman’s rho = −.40, p = 0.001.

Figure 4

Results of the elastic net model for a) gambling frequency, and b) gambling-related cognition scale scores. Values indicate the strength of regression effects (y-axis) for each variable as a function of model parsimony (x-axis). As values increase along the x-axis, the model becomes less parsimonious and additional variables enter the model (lines diverging from 0 along the y-axis). At a given parsimony (x-axis position), the y-axis value of the variable lines indicates the strength of that regression coefficient in the model. The solid vertical line indicates the optimal model, and the dashed vertical line indicates the most parsimonious model within one standard error. Only coefficients with nonzero values between these vertical lines should be considered to have relevance for predictive performance in the model.