Introduction: Mind-wandering (MW) is the cognitive phenomenon where attention drifts from a primary task to unrelated thoughts, influencing various decision-making processes. However, its role in learning and decision-making under uncertainty still needs to be explored.
Objective: This study investigates the impact of state and trait MW (spontaneous and deliberate) on performance in the Iowa Gambling Task (IGT). The IGT is a widely used paradigm that simulates real-life decision-making under uncertainty. Participants repeatedly select from four decks of cards that vary in short-term rewards and long-term losses. Over time, advantageous decks yield higher net gains, whereas disadvantageous decks result in net losses, requiring participants to implicitly learn the probabilities through trial and error.
Method: A sample of 101 participants completed 200 IGT trials interspersed with thought probes assessing state MW and questionnaires measuring trait MW. Learning was measured by comparing deck selections between early (trials 1-100) and later (trials 101-200) phases. Risky decision-making was operationalised as the proportion of disadvantageous deck selections. Statistical analyses were conducted using general linear regression models, examining both main and interaction effects of state and trait MW.
Results: The study revealed a significant interaction between state MW and trait spontaneous MW on learning under uncertainty (β = 0.35, p < 0.01), with state MW positively influencing learning at moderate (slope = 11.61, 95% CI = [1.74, 21.48], p = .02) and high (slope = 21.21, 95% CI = [6.25, 36.16], p = .006) levels of trait spontaneous MW. However, state MW did not significantly affect risky decision-making (β = -0.07, p = .46). These findings suggest that individuals with higher trait spontaneous MW may leverage flexible cognitive states to enhance adaptive learning in uncertain contexts.
Conclusion: The study highlights the nuanced interplay between MW and learning, underscoring the benefits of spontaneous MW traits in unpredictable environments.