This study examined whether age differences in risky decision making are dependent on known probability and value of outcomes (i.e., the expected value [EV]), the valence of anticipated outcomes (gains or losses), and individual differences in working memory and impulsivity. We used a task that varied risk independently from EV so that taking risks could be advantageous or disadvantageous. Results indicated differential developmental courses for the sensitivity to EV and outcome valence from early to late adolescence. An increase in risk-advantageous but a decrease in risk-disadvantageous behavior was obtained between early-to-mid and late adolescence. All adolescents showed higher risky behavior when losses rather than gains were expected. Age differences in the sensitivity to EV were fully mediated by individual differences in working memory but not by self-reported impulsivity, suggesting that decision making under known risk is strongly limited by the maturation of cognitive control processes.
No abstract
When people estimate the quantities of objects (e.g., country populations), are then presented with the objects' actual quantities, and subsequently asked to remember their initial estimates, responses are often distorted towards the actual quantities. This hindsight bias-traditionally considered to reflect a cognitive error-has more recently been proposed to result from adaptive knowledge updating. But how to conceptualize such knowledge-updating processes and their potentially beneficial consequences? Here, we provide a framework that conceptualizes knowledge updating in the context of hindsight bias in real-world estimation by connecting it with research on seeding effects-improvements in people's estimation accuracy after exposure to numerical facts. This integrative perspective highlights a previously neglected facet of knowledge updating, namely, recalibration of metric domain knowledge, which can be expected to lead to transfer learning and thus improve estimation for objects from a domain more generally. We develop an experimental paradigm to investigate the association of hindsight bias with improved estimation accuracy. In Experiment 1, we demonstrate that the classical approach to induce hindsight bias indeed produces transfer learning. In Experiment 2, we provide evidence for the novel prediction that hindsight bias can be triggered via transfer learning; this establishes a direct link from knowledge updating to hindsight bias. Our work integrates two prominent but previously unconnected research programs on the effects of knowledge updating in real-world estimation and supports the notion that hindsight bias is driven by adaptive learning processes. Public Significance StatementWhen people try to recall their previous judgment on some issue (e.g., "What is the population of Sweden?") and had in the meantime learned the true value ("Sweden's population is about 10.4 million"), they seem to misremember their previous judgment to be closer to the true value than it had actually been. This hindsight bias has traditionally been interpreted as reflecting a deficiency of the mind.Here we demonstrate in the context of real-world quantitative estimation that hindsight bias likely represents a side effect of adaptive learning processes. Specifically, we show that hindsight bias emerges because people use the acquired true numerical values of objects to recalibrate the metric underlying their estimates of objects in a domain. Our results suggest that hindsight bias, rather than indicating a mental flaw, is the product of people's smart integration of new information into their world knowledge.
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