Topics in Cognitive Science, Volume 14, Issue 3, Page 467-491, July 2022.

For a long time, the dominant approach to studying decision making under risk has been to use psychoeconomic functions to account for how behavior deviates from the normative prescriptions of expected value maximization. While this neo-Bernoullian tradition has advanced the field in various ways—such as identifying seminal phenomena of risky choice —it contains a major shortcoming: Psychoeconomic curves are mute with regard to the cognitive mechanisms underlying risky choice. This neglect of the mechanisms both limits the explanatory value of neo-Bernoullian (...) models and fails to provide guidance for designing effective interventions to improve decision making. Here we showcase a recent “attentional turn” in research on risk choice that elaborates how deviations from normative prescriptions can result from imbalances in attention allocation and that thus promises to overcome the challenges of the neo-Bernoullian tradition. We argue that a comprehensive understanding of preference formation in risky choice must provide an account on a mechanistic level, and we delineate directions in which existing theories that rely on attentional processes may be extended to achieve this objective. (shrink)

It is difficult to overestimate Paul Meehl's influence on judgment and decision-making research. His 'disturbing little book' Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence is known as an attack on human judgment and a call for replacing clinicians with actuarial methods. More than 40 years later, fast and frugal heuristics - proposed as models of human judgment - were formalized, tested, and found to be surprisingly accurate, often more so than the actuarial models that (...) Meehl advocated. We ask three questions: Do the findings of the two programs contradict each other? More generally, how are the programs conceptually connected? Is there anything they can learn from each other? After demonstrating that there need not be a contradiction, we show that both programs converge in their concern to develop domain-specific models of judgment and nonlinear process models that arise from the bounded nature of judgment. We then elaborate the differences between the programs and discuss how these differences can be viewed as mutually instructive: First, we show that the fast and frugal heuristic models can help bridge the clinical - actuarial divide, that is, they can be developed into actuarial methods that are both accurate and easy to implement by the unaided clinical judge. We then argue that Meehl's insistence on improving judgment makes clear the importance of examining the degree to which heuristics are used in the clinical domain and how acceptable they would be as actuarial tools. (shrink)