Attentional dynamics of evidence accumulation explain why more numerate people make better decisions under risk.

In decisions under risk, more numerate people are typically more likely to choose the option with the highest expected value (EV) than less numerate ones. Prior research indicates that this finding cannot be explained by differences in the reliance on explicit EV calculation. The current work uses the attentional Drift Diffusion Model as a unified computational framework to formalize three candidate mechanisms of pre-decisional information search and processing-namely, attention allocation, amount of deliberation, and distorted processing of value-which may differ between more and less numerate people and explain differences in decision quality.

Attribute attention and option attention in risky choice.

Probability weighting is one of the most powerful theoretical constructs in descriptive models of risky choice and constitutes a central component of cumulative prospect theory (CPT). Probability weighting has been shown to be related to two facets of attention allocation: one analysis showed that differences in the shape of CPT's probability-weighting function are linked to differences in how attention is allocated across attributes (i.e.

Toward an attentional turn in research on risky choice.

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 (e.g.

Stronger attentional biases can be linked to higher reward rate in preferential choice.

When attention is biased to a particular option during information search preceding preferential choice, this option is often more likely to be chosen-even if its value is objectively lower than that of the alternative. This article demonstrates that although attentional biases-even to lower-valued options-may reduce accuracy (the tendency to choose the highest-valued option), they can increase reward rate (the amount of reward obtained per unit of time invested in the choice). To achieve a higher reward rate it is often preferable to choose a lower-valued option quickly rather than spend time trying to identify the highest-valued option.

Nonlinear probability weighting can reflect attentional biases in sequential sampling.

Nonlinear probability weighting allows cumulative prospect theory (CPT) to account for key phenomena in decision making under risk (e.g., certainty effect, fourfold pattern of risk attitudes).

Age differences in risk attitude are shaped by option complexity.

The canonical conclusion from research on age differences in risky choice is that older adults are more risk averse than younger adults, at least in choices involving gains. Most of the evidence for this conclusion derives from studies that used a specific type of choice problem: choices between a safe and a risky option. However, safe and risky options differ not only in the degree of risk but also in the amount of information to be processed-that is, in their complexity.