The cognitive mechanisms behind wishful predictions: A diffusion model decomposition

Abstract

Wishful thinking or desirability bias refers to instances where the desire for an outcome inflates the expectation that it will occur. Although studies have demonstrated influences of outcome desirability on people's predictions, the cognitive mechanisms behind such an effect have remained unclear. Both biased criteria for evidence judgment and biased evidence search/accumulation have been suggested as possible mechanisms. In the present work, we used drift-diffusion modeling to examine on which levels of processing desirability has its impact. Participants (N = 147) made predictions about the color of a randomly selected square from 2-color grids. Crucially, certain color outcomes were made more desirable than others, and the strength of evidence was manipulated by varying the proportion of desired-color squares in the grid. We found that both manipulations—and their interaction—significantly affected predictions. More importantly, drift-diffusion model analyses showed that outcome desirability resulted in a judgment-level bias, where participants required less evidence to predict a desired outcome. Notably, we also found that desirability impacted the evidence accumulation process itself. Participants more readily construed evidence as supporting the desired outcome, indicating that desirability had a top-down influence on how prediction-relevant evidence was accumulated. The present results have implications for existing accounts of how desire impacts expectations and highlight the utility of drift diffusion modeling as a tool for assessing the mechanisms underlying motivated biases.