Variance After-Effects Distort Risk Perception in Humans

Curr Biol. 2016 Jun 6;26(11):1500-4. doi: 10.1016/j.cub.2016.04.023. Epub 2016 May 5.

Abstract

In many contexts, decision-making requires an accurate representation of outcome variance-otherwise known as "risk" in economics. Conventional economic theory assumes this representation to be perfect, thereby focusing on risk preferences rather than risk perception per se [1-3] (but see [4]). However, humans often misrepresent their physical environment. Perhaps the most striking of such misrepresentations are the many well-known sensory after-effects, which most commonly involve visual properties, such as color, contrast, size, and motion. For example, viewing downward motion of a waterfall induces the anomalous biased experience of upward motion during subsequent viewing of static rocks to the side [5]. Given that after-effects are pervasive, occurring across a wide range of time horizons [6] and stimulus dimensions (including properties such as face perception [7, 8], gender [9], and numerosity [10]), and that some evidence exists that neurons show adaptation to variance in the sole visual feature of motion [11], we were interested in assessing whether after-effects distort variance perception in humans. We found that perceived variance is decreased after prolonged exposure to high variance and increased after exposure to low variance within a number of different visual representations of variance. We demonstrate these after-effects occur across very different visual representations of variance, suggesting that these effects are not sensory, but operate at a high (cognitive) level of information processing. These results suggest, therefore, that variance constitutes an independent cognitive property and that prolonged exposure to extreme variance distorts risk perception-a fundamental challenge for economic theory and practice.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Female
  • Humans
  • Male
  • Risk*
  • Visual Perception*