The impact of choice discriminability and outcome valence on visual decision making under risk

• Published in: Vision research (Oxford) Vol. 199; p. 108073
• Main Author: Warren, Paul A.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.10.2022
• Subjects: Decision-making under risk; Perceptual decision-making; Vision and action; Decision-making under risk; Perceptual decision-making; Vision and action
• ISSN: 0042-6989; 1878-5646
• DOI: 10.1016/j.visres.2022.108073

Much of human activity involves perceptual or perceptuo-motor choice between options with uncertain outcomes. Previous research suggests that decisions in these contexts can be near-optimal in some circumstances but can also be significantly biased. Here we investigate how biases might depend on: i) discriminability of available choice outcomes, adjusted by manipulating the Expected Value (EV) function curvature; ii) outcome valence, which changes the tendency for risk seeking/aversive behaviour in cognitive decision making. In three experiments, participants set the size of a catcher in order to catch a dot moving on a random walk (with varying levels of predictability) after it emerged from behind an occluder. Catching and missing the dot were associated with scoring a variable number of outcome points depending on catcher size. In experiment 1 outcomes were most discriminable (high EV curvature) and catcher size settings were near-optimal. In experiments 2 and 3 outcomes were harder to discriminate (low EV curvature) and there was a significant bias to set the catcher size too small. Unlike cognitive decision making, the valence manipulation had little effect. Subsequent analyses suggest observed biases might reflect participants moving settings towards the region with highest EV curvature, where feedback is most informative. These data suggest that: i) unlike cognitive decisions, in this task choices are largely insensitive to outcome valence; ii) EV curvature is potentially an important factor when interpreting performance in such tasks; iii) Choice may be biased towards high EV curvature regions, consistent with value being placed on exploration to increase information return.