Heuristics contribute to sensorimotor decision-making under risk

Research in psychophysics argues that incentivized sensorimotor decisions (such as deciding where to reach to get a reward) maximize expected gain, suggesting that these decisions may be impervious to cognitive biases and heuristics. We tested this hypothesis in two experiments, directly comparing the predictive accuracy of an optimal model and plausible suboptimal models. We obtained strong evidence that people deviated from the optimal strategy by excessively avoiding loss regions when the potential loss was zero and failing to shift far enough away from loss regions when potential losses outweighed the potential gains. Although allowing nonlinear distortions of value and probability information improved the fit of value-maximizing models, behavior was best described by a model encapsulating a simple heuristic strategy. This suggests that visuomotor decisions are likely influenced by biases and heuristics observed in more classical economic decision-making tasks.