Suboptimality in perception
LONG ABSTRACTHuman perceptual decisions are often described as optimal. This view reflects recent successes of Bayesian approaches to both cognition and perception. However, claims regarding optimality have been strongly criticized for their excessive flexibility and lack of explanatory power. Rebuttals from Bayesian theorists in turn claim that critics unfairly pick on select few papers. To resolve the issue regarding the role of optimality in perceptual decision making, we review the vast literature on suboptimal performance in perceptual tasks. Specifically, we discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria, inadequate tradeoff between speed and accuracy, inappropriate confidence ratings, misweightings in cue combination, and findings related to various perceptual illusions and biases. We then extract the proposed explanations for the suboptimal behavior seen in each type of task. Critically, we show that these explanations naturally fit within an overarching Bayesian framework. Specifically, each suboptimality can be explained by alternative likelihood functions, priors, cost functions, or decision rules (LPCDs). We argue that unless the observers likelihood functions, priors, and cost functions are known, statements about the optimality or suboptimality of decision rules are meaningless. Further, the very definition of optimal behavior is debatable and may ultimately require appeals to evolutionary history beyond the current scope of perceptual science. The field should therefore shift its focus away from optimality. We propose a \"LPCD approach\" to perceptual decision making that focuses exclusively on uncovering the LPCD components, without debating whether the uncovered LPCDs are \"optimal\" or not.\n\nSHORT ABSTRACTHuman perceptual decisions are often described as optimal, but this view remains controversial. To resolve the issue, we review the vast literature on suboptimalities in perceptual tasks. We then extract the proposed explanations for the suboptimal behavior seen in each type of task and show that each suboptimality can be explained by alternative likelihood functions, priors, cost functions, or decision rules (LPCDs). We argue that general statements about the optimality or suboptimality of perceptual decisions are meaningless and propose a \"LPCD approach\" to perceptual decision making that focuses on the LPCD components rather than optimality.