The suboptimality of perceptual decision making with multiple alternatives

Abstract

It is becoming widely appreciated that human perceptual decision making is suboptimal but the nature and origins of this suboptimality remain poorly understood. Most past research has employed tasks with two stimulus categories, but such designs cannot fully capture the limitations inherent in naturalistic perceptual decisions where choices are rarely between only two alternatives. We conducted four experiments with tasks involving multiple alternatives and used computational modeling to determine the decision-level representation on which the perceptual decisions were based. The results from all four experiments pointed to the existence of robust suboptimality such that most of the information in the sensory representation was lost during the transformation to a decision-level representation. These results reveal severe limits in the quality of decision-level representations for multiple alternatives and have strong implications about perceptual decision making in naturalistic settings.

A robust confidence–accuracy dissociation via criterion attraction

“…The fitting was performed on the actual stimulus orientations encountered by each individual participant. To find the best fit, I computed the log-likelihood value associated with the full distribution of probabilities of each response type, as done previously ( Yeon and Rahnev 2020 ; Shekhar and Rahnev 2021b ): \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$\begin{equation*}Log\ likelihood = \mathop \sum \limits_{i,j,k} {\rm{log}}({p_{ijk}})*{n_{ijk}}\end{equation*}$$\end{document} where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${p_{ijk}}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${n_{ijk}}$\end{document} are the response probability and the number of trials, respectively, associated with the Distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$i \in \left\{ {1,2} \right\}$\end{document} , confidence rating \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$j \in \left\{ {1,2,3,4} \right\}$\end{document} , and condition \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k$\end{document} , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k = 1$\end{document} corresponds to the low variability condition and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k = 2$\end{document} corresponds to the high variability condition. The best fit was determined as the set of parameters that maximized the log-likelihood value.…”

Perception is Rich and Probabilistic

“…This has generally been studied by determining whether humans can perform flexible and deliberate computations that require access to more than summary statistics [15] . Studies using this approach have yielded mixed results [12,13,14] and there are concerns about whether a failure to perform optimal calculations reflects a lack of information about internal representation, or just an ability to effectively reason about it. Here we try a more direct approach by asking participants to place multiple Gaussian bets per trial over a shape space in order to convey a sense of probability of a perceived shape.…”

“…For example, Block [1] proposes that "competition among unconscious representations yields conscious representations through winner-takes-all processes of elimination or merging". In support of such a view, researchers have found that perceptual decisions show little evidence of knowledge of the probability distributions formed during perception [12] . Other studies reveal conflicting results, but since they use an array of target stimuli per display, e.g.…”

Probabilistic representations as building blocks for higher-level vision

“…These summaries enable rapid assessment of the general properties and layout of natural scenes 29,37 . Similarly, Rahnev 10,38 argued that observers represent only a summary consisting of the most likely stimulus and the associated strength of evidence, and Cohen et al 8 used summary statistics to explain the richness of consciousness experience. Our results argue against such views, since the representations that are bound together are far more detailed than this implies.…”