Robust averaging during perceptual judgment

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 108; no. 32; pp. 13341 - 13346
• Main Authors: de Gardelle, Vincent, Summerfield, Christopher
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 09.08.2011; National Acad Sciences
• Subjects: Adult; Behavioral neuroscience; Biological Sciences; Circles; Cognitive science; Color Perception; Computer Simulation; Decision Making; Decision making models; Empirical evidence; Error rates; Experimentation; Human subjects; Humans; Integration; Judgment; Judgment - physiology; Modeling; Models, Biological; Neurons; Perception - physiology; Psychology; Regression Analysis; Signal to noise ratio; Statistical variance; Supernova remnants; Task Performance and Analysis; Term weighting; uncertainty; variance; Young Adult
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1104517108

An optimal agent will base judgments on the strength and reliability of decision-relevant evidence. However, previous investigations of the computational mechanisms of perceptual judgments have focused on integration of the evidence mean (i.e., strength), and overlooked the contribution of evidence variance (i.e., reliability). Here, using a multielement averaging task, we show that human observers process heterogeneous decision-relevant evidence more slowly and less accurately, even when signal strength, signal-to-noise ratio, category uncertainty, and low-level perceptual variability are controlled for. Moreover, observers tend to exclude or downweight extreme samples of perceptual evidence, as a statistician might exclude an outlying data point. These phenomena are captured by a probabilistic optimal model in which observers integrate the log odds of each choice option. Robust averaging may have evolved to mitigate the influence of untrustworthy evidence in perceptual judgments.