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Atten Percept Psychophys. 2016 Apr;78(3):923-37. doi: 10.3758/s13414-016-1059-x.

Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity.

Author information

National Institutes of Health, 10 Center Drive, Building 10, Room 1D51A, MSC 1065, Bethesda, MD, 20892-1065, USA.
Department of Psychology, Columbia University, 1190 Amsterdam Ave., MC 5501, New York, NY, 10027, USA.
Department of Psychology, University of California Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, CA, 90095-1563, USA.
Brain Research Institute, University of California Los Angeles, 695 Charles E Young Dr. South, Los Angeles, CA, 90095, USA.


Zylberberg et al. [Zylberberg, Barttfeld, & Sigman (Frontiers in Integrative Neuroscience, 6; 79, 2012), Frontiers in Integrative Neuroscience 6:79] found that confidence decisions, but not perceptual decisions, are insensitive to evidence against a selected perceptual choice. We present a signal detection theoretic model to formalize this insight, which gave rise to a counter-intuitive empirical prediction: that depending on the observer's perceptual choice, increasing task performance can be associated with decreasing metacognitive sensitivity (i.e., the trial-by-trial correspondence between confidence and accuracy). The model also provides an explanation as to why metacognitive sensitivity tends to be less than optimal in actual subjects. These predictions were confirmed robustly in a psychophysics experiment. In a second experiment we found that, in at least some subjects, the effects were replicated even under performance feedback designed to encourage optimal behavior. However, some subjects did show improvement under feedback, suggesting the tendency to ignore evidence against a selected perceptual choice may be a heuristic adopted by the perceptual decision-making system, rather than reflecting inherent biological limitations. We present a Bayesian modeling framework that explains why this heuristic strategy may be advantageous in real-world contexts.


Bayesian modeling; Signal detection theory; Visual awareness

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