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Leveraging decision consistency to decompose suboptimality in terms of its ultimate predictability

Published online by Cambridge University Press:  10 January 2019

Valentin Wyart Open the ORCID record for Valentin Wyart [Opens in a new window]
Valentin Wyart*
Affiliation:
Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale, Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL University, 75005 Paris, France. valentin.wyart@ens.frhttp://lnc2.dec.ens.fr/inference-and-decision-making
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Abstract

Although the suboptimality of perceptual decision making is indisputable in its strictest sense, characterizing the nature of suboptimalities constitutes a valuable drive for future research. I argue that decision consistency offers a rarely measured, yet important behavioral metric for decomposing suboptimality (or, more generally, deviations from any candidate model of decision making) into ultimately predictable and inherently unpredictable components.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e248
Copyright
Copyright © Cambridge University Press 2018 

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References

Drugowitsch, J., Wyart, V., Devauchelle, A.-D. & Koechlin, E. (2016) Computational precision of mental inference as critical source of human choice suboptimality. Neuron 92(6):1398–411. Available at: http://dx.doi.org/10.1016/j.neuron.2016.11.005.Google Scholar
Girshick, A. R., Landy, M. S. & Simoncelli, E. P. (2011) Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics. Nature Neuroscience 14(7):926–32. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3125404&tool=pmcentrez&rendertype=abstract.Google Scholar
Green, D. M. & Swets, J. A. (1966) Signal detection theory and psychophysics. John Wiley & Sons.Google Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.Google Scholar
Palminteri, S., Wyart, V. & Koechlin, E. (2017) The importance of falsification in computational cognitive modeling. Trends in Cognitive Sciences 21(6):425–33.Google Scholar
Wei, X.-X. & Stocker, A. A. (2015) A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience 18:1509–17. Available at: http://dx.doi.org/10.1038/nn.4105.Google Scholar
Wyart, V. & Koechlin, E. (2016) Choice variability and suboptimality in uncertain environments. Current Opinion in Behavioral Sciences 11:109–15. Available at: http://dx.doi.org/10.1016/j.cobeha.2016.07.003.Google Scholar