Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-28T14:44:31.776Z Has data issue: false hasContentIssue false
  • English
  • Français

The standard Bayesian model is normatively invalid for biological brains

Published online by Cambridge University Press:  10 January 2019

Rani Moran Open the ORCID record for Rani Moran [Opens in a new window]  and
Konstantinos Tsetsos Open the ORCID record for Konstantinos Tsetsos [Opens in a new window]
Rani Moran
Affiliation:
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, United Kingdom. rani.moran@gmail.comhttps://www.mps-ucl-centre.mpg.de/en/people/rani-moran Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, United Kingdom
Konstantinos Tsetsos
Affiliation:
Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany. k.tsetsos62@gmail.comhttps://sites.google.com/site/konstantinostsetsos/
Get access Rights & Permissions [Opens in a new window]

Abstract

We show that the benchmark Bayesian framework that Rahnev & Denison (R&D) used to assess optimality is actually suboptimal under realistic assumptions about how noise corrupts decision making in biological brains. This model is therefore invalid qua normative standard. We advise against generally forsaking optimality and argue that a biologically constrained definition of optimality could serve as an important driver for scientific progress.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e237
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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
Fawcett, T. W., Fallenstein, B., Higginson, A. D., Houston, A. I., Mallpress, D. E., Trimmer, P. C. & McNamara, J. M. (2014) The evolution of decision rules in complex environments. Trends in Cognitive Sciences 18(3):153–61.Google Scholar
Moran, R. (2015) Optimal decision making in heterogeneous and biased environment. Psychonomic Bulletin & Review 22:38–53.Google Scholar
Osmmy, O., Moran, R., Pfeffer, T., Tsetsos, K., Usher, M. & Donner, T. (2013) The time scale of perceptual evidence integration can be adapted to the environment. Current Biology 23:981–86.Google Scholar
Servan-Schreiber, D., Printz, H. & Cohen, J. D. (1990) A network model of catecholamine effects: Gain, signal-to-noise ratio, and behavior. Science 249(4971):892–95.Google Scholar
Soltani, A., De Martino, B. & Camerer, C. (2012) A range-normalization model of context-dependent choice: A new model and evidence. PLoS Computational Biology 8(7):e1002607.Google Scholar
Tsetsos, K., Moran, R., Moreland, J., Chater, N., Usher, M. & Summerfield, C. (2016a) Economic irrationality is optimal during noisy decision making. Proceedings of the National Academy of Sciences of the United States of America 113(11):3102–107. Available at: http://www.pnas.org/content/early/2016/02/24/1519157113.long.Google Scholar
Tsetsos, K., Moran, R., Moreland, J., Chater, N., Usher, M. & Summerfield, C. (2016b) Reply to Davis-Stober et al.: Violations of rationality in Tsetsos et al. (2016) are not aggregation artifacts. Proceedings of the National Academy of Sciences of the United States of America 113(33):pE4764–66.Google Scholar