Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-05T09:48:51.817Z Has data issue: false hasContentIssue false
  • English
  • Français

Sampling as a resource-rational constraint

Published online by Cambridge University Press:  11 March 2020

Adam N. Sanborn Open the ORCID record for Adam N. Sanborn [Opens in a new window] ,
Jianqiao Zhu ,
Jake Spicer  and
Nick Chater
Adam N. Sanborn
Affiliation:
Department of Psychology, University of Warwick, CoventryCV4 7AL, United Kingdom. [email protected]@[email protected]://warwick.ac.uk/fac/sci/psych/people/asanborn/https://warwick.ac.uk/fac/sci/psych/people/zjianqiao/
Jianqiao Zhu
Affiliation:
Department of Psychology, University of Warwick, CoventryCV4 7AL, United Kingdom. [email protected]@[email protected]://warwick.ac.uk/fac/sci/psych/people/asanborn/https://warwick.ac.uk/fac/sci/psych/people/zjianqiao/
Jake Spicer
Affiliation:
Department of Psychology, University of Warwick, CoventryCV4 7AL, United Kingdom. [email protected]@[email protected]://warwick.ac.uk/fac/sci/psych/people/asanborn/https://warwick.ac.uk/fac/sci/psych/people/zjianqiao/
Nick Chater
Affiliation:
Warwick Business School, University of Warwick, CoventryCV4 7AL, United Kingdom. [email protected]://www.wbs.ac.uk/about/person/nick-chater/
Get access Rights & Permissions [Opens in a new window]

Abstract

Resource rationality is useful for choosing between models with the same cognitive constraints but cannot settle fundamental disagreements about what those constraints are. We argue that sampling is an especially compelling constraint, as optimizing accumulation of evidence or hypotheses minimizes the cost of time, and there are well-established models for doing so which have had tremendous success explaining human behavior.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e22
Check for updates
Copyright
Copyright © Cambridge University Press 2020

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

Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. & Pouget, A. (2012) Not noisy, just wrong: The role of suboptimal inference in behavioral variability. Neuron 74(1):30–39. doi:10.1016/j.neuron.2012.03.016.CrossRefGoogle ScholarPubMed
Bogacz, R., Brown, E., Moehlis, J., Holmes, P. & Cohen, J. (2006) The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review 113(4):700–65. doi:10.1037/0033-295x.113.4.700.CrossRefGoogle ScholarPubMed
Busemeyer, J. R., Pothos, E. M., Franco, R. & Trueblood, J. S. (2011) A quantum theoretical explanation for probability judgment errors. Psychological Review 118(2):193–218.CrossRefGoogle ScholarPubMed
Dasgupta, I., Schulz, E. & Gershman, S. J. (2017) Where do hypotheses come from? Cognitive Psychology 96:1–25. doi:10.1016/j.cogpsych.2017.05.001.CrossRefGoogle Scholar
Fantino, E., Kulik, J., Stolarz-Fantino, S. & Wright, W. (1997) The conjunction fallacy: A test of averaging hypotheses. Psychonomic Bulletin & Review 4(1):96–101.CrossRefGoogle Scholar
Juslin, P., Nilsson, H. & Winman, A. (2009) Probability theory, not the very guide of life. Psychological Review 116(4):856–74.CrossRefGoogle Scholar
Maylor, E. A., Chater, N. & Jones, G. V. (2001) Searching for two things at once: Evidence of exclusivity in semantic and autobiographical memory retrieval. Memory & Cognition 29(8):1185–95.CrossRefGoogle ScholarPubMed
Ratcliff, R. & McKoon, G. (2008) The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation 20(4):873–922.CrossRefGoogle ScholarPubMed
Sanborn, A. N. & Chater, N. (2016) Bayesian brains without probabilities. Trends in Cognitive Sciences 20(12):883–93. doi:10.1016/j.tics.2016.10.003.CrossRefGoogle ScholarPubMed
Shadlen, M. N. & Shohamy, D. (2016) Decision making and sequential sampling from memory. Neuron 90(5):927–39.CrossRefGoogle ScholarPubMed
Spicer, J. & Sanborn, A. N. (2019) What does the mind learn? A comparison of human and machine learning representations. Current Opinion in Neurobiology 55:97–102.CrossRefGoogle Scholar
Tversky, A. & Kahneman, D. (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–31. doi:10.1126/science.185.4157.1124.CrossRefGoogle ScholarPubMed
Vul, E., Goodman, N. D., Griffiths, T. L. & Tenenbaum, J. B. (2014) One and done? Optimal decisions from very few samples. Cognitive Science 38(4):599–637. doi:10.1111/cogs.12101.CrossRefGoogle ScholarPubMed
Wald, A. (1950) Statistical decision functions. John Wiley & Sons.Google Scholar
Zhu, J.-Q., Sanborn, A. N. & Chater, N. (2018a) The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments. Available at: https://doi.org/10.31234/osf.io/af9vy.CrossRefGoogle Scholar
Zhu, J.-Q., Sanborn, A. N. & Chater, N. (2018b) Mental sampling in multimodal representations. In: Advances in neural information processing systems, vol. 31, ed. Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N. & Garnett, R., pp. 5752–63. Curran Associates, Inc.Google Scholar