Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales

Kiyohito Iigaya (), Yashar Ahmadian, Leo P. Sugrue, Greg S. Corrado, Yonatan Loewenstein, William T. Newsome and Stefano Fusi ()
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Kiyohito Iigaya: Columbia University
Yashar Ahmadian: Columbia University
Leo P. Sugrue: Stanford University School of Medicine
Greg S. Corrado: Stanford University School of Medicine
Yonatan Loewenstein: The Hebrew University of Jerusalem
William T. Newsome: Stanford University School of Medicine
Stefano Fusi: Columbia University

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: Abstract Behavior deviating from our normative expectations often appears irrational. For example, even though behavior following the so-called matching law can maximize reward in a stationary foraging task, actual behavior commonly deviates from matching. Such behavioral deviations are interpreted as a failure of the subject; however, here we instead suggest that they reflect an adaptive strategy, suitable for uncertain, non-stationary environments. To prove it, we analyzed the behavior of primates that perform a dynamic foraging task. In such nonstationary environment, learning on both fast and slow timescales is beneficial: fast learning allows the animal to react to sudden changes, at the price of large fluctuations (variance) in the estimates of task relevant variables. Slow learning reduces the fluctuations but costs a bias that causes systematic behavioral deviations. Our behavioral analysis shows that the animals solved this bias-variance tradeoff by combining learning on both fast and slow timescales, suggesting that learning on multiple timescales can be a biologically plausible mechanism for optimizing decisions under uncertainty.

Date: 2019
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DOI: 10.1038/s41467-019-09388-3

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