TY - JOUR
T1 - Two sides of the same coin
T2 - Beneficial and detrimental consequences of range adaptation in human reinforcement learning
AU - Bavard, Sophie
AU - Rustichini, Aldo
AU - Palminteri, Stefano
N1 - Publisher Copyright:
Copyright © 2021 The Authors, some rights reserved.
PY - 2021/4
Y1 - 2021/4
N2 - Evidence suggests that economic values are rescaled as a function of the range of the available options. Although locally adaptive, range adaptation has been shown to lead to suboptimal choices, particularly notable in reinforcement learning (RL) situations when options are extrapolated from their original context to a new one. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counterintuitive prediction: Decreasing task difficulty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of participants performing variants of an RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing task difficulty. Last, we propose a range-adapting model and show that it is able to parsimoniously capture all the behavioral results.
AB - Evidence suggests that economic values are rescaled as a function of the range of the available options. Although locally adaptive, range adaptation has been shown to lead to suboptimal choices, particularly notable in reinforcement learning (RL) situations when options are extrapolated from their original context to a new one. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counterintuitive prediction: Decreasing task difficulty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of participants performing variants of an RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing task difficulty. Last, we propose a range-adapting model and show that it is able to parsimoniously capture all the behavioral results.
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U2 - 10.1126/sciadv.abe0340
DO - 10.1126/sciadv.abe0340
M3 - Article
C2 - 33811071
AN - SCOPUS:85103754658
SN - 2375-2548
VL - 7
JO - Science Advances
JF - Science Advances
IS - 14
M1 - eabe0340
ER -