TY - JOUR
T1 - Rational metareasoning and the plasticity of cognitive control
AU - Lieder, Falk
AU - Shenhav, Amitai
AU - Musslick, Sebastian
AU - Griffiths, Thomas L.
N1 - Funding Information:
This research was supported by the Office of Naval Research through Grant No. MURI N00014-13-1-0431 to TLG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank Matthew Botvinick, Colin Hoy, and S.J. Katarina Slama for comments on an earlier version of the manuscript and Jonathan D. Cohen for useful discussions.
Publisher Copyright:
© 2018 Lieder et al.
PY - 2018/4
Y1 - 2018/4
N2 - The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.
AB - The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.
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U2 - 10.1371/journal.pcbi.1006043
DO - 10.1371/journal.pcbi.1006043
M3 - Article
C2 - 29694347
AN - SCOPUS:85046358518
SN - 1553-734X
VL - 14
JO - PLoS computational biology
JF - PLoS computational biology
IS - 4
M1 - e1006043
ER -