Neuron
Volume 94, Issue 2, 19 April 2017, Pages 401-414.e6
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Article
Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty

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Highlights

  • Metaplasticity can adjust learning according to reward uncertainty

  • Learning is adjusted without knowledge of the environment or explicit optimization

  • Metaplasticity model predicts choice behavior more accurately than optimal models

  • Changes in the activity of model neurons can be used to estimate uncertainty

Summary

Value-based decision making often involves integration of reward outcomes over time, but this becomes considerably more challenging if reward assignments on alternative options are probabilistic and non-stationary. Despite the existence of various models for optimally integrating reward under uncertainty, the underlying neural mechanisms are still unknown. Here we propose that reward-dependent metaplasticity (RDMP) can provide a plausible mechanism for both integration of reward under uncertainty and estimation of uncertainty itself. We show that a model based on RDMP can robustly perform the probabilistic reversal learning task via dynamic adjustment of learning based on reward feedback, while changes in its activity signal unexpected uncertainty. The model predicts time-dependent and choice-specific learning rates that strongly depend on reward history. Key predictions from this model were confirmed with behavioral data from non-human primates. Overall, our results suggest that metaplasticity can provide a neural substrate for adaptive learning and choice under uncertainty.

Keywords

metaplasticity
uncertainty
volatility
sub-optimality
decision making
reward
learning rate

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