Citation

Herd S, Krueger K, Nair A, Mollick J, O'Reilly R. Cogn. Affect. Behav. Neurosci. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Holtzbrinck Springer Nature Publishing Group)

DOI

10.3758/s13415-020-00842-0

PMID

unavailable

Abstract

We present a theory and neural network model of the neural mechanisms underlying human decision-making. We propose a detailed model of the interaction between brain regions, under a proposer-predictor-actor-critic​ ​framework. This theory is based on detailed animal data and theories of action-selection. Those theories are adapted to serial operation to bridge levels of analysis and explain human decision-making. Task-relevant areas of cortex propose a candidate plan using fast, model-free, parallel neural computations. Other areas of cortex and medial temporal lobe can then predict​ likely outcomes of that plan in this situation. This optional prediction- (or model-) based computation can produce better accuracy and generalization, at the expense of speed. Next, linked regions of basal ganglia act​ to accept or reject the proposed plan based on its reward history in similar contexts. If that plan is rejected, the process repeats to consider a new option. The reward-prediction system acts as a critic​ to determine the value of the outcome relative to expectations and produce dopamine as a training signal for cortex and basal ganglia. By operating sequentially and hierarchically, the same mechanisms previously proposed for animal action-selection could explain the most complex human plans and decisions. We discuss explanations of model-based decisions, habitization, and risky behavior based on the computational model.

Language: en

Keywords

Decision-making; Basal ganglia; Controlled processing; Model-based; Neural network