Neural Mechanisms of Human Decision-Making
Seth Herd, Kai Krueger, Ananta Nair, Jessica Mollick, and Randall, OReilly

TL;DR
This paper introduces a comprehensive neural model of human decision-making, integrating multiple brain regions and mechanisms to explain how decisions are proposed, predicted, evaluated, and reinforced, drawing parallels with animal action-selection.
Contribution
It presents a detailed, hierarchical computational model of human decision-making based on neural mechanisms, linking cortical, basal ganglia, and reward systems.
Findings
Model explains complex decision processes with neural plausibility.
Predicts how reward history influences plan acceptance.
Generates hypotheses on risky decision-making mechanisms.
Abstract
We present a computational and theoretical 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. Task-relevant areas of cortex propose a candidate plan using fast, model-free, parallel constraint-satisfaction computations. Other areas of cortex and medial temporal lobe can then predict likely outcomes of that plan in this situation. This step is optional. This prediction-(or model-) based computation produces 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. Finally 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…
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