@article{ref1,
title="Neural mechanisms of human decision-making",
journal="Cognitive, affective and behavioral neuroscience",
year="2021",
author="Nair, Ananta and Krueger, Kai and Herd, Seth and Mollick, Jessica and O'Reilly, Randall",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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.<p /> <p>Language: en</p>",
language="en",
issn="1530-7026",
doi="10.3758/s13415-020-00842-0",
url="http://dx.doi.org/10.3758/s13415-020-00842-0"
}