Neural Mechanisms of Human Decision-Making.
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-selec...
| Published in: | Cognitive, Affective & Behavioral Neuroscience Vol. 21; no. 1; pp. 35 - 58 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Feb2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149471891&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149471891 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15307026 NA4 jtl: Cognitive, Affective & Behavioral Neuroscience issn: 15307026 maglogo: N pubinfo: dt: Feb2021 vid: 21 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149471891 147941379 10.3758/s13415-020-00842-0 149471891 ppf: 35 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Neural Mechanisms of Human Decision-Making. aug: au: Herd, Seth Krueger, Kai Nair, Ananta Mollick, Jessica O'Reilly, Randall affil: eCortex, Inc., Boulder, CO, USA sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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