Properties of decision-making tasks govern the tradeoff between model-based and model-free learning
Abstract
<jats:title>Abstract</jats:title><jats:p>When decisions must be made between uncertain
options, optimal behavior depends on accurate estimations of the likelihoods of different
outcomes. The contextual factors that govern whether these estimations depend on model-free
learning (tracking outcomes) vs. model-based learning (learning generative stimulus
distributions) are poorly understood. We studied model-free and model-based learning
using serial decision-making tasks in which subjects selected a rule and then used
it to flexibly act on visual stimuli. A factorial approach defined a family of behavioral
models that could integrate model-free and model-based strategies to predict rule
selection trial-by-trial. Bayesian model selection demonstrated that the subjects
strategies varied depending on lower-level task characteristics such as the identities
of the rule options. In certain conditions, subjects integrated learned stimulus distributions
and tracked reward rates to guide their behavior. The results thus identify tradeoffs
between model-based and model-free decision strategies, and in some cases parallel
utilization, depending on task context.</jats:p>
Type
Journal articlePermalink
https://hdl.handle.net/10161/19285Published Version (Please cite this version)
10.1101/730663Publication Info
Abzug, Zachary; Sommer, Marc; & Beck, Jeffrey (2019). Properties of decision-making tasks govern the tradeoff between model-based and model-free
learning. bioRxiv. 10.1101/730663. Retrieved from https://hdl.handle.net/10161/19285.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
Collections
More Info
Show full item recordScholars@Duke
Marc A. Sommer
Professor of Biomedical Engineering
We study circuits for cognition. Using a combination of neurophysiology and biomedical
engineering, we focus on the interaction between brain areas during visual perception,
decision-making, and motor planning. Specific projects include the role of frontal
cortex in metacognition, the role of cerebellar-frontal circuits in action timing,
the neural basis of "good enough" decision-making (satisficing), and the neural mechanisms
of transcranial magnetic stimulation (TMS).

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info