ArticleNature communications2021
Entropy-based metrics for predicting choice behavior based on local response to reward.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
- Polarization Holographic 4D Phenotyping Reveals Nanoplastic Induced Dysfunction in Zooplankton.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Posterior parietal cortex mediates rarity-induced decision bias.National science review · 2026Article
- Ventrolateral prefrontal cortex in macaques guides decisions in different learning conditions.Nature communications · 2026Article
- Uncertainty and reward histories have distinct effects on decisions after wins and losses.Scientific reports · 2026Article
- Sex differences in task engagement and lapse rate during reward learning.Oxford open neuroscience · 2026Article
- Contribution of amygdala to dynamic model arbitration under uncertainty.Nature communications · 2025Article
- An Information-Theoretic Framework for Understanding Learning and Choice Under Uncertainty.Entropy (Basel, Switzerland) · 2025Article
- Disrupted decision making in adult male rats after prolonged withdrawal from ethanol vapor exposure.Alcohol, clinical & experimental research · 2025Article
- Chronic ethanol exposure produces sex-dependent impairments in value computations in the striatum.Science advances · 2025Article
- Chronic Ethanol Exposure Produces Persistent Impairment in Cognitive Flexibility and Decision Signals in the Striatum.bioRxiv : the preprint server for biology · 2025Article
- The role of rat prelimbic cortex in decision making.bioRxiv : the preprint server for biology · 2024Article
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- Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys.Cognitive, affective & behavioral neuroscience · 2023Article
- Undermatching Is a Consequence of Policy Compression.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- An Information Theoretic Approach to Symbolic Learning in Synthetic Languages.Entropy (Basel, Switzerland) · 2022Article
- Entropy-based metrics for predicting choice behavior based on local response to reward.Nature communications · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
For decades, behavioral scientists have used the matching law to quantify how animals distribute their choices between multiple options in response to reinforcement they receive. More recently, many reinforcement learning (RL) models have been developed to explain choice by integrating reward feedback over time. Despite reasonable success of RL models in capturing choice on a trial-by-trial basis, these models cannot capture variability in matching behavior. To address this, we developed metrics based on information theory and applied them to choice data from dynamic learning tasks in mice and monkeys. We found that a single entropy-based metric can explain 50% and 41% of variance in matching in mice and monkeys, respectively. We then used limitations of existing RL models in capturing entropy-based metrics to construct more accurate models of choice. Together, our entropy-based metrics provide a model-free tool to predict adaptive choice behavior and reveal underlying neural mechanisms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.