ArticleBehavioral neuroscience2022
How do real animals account for the passage of time during associative learning?
Article in Behavioral neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- Duration between rewards controls the rate of behavioral and dopaminergic learning.Nature neuroscience · 2026Article
- Information, certainty, and learning.eLife · 2026Article
- Reconciling time and prediction error theories of associative learning.Nature communications · 2025Article
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- Prospective contingency explains behavior and dopamine signals during associative learning.Nature neuroscience · 2025Article
- Mesolimbic dopamine ramps reflect environmental timescales.bioRxiv : the preprint server for biology · 2025Article
- Learning to express reward prediction error-like dopaminergic activity requires plastic representations of time.Nature communications · 2024Article
- Dopamine transients follow a striatal gradient of reward time horizons.Nature neuroscience · 2024Article
- The role of prospective contingency in the control of behavior and dopamine signals during associative learning.bioRxiv : the preprint server for biology · 2024Article
- Dual credit assignment processes underlie dopamine signals in a complex spatial environment.Neuron · 2023Article
- Emergence of belief-like representations through reinforcement learning.PLoS computational biology · 2023Article
- Striatal Dopamine: The Cement of the Brain?: Jeong H, Taylor A, Floeder JR, et al. Mesolimbic dopamine release conveys causal associations. Science 2022;378:eabq6740.: Jeong H, Taylor A, Floeder JR, et al. Mesolimbic dopamine release conveys causal associations. Science 2022;378:eabq6740.Movement disorders : official journal of the Movement Disorder Society · 2023Article
- Emergence of belief-like representations through reinforcement learning.bioRxiv : the preprint server for biology · 2023Article
- Dual credit assignment processes underlie dopamine signals in a complex spatial environment.bioRxiv : the preprint server for biology · 2023Article
- Mesolimbic dopamine release conveys causal associations.Science (New York, N.Y.) · 2022Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
Animals routinely learn to associate environmental stimuli and self-generated actions with their outcomes such as rewards. One of the most popular theoretical models of such learning is the reinforcement learning (RL) framework. The simplest form of RL, model-free RL, is widely applied to explain animal behavior in numerous neuroscientific studies. More complex RL versions assume that animals build and store an explicit model of the world in memory. To apply these approaches to explain animal behavior, typical neuroscientific RL models make implicit assumptions about how real animals represent the passage of time. In this perspective, I explicitly list these assumptions and show that they have several problematic implications. I hope that the explicit discussion of these problems encourages the field to seriously examine the assumptions underlying timing and reinforcement learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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.