Evidence map›Paper›PMID 35333867›Full record

ArticlePLoS computational biology2022

Minimal cross-trial generalization in learning the representation of an odor-guided choice task.

Mingyu Song, Yuji K Takahashi, Amanda C Burton, Matthew R Roesch, Geoffrey Schoenbaum, Yael Niv, Angela J Langdon

Abstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Neuronal implementation of the temporal difference learning algorithm in the midbrain dopaminergic system.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mingyu SongPrinceton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.ORCID 0000-0002-6833-8329
Yuji K TakahashiNational Institute on Drug Abuse Intramural Research Program, NIH, Baltimore, Maryland, United States of America.
Amanda C BurtonDepartment of Psychology, University of Maryland, College Park, Maryland, United States of America.
Matthew R RoeschDepartment of Psychology, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0003-2854-6593
Geoffrey SchoenbaumNational Institute on Drug Abuse Intramural Research Program, NIH, Baltimore, Maryland, United States of America.
Yael NivPrinceton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.
Angela J LangdonPrinceton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.ORCID 0000-0003-4742-6976

Funding

Orbitofrontal cortex as a cognitive map of task statesR01DA042065 · NIDA · PRINCETON UNIVERSITY · PI NIV, YAEL · 2016 to 2020
$1.8M
Decoding the dynamic representation of reward predictions across mesocorticostriatal circuits during learningR01DA050647 · NIDA · PRINCETON UNIVERSITY · PI NIV, YAEL · 2020 to 2021
$567k
Decoding the dynamic representation of reward predictions across mesocorticostriatal circuits during learningU01DA050647 · NIDA · PRINCETON UNIVERSITY · PI NIV, YAEL · 2022 to 2022
$284k
NIDA NIH HHS R01 DA042065NIDA NIH HHS R01 DA050647NIDA NIH HHS U01 DA050647
6 · The paper itself

Abstract

There is no single way to represent a task. Indeed, despite experiencing the same task events and contingencies, different subjects may form distinct task representations. As experimenters, we often assume that subjects represent the task as we envision it. However, such a representation cannot be taken for granted, especially in animal experiments where we cannot deliver explicit instruction regarding the structure of the task. Here, we tested how rats represent an odor-guided choice task in which two odor cues indicated which of two responses would lead to reward, whereas a third odor indicated free choice among the two responses. A parsimonious task representation would allow animals to learn from the forced trials what is the better option to choose in the free-choice trials. However, animals may not necessarily generalize across odors in this way. We fit reinforcement-learning models that use different task representations to trial-by-trial choice behavior of individual rats performing this task, and quantified the degree to which each animal used the more parsimonious representation, generalizing across trial types. Model comparison revealed that most rats did not acquire this representation despite extensive experience. Our results demonstrate the importance of formally testing possible task representations that can afford the observed behavior, rather than assuming that animals' task representations abide by the generative task structure that governs the experimental design.

Indexed as

OdorantsRewardAnimalsCuesGeneralization, PsychologicalHumansRatsReinforcement, Psychology

Identifiers

PMID35333867
PMCPMC8986096

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

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.