Evidence map›Paper›PMID 36305588›Full record

ArticleeLife2022

High-throughput automated methods for classical and operant conditioning of

Elise C Croteau-Chonka, Michael S Clayton, Lalanti Venkatasubramanian, Samuel N Harris, Benjamin M W Jones, Lakshmi Narayan, Michael Winding, Jean-Baptiste Masson, Marta Zlatic, Kristina T Klein

Open access · goldAbstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
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

6 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Future avenues inLearning & memory (Cold Spring Harbor, N.Y.) · 2024
    Review
  5. Article
  6. 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

10 authors at 3 institutions in 3 countries.

Elise C Croteau-Chonka *Department of Zoology, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0001-5116-3772
Michael S Clayton *MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.ORCID 0000-0003-1152-9199
Lalanti VenkatasubramanianDepartment of Zoology, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-9280-8335
Samuel N HarrisMRC Laboratory of Molecular Biology, Cambridge, United Kingdom.
Benjamin M W JonesMRC Laboratory of Molecular Biology, Cambridge, United Kingdom.
Lakshmi NarayanJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.
Michael WindingDepartment of Zoology, University of Cambridge, Cambridge, United Kingdom.
Jean-Baptiste MassonJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.
Marta Zlatic *Department of Zoology, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-3149-2250
Kristina T Klein *Department of Zoology, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-8772-3628
Howard Hughes Medical Institute · USMRC Laboratory of Molecular Biology · GBUniversity of Cambridge · GB

Funding

Howard Hughes Medical InstituteMedical Research Council MC_UP_1201/20Wellcome Trust 205050/B/16/ZWellcome Trust 205050/Z/16/Z
6 · The paper itself

Abstract

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The

Indexed as

Conditioning, OperantDrosophilaAnimalsConditioning, ClassicalDrosophila melanogasterLarvabehavior detectionclosed-loop trackingcomputational biologycomputer visionD. melanogasterDrosophila melanogaster larvaneuroscienceoperant conditioningstimulationsystems biologytrace conditioning

Identifiers

PMID36305588
PMCPMC9678368
OpenAlexW4307462330

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.