Evidence map›Paper›PMID 37284936›Full record

ArticleLearning & behavior2023

Simple questions on simple associations: regularity extraction in non-human primates.

Jeremy Yeaton, Laure Tosatto, Joël Fagot, Jonathan Grainger, Arnaud Rey

Open access · hybridAbstract read
In one paragraph

Article in Learning & behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 30% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
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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

5 authors at 1 institution in 2 countries.

Jeremy YeatonAix Marseille Univ, CNRS, LPC, Marseille, France. jyeaton@uci.edu.ORCID 0000-0002-6650-8080
Laure TosattoAix Marseille Univ, CNRS, LPC, Marseille, France.
Joël FagotAix Marseille Univ, CNRS, LPC, Marseille, France.
Jonathan GraingerAix Marseille Univ, CNRS, LPC, Marseille, France.
Arnaud ReyAix Marseille Univ, CNRS, LPC, Marseille, France.
Centre National de la Recherche Scientifique · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When human and non-human animals learn sequences, they manage to implicitly extract statistical regularities through associative learning mechanisms. In two experiments conducted with a non-human primate species (Guinea baboons, Papio papio), we addressed simple questions on the learning of simple AB associations appearing in longer noisy sequences. Using a serial reaction time task, we manipulated the position of AB within the sequence, such that it could be either fixed (by appearing always at the beginning, middle, or end of a four-element sequence; Experiment 1) or variable (Experiment 2). We also tested the effect of sequence length in Experiment 2 by comparing the performance on AB when it was presented at a variable position within a sequence of four or five elements. The slope of RTs from A to B was taken for each condition as a measurement of learning rate. While all conditions differed significantly from a no-regularity baseline, we found strong evidence that the learning rate did not differ between the conditions. These results indicate that regularity extraction is not impacted by the position of the regularity within a sequence and by the length of the sequence. These data provide novel general empirical constraints for modeling associative mechanisms in sequence learning.

Indexed as

LearningPapio papioAnimalsReaction TimeAnimal cognitionAssociative learningSequence learningStatistical learning

Identifiers

PMID37284936
PMCPMC10716064
OpenAlexW4379599067

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.