Evidence map›Paper›PMID 34619516›Full record

ArticleCognition2022

The Lords of the Rings: People and pigeons take different paths mastering the concentric-rings categorization task.

Ellen M O'Donoghue, Matthew B Broschard, John H Freeman, Edward A Wasserman

Abstract read
In one paragraph

Article in Cognition, 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
–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

6 citing papers in PubMed.

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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

4 authors.

Ellen M O'DonoghueDepartment of Psychological and Brain Sciences, The University of Iowa, United States of America. Electronic address: ellen-odonoghue@uiowa.edu.
Matthew B BroschardDepartment of Psychological and Brain Sciences, The University of Iowa, United States of America.
John H FreemanDepartment of Psychological and Brain Sciences, The University of Iowa, United States of America.
Edward A WassermanDepartment of Psychological and Brain Sciences, The University of Iowa, United States of America.

Funding

ONTOGENESIS OF CATEGORIZATION: LINKING DEVELOPMENT, MECHANISMS, AND BIOLOGYP01HD080679 · NICHD · OHIO STATE UNIVERSITY · PI LOVE, BRADLEY C · 2016 to 2020
$5.8M
NICHD NIH HHS P01 HD080679
6 · The paper itself

Abstract

COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, & Waldron, 1998) is a prominent model of categorization which hypothesizes that humans have two independent categorization systems - one declarative, one associative - that can be recruited to solve category learning tasks. To date, most COVIS-related research has focused on just two experimental tasks: linear rule-based (RB) tasks, which purportedly encourage declarative rule use, and linear information-integration (II) tasks, which purportedly require associative learning mechanisms. We introduce and investigate a novel alternative: the concentric-rings task, a nonlinear category structure that both humans and pigeons can successfully learn and transfer to untrained exemplars. Yet, despite their broad behavioral similarities, humans and pigeons achieve their successful learning through decidedly different means. As predicted by COVIS, pigeons appear to rely solely on associative learning mechanisms, whereas humans appear to initially test but subsequently reject unidimensional rules. We discuss how variants of our concentric-rings task might yield further insights into which category-learning mechanisms are shared across species, as well as how categorization strategies might change throughout training.

Indexed as

ColumbidaeLearningAnimalsConcept FormationHumansProblem SolvingCategorizationCOVISDimensional learningPigeonsRule formation

Identifiers

PMID34619516
PMCPMC8639790

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Registered trials

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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.