Evidence map›Paper›PMID 27232521›Full record

ArticleCognition2016

What is automatized during perceptual categorization?

Jessica L Roeder, F Gregory Ashby

Abstract read
In one paragraph

Article in Cognition, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

2 authors.

Jessica L RoederDepartment of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 93106, USA. Electronic address: Jessica.Roeder@austin.utexas.edu.
F Gregory AshbyDepartment of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.

Funding

The Cognitive Neuroscience of Human Category LearningR01MH063760 · NIMH · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI ASHBY, F. GREGORY, HELIE, SEBASTIEN · 2002 to 2018
$3.6M
NIMH NIH HHS R01 MH063760
6 · The paper itself

Abstract

An experiment is described that tested whether stimulus-response associations or an abstract rule are automatized during extensive practice at perceptual categorization. Twenty-seven participants each completed 12,300 trials of perceptual categorization, either on rule-based (RB) categories that could be learned explicitly or information-integration (II) categories that required procedural learning. Each participant practiced predominantly on a primary category structure, but every third session they switched to a secondary structure that used the same stimuli and responses. Half the stimuli retained their same response on the primary and secondary categories (the congruent stimuli) and half switched responses (the incongruent stimuli). Several results stood out. First, performance on the primary categories met the standard criteria of automaticity by the end of training. Second, for the primary categories in the RB condition, accuracy and response time (RT) were identical on congruent and incongruent stimuli. In contrast, for the primary II categories, accuracy was higher and RT was lower for congruent than for incongruent stimuli. These results are consistent with the hypothesis that rules are automatized in RB tasks, whereas stimulus-response associations are automatized in II tasks. A cognitive neuroscience theory is proposed that accounts for these results.

Indexed as

Association LearningConcept FormationVisual PerceptionHumansModels, NeurologicalModels, PsychologicalReaction TimeAutomatic learningCategorizationImplicit-explicit cognitionPerception

Identifiers

PMID27232521
PMCPMC4939132

What OpenQuestion holds

Textmetadata
LicenceTDM
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.