Evidence map›Paper›PMID 41003767›Full record

ArticlePsychological research2025

Procedural and declarative knowledge simultaneously contribute to category response selection.

Priya B Kalra, Laura J Batterink, John P Minda

Abstract read
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In one paragraph

Article in Psychological research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Priya B KalraCenter for Brain and Mind, Western University, London, Canada. pkalra7@uwo.ca.
Laura J Batterink *Center for Brain and Mind, Western University, London, Canada.
John P Minda *Center for Brain and Mind, Western University, London, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Skilled behaviour in real-world contexts often relies on a combination of both declarative and procedural learning. However, precisely how declarative and procedural knowledge interact is not yet fully understood. Previous findings have shown that procedural and declarative learning may interact or compete at the systems level during encoding, consolidation, and retrieval, but beyond this, it is not known whether declarative and procedural representations themselves interact. The goal of the current study is to investigate whether procedural and declarative knowledge can contribute simultaneously to categorization response selection behavior. We designed a stimulus set in which information learned by each system sometimes supports different responses, and created trials in the test phase that are designed to maximize such divergence. Participants were instructed to use a completely diagnostic, verbalizable, shape-based rule to categorize exemplars, receiving feedback after each trial. However, unbeknownst to participants, the categories also differed probabilistically in their color distributions. Participants used both color (learned procedurally) and shape (learned declaratively) to categorize exemplars, responding more quickly when both sources indicated the same category judgement, and more slowly when they conflicted. Debriefing confirmed that most participants were unaware of the color distributions. These results show simultaneous trial-level contributions from both declarative and procedural memory systems. Our findings represent a novel form of interaction between the two systems and have implications for domains beyond the laboratory, such as decision-making and classroom instruction.

Indexed as

Concept FormationKnowledgeLearningAdultFemaleHumansMaleReaction TimeYoung Adult

Identifiers

What OpenQuestion holds

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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.