Evidence map›Paper›PMID 40977085›Full record

ArticleBrain and behavior2025

The Combined Effect of Visual Stimulus Complexity and Semantic Content on Audiovisual Associative Equivalence Learning.

Kálmán Tót, Noémi Harcsa-Pintér, Adél Papp, Balázs Bodosi, Attila Nagy, Gabriella Eördegh

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Article in Brain and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Kálmán TótDepartment of Physiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.ORCID https://orcid.org/0000-0002-7641-7182
Noémi Harcsa-PintérDepartment of Physiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
Adél PappDepartment of Physiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
Balázs BodosiDepartment of Physiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
Attila NagyDepartment of Physiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
Gabriella EördeghDepartment of Theoretical Health Sciences and Health Management, Faculty of Health Sciences and Social Studies, University of Szeged, Szeged, Hungary.

Funding

2023/5S479Szent-Györgyi Albert Orvostudományi Kar, Szegedi Tudományegyetem
6 · The paper itself

Abstract

backgroundThe Rutgers Acquired Equivalence Test (RAET) is an associative learning task that requires participants to learn pairs of visual stimuli and then recall and generalize these associations. To further explore this cognitive task, we developed three audiovisual learning tests with the same structure as the original RAET.

methodsEach audiovisual test applied the same four distinct auditory antecedents but differed in visual consequents in complexity and semantic content, that is, cartoon faces (SoundFace), colored fish (SoundFish), and geometric shapes (SoundPolygon), respectively. The present study investigated the effect of these different visual stimuli on performance in audiovisual associative equivalence learning. Learning performance was assessed across three phases: acquisition, retrieval, and generalization. A total of 52 participants (25 females, 27 males, mean age = 25.88 ± 10.28 years) completed the tasks. Statistical analyses, including Friedman's ANOVA and Wilcoxon matched-pairs tests with Bonferroni correction, were applied to evaluate differences in performance across the tests.

resultsParticipants consistently performed significantly (p < 0.01) better and responded faster in learning, retrieval, and generalization phases of the SoundFace test compared to the SoundFish and SoundPolygon tests, which did not significantly differ from each other. Additionally, a semantic association task confirmed that face and fish stimuli were significantly (p < 0.01) richer in semantic content than polygons, yet only face stimuli significantly facilitated audiovisual learning outcomes.

conclusionThese results suggest that the semantic content of visual stimuli-which could influence their verbalizability-is not sufficient on its own to enhance performance in audiovisual associative learning. Additionally, the number and variety of different features in visual stimulus sets (such as faces, fish, or polygons) may also significantly influence performance in audiovisual equivalence learning.

Indexed as

Association LearningAuditory PerceptionVisual PerceptionAcoustic StimulationAdultFemaleGeneralization, PsychologicalHumansMaleMental RecallPhotic StimulationSemanticsYoung Adultassociative learning | audiovisual | complexity | human | psychophysics | semantic content

Identifiers

PMID40977085
PMCPMC12451068

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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.