Evidence map›Paper›PMID 33852575›Full record

ArticlePloS one2021

No fruits without color: Cross-modal priming and EEG reveal different roles for different features across semantic categories.

Georgette Argiris, Raffaella I Rumiati, Davide Crepaldi

Abstract read
In one paragraph

Article in PloS one, 2021. 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.

Georgette ArgirisInternational School for Advanced Studies (SISSA), Trieste, Italy.ORCID 0000-0003-0999-2926
Raffaella I RumiatiInternational School for Advanced Studies (SISSA), Trieste, Italy.
Davide CrepaldiInternational School for Advanced Studies (SISSA), Trieste, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Category-specific impairments witnessed in patients with semantic deficits have broadly dissociated into natural and artificial kinds. However, how the category of food (more specifically, fruits and vegetables) fits into this distinction has been difficult to interpret, given a pattern of deficit that has inconsistently mapped onto either kind, despite its intuitive membership to the natural domain. The present study explores the effects of a manipulation of a visual sensory (i.e., color) or functional (i.e., orientation) feature on the consequential semantic processing of fruits and vegetables (and tools, by comparison), first at the behavioral and then at the neural level. The categorization of natural (i.e., fruits/vegetables) and artificial (i.e., utensils) entities was investigated via cross-modal priming. Reaction time analysis indicated a reduction in priming for color-modified natural entities and orientation-modified artificial entities. Standard event-related potentials (ERP) analysis was performed, in addition to linear classification. For natural entities, a N400 effect at central channel sites was observed for the color-modified condition compared relative to normal and orientation conditions, with this difference confirmed by classification analysis. Conversely, there was no significant difference between conditions for the artificial category in either analysis. These findings provide strong evidence that color is an integral property to the categorization of fruits/vegetables, thus substantiating the claim that feature-based processing guides as a function of semantic category.

Indexed as

SemanticsAdolescentAdultBrain MappingCognitionColorElectroencephalographyEvoked PotentialsEvoked Potentials, VisualFemaleFoodFruitHumansMalePhotic StimulationReaction Time

Identifiers

PMID33852575
PMCPMC8046255

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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.