Evidence map›Paper›PMID 38724883›Full record

ArticleMemory & cognition2024

Visual word identification beyond common words: The role of font and letter case in brand names.

Melanie Labusch, Jon Andoni Duñabeitia, Manuel Perea

Abstract read
In one paragraph

Article in Memory & cognition, 2024. 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. Letters, Words, Sentences, and Reading.Journal of cognition · 2024
    Review
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.

Melanie LabuschDepartment of Methodology and ERI-Lectura, Universitat de València, Av. Blasco Ibáñez, 21, 46010, Valencia, Spain.
Jon Andoni DuñabeitiaUniversidad Nebrija, Madrid, Spain.
Manuel PereaDepartment of Methodology and ERI-Lectura, Universitat de València, Av. Blasco Ibáñez, 21, 46010, Valencia, Spain. mperea@uv.es.ORCID 0000-0002-3291-1365

Funding

Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana CIAICO/2021/172Ministerio de Ciencia e Innovación PID2020-116740GB-I00 (MCIN/ AEI/10.13039/501100011
6 · The paper itself

Abstract

While abstractionist theories of visual word recognition propose that perceptual elements like font and letter case are filtered out during lexical access, instance-based theories allow for the possibility that these surface details influence this process. To disentangle these accounts, we focused on brand names embedded in logotypes. The consistent visual presentation of brand names may render them much more susceptible to perceptual factors than common words. In the present study, we compared original and modified brand logos, varying in font or letter case. In Experiment 1, participants decided whether the stimuli corresponded to existing brand names or not, regardless of graphical information. In Experiment 2, participants had to categorize existing brand names semantically - whether they corresponded to a brand in the transportation sector or not. Both experiments showed longer response times for the modified brand names, regardless of font or letter-case changes. These findings challenge the notion that only abstract units drive visual word recognition. Instead, they favor those models that assume that, under some circumstances, the traces in lexical memory may contain surface perceptual information.

Indexed as

Pattern Recognition, VisualRecognition, PsychologyAdultFemaleHumansMaleReadingSemanticsYoung AdultAbstractionist accountsBrand namesInstance theoriesVisual word recognition

Identifiers

PMID38724883
PMCPMC11522189

What OpenQuestion holds

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LicenceCC BY
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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.