Evidence map›Paper›PMID 33569710›Full record

ArticleBehavior research methods2021

RealPic: Picture norms of real-world common items.

Cristiane Souza, Margarida V Garrido, Magda Saraiva, Joana C Carmo

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Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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  10. Aesthetic appeal influences visual search performance.Attention, perception & psychophysics · 2022
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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

4 authors.

Cristiane SouzaIscte-Instituto Universitário de Lisboa, CIS-IUL, Av. das Forças Armadas, 1649-026, Lisboa, Portugal. Cristiane_Anunciacao_Souza@iscte-iul.pt.
Margarida V GarridoIscte-Instituto Universitário de Lisboa, CIS-IUL, Av. das Forças Armadas, 1649-026, Lisboa, Portugal.
Magda SaraivaIscte-Instituto Universitário de Lisboa, CIS-IUL, Av. das Forças Armadas, 1649-026, Lisboa, Portugal.
Joana C CarmoFaculdade de Psicologia, Universidade de Lisboa, Lisboa, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pictures are often used as stimuli in several fields, such as psychology and neuroscience. However, co-occurring image-related properties might impact their processing, emphasizing the importance of validating such materials to guarantee the quality of research and professional practices. This is particularly relevant for pictures of common items because of their wide applicability potential. Normative studies have already been conducted to create and validate such pictures, yet most of them focused on stimulus without naturalistic elements (e.g., line drawings). Norms for real-world pictures of common items are rare, and their normative examination does not always simultaneously assess affective, semantic and perceptive dimensions, namely in the Portuguese context. Real-world pictures constitute pictorial representations of the world with realistic details (e.g., natural color or position), thus improving their ecological validity and their suitability for empirical studies or intervention purposes. Consequently, the establishment of norms for real-world pictures is mandatory for exploring their ecological richness and to uncover their impact across several relevant dimensions. In this study, we established norms for 596 real-world pictures of common items (e.g., tomato, drum) selected from existing databases and distributed into 12 categories. The pictures were evaluated on nine dimensions by a Portuguese sample. The results present the norms by item, by dimension and their correlations as well as cross-cultural analyses. RealPic is a culturally based dataset that offers systematic and flexible standards and is suitable for selecting stimuli while controlling for confounding effects in empirical tasks and interventional applications.

Indexed as

SemanticsDatabases, FactualHumansAffectiveCross-cultural analysisNormsPerceptiveReal-world picturesSemantic

Identifiers

PMID33569710

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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.