Evidence map›Paper›PMID 32876677›Full record

ArticleJournal of vision2020

Overt attentional correlates of memorability of scene images and their relationships to scene semantics.

Muxuan Lyu, Kyoung Whan Choe, Omid Kardan, Hiroki P Kotabe, John M Henderson, Marc G Berman

Abstract read
In one paragraph

Article in Journal of vision, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Muxuan LyuDepartment of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong, China.
Kyoung Whan ChoeDepartment of Psychology, The University of Chicago, Chicago, IL, USA.
Omid KardanDepartment of Psychology, The University of Chicago, Chicago, IL, USA.
Hiroki P KotabeKotabe Labs, Chicago, IL, USA.
John M HendersonCenter for Mind and Brain and Department of Psychology, University of California, Davis, Davis, CA, USA.
Marc G BermanDepartment of Psychology, The University of Chicago, Chicago, IL, USA.

Funding

Guiding Attention in Real-World ScenesR01EY027792 · NEI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI John M Henderson · 2017 to 2026
$2.5M
NEI NIH HHS R01 EY027792
6 · The paper itself

Abstract

Computer vision-based research has shown that scene semantics (e.g., presence of meaningful objects in a scene) can predict memorability of scene images. Here, we investigated whether and to what extent overt attentional correlates, such as fixation map consistency (also called inter-observer congruency of fixation maps) and fixation counts, mediate the relationship between scene semantics and scene memorability. First, we confirmed that the higher the fixation map consistency of a scene, the higher its memorability. Moreover, both fixation map consistency and its correlation to scene memorability were the highest in the first 2 seconds of viewing, suggesting that meaningful scene features that contribute to producing more consistent fixation maps early in viewing, such as faces and humans, may also be important for scene encoding. Second, we found that the relationship between scene semantics and scene memorability was partially (but not fully) mediated by fixation map consistency and fixation counts, separately as well as together. Third, we found that fixation map consistency, fixation counts, and scene semantics significantly and additively contributed to scene memorability. Together, these results suggest that eye-tracking measurements can complement computer vision-based algorithms and improve overall scene memorability prediction.

Indexed as

AttentionSpatial MemoryAlgorithmsEye-Tracking TechnologyFemaleFixation, OcularHumansMaleSemantics

Identifiers

PMID32876677
PMCPMC7476653

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.