Evidence map›Paper›PMID 32174816›Full record

ReviewFrontiers in integrative neuroscience2020

What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective.

Di Fu, Cornelius Weber, Guochun Yang, Matthias Kerzel, Weizhi Nan, Pablo Barros, Haiyan Wu, Xun Liu, Stefan Wermter

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in integrative neuroscience, 2020. 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
3.0field-weighted citation impact, top 9% of its field
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

11 citing papers in PubMed, 29 citations in OpenAlex.

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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

9 authors at 4 institutions in 2 countries.

Di FuCAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
Cornelius WeberDepartment of Informatics, University of Hamburg, Hamburg, Germany.
Guochun YangCAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
Matthias KerzelDepartment of Informatics, University of Hamburg, Hamburg, Germany.
Weizhi NanDepartment of Psychology, Center for Brain and Cognitive Sciences, School of Education, Guangzhou University, Guangzhou, China.
Pablo BarrosDepartment of Informatics, University of Hamburg, Hamburg, Germany.
Haiyan WuCAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
Xun LiuCAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
Stefan WermterDepartment of Informatics, University of Hamburg, Hamburg, Germany.
Universität Hamburg · DEInstitute of Psychology, Chinese Academy of Sciences · CNGuangzhou University · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for intelligent computational agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.

Indexed as

auditory attentioncomputational modelingcrossmodal learningdeep learningselective attentionvisual attention

Identifiers

PMID32174816
PMCPMC7056875
OpenAlexW3008176820

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.