Pupil diameter differentiates expertise in dental radiography visual search.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 30 09 2019
accepted: 13 05 2020
entrez: 30 5 2020
pubmed: 30 5 2020
medline: 25 7 2020
Statut: epublish

Résumé

Expert behavior is characterized by rapid information processing abilities, dependent on more structured schemata in long-term memory designated for their domain-specific tasks. From this understanding, expertise can effectively reduce cognitive load on a domain-specific task. However, certain tasks could still evoke different gradations of load even for an expert, e.g., when having to detect subtle anomalies in dental radiographs. Our aim was to measure pupil diameter response to anomalies of varying levels of difficulty in expert and student dentists' visual examination of panoramic radiographs. We found that students' pupil diameter dilated significantly from baseline compared to experts, but anomaly difficulty had no effect on pupillary response. In contrast, experts' pupil diameter responded to varying levels of anomaly difficulty, where more difficult anomalies evoked greater pupil dilation from baseline. Experts thus showed proportional pupillary response indicative of increasing cognitive load with increasingly difficult anomalies, whereas students showed pupillary response indicative of higher cognitive load for all anomalies when compared to experts.

Identifiants

pubmed: 32469952
doi: 10.1371/journal.pone.0223941
pii: PONE-D-19-27421
pmc: PMC7259659
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0223941

Commentaires et corrections

Type : ErratumIn

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Nora Castner (N)

Human-Computer Interaction, Institute of Computer Science, University Tübingen, Tübingen, Germany.

Tobias Appel (T)

Human-Computer Interaction, Institute of Computer Science, University Tübingen, Tübingen, Germany.

Thérése Eder (T)

Multiple Representations Lab, Leibniz-Institut für Wissensmedien, Tübingen, Germany.

Juliane Richter (J)

Multiple Representations Lab, Leibniz-Institut für Wissensmedien, Tübingen, Germany.

Katharina Scheiter (K)

Multiple Representations Lab, Leibniz-Institut für Wissensmedien, Tübingen, Germany.
University Tübingen, Tübingen, Germany.

Constanze Keutel (C)

Department of Oral- and Maxillofacial Radiology, University Clinic for Dentistry, Oral Medicine, and Maxillofacial Surgery, University of Tübingen, Tübingen, Germany.

Fabian Hüttig (F)

Department of Prosthodontics, University Clinic for Dentistry, Oral Medicine, and Maxillofacial Surgery, University of Tübingen, Tübingen, Germany.

Andrew Duchowski (A)

Visual Computing, Clemson University, Clemson, South Carolina, United States of America.

Enkelejda Kasneci (E)

Human-Computer Interaction, Institute of Computer Science, University Tübingen, Tübingen, Germany.

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