Active visual search in naturalistic environments reflects individual differences in classic visual search performance

• Published in: Scientific reports Vol. 13; no. 1; pp. 631 - 9
• Main Authors: Botch, Thomas L., Garcia, Brenda D., Choi, Yeo Bi, Feffer, Nicholas, Robertson, Caroline E.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.01.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/477; 631/477/2811; Airports; Attention; Computer applications; Computer science; Efficiency; Environment; Exploratory behavior; Eye Movements; Humanities and Social Sciences; Humans; Individuality; Laboratories; Memory; multidisciplinary; Paradigms; Science; Science (multidisciplinary); Virtual Reality; Visual Perception
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-27896-7

Visual search is a ubiquitous activity in real-world environments. Yet, traditionally, visual search is investigated in tightly controlled paradigms, where head-restricted participants locate a minimalistic target in a cluttered array that is presented on a computer screen. Do traditional visual search tasks predict performance in naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality technology to test the degree to which classic and naturalistic search are limited by a common factor, set size, and the degree to which individual differences in classic search behavior predict naturalistic search behavior in a large sample of individuals (N = 75). In a naturalistic search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic search task, participants searched for a target within a simple array of colored letters using only eye-movements. In each task, we found that participants’ search performance was impacted by increases in set size—the number of items in the visual display. Critically, we observed that participants’ efficiency in classic search tasks—the degree to which set size slowed performance—indeed predicted efficiency in real-world scenes. These results demonstrate that classic, computer-based visual search tasks are excellent models of active, real-world search behavior.