Pupillary Response in Visual Imagery

The extent and nature of the overlap between visual imagery and visual perception have been debated over the past century. Can visual imagery result in presumably automatic physiological response such as the pupillary light reflex (PLR)? Laeng & Sulutvedt (2014) reported pupillary responses to dark and bright imagined scenarios. Based on such findings, Kay, Keogh, and Pearson (2022) proposed using the magnitude of imagery-induced PLR as a measure of the ability to generated vivid imagery. We aimed to replicate Kay et al.'s (2022) findings on the PLR response in visual imagery. Ninety-five normally sighted participants were asked to view 16 stimuli in four luminance levels, and then imagine the previously seen stimulus. Pupillary responses were measured during both the perception and imagery periods. PLR response was examined by comparing the pupil diameter in the two darker luminance conditions against that in the two brighter conditions. PLR response was statistically significant in both the perception and the imagery periods (perception: F(1,94) = 598, p < .001; imagery: F(1,94) = 14.7, p < .001). Statistically significant bivariate correlations were consistently observed among the self-report questionnaires (VVIQ, OSIVQ, and SUIS) and the trial-by-trial vividness ratings, suggesting a shared mechanism underlying the subjective evaluation of imagery vividness. However, we could not replicate Kay et al.’s (2022) findings on the association between trial-by-trial vividness ratings and the magnitude of PLR response during the imagery periods. Subjective reports of imagery vividness could reflect both the ability to generate vivid imagery and the metacognitive evaluation of such ability.

Authors

Institutions

Publication Details

Journal
Journal of Vision
Published
2024-09-15
DOI
https://doi.org/10.1167/jov.24.10.622
Citations
1
Primary Topic
Visual perception and processing mechanisms
Type
article
Field-Weighted Citation Impact
0.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pupillary Response in Visual Imagery

Sing Hang Cheung, Sin Wan Sharon Hon
1 citations
Journal of Vision
Visual perception and processing mechanisms
0.34
article

Pupillary Response in Visual Imagery

Sing Hang Cheung, Sin Wan Sharon Hon
article en
1 citations

Abstract

The extent and nature of the overlap between visual imagery and visual perception have been debated over the past century. Can visual imagery result in presumably automatic physiological response such as the pupillary light reflex (PLR)? Laeng & Sulutvedt (2014) reported pupillary responses to dark and bright imagined scenarios. Based on such findings, Kay, Keogh, and Pearson (2022) proposed using the magnitude of imagery-induced PLR as a measure of the ability to generated vivid imagery. We aimed to replicate Kay et al.'s (2022) findings on the PLR response in visual imagery. Ninety-five normally sighted participants were asked to view 16 stimuli in four luminance levels, and then imagine the previously seen stimulus. Pupillary responses were measured during both the perception and imagery periods. PLR response was examined by comparing the pupil diameter in the two darker luminance conditions against that in the two brighter conditions. PLR response was statistically significant in both the perception and the imagery periods (perception: F(1,94) = 598, p < .001; imagery: F(1,94) = 14.7, p < .001). Statistically significant bivariate correlations were consistently observed among the self-report questionnaires (VVIQ, OSIVQ, and SUIS) and the trial-by-trial vividness ratings, suggesting a shared mechanism underlying the subjective evaluation of imagery vividness. However, we could not replicate Kay et al.’s (2022) findings on the association between trial-by-trial vividness ratings and the magnitude of PLR response during the imagery periods. Subjective reports of imagery vividness could reflect both the ability to generate vivid imagery and the metacognitive evaluation of such ability.

Journal of VisionVol. 24(10)
Northeastern University (US), University of Hong Kong (HK)
Openalex Percentile: Top 40%
Visual perception and processing mechanisms
0.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.