Pupillary Response in Visual Imagery

Pupillary Response in Visual Imagery | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 May 2025 V1 Latest version Share on Pupillary Response in Visual Imagery Authors : Sharon Hon 0009-0008-2831-0453 [email protected] and Sing-Hang Cheung Authors Info & Affiliations https://doi.org/10.22541/au.174642498.85826657/v1 Published Journal of Vision Version of record Peer review timeline 170 views 66 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share 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 responses 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 generate 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. The PLR response was examined by comparing the pupil diameter in the two darker luminance conditions against that in the two brighter conditions. The PLR response was statistically significant in both the perception and 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. Our results indicate that while pupillary responses may reflect the presence of visual imagery, they do not consistently track the vividness of that imagery. This study highlights the potential of pupillometry as an objective measure of visual imagery but underscores the need for further validation and refinement to improve its reliability and applicability. Pupillary Response in Visual Imagery Sin Wan Sharon Hon 1 and Sing-Hang Cheung 1 1 Department of Psychology, The University of Hong Kong Author Note Sin Wan Sharon Hon https://orcid.org/0009-0008-2831-0453 Sing-Hang Cheung https://orcid.org/0000-0001-5182-0752 We have no conflict of interest to disclose. Correspondence concerning this article should be addressed to Sin Wan Sharon Hon ( [email protected] ) or Sing-Hang Cheung ( [email protected] ), The University of Hong Kong, Pokfulam, Hong Kong. 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 responses 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 generate 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. The PLR response was examined by comparing the pupil diameter in the two darker luminance conditions against that in the two brighter conditions. The PLR response was statistically significant in both the perception and 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. Our results indicate that while pupillary responses may reflect the presence of visual imagery, they do not consistently track the vividness of that imagery. This study highlights the potential of pupillometry as an objective measure of visual imagery but underscores the need for further validation and refinement to improve its reliability and applicability. Keywords: visual imagery, visual perception, pupillometry Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Apparatus The stimuli in the experiment were presented on a 17-inch LCD display monitor (model: Dell M783c, 1280 x 1024 at 75 Hz). Participants were asked to rest their chin on a chin rest during the experiment to limit head movements, with a viewing distance of 57 cm. The tasks were performed with ambient light off to minimize fluctuations in stimulus luminance. For the pupillary response task, we recorded pupil size and eye movement using Eyelink-1000. We continuously sampled the pupil area of the participants’ dominant eye at 1000 Hz throughout the task. We scaled pupil area values to millimeters (mm) using the artificial pupil diameter and the algorithm provided by SR research. Stimulus The experiment was conducted using PsychoPy (v2023.1.2). We presented sixteen achromatic shape stimuli for participants to perceive and later imagine when absent, across sixteen trials. These stimuli were evenly distributed among four luminance conditions: ’White’, ’Light gray’, ’Dark gray’, or ’Black’. Each stimulus was a single equilateral triangle with sides measuring 12.5 cm, subtending 12.5° of visual angle. The stimulus orientation was 0°, 90°, 180°, or 270°. The luminance levels of our stimuli differed from those used in Kay et al. (2022). The luminance levels used in our current study were chosen to optimize the perceptual PLR. The luminance levels 189 cd/m 2 (White), 94.4 cd/m 2 (Light gray), 4.09 cd/m 2 (Dark gray), and 0.67 cd/m 2 (Black). We presented all stimuli against a gray background (21.8 cd/m 2 luminance). The same gray background was also used during the baseline and imagery phases. A fixation cross was presented on a black background (0.67 cd/m 2 luminance) during the rest phase of each trial. The four stimulus luminance levels and the background luminance level were chosen to ensure that our participants could differentiate the different luminance levels. These chosen luminance levels also resulted in robust differences in perceptual PRL responses. Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Procedures Informed consent was obtained from the participants before they completed the self-report measures of VVIQ, OSIVQ, and SUIS. Before the eye-tracking task, participants completed a simple dominant eye test. They were also given practice trials to familiarize themselves with the experimental procedures. They were instructed to minimize body movements and maintain focus on the screen during the tasks. Each eye-tracking data collection session began with the eye-tracker calibration which required the participants to look at nine dots sequentially. Each trial started with a white fixation cross displayed for one second at the center of a gray screen. Following this, a stimulus was presented for five seconds at the center of the screen. Participants were asked to concentrate on the stimuli and to remember their size, orientation, and level of brightness. This perception phase was followed by an eight-second black screen, allowing any after-image to fade and the pupils to return to resting levels. The gray screen was then displayed again for six seconds. During this time, participants were cued to imagine the stimuli observed during the perceptual phase. Finally, participants reported the vividness of their imagery on a scale of 1–4, with 1 being ’not vivid at all – no shape appeared in imagery’ and 4 being ’very vivid – almost like seeing it’ (see Figure 1 for pupillometry imagery experiment timeline). As a post-task measure, they completed the NASA Task Load Index (NASA-TLX) to assess their subjective workload during the pupillometry experiment. This questionnaire evaluated their perceived mental and physical demands, effort, and frustration experienced throughout the task. Then, the participants attended a debriefing session where the purpose and hypothesis of the study were explained. They received compensation in the form of course credits or monetary rewards. Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Analysis and Results We recorded pupillary responses during both the perception and imagery periods (see Figure 3 for the mean pupil size changes). Data from the two darker luminance conditions were grouped under “Dark Stimuli”, and similarly data from the two brighter luminance conditions were grouped under “Bright Stimuli”. A significant pupillary light reflex (PLR) response was observed during the perception period ( F (1,94) = 598, p < .001), and the imagery period ( F (1,94) = 14.7, p < .001). Post hoc comparisons revealed that during perception, pupil size was significantly larger when exposed to the Dark Stimuli ( M = 0.321, SD = 0.19) compared to the Bright Stimuli ( M = –0.48, SD = 0.22, p < .001, d = 2.51, 95% CI [2.10, 2.92]). Similarly, during imagery, pupil size was significantly larger in response to the Dark Stimuli ( M = –0.09, SD = 0.13) than the Bright Stimuli ( M = –0.15, SD = 0.14, p <.001, d = 0.39, 95% CI [0.18, 0.60]) (see Figures 2 & 3). Pupillary difference scores were determined by subtracting the Bright condition means from the Dark condition means. These scores were also grouped according to the within-trial vividness ratings. This was done to determine if changes in pupil size during imagery were indicative of subjects’ personal experience of the vividness of visual imagery. A linear mixed-effects model analysis was conducted to investigate the effect of pupil-difference scores on trial-by-trial vividness ratings. We examined the relationship between pupil-difference scores and subjective vividness ratings during imagery tasks. The results suggest that pupil size changes may be associated with how vividly participants experience mental images. Specifically, the largest pupil-difference scores were observed for Vividness Rating 1, with progressively smaller differences for higher ratings. This trend indicates that greater pupil constriction may correspond to lower vividness ratings, suggesting a potential link between physiological responses and subjective vividness. However, the pupillary light response did not consistently track moment-to-moment vividness in this study, differing from findings reported by Kay et al. (2022). We found significant bivariate correlations among the self-report questionnaires—VVIQ, OSIVQ, and SUIS—and the trial-by-trial vividness ratings. We found positive correlations between VVIQ and OSIVQ ( r (95)=0.303, p =.003), between VVIQ and SUIS ( r (95)=0.344, p <.001), and between OSIVQ and SUIS ( r (95)=0.364, p <.001). We also found positive correlations between trial-by-trial vividness ratings and VVIQ ( r (95)=0.382, p <.001,), SUIS ( r (95)=0.274, p =.007), and OSIVQ ( r (95)=0.251, p =.014) (see Figure 9). We used the NASA-TLX scale to assess participants’ perceived workload. Participants rated their Own Performance relatively high (14.1), while mental demand (8.2), effort (7.9), and physical demand (6.4) were rated at moderate levels. In contrast, lower ratings for temporal demand (4.4) and frustration (3.7) suggest that participants experienced minimal time pressure and frustration during the task (see Figure 8). Split-Half Reliability Analysis We conducted split-half reliability analysis on the pupillometry measures. The split-half correlations for different measures are listed in Table 1. In each condition, the 16 trials were first grouped under four bins of four trials. Three estimates of split-half correlations were calculated by using Bins 1 and 2 as the first half, Bins 1 and 3 as the first half, and Bins 1 and 4 as the first half. The average across the three estimates were reported in the last column of Table 1. The average split-half correlation of pupillary difference score in perception was .728, indicating acceptable internal consistency. In contrast, the average split-half correlation of pupillary difference score in imagery was .097, which suggested insufficient reliability. These results highlighted a discrepancy between the reliability of pupillary difference scores in perception and in imagery. Split-half correlations for the trial-by-trial vividness ratings were also included in Table 1 for reference. It was clear that the current pupillometry-based imagery measures did not exhibit sufficient reliability. The observed split-half correlations underscored the need for further refinement and validation of these measures to improve their consistency and useability in future research. Discussion Our study explored the shared mechanisms underlying perception and imagery, finding that pupil dilation responded to both imagined and perceived brightness. However, unlike Kay et al. (2022), our findings did not show a consistent increase in pupil-difference scores with higher vividness ratings, suggesting a distinct pattern of physiological response during imagery. Recent studies have linked pupil responses to the intensity of visual imagery. Kay et al. (2022) found a strong correlation between pupillary difference scores and imagery vividness, suggesting that physiological responses could reflect mental visualization depth. Earlier research by Pearson et al. (2011) and Rademaker and Pearson (2012) showed that individuals with high metacognitive awareness of their visual imagery abilities could accurately evaluate their mental image intensity. These findings offered a framework for assessing visual imagery strength. Pearson and colleagues also found evidence that the visual cortex was active during imagery, supporting a shared neural basis with perception and linking subjective experiences to measurable physiological responses. However, we did not replicate this relationship. Our findings showed no significant correlation between PLR response intensity and vividness ratings, suggesting that pupil dilation did not track visual imagery vividness moment-to-moment. Our failed replication could be potentially explained by the observed response pattern in the in-trial vividness rating task. A small number of Vividness Rating 1 responses were observed in the current study. The potential lack of variability in the vividness responses could limit the modeling of PLR response as a function of vividness. (see Figure 7). Future research could encourage participants to use a wider range of vividness in the in-trial rating task. More importantly, our analysis revealed the unacceptably low reliability of the imagery-based PLR responses. That could be the main limiting factor in using the imagery-based PLR response to predict subjective self-report measures. Our study also provided insights into subjective imagery vividness evaluations. Significant correlations were consistently observed among self-report measures, including the Vividness of Visual Imagery Questionnaire (VVIQ), the Object-Spatial Imagery and Verbal Questionnaire (OSIVQ), and the Spontaneous Use of Imagery Scale (SUIS). These correlations were also seen in the trial-by-trial vividness ratings. These correlations, along with vividness ratings on a per-trial basis, suggest a shared mechanism underlying the subjective evaluation of imagery vividness. These findings highlight the consistent relationship between self-reported imagery vividness and trial-specific vividness ratings, linking the stable individual traits to the state-dependent fluctuations in imagery experiences. In Laeng and Sulutvedt’s (2014) Experiment 2, which introduced the imaginary lightness paradigm, the authors reported significant differences in pupillary diameters during imagery ( t (17)=5.16, p <.0001, Cohen’s d =1.25, 95% CI [0.28, 2.22]). Kay et al. (2022) also observed significant pupil size differences during imagery ( t (41)=7.66, p <.001, Cohen’s d =1.18, 95% CI [0.78, 1.57]). However, we found a weaker effect ( t (94)=3.83, p <.001, d =0.39, 95% CI [0.18, 0.60]), despite the larger sample we used (see Table 2). This highlights the individual variability in pupillary responses to imagery and the intricate relationship between imagery vividness and its physiological manifestation. We revisited the PLR data during perception, aiming to clarify the reasons behind the small PLR effect we observed in the imagery condition. The effect size for our PLR effect during perception was d = 2.51. This was a large effect, indicating a significant increase in pupil size during perception when exposed to the Dark Stimuli compared to the Bright Stimuli. Kay et al. (2022) reported similar results ( t (41)=12.63, p <.001, d = 1.95, 95% CI = [1.43, 2.46]), with a slightly smaller effect size. Laeng and Sulutvedt (2014) did not provide available data for comparing our PLR data during perception with theirs. Our study achieved a larger effect size because we aimed to optimize the luminance settings for a robust PLR effect during perception. Specifically, we chose the luminance levels in order to better isolate and measure the effects of luminance changes on pupil size during perception. Both Laeng and Sulutvedt’s (2014) Experiment 2 and Kay et al.’s (2022) study found a larger PLR effect for the imagery condition than what we found in our study. In contrast, our study demonstrated a larger PLR effect size during perception than what Kay et al. observed. These comparisons highlight the importance of taking into account the variability in observed effects across studies when one tries to interpret, and even generalize the findings observed from a single study. We speculate that the smaller PLR effect in imagery observed in our study was partially due to the low reliability of the imagery PLR measures. Discrepancies between studies may arise from variations in task complexity or participant engagement. We used the NASA-TLX scale to assess workload. Our results indicated that participants found the task manageable, but ease of execution might have led to reduced engagement. Future studies could incorporate additional measures, such as real-time questioning, to assess engagement levels and mental imagery extent. Attention and task engagement significantly influence imagery-related physiological responses. Smallwood & Schooler (2015) and Unsworth & Robison (2017) found that enhanced attention strengthens mental imagery and intensifies physiological reactions, such as pupil dilation. A limitation of our study was the absence of catch trials in the pupillometry task, a methodological feature also noted by Kay et al. (2022). Catch trials, where participants describe their visualizations, could enhance engagement assessment and data reliability. Future studies should incorporate such trials and experimental manipulations to assess how task complexity influences engagement and imagery intensity. However, adding complexity, such as a memory component, may introduce non-visual cognitive strategies, potentially diluting the pupillary light reflex (Pearson & Keogh, 2019). Another key finding is that pupillary response to imagery light varies across individuals. Histogram comparisons of pupillary responses during perception (Figure 4) and imagery (Figure 5) revealed distinct distribution patterns, highlighting variability in pupil size changes across conditions. In the perception phase (Figure 4), the frequency distribution followed a bell-shaped curve, with most pupil size differences clustering around the mean, suggesting a consistent physiological response. In contrast, the imagery phase (Figure 5) exhibited a more varied distribution, with greater deviations from the average, indicating increased individual differences in pupillary responses. These findings raise questions about the robustness of pupillometry as a reliable measurement tool for imagery-related cognitive processes, given the variability in physiological responses between perception and imagery tasks. Comparing perception and imagery effects (Figure 6), we observed that perception elicited stronger pupil responses in most individuals, while imagery responses were more subtle and uniform. This variability suggests that pupillary response is not a universally reliable indicator of mental imagery strength. Overall, while our study observed significant pupillary light responses during both the perception and imagery periods. However, the split-half reliability analysis conducted on pupillometry measures indicated that insufficient reliability of these measures, especially for visual imagery. Our results suggest that the current pupillometry-based imagery measures require further refinement and validation. While significant correlations were found among the self-report measures (VVIQ, OSIVQ, SUIS) and the trial-specific vividness ratings, pupillary difference scores did not reliably reflect subjective imagery vividness evaluations. Robustness of pupillometry measures of visual imagery vividness needs to be further improved. Despite these limitations, involuntary pupillary responses remain a promising tool for assessing engagement and depth of visual imagery processes (Beatty & Lucero-Wagoner, 2000; Bradley et al., 2008). Unlike self-report measures, which may be influenced by biases, pupillary responses provide objective insight into underlying physiological mechanisms. While our findings suggest further development and validation studies are needed, pupillary response remains a valuable metric in investigating the relationship between visual and other mental processes. Conclusion This study explores the use of pupillometry in assessing visual imagery and examines the accuracy and generalizability of previous findings. We found that pupils responded to both imagined and perceived brightness, a result consistent with previous studies. However, we found no significant association between vividness ratings and the Pupillary Light Reflex (PLR) magnitude during the imagery periods. Further validation is required for this physiological measure to be considered an indicator of imagery strength. Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Official Journal of the Society for Applied Research in Memory and Cognition, 23 (5), 638-663. https://doi.org/10.1002/acp.1473 Bradley, M. M., Miccoli, L., Escrig, M. A., & Lang, P. J. (2008). 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Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199682737.001.0001 Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160 (1), 106. https://doi.org/10.1113/jphysiol.1962.sp006837 Kay, L., Keogh, R., Andrillon, T., & Pearson, J. (2022). The pupillary light response as a physiological index of aphantasia, sensory and phenomenological imagery strength. Elife, 11 , e72484. https://doi.org/10.7554/eLife.72484 Kosslyn, S. M., DiGirolamo, G. J., Thompson, W. L., & Alpert, N. M. (1998). Mental rotation of objects versus hands: Neural mechanisms revealed by positron emission tomography. Psychophysiology, 35 (2), 151-161. https://doi.org/10.1111/1469-8986.3520151 Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nature Reviews Neuroscience, 2 (9), 635-642. https://doi.org/10.1038/35090055 Kosslyn, S. M., Sukel, K. E., & Bly, B. M. (1999). Squinting with the mind’s eye: Effects of stimulus resolution on imaginal and perceptual comparisons. Memory & Cognition , 27, 276-287. https://doi.org/10.3758/bf03211412 Kosslyn, S. M., Thompson, W. L., & Alpert, N. M. (1997). Neural systems shared by visual imagery and visual perception: A positron emission tomography study. Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Figure 1 Pupillometry Imagery Experiment Timeline Note. The graph outlines sequential steps in a trial assessing participants’ perception and imagery vividness. Each trial began with a white fixation cross shown for one second on a grey screen. Then, a stimulus was displayed for five seconds at the screen’s center. Participants were asked to focus on this stimulus and memorize its size, orientation, and brightness. An eight-second black screen followed, allowing any after-image to fade and pupils to return to resting levels. The grey screen reappeared for six seconds, during which participants were prompted to imagine the earlier stimuli. Lastly, participants rated their imagery’s vividness on a 1-4 scale, where 1 meant ’not vivid at all – no shape appeared in imagery’, and 4 signified ’very vivid – almost like seeing it. Figure 2 Mean Pupil Size Waveforms, Presented as mm Change from Baseline. Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Figure 3 Mean Pupil Size Changes from Baseline During Perception and Imagery Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Figure 4 Variability of Pupil Size Differences During Perception Notes. This graph shows the frequency distribution of pupil size differences (in millimeters) among individuals during perception period. The histogram bars indicate the number of occurrences within specific intervals of pupil size differences. The bell-shaped curve shows the overall distribution trend. The mean value of the dataset is marked by a dashed line, representing the central tendency. This graph helps infer common physiological responses during perception activities. It suggests a typical range of pupil size variations and identifies significant deviations from the average. Figure 5 Variability of Pupil Size Differences During Imagery Notes. This graph illustrates the variation in pupil size changes among individuals during imagery tasks. The bars represent the frequency of different pupil size differences (measured in millimeters), while the curve shows the predicted distribution based on the data. The vertical dashed line denotes the average difference in pupil size. This visualization is valuable for researchers studying the physiological impacts of imagery tasks on the human eye, as it highlights the average and range of pupil size changes across participants. Figure 6 Pupil Size Differences: Perception vs. Imagery Notes. The box plot compares pupil size variations during perception and imagery tasks. Each box plot summarizes the range and distribution of pupil size differences (in millimeters). Whiskers extend to the extremes, with outliers represented as individual points. The perception differences box plot shows a wider range of variability than the imagery differences, indicating a more consistent response during imagery tasks. The median of each box offers a visual representation of the central tendency within each group. This is essential for understanding how different cognitive processes might affect physiological responses, such as pupil dilation and constriction. Figure 7 Relationship Between Subjective Vividness Ratings and Pupil-Difference Score Notes. This bar chart shows the relationship between subjective vividness ratings and pupil-difference scores, which reflect changes in pupil size under different lighting conditions during imagery tasks. Each bar represents a vividness rating, from Rating 1 to Rating 4, and its height indicates the average pupil-difference score for that rating. The trend suggests that higher vividness ratings may correlate with smaller changes in pupil size, hinting at a potential link between subjective vividness and physiological response during imagery. Figure 8 Box Plot of Scores across Subscales for Individual Differences Notes. The chart provides insights into the variability of each workload dimension among individuals, with wider boxes indicating greater diversity in responses. Figure 9 Heatmap of Correlation Coefficients with Significance Levels Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Pupillary difference perception 0.696 0.783 0.706 0.728 Pupillary difference imagery 0.078 0.095 0.117 0.097 Trial-by-trial vividness Dark stimuli 0.827 0.828 0.87 0.842 Trial-by-trial vividness Bright stimuli 0.767 0.759 0.754 0.760 Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Comparison of Effect Sizes Across Studies Untitled Document Generated on Sun May 4 03:58:08 2025 by LaTeXML Information & Authors Information Version history V1 Version 1 05 May 2025 Peer review timeline Published Journal of Vision Version of Record 15 Sep 2024 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Sharon Hon 0009-0008-2831-0453 [email protected] The University of Hong Kong Department of Psychology View all articles by this author Sing-Hang Cheung The University of Hong Kong Department of Psychology View all articles by this author Metrics & Citations Metrics Article Usage 170 views 66 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sharon Hon, Sing-Hang Cheung. Pupillary Response in Visual Imagery. Authorea . 05 May 2025. DOI: https://doi.org/10.22541/au.174642498.85826657/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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