In the pupillometry imagery task (based on Laeng and Sulutvedt, 2014; see Figure 1A), participants who reported having visual imagery were presented with one or four ‘Bright’ or ‘Dark’ triangles for 5 s (see Figure 1—figure supplement 1 for images used). Following this they viewed a blank screen for 8 s (which allowed any after-images to fade) and were then instructed to imagine the prior image/s for 6 s, after which they rated the vividness of their imagery from 1 to 4. Pupils showed a clear pupillary light response to perceptual images (Figure 1B; perception section; a significant effect of perceptual luminance F(1, 41) = 190.02, p < 0.001.) This trend was mirrored in the imagery period showing a significant main effect of imagery luminance (Figure 1B, box insets: imagery section; F(1, 41) = 67.42, p < 0.0001), indicating that imagery also demonstrates a pupillary light response. Post hoc analysis using the Bonferroni correction for multiple comparisons found that for both Set-Size-One and Set-Size-Four, the pupil size in the Dark condition was significantly greater than in the Light condition during imagery (p < 0.001 and p < 0.05, respectively, see Figure 1C). There was no main effect of set size during perception F(1, 42) = 2.67, p = 0.11. However, there was a significant main effect of set size during imagery F(1, 41) = 6.48, p = 0.015, with less constriction/more dilation for Set-Size-Four (when averaged across the brightness conditions). This is consistent with previous studies suggesting that pupil size is influenced by cognitive load (Kahneman and Beatty, 1966; Laeng et al., 2011; van der Wel and van Steenbergen, 2018). Post hoc analysis also demonstrated that in the Bright condition, Set-Size-Four resulted in significantly more pupil dilation during imagery than Set-Size-One (p = 0.001). However, in the Dark condition pupil dilation during Set-Size-Four imagery was not significantly different to Set-Size-One (p = 0.266).

Prior behavioural work suggests we have reasonable metacognition of visual imagery, that is we are able to estimate the strength of imagery on a trial-by-trial basis (Pearson et al., 2011; Rademaker and Pearson, 2012). Here, we compared pupil responses to the trial-by-trial ratings of vividness. Pupil-difference scores are shown as a function of intraindividual vividness ratings for Set-Size-One and Set-Size-Four (see Figure 1D). A 2 × 4 linear mixed-effects analysis (2 (set size: 1, 4) × 4 (vividness rating: 1, 2, 3, 4)) demonstrated there was a significant effect of vividness (χ²(3) = 49.54, p = 1.004e⁻¹⁰), with larger pupillary light response for more vivid imagery trials (for both set sizes, see Figure 1D and fixed effects estimates in Supplementary file 1). These data demonstrate that the pupillary light response tracks the phenomenological vividness of visual imagery from moment to moment.

If the sensory strength of imagery is indeed driving the imagery pupillary light response, then the degree to which this response occurs should be related to independent objective measures of imagery strength in each individual. To assess this, we utilized the binocular rivalry method (Pearson, 2014; Pearson et al., 2008), which allows the objective assessment of the sensory strength of imagery, without relying on any subjective reports (Chang and Pearson, 2018). This is achieved by measuring the degree to which an individual’s imagery biases subsequent binocular rivalry perception. We compared pupil-difference scores (imagery of dark stimuli–bright stimuli, such that larger scores indicate a larger pupillary light response) with imagery strength measured using the binocular rivalry paradigm, in which higher priming scores indicate stronger imagery (Figure 2A; Pearson et al., 2008; Pearson et al., 2011). Within the general population, degree of pupil change in the Set-Size-One condition correlated positively with imagery strength, using Pearson’s correlation coefficient (r_p(41) = 0.62, p = <0.0001, see Figure 2B: green circles and green trendline). The Set-Size-Four pupil data set violated normality (Shapiro–Wilk test, p = 0.003), therefore, the Spearman’s correlational coefficient was used to assess its relationship with binocular rivalry priming. A significant positive correlation was found between Set-Size-Four pupil-difference scores and binocular rivalry priming (r_s(41) = 0.46, p = 0.002, see Figure 2C: green circles and green trendline). This provides further evidence that the sensory strength of imagery content is driving the imagery pupillary light response.

Our results indicate that the strength of the content of imagery drives the imagery pupillary light response in participants who experience visual imagery. The involuntary nature of this response provides a valuable objective measure of imagery strength. Accordingly, we sought to utilize this finding to test the veracity of a condition called aphantasia, that is if these individuals truly lack visual imagery, they should not show a pupillary light response to imagined images. However, if aphantasic individuals do show an imagery-based pupillary light response, one might interpret this as a form of imagery existing, but below threshold for conscious phenomenological awareness. We ran this same study in 18 aphantasic participants and compared their performance to that of the general population. These participants had contacted the lab reporting their lack of visual imagery and asked to participate in our research. They were also unaware of the goals and hypotheses of the current study. Aphantasia was confirmed in these individuals using self-report questionnaires (Vividness of Visual Imagery Questionnaire [VVIQ] score <32) and by means of our binocular rivalry priming method (priming <65%), based on cut-off points used in previous research (Keogh and Pearson, 2018).

Here, we again found a strong effect of stimulus luminance in the perceptual phase of the task for the aphantasic participants (Figure 3A: perception section; F(1, 17) = 81.18, p < 0.001), reflecting a functional pupillary light response. However, we found no significant effect of luminance on pupil size during imagery Figure 3A, box insets: imagery section; F(1, 17) = 0.193, p = 0.67 and Figure 3B shows the lack of pupil diameter change for bright stimuli (red bars) and dark stimuli (blue bars). Similarly, to the general population, there was no main effect of set size during perception F(1, 17) = 1.92, p = 0.18, however interestingly, there was a significant main effect of set size during imagery F(1, 17) = 6.185, p = 0.02, with greater pupil diameters for Set-Size-Four compared to Set-Size-One (when averaged across the brightness conditions). This suggests that the aphantasic participants were actively engaging in the imagery task and exerting greater cognitive effort for the larger set size (van der Wel and van Steenbergen, 2018). In comparison to the general population, 61.11% (11/18) of the aphantasic individuals had difference scores that were lower than or equal to 0 for set size one as compared to 9.5% (4/42) of the general population (see Figure 2B). To confirm this absence of an imagery effect in the aphantasia population, we compared the pupil-difference score obtained when comparing the bright and dark conditions for the control and aphantasia groups, and computed a Bayes Factor (H0: score = 0; H1: score ≠ 0; see Materials and methods). Controls showed very strong evidence for H1 (BF₁₀ > 10¹⁰; Bayesian one-sample t-test), whereas the aphantasia population showed evidence for the null effect (BF₀₁ = 3.180). A direct comparison between the control and aphantasia groups using a Bayesian repeated measure analysis of variance (ANOVA; see Materials and methods) showed very strong evidence for an effect of group (BF₁₀ > 10⁶). Finally, and as expected, pupil-difference scores (imagery of dark stimuli–bright stimuli) did not significantly predict imagery strength (measured using the binocular rivalry paradigm) for the aphantasic population (Figure 2B: yellow triangles; Set-Size-One: r_p(17) = 0.20, p = 0.44); Set-Size-Four: (r_p(17) = −0.08, p = 0.76). It should be noted that we could not perform an analysis on the vividness data in the same way as was done with the general population (Figure 1D) as the aphantasic individuals did not have any variation in their vividness ratings, reflecting their lack of subjective visual imagery (see Figure 3—figure supplement 1).

Age disparities between the groups are a potential confounding variable. This factor is of particular importance because the sensitivity of the pupillary light response, as well as maximum pupillary constriction velocity and acceleration, are thought to decline with age, beginning at 40–50 years old (Fotiou et al., 2007; Lobato-Rincón et al., 2014). However, trial time-course pupil waveforms are very similar for both general and aphantasic populations (Figures 1B and 3A, respectively). Both groups exhibited similar levels of pupil change during the perception phase of the task. Furthermore, a two-way ANCOVA was run on pupil-difference scores between general population and aphantasic groups with age as a covariate. Levene’s test and normality checks were carried out and the assumptions were met. We found a significant difference in pupil-difference score (F(1, 57) = 4.763, p = 0.033) between the groups when accounting for age. This provides evidence that decreased pupil responsiveness with age was not driving the observed effects.

Another possible explanation of our findings could be that the passive viewing of the perceptual images, lingering visual persistence and sluggish pupil responses could be driving our results. If this is the case, we would expect that pupil diameter during the perception of the images should correlate with pupil size during imagery for the corresponding images. Further, the pupillary light reflex during perception should be more pronounced in the control than the aphantasic populations. To investigate this possible alternative explanation of our data we first assessed the correlations between pupil diameter during perception of bright and dark images for Set-Size-One and -Four and their corresponding imagery conditions (control participants only). We found there were no significant correlations between any of the perception and imagery conditions, or the difference scores for set size one and four (all p > 0.40, see Figure 2—figure supplement 1). This lack of a correlation suggests that those individuals who have the largest pupillary light response while viewing the images, do not also have the greatest imagery driven pupillary light responses, making it unlikely that the pupil response while seeing the image is driving the mental imagery pupillary response. Next, we assessed whether the aphantasic individuals demonstrated any significant difference in their pupil responses to perceptual stimuli by running a 2 (image: bright and dark) × 2 (set size: 1 and 4) × 2 (group: aphantasic and controls) repeated measures ANOVA on the pupil diameter during the 5-s perceptual period of the task (see Figure 1A for task timeline). There was no main effect of imagery group F(1, 58) = 1.15, p = 0.29 and no significant interactions between imagery groups and any other factor (all p > 0.22, see Figure 3—figure supplement 2). These findings suggest the observed pupil responses during the imagery period of the task is unlikely to be a carry-over effect of the previous sensory response to perceived images.

Pupil size has been shown to depend on eye position (Drewes et al., 2014; Gagl et al., 2011) and the preparation to make a saccade to an upcoming image (Jainta et al., 2011; Mathôt et al., 2015; Wang et al., 2018). Pupil modulation and eye position are also both controlled by largely overlapping circuitry (Wang and Munoz, 2018). It could then be the case that group differences in eye position or saccades (either while viewing or imagining the triangles) may explain our data. To assess how eye movements and position (eccentricity) might be related to our findings, we analysed both eye position and saccades made while viewing and imagining the images, to see if these differed as a function of group. Eccentricity was extracted using the ‘saccades’ package in R (von der Malsburg, 2015) and saccades were detected using a velocity-based algorithm (Engbert and Kliegl, 2003) using the same R package. There were no significant differences between the groups for the number of saccades made during perception or imagery of the stimuli (see Figure 3—figure supplement 4 and Figure 3—figure supplement 5). There were also no differences in mean eccentricity values when comparing the two groups (Figure 3—figure supplement 3), and no correlation with eccentricity and the pupillary light response (Figure 2—figure supplement 3), binocular priming (Figure 2—figure supplement 4), or vividness ratings (Figure 2—figure supplement 5), suggesting that differences in fixation or eye movements between the two groups is unlikely to drive the observed group differences in regard to the mental imagery pupillary light response.

Taken together these data from the general population and aphantasic individuals suggest that it is the content and ability to form vivid visual images, not the voluntary attempt to do so or the semantic content, that is driving the imaginary pupillary light response, providing the first evidence that these pupil changes are due to the sensory strength of imagery content and are not driven by higher-level semantic content.