Task-dependent pupillary responses to glossiness and attractiveness judgments

Human pupillary responses are influenced not only by low-level visual properties but also by cognitive and affective factors related to task demands. However, the temporal dynamics of how different evaluative tasks modulate pupil size remain poorly understood. In this study, we investigated how pupillary responses vary when observers evaluate the same set of object images for either glossiness or attractiveness. These two perceptual attributes were selected to exemplify distinct cognitive demands: one rooted in surface-level visual analysis and the other involving emotional valuation. The stimuli consisted of 60 grayscale object photographs selected from the THINGS database, representing common real-world items without social or facial content. Low-level image statistics were controlled using histogram matching to equalize luminance and contrast across all stimuli. Participants viewed each image for 3000 ms while maintaining central fixation and rated either its glossiness or attractiveness on a 7-point scale in separate task blocks. Ground-average waveforms revealed task-dependent modulations of pupil size across rating levels, consistent with prior reports that pupillary responses vary with evaluative context even for identical stimuli. Using temporal principal component analysis and generalized additive modeling, we found that higher glossiness ratings were associated with greater pupil constriction at early, light-reflex-like latencies, whereas higher attractiveness ratings elicited greater pupil dilation at later time points. These findings suggest that distinct temporal profiles of pupil size reflect task-specific processing demands, potentially aligning with the notion that visual and affective evaluations may unfold in temporally distinct stages, as suggested in prior theoretical models. Our results underscore the value of pupillometry as a non-invasive tool for dissociating task-dependent perceptual processes, with potential applications in cognitive neuroscience and affective computing.