Pupil size is modulated by the size of equal-luminance gratings

Glossiness perception and its pupillary response

Recent studies have revealed that pupillary response changes depend on perceptual factors such as subjective brightness caused by optical illusions and luminance. However, the manner in which the perceptual factor that is derived from the glossiness perception of object surfaces affects the pupillary response remains unclear. We investigated the relationship between the glossiness perception and pupillary response through a glossiness rating experiment that included recording the pupil diameter. We prepared general object images (original) and randomized images (shuffled) that comprised the same images with randomized small square regions as stimuli. The image features were controlled by matching the luminance histogram. The observers were asked to rate the perceived glossiness of the stimuli presented for 3,000 ms and the changes in their pupil diameters were recorded. Images with higher glossiness ratings constricted the pupil size more than those with lower glossiness ratings at the peak constriction of the pupillary responses during the stimulus duration. The linear mixed-effects model demonstrated that the glossiness rating, image category (original/shuffled), variance of the luminance histogram, and stimulus area were most effective in predicting the pupillary responses. These results suggest that the illusory brightness obtained by the image regions of high-glossiness objects, such as specular highlights, induce pupil constriction.

Pupillary Responses Reflect Dynamic Changes in Multiple Cognitive Factors During Associative Learning in Primates

Associative learning involves complex interactions of multiple cognitive factors. While adult subjects can articulate these factors verbally, for model animals such as macaques, we rely on behavioral outputs. In our study, we used pupillary responses as an alternative measure to capture these underlying cognitive changes. We recorded the dynamic changes in the pupils of three male macaques when they learned the associations between visual stimuli and reward sizes under the classical Pavlovian experimental paradigm. We found that during the long-term learning process, the gradual changes in the pupillary response reflect the changes in the cognitive state of the animals. The pupillary response can be explained by a linear combination of components corresponding to multiple cognitive factors. These components reflect the impact of visual stimuli on the pupils, the prediction of reward values associated with the visual stimuli, and the macaques' understanding of the current experimental reward rules. The changing patterns of these factors during interday and intraday learning clearly demonstrate the enhancement of current reward-stimulus association and the weakening of previous reward-stimulus association. Our study shows that the dynamic response of pupils can serve as an objective indicator to characterize the psychological changes of animals, understand their learning process, and provide important tools for exploring animal behavior during the learning process.

Pupillary responses to differences in luminance, color and set size

The pupil responds to a salient stimulus appearing in the environment, in addition to its modulation by global luminance. These pupillary responses can be evoked by visual or auditory stimuli, scaled with stimulus salience, and enhanced by multisensory presentation. In addition, pupil size is modulated by various visual stimulus attributes, such as color, area, and motion. However, research that concurrently examines the influence of different factors on pupillary responses is limited. To explore how presentation of multiple visual stimuli influences human pupillary responses, we presented arrays of visual stimuli and systematically varied their luminance, color, and set size. Saliency level, computed by the saliency model, systematically changed with set size across all conditions, with higher saliency levels in larger set sizes. Pupillary constriction responses were evoked by the appearance of visual stimuli, with larger pupillary responses observed in larger set size. These effects were pronounced even though the global luminance level was unchanged using isoluminant chromatic stimuli. Furthermore, larger pupillary constriction responses were obtained in the blue, compared to other color conditions. Together, we argue that both cortical and subcortical areas contribute to the observed pupillary constriction modulated by set size and color.

Deep learning-based pupil model predicts time and spectral dependent light responses

Although research has made significant findings in the neurophysiological process behind the pupillary light reflex, the temporal prediction of the pupil diameter triggered by polychromatic or chromatic stimulus spectra is still not possible. State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. Here we propose a deep learning-driven concept of a pupil model, which reconstructs the pupil's time course either from photometric and colourimetric or receptor-based stimulus quantities. By merging feed-forward neural networks with a biomechanical differential equation, we predict the temporal pupil light response with a mean absolute error below 0.1 mm from polychromatic (2007 [Formula: see text] 1 K, 4983 [Formula: see text] 3 K, 10,138 [Formula: see text] 22 K) and chromatic spectra (450 nm, 530 nm, 610 nm, 660 nm) at 100.01 ± 0.25 cd/m2. This non-parametric and self-learning concept could open the door to a generalized description of the pupil behaviour.