Effects of pupillary responses to luminance and attention on visual spatial discrimination

Method to Quickly Map Multifocal Pupillary Response Fields (mPRF) Using Frequency Tagging

We present a method for mapping multifocal Pupillary Response Fields in a short amount of time using a visual stimulus covering 40° of the visual angle divided into nine contiguous sectors simultaneously modulated in luminance at specific, incommensurate, temporal frequencies. We test this multifocal Pupillary Frequency Tagging (mPFT) approach with young healthy participants (N = 36) and show that the spectral power of the sustained pupillary response elicited by 45 s of fixation of this multipartite stimulus reflects the relative contribution of each sector/frequency to the overall pupillary response. We further analyze the phase lag for each temporal frequency as well as several global features related to pupil state. Test/retest performed on a subset of participants indicates good repeatability. We also investigate the existence of structural (RNFL)/functional (mPFT) relationships. We then summarize the results of clinical studies conducted with mPFT on patients with neuropathies and retinopathies and show that the features derived from pupillary signal analyses, the distribution of spectral power in particular, are homologous to disease characteristics and allow for sorting patients from healthy participants with excellent sensitivity and specificity. This method thus appears as a convenient, objective, and fast tool for assessing the integrity of retino-pupillary circuits as well as idiosyncrasies and permits to objectively assess and follow-up retinopathies or neuropathies in a short amount of time.

Autogenous cerebral processes: an invitation to look at the brain from inside out

While external stimulation can reliably trigger neuronal activity, cerebral processes can operate independently from the environment. In this study, we conceptualize autogenous cerebral processes (ACPs) as intrinsic operations of the brain that exist on multiple scales and can influence or shape stimulus responses, behavior, homeostasis, and the physiological state of an organism. We further propose that the field should consider exploring to what extent perception, arousal, behavior, or movement, as well as other cognitive functions previously investigated mainly regarding their stimulus-response dynamics, are ACP-driven.

The influence of pupil responses on subjective brightness perception

When the pupil dilates, the amount of light that falls onto the retina increases. However, in daily life, this does not make the world look brighter. Here we asked whether pupil size (resulting from active pupil movement) influences subjective brightness in the absence of indirect cues that, in daily life, support brightness constancy. We measured the subjective brightness of a tester stimulus relative to a referent as a function of pupil size during tester presentation. In Experiment 1, we manipulated pupil size through a secondary working-memory task (larger pupils with higher load and after errors). We found some evidence that the tester was perceived as darker, rather than brighter, when pupils were larger. In Experiment 2, we presented a red or blue display (larger pupils following red displays). We again found that the tester was perceived as darker when pupils were larger. We speculate that the visual system takes pupil size into account when making brightness judgments. Finally, we highlight the challenges associated with manipulating pupil size. In summary, the current study (as well as a recent pharmacological study on the same topic by another team) is intriguing first steps towards understanding the role of pupil size in brightness perception.

Fixation Duration and Pupil Size as Diagnostic Tools in Parkinson's Disease

BACKGROUND

Visual and oculomotor problems are very common in Parkinson's disease (PD) and by using eye-tracking such problems could be characterized in more detail. However, eye-tracking is not part of the routine clinical investigation of parkinsonism.

OBJECTIVE

To evaluate gaze stability and pupil size in stable light conditions, as well as eye movements during sustained fixation in a population of PD patients and healthy controls (HC).

METHODS

In total, 50 PD patients (66% males) with unilateral to mild-to-moderate disease (Hoehn & Yahr 1-3, Schwab and England 70-90%) and 43 HC (37% males) were included in the study. Eye movements were recorded with Tobii Pro Spectrum, a screen-based eye tracker with a sampling rate of 1200 Hz. Logistic regression analysis was applied to investigate the strength of association of eye-movement measures with diagnosis.

RESULTS

Median pupil size (OR 0.811; 95% CI 0.666-0.987; p = 0.037) and longest fixation period (OR 0.798; 95% CI 0.691-0.921; p = 0.002), were the eye-movement parameters that were independently associated with diagnosis, after adjustment for sex (OR 4.35; 95% CI 1.516-12.483; p = 0.006) and visuospatial/executive score in Montreal Cognitive Assessment (OR 0.422; 95% CI 0.233-0.764; p = 0.004). The area under the ROC curve was determined to 0.817; 95% (CI) 0.732-0.901.

CONCLUSION

Eye-tracking based measurements of gaze fixation and pupil reaction may be useful biomarkers of PD diagnosis. However, larger studies of eye-tracking parameters integrated into the screening of patients with suspected PD are necessary, to further investigate and confirm their diagnostic value.

New insight into Parkinson's disease-related impairment of the automatic control of upright stance

Parkinson's disease (PD) affects the automatic control of body movements. In our study, we tested PD-related impairments in automatic postural control in quiet upright stance. Twenty PD patients (mean age: 60 ± 8 years; Hoehn and Yahr: 2.00 ± 0.32, on-drug) and twenty age-matched controls (61 ± 7 years) were recruited. We studied interrelations between center-of-pressure movements, body movements (head, neck, and lower back), eye movements and variability of pupil size. Participants performed two fixation tasks while standing, during which they looked at: (a) a cross surrounded by a white background; and (b) a cross surrounded by a structured visual background (images used: rooms in houses). PD patients exhibited stronger and weaker correlations between eye and center-of-pressure/body movement variables than age-matched controls in the white and structured fixation tasks, respectively. Partial correlations, controlling for variability of pupil size showed that PD patients used lower and greater attentional resources than age-matched controls to control their eye and center-of-pressure/body movements simultaneously in the white fixation and structured fixation tasks, respectively. In the white fixation task, PD patients used attentional resources to optimize visuomotor coupling between eye and body movements to control their posture. In the structured fixation task, the salient visual stimuli distracted PD patients' attention and that possibly affected postural control by deteriorating the automatic visuomotor coupling. In contrast, age-matched controls were able to use surrounding visual background to improve the automatic coupling between eye and center-of-pressure movements to control their posture. These results suggest that cluttered environments may distract PD patients and deteriorate their postural control.

Working together to orient faster: The combined effects of alerting and orienting networks on pupillary responses at 8 months of age

Multiple visual attention mechanisms are active already in infancy, most notably one supporting orienting towards stimuli and another, maintaining appropriate levels of alertness, when exploring the environment. They are thought to depend on separate brain networks, but their effects are difficult to isolate in existing behavioural paradigms. Better understanding of the contribution of each network to individual differences in visual orienting may help to explain their role in attention development. Here, we tested whether alerting and spatial cues differentially modulate pupil dilation in 8-month-old infants in a visual orienting paradigm. We found differential effects in the time course of these responses depending on the cue type. Moreover, using Principal Component Analysis (PCA) we identified two main components of pupillary response, which may reflect the alerting and orienting network activity. In a regression analysis, these components together explained nearly 40 % of variance in saccadic latencies in the spatial cueing condition of the task. These results likely demonstrate that both networks work together in 8-month-old infants and that their activity can be indexed with pupil dilation combined with PCA, but not with raw changes in pupil diameter.

The effect of pupil size and peripheral brightness on detection and discrimination performance

It is easier to read dark text on a bright background (positive polarity) than to read bright text on a dark background (negative polarity). This positive-polarity advantage is often linked to pupil size: A bright background induces small pupils, which in turn increases visual acuity. Here we report that pupil size, when manipulated through peripheral brightness, has qualitatively different effects on discrimination of fine stimuli in central vision and detection of faint stimuli in peripheral vision. Small pupils are associated with improved discrimination performance, consistent with the positive-polarity advantage, but only for very small stimuli that are at the threshold of visual acuity. In contrast, large pupils are associated with improved detection performance. These results are likely due to two pupil-size related factors: Small pupils increase visual acuity, which improves discrimination of fine stimuli; and large pupils increase light influx, which improves detection of faint stimuli. Light scatter is likely also a contributing factor: When a display is bright, light scatter creates a diffuse veil of retinal illumination that reduces perceived image contrast, thus impairing detection performance. We further found that pupil size was larger during the detection task than during the discrimination task, even though both tasks were equally difficult and similar in visual input; this suggests that the pupil may automatically assume an optimal size for the current task. Our results may explain why pupils dilate in response to arousal: This may reflect an increased emphasis on detection of unpredictable danger, which is crucially important in many situations that are characterized by high levels of arousal. Finally, we discuss the implications of our results for the ergonomics of display design.

At least two distinct mechanisms control binocular luster, rivalry, and perceived rotation with contrast and average luminance disparities

When one views a square-wave grating and dichoptically changes the average luminance or contrast of the monocular images, at least three perceptual phenomena might occur. These are the Venetian blind effect, or a perceived rotation of the bars around individual vertical axes; binocular luster, or a perceived shimmering; and binocular rivalry, or an alternating perception between the views of the two eyes. Perception of luster and rivalry occur when the "light bars" in the grating dichoptically straddle the background luminance (one eye's image has a higher luminance than the background and the other eye's image has a lower luminance than the background), with little impact from the "dark bars." Perception of rotation, on the other hand, is related to average luminance or contrast disparity, independent of whether or not the "light bars" straddle the background luminance. The patterns for perceived rotation versus binocular luster and binocular rivalry suggest at least two separate mechanisms in the visual system for processing luminance and contrast information over and above their differing physiological states suggested by their different appearances. While luster and rivalry depend directly on the relation between stimuli and the background, perceived rotation depends on the magnitude of the luminance or contrast disparity, as described by the generalized difference model.