Non-image-forming vision as measured through ipRGC-mediated pupil constriction is not modulated by covert visual attention.

In brightness, the pupil constricts, while in darkness, the pupil dilates; this is known as the pupillary light response (PLR). The PLR is driven by all photoreceptors: rods and cones, which contribute to image-forming vision, and intrinsically photosensitive retinal ganglion cells (ipRGCs), which mainly contribute to non-image-forming vision. Rods and cones cause immediate pupil constriction upon light exposure, whereas ipRGCs cause sustained constriction throughout light exposure. Recent studies have shown that covert attention modulated the initial PLR; however, it remains unclear whether the same holds for the sustained PLR. We tested this by leveraging ipRGCs' responsiveness to blue light, causing the most prominent sustained constriction. While replicating previous studies by showing that pupils constricted more when either directly looking at, or covertly attending to, bright as compared to dim stimuli (with the same color), we also found that the pupil constricted more when directly looking at blue as compared to red stimuli (with the same luminosity). Crucially, however, in two high-powered studies (n = 60), we did not find any pupil-size difference when covertly attending to blue as compared to red stimuli. This suggests that ipRGC-mediated pupil constriction, and possibly non-image-forming vision more generally, is not modulated by covert attention.

Effects of pupil size as manipulated through ipRGC activation on visual processing.

The size of the eyes' pupils determines how much light enters the eye and also how well this light is focused. Through this route, pupil size shapes the earliest stages of visual processing. Yet causal effects of pupil size on vision are poorly understood and rarely studied. Here we introduce a new way to manipulate pupil size, which relies on activation of intrinsically photosensitive retinal ganglion cells (ipRGCs) to induce sustained pupil constriction. We report the effects of both experimentally induced and spontaneous changes in pupil size on visual processing as measured through EEG. We compare these to the effects of stimulus intensity and covert visual attention, because previous studies have shown that these factors all have comparable effects on some common measures of early visual processing, such as detection performance and steady-state visual evoked potentials; yet it is still unclear whether these are superficial similarities, or rather whether they reflect similar underlying processes. Using a mix of neural-network decoding, ERP analyses, and time-frequency analyses, we find that induced pupil size, spontaneous pupil size, stimulus intensity, and covert visual attention all affect EEG responses, mainly over occipital and parietal electrodes, but-crucially-that they do so in qualitatively different ways. Induced and spontaneous pupil-size changes mainly modulate activity patterns (but not overall power or intertrial coherence) in the high-frequency beta range; this may reflect an effect of pupil size on oculomotor activity and/ or visual processing. In addition, spontaneous (but not induced) pupil size tends to correlate positively with intertrial coherence in the alpha band; this may reflect a non-causal relationship, mediated by arousal. Taken together, our findings suggest that pupil size has qualitatively different effects on visual processing from stimulus intensity and covert visual attention. This shows that pupil size as manipulated through ipRGC activation strongly affects visual processing, and provides concrete starting points for further study of this important yet understudied earliest stage of visual processing.

Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis.

Cognitive pupillometry is the measurement of pupil size to investigate cognitive processes such as attention, mental effort, working memory, and many others. Currently, there is no commonly agreed-upon methodology for conducting cognitive-pupillometry experiments, and approaches vary widely between research groups and even between different experiments from the same group. This lack of consensus makes it difficult to know which factors to consider when conducting a cognitive-pupillometry experiment. Here we provide a comprehensive, hands-on guide to methods in cognitive pupillometry, with a focus on trial-based experiments in which the measure of interest is the task-evoked pupil response to a stimulus. We cover all methodological aspects of cognitive pupillometry: experimental design, preprocessing of pupil-size data, and statistical techniques to deal with multiple comparisons when testing pupil-size data. In addition, we provide code and toolboxes (in Python) for preprocessing and statistical analysis, and we illustrate all aspects of the proposed workflow through an example experiment and example scripts.

Touch-induced pupil size reflects stimulus intensity, not subjective pleasantness.

Interpersonal touch is known to influence human communication and emotion. An important system for interpersonal touch is the C-tactile (CT) system, which is activated by a soft stroke on hairy skin with a velocity of 1-10 cms-1. This system been proposed to play a unique role in hedonic valence and emotion of touch. For other sensory modalities, hedonic processing has been associated with pupil dilation. However, it is unclear whether pupil dilation can be modulated by hedonic touch. The current study investigated in two experiments how pupil size reacts to both affective and non-affective stroking. Pupil-size data were obtained to investigate differences between stroking conditions. In addition, an adjusted version of the Touch Perception Task (TPT) was used to assess subjective touch pleasantness ratings. In Experiment 1, affective (3 cms-1) and non-affective (0.3 and 30 cms-1) stroking was applied to the dorsal side of the right hand. Results revealed that stroking velocity had a significant effect on TPT-item scores, showing higher that affective touch was rated as more pleasant compared to non-affective touch, thereby replicating the previous studies. Results, however, revealed no specific pupil dilation for the 3 cms-1 condition; instead, a logarithmic relation was found between pupil-size dilation and stroking velocity. This relation was confirmed in a second experiment. Furthermore, the palm of the hand was used as a control site for tactile stimulation, for which similar findings were obtained as for the dorsal side of the hand. In addition, skin conductance recordings showed a pattern of response to different stroking velocities similar to pupil dilation. These results suggest that pupil-size dilation does respond to tactile input, but that this response is related to arousal caused by changes in stimulus intensity (e.g., stroking velocity) rather than specific C-tactile stimulation.

Safe and sensible preprocessing and baseline correction of pupil-size data.

Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, i.e., analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size/baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size - baseline). We discuss the role of baseline correction as a part of preprocessing of pupillometric data, and make five recommendations: (1) before baseline correction, perform data preprocessing to mark missing and invalid data, but assume that some distortions will remain in the data; (2) use subtractive baseline correction; (3) visually compare your corrected and uncorrected data; (4) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and (5) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

MEGALEX: A megastudy of visual and auditory word recognition.

Using the megastudy approach, we report a new database (MEGALEX) of visual and auditory lexical decision times and accuracy rates for tens of thousands of words. We collected visual lexical decision data for 28,466 French words and the same number of pseudowords, and auditory lexical decision data for 17,876 French words and the same number of pseudowords (synthesized tokens were used for the auditory modality). This constitutes the first large-scale database for auditory lexical decision, and the first database to enable a direct comparison of word recognition in different modalities. Different regression analyses were conducted to illustrate potential ways to exploit this megastudy database. First, we compared the proportions of variance accounted for by five word frequency measures. Second, we conducted item-level regression analyses to examine the relative importance of the lexical variables influencing performance in the different modalities (visual and auditory). Finally, we compared the similarities and differences between the two modalities. All data are freely available on our website ( https://sedufau.shinyapps.io/megalex/ ) and are searchable at www.lexique.org , inside the Open Lexique search engine.

Pupillary Responses to Words That Convey a Sense of Brightness or Darkness.

Theories about embodiment of language hold that when you process a word's meaning, you automatically simulate associated sensory input (e.g., perception of brightness when you process lamp) and prepare associated actions (e.g., finger movements when you process typing). To test this latter prediction, we measured pupillary responses to single words that conveyed a sense of brightness (e.g., day) or darkness (e.g., night) or were neutral (e.g., house). We found that pupils were largest for words conveying darkness, of intermediate size for neutral words, and smallest for words conveying brightness. This pattern was found for both visually presented and spoken words, which suggests that it was due to the words' meanings, rather than to visual or auditory properties of the stimuli. Our findings suggest that word meaning is sufficient to trigger a pupillary response, even when this response is not imposed by the experimental task, and even when this response is beyond voluntary control.

Don't admit defeat: A new dawn for the item in visual search.

Even though we lack a precise definition of "item," it is clear that people do parse their visual environment into objects (the real-world equivalent of items). We will review evidence that items are essential in visual search, and argue that computer vision - especially deep learning - may offer a solution for the lack of a solid definition of "item."

The role of object affordances and center of gravity in eye movements toward isolated daily-life objects.

The purpose of the current study was to investigate to what extent low-level versus high-level effects determine where the eyes land on isolated daily-life objects. We operationalized low-level effects as eye movements toward an object's center of gravity (CoG) or the absolute object center (OC) and high-level effects as visuomotor priming by object affordances. In two experiments, we asked participants to make saccades toward peripherally presented photographs of graspable objects (e.g., a hammer) and to either categorize them (Experiment 1) or to discriminate them from visually matched nonobjects (Experiment 2). Objects were rotated such that their graspable part (e.g., the hammer's handle) pointed toward either the left or the right whereas their action-performing part (e.g., the hammer's head) pointed toward the other side. We found that early-triggered saccades were neither biased toward the object's graspable part nor toward its action-performing part. Instead, participants' eyes landed near the CoG/OC. Only longer-latency initial saccades and refixations were subject to high-level influences, being significantly biased toward the object's action-performing part. Our comparison with eye movements toward visually matched nonobjects revealed that the latter was not merely the consequence of a low-level effect of shape, texture, asymmetry, or saliency. Instead, we interpret it as a higher-level, object-based affordance effect that requires time, and to some extent also foveation, in order to build up and to overcome default saccadic-programming mechanisms.

Effects of number, complexity, and familiarity of flankers on crowded letter identification.

We tested identification of target letters surrounded by a varying number (2, 4, 6) of horizontally aligned flanking elements. Strings were presented left or right of a central fixation dot, and targets were always at the center of the string. Flankers could be other letters, digits, symbols, simple shapes, or false fonts, and thus varied both in terms of visual complexity and familiarity. Two-alternative forced choice (2AFC) speed and accuracy was measured for choosing the target letter versus an alternative letter that was not present in the string. Letter identification became harder as the number of flankers increased. Greater flanker complexity led to more interference in target identification, whereas more complex targets were easier to identify. Effects of flanker complexity were found to depend on visual field and position of flankers, with the strongest effects seen for leftward flankers in the left visual field. Visual complexity predicted flanker interference better than familiarity, and better than target-flanker similarity. These results provide further support for an excessive feature-integration account of the interfering effects of both adjacent and nonadjacent flanking elements in horizontally aligned strings.

The pupillary light response reflects exogenous attention and inhibition of return.

Here we show that the pupillary light response reflects exogenous (involuntary) shifts of attention and inhibition of return. Participants fixated in the center of a display that was divided into a bright and a dark half. An exogenous cue attracted attention to the bright or dark side of the display. Initially, the pupil constricted when the bright, as compared to the dark, side of the display was cued, reflecting a shift of attention toward the exogenous cue. Crucially, this pattern reversed about 1 s after cue presentation. This later-occurring, relative dilation (when the bright side was cued) reflected disengagement from the previously attended location, analogous to the behavioral phenomenon of inhibition of return. Indeed, we observed a reliable correlation between "pupillary inhibition" and behavioral inhibition of return. Our results support the view that inhibition of return results from habituation to (or short-term depression of) visual input. We conclude that the pupillary light response is a complex eye movement that reflects how we selectively parse and interpret visual input.

PyGaze: an open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments.

The PyGaze toolbox is an open-source software package for Python, a high-level programming language. It is designed for creating eyetracking experiments in Python syntax with the least possible effort, and it offers programming ease and script readability without constraining functionality and flexibility. PyGaze can be used for visual and auditory stimulus presentation; for response collection via keyboard, mouse, joystick, and other external hardware; and for the online detection of eye movements using a custom algorithm. A wide range of eyetrackers of different brands (EyeLink, SMI, and Tobii systems) are supported. The novelty of PyGaze lies in providing an easy-to-use layer on top of the many different software libraries that are required for implementing eyetracking experiments. Essentially, PyGaze is a software bridge for eyetracking research.

Functional benefits of cognitively driven pupil-size changes.

Pupil-size changes are typically associated with the pupil light response (PLR), where they are driven by the physical entry of light into the eye. However, pupil-size changes are also influenced by various cognitive processes, where they are driven by higher-level cognition. For example, the strength of the PLR is not solely affected by physical properties of the light but also by cognitive factors, such as whether the source of light is attended or not, which results in an increase or decrease in the strength of the PLR. Surprisingly, although cognitively driven pupil-size changes have been the focus of extensive research, their possible functions are rarely discussed. Here we consider the relative (dis)advantages of small versus large pupils in different situations from a theoretical point of view, and compare these to empirical results showing how pupil size actually changes in these situations. Based on this, we suggest that cognitively driven pupil-size changes optimize vision either through preparation, embodied representations, or a differential emphasis on central or peripheral vision. More generally, we argue that cognitively driven pupil-size changes are a form of sensory tuning: a subtle adjustment of the eyes to optimize vision for the current situation and the immediate future. This article is categorized under: Neuroscience > Cognition Neuroscience > Physiology Neuroscience > Behavior.

Attention-based rehearsal: Eye movements reveal how visuospatial information is maintained in working memory.

The human eye scans visual information through scan paths, series of fixations. Analogous to these scan paths during the process of actual "seeing," we investigated whether similar scan paths are also observed while subjects are "rehearsing" stimuli in visuospatial working memory. Participants performed a continuous recall task in which they rehearsed the precise location and color of three serially presented discs during a retention interval, and later reproduced either the precise location or the color of a single probed item. In two experiments, we varied the direction along which the items were presented and investigated whether scan paths during rehearsal followed the pattern of stimulus presentation during encoding (left-to-right in Experiment 1; left-to-right/right-to-left in Experiment 2). In both experiments, we confirmed that the eyes follow similar scan paths during encoding and rehearsal. Specifically, we observed that during rehearsal participants refixated the memorized locations they saw during encoding. Most interestingly, the precision with which these locations were refixated was associated with smaller recall errors. Assuming that eye position reflects the focus of attention, our findings suggest a functional contribution of spatial attention shifts to working memory and are in line with the hypothesis that maintenance of information in visuospatial working memory is supported by attention-based rehearsal. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Baseline Pupil Size Seems Unrelated to Fluid Intelligence, Working Memory Capacity, and Attentional Control.

Over the past few years, several studies have explored the relationship between resting-state baseline pupil size and cognitive abilities, including fluid intelligence, working memory capacity, and attentional control. However, the results have been inconsistent. Here we present the findings from two experiments designed to replicate and expand previous research, with the aim of clarifying previous mixed findings. In both experiments, we measured baseline pupil size while participants were not engaged in any tasks, and assessed fluid intelligence using a matrix task. In one experiment we also measured working memory capacity (letter-number-sequencing task) and attentional control (attentional-capture task). We controlled for several personal and demographic variables known to influence pupil size, such as age and nicotine consumption. Our analyses revealed no relationship between resting-state pupil size (average or variability) and any of the measured constructs, neither before nor after controlling for confounding variables. Taken together, our results suggest that any relationship between resting-state pupil size and cognitive abilities is likely to be weak or non-existent.

Revealing visual working memory operations with pupillometry: Encoding, maintenance, and prioritization.

Pupillary dynamics reflect effects of distinct and important operations of visual working memory: encoding, maintenance, and prioritization. Here, we review how pupil size predicts memory performance and how it provides novel insights into the mechanisms of each operation. Visual information must first be encoded into working memory with sufficient precision. The depth of this encoding process couples to arousal-linked baseline pupil size as well as a pupil constriction response before and after stimulus onset, respectively. Subsequently, the encoded information is maintained over time to ensure it is not lost. Pupil dilation reflects the effortful maintenance of information, wherein storing more items is accompanied by larger dilations. Lastly, the most task-relevant information is prioritized to guide upcoming behavior, which is reflected in yet another dilatory component. Moreover, activated content in memory can be pupillometrically probed directly by tagging visual information with distinct luminance levels. Through this luminance-tagging mechanism, pupil light responses reveal whether dark or bright items receive more attention during encoding and prioritization. Together, conceptualizing pupil responses as a sum of distinct components over time reveals insights into operations of visual working memory. From this viewpoint, pupillometry is a promising avenue to study the most vital operations through which visual working memory works. This article is categorized under: Psychology > Attention Psychology > Memory Psychology > Theory and Methods.

A retinotopic attentional trace after saccadic eye movements: evidence from event-related potentials.

Saccadic eye movements are a major source of disruption to visual stability, yet we experience little of this disruption. We can keep track of the same object across multiple saccades. It is generally assumed that visual stability is due to the process of remapping, in which retinotopically organized maps are updated to compensate for the retinal shifts caused by eye movements. Recent behavioral and ERP evidence suggests that visual attention is also remapped, but that it may still leave a residual retinotopic trace immediately after a saccade. The current study was designed to further examine electrophysiological evidence for such a retinotopic trace by recording ERPs elicited by stimuli that were presented immediately after a saccade (80 msec SOA). Participants were required to maintain attention at a specific location (and to memorize this location) while making a saccadic eye movement. Immediately after the saccade, a visual stimulus was briefly presented at either the attended location (the same spatiotopic location), a location that matched the attended location retinotopically (the same retinotopic location), or one of two control locations. ERP data revealed an enhanced P1 amplitude for the stimulus presented at the retinotopically matched location, but a significant attenuation for probes presented at the original attended location. These results are consistent with the hypothesis that visuospatial attention lingers in retinotopic coordinates immediately following gaze shifts.