Investigating pupil dilation response to sounds at increasing volume level: an infant eye-tracking study.
Creators: name: Falck-Ytter, Terje
name: Bussu, Giorgia
Publisher: name: Open Science Framework
Year: 2023
Subjects: subject: Developmental Psychology
subject: Psychology
subject: FOS: Psychology
subject: Social and Behavioral Sciences
subject: pupillometry
subject: eye-tracking
subject: sleep
subject: infancy
subject: regulation
subject: arousal

description: CONTRIBUTORS Giorgia Bussu, Terje Falck-Ytter EXPLANATION FOR PRE-REGISTRATION This registration aims to provide a time-stamped proof of the analytic study plan before data analysis begins. This registration is posterior to the first data collection phase and pre-processing, yet before the second phase of data collection. Background The ability to orient to salient stimuli develops very early in infancy. Visual orienting is influenced by global arousal, with higher levels of arousal being linked to more vigilant attentional behaviours and manifesting in faster orienting to salient and task-relevant stimuli (de Barbaro et al., 2017). However, very high levels of arousal appear to be linked to quick but unselective responses (Mather et al., 2016). Research interest as recently increased in the use of the eye pupil as an accessible and easy way to understand how the brain processes cognitive and emotional load, and how it allocates attention (Laeng et al., 2012; Zekveld &amp; Kramer, 2014)]. Pupil size is commonly used within infant cognition as a measure of arousal. Neurophysiological work in animal studies has linked fluctuations in pupil size to autonomic influences and descending cortical signals resulting from the balance in brain activity across two large systems (Joshi &amp; Gold, 2020): the locus coeruleus-norephinephrine (LC-NE) system (Aston-Jones &amp; Cohen, 2005; Mather et al., 2016), and the basal forebrain-acetylcholine (BF-Ach) system (van den Brink et al., 2016). Therefore, pupillometry can be used as an index of global arousal level and alertness to stimuli (Laeng et al., 2012). Attention bias towards socially relevant stimuli, and particularly orienting to faces, enables infants to process social information and provides them with opportunities to develop social skills (Reynolds &amp; Roth, 2018). In a world full of different sensory stimuli, attention is crucial for an infant to orient to their caregiver (Bast et al., 2019). The ability to orient towards faces is present from birth and improves with age. A fast-subcortical pathway involving the superior colliculus, the pulvinar, the amygdala and higher-level visual areas has been previously proposed as a model for regulation of attention and oculomotor response to faces (Johnson et al., 2015). Evidence for selective attention to socially relevant stimuli in infancy comes from pop-out studies measuring preferential orienting to faces in the first months of life (Portugal et al., 2022). Phasic changes in neuromodulatory activity (phasic arousal) have been previously investigated in adults through task-evoked changes in pupil diameter and related to task performance (de Gee et al., 2017; Urai et al., 2017; van Kempen et al., 2019). While it is not possible to instruct infants to perform a specific task, the pop-out paradigm represents an ecologically valid solution to investigate influences of phasic arousal on visual attention in infancy. Visual orienting abilities in the context of a pop-out task appear to be modulated by level of arousal (Kleberg et al., 2018) and motivation (Hsieh et al., 2011). Surprisingly occurring sounds outside the focus of attention can elicit increase in arousal levels and involuntarily capture attention. Kleberg and colleagues (Kleberg et al., 2018) investigated changes in orienting response to faces in 6.5-month-old infants in relation to phasic increases of arousal induced by auditory cues and measured with pupil dilation. Results showed a negative linear response of first gaze latency to any visual stimulus in the pop-out array in relation to pupil dilation, supporting the idea that higher alertness is linked to faster orienting. In terms of target selection, instead, the authors found evidence for an inverted U-shaped relationship between levels of arousal, as indexed by pupil dilation, and visual orienting to the target, with optimal target selection for intermediate levels of arousal. However, findings need further replication and extension to a wider range of conditions to elucidate mechanistic associations between sensory features of the incoming stimuli, elicited arousal and associated orienting. While previous studies have examined the effect of auditory stimuli manipulations in older age groups (Zekveld et al., 2018), further work is needed to understand how characteristics of the auditory cues selected to elicit phasic arousal can influence the observed pupillary response and its relationship to visual attention in infancy. Previous work has shown that the pupillary dilation response can be robustly evoked by salient auditory and visual stimuli across species (A. D. Bala &amp; Takahashi, 2000; C. A. Wang et al., 2014; C.-A. Wang &amp; Munoz, 2014). A recent study used pupillometry to estimate detection and discrimination thresholds in guinea pigs across a wide range of stimuli in an auditory oddball paradigm (Montes-Lourido et al., 2021). Results showed that pupil dilation responses are modulated by both low-level (i.e., sound intensity) and high-level changes (i.e., tones vs. conspecific vocalizations), providing a reliable and easy-to-use method of estimating sensory thresholds in large experimental cohorts and across species. In adult humans as in the barn owl, pupil dilation response was found to scale in amplitude with the sound volume (A. D. S. Bala et al., 2020; A. D. Bala &amp; Takahashi, 2000). In particular, previous work with adults showed a nearly perfect linear correlation relation between pupil dilation response to sounds and sound loudness (Liao et al., 2016). This suggests that the pupillary dilation response might actually represent a sensitive index of sensory salience, in addition to being a robust signal of stimulus detection. Individual differences in perceptual sensitivity may be modulated by personality traits (Hubert Lyall &amp; Järvikivi, 2021). Keuler and colleagues (Keuler et al., 2011) showed a moderate correlation between temperamental negative affect and fear, and auditory and tactile sensory over-responsivity in toddlerhood. Similarly, the Big Five personality dimensions (i.e., neuroticism, extroversion, openness, agreeableness, and conscientiousness) accounted for 33% of the variance in noise sensitivity in an adult population (Shepherd et al., 2015). However, sensory responsivity was measured through questionnaire assessing sensory behaviours and further work should investigate whether the association with personality traits remains for physiological response to sensory stimulation, and how it changes through early development. Previous research linked physiological reactivity, as measured by amplitude and latency of pupil dilation, to temperament and personality traits. In particular, larger pupil dilation as a function of sound volume has been linked to neuroticism in adults (Antikainen &amp; Niemi, 1983). Furthermore, a recent study has investigated the association between pupil dilation response to faces and the development of temperament very early in infancy, at age 2 and 4 months (Zeng et al., 2022). Taken together, these studies provide promising evidence for the use of pupillometry to investigate the association between autonomic regulation and temperament early in development, facilitating the identification of potential early signs for alterations in social functioning and/or neurodevelopmental conditions. Developmental research has shown increasing interest recently in understanding the role of sleep in early development [(Gossé et al., 2022); see (Mason et al., 2021) for a review]. Sensitivity to unwanted sounds is common in the general population as well as clinical manifestations. Research indicates that individuals reporting noise sensitivity have more irregular sleep patterns [Dang-Vu 2010], a stronger emotional response to sounds, and difficulties habituating and therefore filtering out irrelevant sounds (Kolesnik et al., 2019), resulting in altered attention. Findings show substantial individual differences in sleep patterns and sleep consolidation in infancy, which appear to be associated with later neurocognitive development. Auditory arousal thresholds as measured through EEG have been previously linked to sleep processes underlying awakening from sleep across development (Busby et al., 1994), suggesting a potential link across different physiological mechanisms for autonomic regulation. Understanding individual variability in sleep as through the association with variability in sensory processing, in terms of auditory signal detection and modulation of physiological arousal to sounds, can have relevant implications for our understanding of different developmental pathways. RESEARCH AIMS AND HYPOTHESES Research on neurological development of attention in relation to arousal has largely focused on adults and animals, with very few studies looking at these mechanisms in infancy, and only a handful of studies investigating this association across different experimental manipulations. While the field demands for additional evidence on the development of alerting and attention systems in infancy, several questions remain untested on the potential developmental link between these systems and different domains of physiological and behavioural regulation. This project aims to investigate: 1. Normative properties of pupillary response to sounds in infancy. Particularly, we aim to understand how the pupil responds to sounds at increasing volume level (Aim 1). 2. How individual differences in pupillary response to sounds associates with individual developmental scores in infancy (Aim 2). Particularly, we aim to test associations with: a. Temperament. b. Sleep pattern. c. Sensory profile. We expect to find: 1. a monotonic association between pupil dilation to sounds and increasing sound volume. 2. steeper pupil dilation response to sounds with increasing volume being linked to more irregular sleep patterns, higher sensory sensitivity, and more fearful and negative affective behavior. Materials and methods Participants A total of 40 5-month-old infants have been recruited for the study, balanced in terms of biological sex at birth (22 boys). Exclusion criteria were: 1. Visual or auditory impairment. 2. Other medical diagnoses that could affect development. 3. Premature birth (earlier than pregnancy week 37). Eye-tracking protocol For the duration of the experiment, the infant was seated on the caregiver’s lap at an approximate distance of 55cm from a Tobii Pro Spectrum eye-tracker and monitor. The exact distance was adjusted during the experimental session to ensure optimal signal to the eye-tracker. The caregiver was wearing a pair of perforated black glasses to avoid the caregiver’s eyes being registered by the eye-tracker, and was instructed to avoid pointing to the screen or in any way influencing the infant’s looking behavior during the whole procedure. The experimenter monitored the participant by direct observation and by use of a separate screen that indicated participant gaze position. A 5-point calibration procedure was completed before starting the task battery, including a pop-out visual attention task, which provided the data for this study. Pop-out visual attention task. In order to test auditory perception in infants using pupillometry, it is necessary to embed the experiment within an experimental context that maintains their interest to the stimulus screen. In this task, we manipulated sound volume across 7 different levels. The experimental paradigm is a modified version of the one used in previous studies (Kleberg et al., 2018) and consists of a pop-out visual attention task in which a picture of a face is presented among distractors. Visual stimulation is preceded by auditory cues at different volume levels (i.e., seven levels: 0dB, 20dB, 30dB, 40dB, 50dB, 60dB, 70dB) to assess sound detection threshold and modulation of arousal and visual orienting by sound volume. Increasing sound volume levels are presented in sequential trials (see Task 1 in Figure 1). The combination of the auditory experiment and the visual task allows us to test how auditory alerting is linked to visual orienting. Structure and duration. Trial start was triggered by participant’s gaze to the attention grabber for two fifths of the time during an interval of 500ms. If the infant did not gaze at the attention grabber for 5s, the experimenter played an attention-grabbing sound. Once gaze was captured, the attention grabber disappeared and a sound was played at increasing volume level. After 80-400ms (exact duration randomly determined), an array of four images was displayed for 3000ms. After the visual array was finished, an entirely white screen was displayed for 500ms until trial end. Each trial (i.e., one sound presentation of the pop-out visual attention task) lasts between 3600ms and 4500ms. Each experimental block (including the pop-out visual attention task) consisted of 13 trials (n=7 trials for the pop-out visual attention task), and lasted for approximately 60 seconds. Each block was repeated six times to provide 6 trials for each volume level (see Figure 1). Figure 1. The experiment consists of six blocks, each including 13 trials (coloured boxes). The pop-out visual attention task (Task 1) is shown in blue, with different shades indicating the increasing volume of presented sound cues, starting from a silent trial. Auditory Stimuli Sound cues were selected from an array of social and non-social sounds and randomly presented across participants, ensuring that the same sound is repeated at different volume levels within each experimental block for the pop-out task and that the same amount of social/non-social sounds was played for each participant, but randomized between blocks. Social sound stimuli consisted of three infant-directed speech sounds balanced (including both male/female speakers pronouncing the Swedish word hejsan and the same female speaker pronouncing the Swedish word oj). Non-social sound stimuli consisted of three recordings of everyday objects (i.e., toy car rolling, phone message sound, toy squeak). Each sound will have a sudden onset and a duration ranging between 599ms and 989ms. All sound files shared the same maximum amplitude. Visual Stimuli Visual stimuli for the pop-out experiment included 56 images for each of the categories: faces (varying by sex, age, and ethnicity; Ma et al., 2015), man-made objects (e.g. hammer), natural objects (e.g. leaf), and computer-generated 3D shapes (e.g. cube). Each trial included one image from each category, of size 9° × 9° (visual degrees), arranged in a cross-like shape where each image midpoint is at the same horizontal (12°) and vertical (6°) distance from the monitor midpoint. For each trial and location on the array, the specific image displayed will be selected randomly (within category) for each participant but ensuring that (a) each image appears only once during the experiment, (b) one image from each category is included in each trial, (c) images of the same category never appear in the same position for two trials in a row. Questionnaires Caregivers receiver questionnaires to fill in at home (or during the lab visit) about the infant’s temperament (Infant Behavior Questionnaire Revised, IBQ-R (Rothbart et al., 2001)), sensory processing (Infant/Toddler Sensory Profile (Dunn &amp; Daniels, 2002)), and sleep habits (infant version of the Sleep and Settle Questionnaire, (Matthey et al., 2001)). PRE-PROCESSING Gaze data Gaze data was first linearly interpolated (over gaps in the data shorter than 150ms) and averaged for the left and right eye. Areas of interest (AOIs) of 11 × 11° (visual angle) were defined around the four stimulus positions (this is slightly bigger than the presented stimuli) and binary vectors were computed for gaze in each AOI. Sum of time in each AOI (in seconds) was calculated, as well as the latency to enter the AOI for the first time – for this, only the first entry which lasted 100ms or more was considered (latency to enter was defined from the first accepted sample). In addition, proportion raw (before interpolation) missing data in the entire trial and proportion on screen data after interpolation during visual presentation were computed. Trials were rejected if: 1) interpolated gaze was not at the central attention-grabbing area/AOI for more than 40% of the 500ms before sound onset, 2) interpolated gaze was not at the central attention-grabbing area during the 200ms before visual onset, 3) interpolated gaze was missing or outside the screen during the 500ms after visual onset, or was missing or outside the screen for more than 25% during the subsequent 500ms*, 4) Gaze was not detected at any AOI (entry which lasted 100ms or more) during the entire visual presentation, and 5) Latency to first AOI was below 200ms or higher than 1 second**. *This criterion was decided based on a compromise between being liberal enough so that we do not exclude trials where we have a valid first latency and first target (but the baby disengaged from stimuli quickly after). However, this means that in some trials we risk including pupil response when gaze went offscreen and came back during the 2nd second of the trial. **These thresholds were decided based on seeing the distribution of latencies to first AOI in all valid trials across all participants. These cut-offs represent 0.9% and 93.3% of the valid trials, respectively. Kleberg (Kleberg et al., 2018) criteria were between 150ms (based on them likely being anticipatory gaze shifts, (Kenward et al., 2017)) and 2130ms (97.5% of their sample); we were a bit more conservative because we are not excluding too many trials with this approach. Pupil data Pupil size data was first smoothed (with a moving average filter with 100ms window), then averaged with a dynamic offset mean (i.e., taking into consideration the dynamic offset between the sizes of the two pupils in the gaps where one eye was missing, (Kret &amp; Sjak-Shie, 2019)). Invalid samples (i.e., outside the 1.5-9 mm range or higher/lower than 3 standard deviations from the trial mean) were recoded into missing samples. Pupil samples when gaze was detected outside the screen were also recoded into missing samples. The pupil data was then linearly interpolated (over gaps in the data shorter than 150ms). Baseline pupil size was computed by taking the mean interpolated pupil size during the 200ms* preceding the sound onset (or the corresponding marker for silent trials). Response pupil size was computed by taking the mean interpolated pupil size during the first 2 seconds from the visual onset**. Pupil dilation was computed by subtracting the mean baseline from the mean response. In addition, proportion raw (before interpolation) missing data in the entire trial and proportion missing data after interpolation during baseline and response separately were computed. Only valid trials for gaze data were considered for pupil measurements. *This is likely a period where participants have data given the contingency rule of the task, it is before the experimental manipulation of interest, and is of reasonable duration for a baseline (Mathôt &amp; Vilotijević, 2022). **This was decided based on previous work and seeing the normalized pupil response across all trials and participants (see Figure 2). It's between Kleberg ((Kleberg et al., 2018); 0-1.5 seconds) and Rudling ((Rudling et al., 2022), 0-3 seconds). Figure 2. Time-course of pupil dilation averaged across trials and across participants (n=39 participants providing data). Different lines show the average pupil dilation response after sound presentation (marked by the empty vertical area around 0 on the x-axis) at different volume levels: 0dB (Volume 1), 20dB (Volume 2), 30dB (Volume 3), 40dB (Volume4), 50dB (Volume 5), 60dB (Volume 6), and 70dB (Volume 7). The time-window for calculation of the baseline pupil size is marked by the grey shaded area, while the response was averaged across the 2000ms after the sound was played. Average across trials Average measures of pupil dilation response were calculated within participant across all valid trials for each condition separately (based on volume level), defined as the mean pupil baseline subtracted from the mean pupil response. This yielded to a total of 7 average measures per participant. In addition, number of valid trials considered in each condition were computed. ANALYSIS PLAN After cleaning and pre-processing, our preliminary sample size consists of 34 infants (19 boys). We will use mixed-effect ANOVA to test for main and interaction effects of sex (2 levels; between-subject effect) and sound volume (7 levels; within-subject effect) on pupil dilation response to auditory cues. When interaction effects are significant, we will test significance of simple main effects through t-tests. Non-parametric approaches (e.g., Wilcoxon test) will be used accordingly, if model assumptions are violated. Given a sample size of 34 participants across the 2 sex-stratified groups, we will have more than 99% statistical power to detect a within-between subject interaction effect of moderate effect size (f=0.25). Next, we will use generalized linear regression models to test for the functional form of pupil dilation responses to sound cues at increasing volume level (Aim 1). For that, we will use as dependent variable average amplitude of pupil dilation response to sounds (additive size in mm compared to baseline). Intercept and slope will be used as independent variables in subsequent analyses testing the association between pupillary response to sounds and domain scores from the collected questionnaire data (Aim 2), with age and sex included as covariates in the model. In particular, we will test associations with the following questionnaire-based metrics used as dependent variables: 1. Temperament domains (IBQ-R): negative affect (Aim 2.a). 2. Sleep habits (CSHQ-I): night waking (Aim 2.b). 3. Infant-Toddler Sensory Profile (ITSP): sensory sensitivity (Aim 2.c). However, based on our current sample size, these analyses will only have approximately 60% statistical power to detect a moderate effect (f=0.15) in increase of variance in developmental scores explained by the slope of pupillary response to increasing volume level with a multiple regression approach (4 predictors in total). The prospective sample size after a second phase of data collection will be of 70 infants in total, increasing statistical power to approximately 90% for detecting a moderate effect (f=0.15) in increase of variance in developmental scores explained by the slope of pupillary response to increasing volume level with a multiple regression approach including phase of data collection as an additional covariate (5 predictors in total). Furthermore, we will test additional questions to further investigate the association between arousal and visual attention early in development. In particular, we will use generalized linear models to test: 1. Main and interaction effects of age, sex and volume level on alerting, as indexed by the following gaze measures extracted during the pop-out visual attention task: a. Latency of first look to any object (mean time in seconds; dependent variable). b. First look to the face (binary ‘yes’ or ‘no’ variable; dependent variable). c. 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