Behavior Research Methods

Social gaze is a crucial yet often overlooked aspect of nonverbal communication. During conversations, it typically operates subconsciously, following automatic co-regulation patterns. However, deviations from typical patterns, such as avoiding eye contact or excessive gazing, can significantly affect social interactions and perceived relationship quality. The principles and effects of social gaze have intrigued researchers across various fields, including communication science, social psychology, animal biology, and psychiatry. Despite its significance, research in social gaze has been limited by methodological challenges in assessing eye movements and gaze direction during natural social interactions. To address these obstacles, we have developed a new approach combining mobile eye tracking technology with automated analysis tools. In this paper, we introduce, validate, and apply a pipeline for recording and analyzing gaze behavior in dyadic conversations. We present a sample study where dyads engaged in two types of interactions: a get-to-know conversation and a conflictual conversation. Our new analysis pipeline corroborated previous findings, such as people directing more eye gaze while listening than talking, and gaze typically lasting about three seconds before averting. These results demonstrate the potential of our methodology to advance the study of social gaze in natural interactions.

Eye movement (EM) detection is a critical step in most eye-tracking (ET) research, typically relying on detectors - specialized algorithms designed to segment raw ET data into discrete oculomotor events. However, variability in detection algorithms and the lack of standardized evaluation frameworks hinder transparency and reproducibility across studies. In this work, we introduce pEYES, an open-source toolkit designed to streamline EM detection and enable robust, quantitative comparisons between detectors. The toolkit provides implementations for several widely used threshold-based detectors, along with multiple standardized evaluation procedures for assessing detection performance. Using pEYES, we evaluated seven detection algorithms on two publicly-available human-annotated datasets containing recordings of subjects freely viewing color images. Performance was assessed using metrics such as Cohens Kappa, Relative Timing Offset and Deviation, and a sensitivity index (') for fixation and saccade onsets and offsets. Engberts adaptive velocity-threshold algorithm consistently matched or outperformed the other detectors, occasionally achieving human-level precision. In contrast, several other detectors exhibited substantial variability in performance between datasets. We also found systematic differences in detection scores between fixation and saccade boundaries, with fixation offsets and saccade onsets detected more reliably than their counterparts. These findings highlight the importance of task- and dataset-specific detector selection in EM analysis. The pEYES toolkit is freely available, and its codebase - along with the analyses presented in this report - is accessible at https://github.com/huji-hcnl/pEYES. We invite the research community to use, extend, and contribute to its ongoing development. Through open collaboration, we aim to advance the rigor and reproducibility of EM detection practices.

Over the last years, several developments of remote webcam-based eye tracking (ET) prototypes have emerged, testing their feasibility and potential for web-based experiments. This growing interest is mainly explained by the possibility to perform tasks remotely, which allows the study of larger and hard-to-reach populations and potential applications in telemedicine. Nevertheless, a decrease in the quality of the camera and a noisier environment bring new implementation challenges. In this study, we present a new prototype of remote webcam-based ET. First, we introduced improvements to the state-of-the-art remote ET prototypes for cognitive and clinical tasks, e.g. without the necessity of constant mouse interactions. Second, we assessed its spatiotemporal resolution and its reliability within an experiment. Third, we ran a classical experiment, the anti-saccade task, to assess its functionality and limitations. This cognitive test compares horizontal eye movements toward (pro-saccades) or away from (anti-saccades) a target, as a measure of inhibitory control. Our results replicated previous findings obtained with high-quality laboratory ETs. Briefly, higher error rates in anti-saccades compared to pro-saccades were observed, and incorrect responses presented faster reaction times. Our web-ET prototype showed a stable calibration over time and performed well in a classic cognitive experiment. Finally, we discussed the potential of this prototype for clinical applications and its limitations for experimental use.

Psychophysical methods are widely used in neuroscience to investigate the quantitative relation between a physical property of the world and its perceptual representation provided by the senses. Recent studies introduced the Generalized Linear Mixed Model (GLMM) to fit the responses of multiple participants in psychophysical experiments. Another approach (two-level approach) requires fitting psychometric functions to each individual participant data using a Generalized Linear Model (GLM), and then testing the hypotheses on the multiple participants by means of a second level analysis. For either options, the implementation of the statistical analysis in R is possible and beneficial. Here, we introduce the package MixedPsy to model and fit psychometric data in R, either with two-level and GLMM approaches. The package, freely available in the CRAN repository, uses different methods for the estimation of Point of Subjective Equivalence (PSE) and Just Noticeable Difference (JND), and provides utilities for immediate visualization and plotting of the fitted results. This manuscript aims to provide researchers with a practical tutorial for implementing a complete analysis pipeline for psychophysical data using MixedPsy and other packages and basic functionalities of the R programming environment.

Visual function tests are important in basic and clinical vision research but are typically limited to very few aspects of human vision, coarse diagnostic resolution, and require an administrator. Recently, the generalizable, response-adaptive, self-administered Angular Indication Measurement (AIM) and Foraging Interactive D-prime (FInD) methods were developed to assess vision across different visual functions. The AIM and FInD paradigms show a range of visual stimuli per display (4x4 stimuli) spanning {+/-}2{sigma} around an adaptively estimated perceptual threshold across multiple displays. Here, we investigated the repeatability and behavioral relationships of the AIM and FInD paradigms for near visual acuity, contrast sensitivity function (CSF), form, and motion coherence threshold measurements using a novel tablet-based vision test tool. 31 healthy participants were recruited and completed two repetitions of each experiment in random order. Bland-Altman analyses were performed to calculate the Coefficient of Repeatability (precision) and Mean Bias (accuracy). Linear regressions and hierarchical cluster analysis were used to investigate the relationship between outcome parameters. Results show that AIM Form coherence and FInD Form horizontal coherence showed significant retest bias; all other tests were bias-free. Cluster analysis revealed overall clustering of CSF, form and motion outcomes. We further show significant correlations within CSF and between motion coherence outcomes but few significant correlations between form coherence outcomes. AIM and FInD near vision tests are generalizable across multiple visual functions and are precise and reliable. Most functions tested were bias-free. CSF, form, and motion outcomes clustered together, and CSF and motion outcomes correlated with one another. The combination of a generalizable, response-adaptive, and self-administered approach may be a suitable set of tests for basic science and clinical use cases.

Pupillometry provides a non-invasive window into the mind and brain, particularly as a psychophysiological readout of autonomic and cognitive processes like arousal, attention, stress, and emotional states. Pupillometry research lacks a robust, standardized framework for data preprocessing, whereas in functional magnetic resonance imaging and electroencephalography, researchers have converged on tools such as fMRIPrep, EEGLAB and MNE-Python; these tools are considered the gold standard in the field. Many established pupillometry preprocessing packages and workflows fall short of serving the goal of enhancing reproducibility, especially since most existing solutions lack designs based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. To promote FAIR and open science practices for pupillometry research, we developed eyeris, a complete pupillometry preprocessing suite designed to be intuitive, modular, performant, and extensible (https://github.com/shawntz/eyeris). Out-of-the-box, eyeris provides a recommended preprocessing workflow and considers signal processing best practices for tonic and phasic pupillometry. Moreover, eyeris further enables open and reproducible science workflows, as well as quality control workflows by following a well-established file management schema and generating interactive output reports for both record keeping/sharing and quality assurance of preprocessed pupil data prior to formal analysis. Taken together, eyeris provides a robust all-in-one transparent and adaptive solution for high-fidelity pupillometry preprocessing with the aim of further improving reproducibility in pupillometry research. Impact StatementPupillometry research currently lacks a standardized, integrated preprocessing framework comparable to tools widely adopted in EEG and fMRI research. We introduce eyeris, an open-source R package that fills this gap through a modular, transparent pipeline with signal processing best practices, interactive diagnostic reports for quality control, and scalable database storage. eyeris advances pupillometry methods by promoting reproducible, FAIR-compliant workflows accessible to researchers at all levels of programming expertise.

Home cage monitoring (HCM) captures longitudinal animal behavioural data without human intervention. However, the systems complexity is rarely addressed in their design, increasing the risk of data loss, which wastes workhours, resources, and animal lives. To assess the feasibility of implementing modern, robust architectures in complex operant HCM paradigms, the VersatiLe Autonomous DevIce for Scheduled Learning Assessment Via Wi-Fi (VLADISLAV) was developed and employed to test cognitive deficits in the intracerebroventricular streptozotocin-induced rat model of sporadic Alzheimers disease (sAD). Reliability was modelled against a system architecture common in commercial HCM systems by modelling the failure rate of the devices critical components across typical durations of animal experiments. VLADISLAV assessed multiple cognitive dimensions of a rat model of sAD with automated, scheduled testing. Its design enabled simultaneous, redundant recording to multiple devices in real time, as well as batch remote control and supervision of tens of VLADISLAVs. VLADISLAV is estimated to reduce component failure rate [~]200-fold at {euro}40/device. Data loss due to system failure shouldnt be accepted as a normal occurrence and robust system design is an ethical imperative. VLADISLAVs robustness and utility demonstrate the potential of embedded networked systems, used in other industries and consumer electronics for over a decade. Today, the open source ecosystem enables cost-effective implementation of such architectures in HCM by biomedical researchers with no electronic engineering education, preventing data loss and facilitating researchers and technicians day-to-day work. Considering these findings, it is apparent that the implementation of modern architectures in HCM is long overdue.

Online methods allow testing of larger, more diverse populations, with much less effort than in-lab testing. However, many psychophysical measurements, including visual crowding, require accurate eye fixation, which is classically achieved by testing only experienced observers who have learned to fixate reliably, or by using a gaze tracker to restrict testing to moments when fixation is accurate. Alas, both approaches are impractical online since online observers tend to be inexperienced, and online gaze tracking, using the built-in webcam, has a low precision ({+/-}4 deg, Papoutsaki et al., 2016). The EasyEyes open-source software reliably measures peripheral thresholds online with accurate fixation achieved in a novel way, without gaze tracking. EasyEyes tells observers to use the cursor to track a moving crosshair. At a random time during successful tracking, a brief target is presented in the periphery. The observer responds by identifying the target. To evaluate EasyEyes fixation accuracy and thresholds, we tested 12 naive observers in three ways in a counterbalanced order: first, in the lab, using gaze-contingent stimulus presentation (Kurzawski et al., 2023; Pelli et al., 2016); second, in the lab, using EasyEyes while independently monitoring gaze; third, online at home, using EasyEyes. We find that crowding thresholds are consistent (no significant differences in mean and variance of thresholds across ways) and individual differences are conserved. The small root mean square (RMS) fixation error (0.6 deg) during target presentation eliminates the need for gaze tracking. Thus, EasyEyes enables fixation-dependent measurements online, for easy testing of larger and more diverse populations.

The eye is the vanguard of the reception process, constituting the point where visual information arrives and is transformed into neural signals. While we view dynamic media contents, a fine-tuned interplay of mechanisms causes our pupils to dilate and constrict over time - and putatively similarly across audience members exposed to the same messages. Research that once pioneered pupillometry did actually use dynamic media as stimuli, but this trend then stalled, and pupillometry remained underdeveloped in the study of naturalistic media stimuli. Here, we introduce a VR-based approach to capture audience members pupillary responses during media consumption and suggest an innovative analytic framework. Specifically, we expose audiences to a set of 30 different video messages and compute the cross-receiver similarity of pupillometric responses. Based on this data, we identify the specific video an individual is watching. Our results show that this pupil-pulse-tracking enables highly accurate decoding of video identity. Moreover, we demonstrate that the decoding is relatively robust to manipulations of video size and distractor presence. Finally, we examine the relationship between pupillary responses and subsequent memory. Theoretical implications for objectively quantifying exposure and states of audience engagement are discussed. Practically, we anticipate that this pupillary audience response measurement approach could find application in media measurement across contexts, ranging from traditional screen-based media (commercials, movies) to social media (e.g., TikTok and YouTube), and to next-generation virtual media environments (e.g., Metaverse, gaming).

Precise measurement of color discrimination across color space is limited by the time and effort required to collect large psychophysical datasets. We investigated whether immersive virtual reality (VR) can support high-throughput measurement of color discrimination without compromising data quality. A standard 4-alternative forced-choice odd-one-out task was embedded in an interactive VR environment inspired by the rhythm game Beat Saber, in which participants indicated the location of a chromatic target by slicing approaching cubes. Stimuli were presented on a color-calibrated VR headset, and chromaticities were specified in DKL space. Discrimination thresholds were measured for hue and chroma shifts around two reference colors. Participants sustained response rates of approximately one trial per second while maintaining stable performance. Thresholds replicated established asymmetries in color discrimination: hue thresholds were lower than chroma thresholds, and the hue-chroma ratio differed between color quadrants. A control experiment comparing VR-based slicing responses with matched keyboard responses revealed comparable psychometric fits and threshold estimates, indicating that motor engagement did not degrade measurement precision. Questionnaire measures further showed significantly higher intrinsic motivation, enjoyment, and stimulation in the immersive condition relative to a classic static psychophysical task, without increases in reported pressure or discomfort. These results demonstrate that calibrated immersive VR can yield reliable color discrimination measurements at substantially increased throughput, providing a scalable approach for mapping the metric structure of color space.

Wearable light loggers and optical radiation dosimeters are increasingly used in chronobiology and circadian health research, yet their data often lack contextual information (e.g., sleep, activity, environmental conditions) and may be compromised by non-wear periods, compliance issues, or technical faults. To address these limitations, we conducted interviews (n=21) and a survey (n=16) with domain experts to distill and iteratively develop auxiliary data and quality-control strategies aimed at improving the accuracy and interpretability of wearable light measurements. From this process, we established a six-domain auxiliary data framework encompassing wear/non-wear logging, sleep monitoring, light-source context, participant behaviour, user experience, and environmental light levels. Survey responses showed strong consensus on the value of auxiliary information (mean importance 4.0/5), with sleep and wear-time tracking rated as the most essential additions. To support practical adoption, we provide implementation tools, including extensions to the open-source R package LightLogR, enabling streamlined integration of wearable and auxiliary data as well as systematic quality assurance and control. Experts agreed that combining contextual records with rigorous QA/QC procedures substantially improves the reliability of field-collected light-exposure data. These recommendations and tools aim to help researchers in chronobiology, wearable sensing, and health sciences maximise data quality and enhance interpretation in real-world light-exposure studies.

Purpose: To evaluate the feasibility, validity, and scalability of PLFest, an open-source, Unity-based, cross-platform application designed for standardized, multi-site visual and cognitive assessment and training. Methods: Two hundred sixty participants (mean age = 23 years) were recruited across four university sites in the United States. Participants completed a battery of five visual assessments administered through PLFest, including visual acuity, contrast sensitivity, spatial frequency cutoff, contrast sensitivity at spatial-frequency cutoff, and visual search. Five cognitive assessments measuring visuospatial working memory, verbal working memory, fluid reasoning, inhibitory control, and selective attention were also administered. Descriptive statistics and performance distributions were examined and compared with normative data. Results: Visual acuity and contrast sensitivity measures closely matched previously reported normative values obtained using established clinical and psychophysical methods. Spatial frequency cutoff and visual search tasks produced stable threshold estimates while showing substantial inter-individual variability. Performance across all cognitive assessments was consistent with published validation studies of the corresponding tasks. Across the full battery, adaptive procedures demonstrated reliable convergence and generated well-distributed performance measures without evidence of substantial floor or ceiling effects. Importantly, these findings were observed across four geographically distributed testing sites using standardized consumer-grade tablet hardware. Conclusions: PLFest provides reliable and scalable assessment of visual and cognitive function using portable consumer devices. The platform supports standardized data collection across distributed research settings while maintaining performance characteristics consistent with established laboratory and clinical benchmarks. These findings support the use of PLFest as a reliable framework for large-scale studies of vision and cognition. Translational Relevance: By reducing dependence on specialized laboratory infrastructure and trained personnel, PLFest may facilitate broader access to visual and cognitive assessment, enabling large-scale research, screening, and future rehabilitation applications.

The recent introduction of organic light-emitting diode (OLED) monitors with refresh rates of 240 Hz or more opens new possibilities for their use as precise stimulation devices in vision research, experimental psychology, and electrophysiology. These affordable high-speed monitors, targeted at video gamers, promise several advantages over the cathode ray tube (CRT) and liquid crystal display (LCD) monitors commonly used in these fields. Unlike LCDs, OLED displays have self-emitting pixels that can show true black, resulting in superior contrast ratios, a broad color gamut, and good viewing angles. More importantly, the latest gaming OLEDs promise excellent timing properties with minimal input lags and rapid transition times. However, OLED technology also has potential drawbacks, notably Auto-Brightness Limiting (ABL) behavior, where the local luminance of a stimulus can change with the number of currently illuminated pixels. This study characterized a 240 Hz OLED monitor, the ASUS PG27AQDM, in terms of its timing properties, spatial uniformity, viewing angles, warm-up times, and ABL behavior. We also compared its responses to those of CRTs and LCDs. Results confirm the monitors excellent temporal properties with CRT-like transition times (around 0.3 ms), wide viewing angles, and decent spatial uniformity. Additionally, we found that ABL could be prevented with appropriate settings. We illustrate the monitors benefits in two time-critical paradigms: Rapid "invisible" flicker stimulation and the gaze-contingent presentation of stimuli during eye movements. Ourfindings suggest that the newest gaming OLEDs are precise and cost-effective stimulation devices for visual experiments that have several key advantages over CRTs and LCDs.

Joint music making offers an ecologically powerful framework for investigating human social interaction and synchronization. Yet, experimental paradigms often rely on traditional instruments that limit accessibility, reproducibility, and experimental control. In parallel, the use of music for therapy and rehabilitation is expanding, motivating the development of digital musical instruments that can serve research, educational, and clinical purposes. Here, we introduce the e-Music Box Roma (eMB Roma), an open, reproducible digital musical instrument designed to study music making behavior regardless of musical training. The eMB Roma plays preregistered music with tempo controlled by hand rotary movements. Building on the original e-Music Box (Novembre et al., 2015), the eMB Roma retains its intuitive rotary hand control while introducing major innovations: a fully open and 3D-printable design, modular hardware with integrated slider and button controls, polyphonic output with multiple simultaneous instruments, and MIDI compatibility. Additionally, a dedicated graphical user interface allows real-time monitoring, experiment control, device synchronization (like neuroimaging or motion capture devices), and both solo and joint music-making paradigms. The eMB Roma provides a flexible and accessible platform for research contexts, allowing experimental control, reproducibility, and future extensions. Its open design and modularity make it suitable not only for research but also for therapeutic, rehabilitation, and educational applications, where it can support personalized interventions and quantitative assessment of motor performance.

Light influences human physiology profoundly, affecting the circadian clock and suppressing the endogenous hormone melatonin. Experimental studies often employ either homogenous full-field stimulation, or overhead illumination, which are hard to standardize across studies and laboratories. Here, we present a novel technique to examine non-visual responses to light using virtual-reality (VR) head-mounted displays (HMDs) for delivering standardized and calibrated light stimuli to observers in a reproducible and controlled manner. We find that VR HMDs are well-suited for delivering standardized stimuli defined in luminance and across time, with excellent properties up to 10 Hz. We examine melatonin suppression to continuous luminance-defined light stimuli in a sample of healthy participants (n=32, mean{+/-}SD age: 27.2{+/-}5.6), and find robust melatonin suppression in 24 out of 32 participants (75% of the sample). Our findings demonstrate that VR HMDs are well-suited for studying the mechanisms underlying human non-visual photoreception in a reproducible and standardized fashion.

The visual diet of humans is complex in space, time, spectrum, and conse- quently the activation of the retinal photoreceptors. While analyses of natural scenes have yielded valuable insights, the naturalistic natural stimulus on the retina is not very well understood. In the present study, we performed eye tracking in naturalistic indoor and outdoor real-world scenes. We recorded pupil size, several saccade and fixation metrics, as well as subjective scene perception ratings derived from subjective questionnaires. For the first five seconds of eye tracking, the descriptive data analysis revealed significantly increased average saccade frequency (p =.0025), amplitude (p =.0049), peak velocity (p =.0072), as well as pupil size (p = 1.307e-09) in the indoor environ- ments. After this initial phase, these differences vanished, except for pupil size. Using an exploratory analysis on the whole 4-minute measurement, we found that saccade and fixation metrics, along with scene ratings, showed significantly different correlations between indoor and outdoor conditions. Despite the inherent constraints of such a naturalistic study (reduced ability to exert control over the precise task and the environmental conditions), we contend that the dataset holds substantial value for field of eye movement research, as it has effectively minimized confounding factors that have been prevalent in previous eye tracking studies. HighlightsO_LIIncreased amplitude, frequency, and peak velocity of saccades in real- world indoor environments within the initial 5 seconds of eye tracking C_LIO_LIDifferent correlations of eye movement metrics and subjective scene per- ception between real-world indoor and outdoor environments C_LIO_LIEstablishing an eye movement dataset of various saccade in fixation metrics in naturalistic real-world environments with reduced confounding factors C_LI

AO_SCPLOWBSTRACTC_SCPLOWHead-mounted tools for eye/head tracking are increasingly used for assessment of visual behavior in navigation, sports, sociology, and neuroeconomics. Here we introduce an open-source python software (TP3Py) for collection and analysis of portable eye/head tracking signals using Tobii Pro Glasses 3. TP3Pys modular pipeline provides a platform for incorporating user-oriented functionalities and comprehensive data acquisition to accelerate the development in behavioral and tracking research. Tobii Pro Glasses 3 is equipped with embedded cameras viewing the visual scene and the eyes, inertial measurement unit (IMU) sensors, and video-based eye tracker implemented in the accompanying unit. The program establishes a wireless connection to the glasses and, within separate threads, continuously leverages the received data in numerical or string formats accessible for saving, processing, and graphical purposes. Built-in modules for presenting eye, scene, and IMU data to the experimenter have been adapted as well as communicating modules for sending the raw signals to stimulus/task controllers in live fashion. Closed-loop experimental designs are limited due to the 140ms time delay of the system, but this limitation is compensated by the portability of the eye/head tracking. An offline data viewer has been also incorporated to allow more time-consuming computations. Lastly, we demonstrate example recordings involving vestibulo-ocular reflexes, saccadic eye movements, optokinetic responses, or vergence eye movements to highlight the programs measurement capabilities to address various experimental goals. TP3Py has been tested on Windows with Intel processors, and Ubuntu operating systems with Intel or ARM (Raspberry Pie) architectures.

PurposeThere is a need for outcome measures that predict real-world communication abilities in hearing-impaired people. We outline a potential method for this and use it to answer the question of when, and how much, hearing-impaired listeners look towards a new talker in a conversation. MethodTwenty-two older hearing-impaired adults followed a pre-recorded two-person audiovisual conversation in the presence of babble noise. We compared their eye-gaze direction to the conversation in two multilevel logistic regression (MLR) analyses. First, we split the conversation into events classified by the number of active talkers within a turn or a transition, and we tested if these predicted the listeners gaze. Second, we mapped the odds that a listener gazed towards a new talker over time during a conversation transition. ResultsWe found no evidence that our conversation events predicted changes in the listeners gaze, but the listeners gaze towards the new talker during a silent-transition was predicted by time: The odds of looking at the new talker increased in an s-shaped curve from at least 0.4 seconds before to 1 second after the onset of the new talkers speech. A comparison of models with different random effects indicated that more variance was explained by differences between individual conversation events than by differences between individual listeners. ConclusionMLR modelling of eye-gaze during talker transitions is a promising approach to study a listeners perception of realistic conversation. Our experience provides insight to guide future research with this method.

In contemporary urban environments, advertisements are ubiquitous, capturing the attention of individuals navigating through cityscapes. This study simulates this situation by using VR as an advertising research tool and combining it with eye-tracking to rigorously assess attention to and retention of visual advertisements. Specifically, participants drove through a virtual city with 40 AI-generated, experimentally manipulated, and randomly assigned advertisements (20 commercial, 20 health) distributed throughout. Our results confirm theoretical predictions about the link between exposure, visual attention, and incidental memory. Specifically, we found that attended ads are likely to be recalled and recognized, and concurrent task demands (counting sales signs) decreased visual attention and subsequent recall and recognition of the ads. Finally, we identify the influence of ad placement in the city as a very important but previously hard-to-study factor influencing advertising effects. This paradigm offers great flexibility for biometric advertising research and can be adapted to varying contexts, including highways, airports, and subway stations, and theoretically manipulate other variables. Moreover, considering the metaverse as a next-generation advertising environment, our work demonstrates how causal mechanisms can be identified in ways that are of equally high interest to both theoretical as well as applied advertising research.

Exposure to the optical environment -- often referred to as visual experience -- profoundly influences human physiology and behavior across multiple time scales. In controlled laboratory settings, stimuli can be held constant or manipulated parametrically. However, such exposures rarely replicate real-world conditions, which are inherently complex and dynamic, generating high-dimensional datasets that demand rigorous and flexible analysis strategies. This tutorial presents an analysis pipeline for visual experience datasets, with a focus on reproducible workflows for human chronobiology and myopia research. Light exposure and its retinal encoding affect human physiology and behavior across multiple time scales. Here we provide step-by-step instructions for importing, visualizing, and processing viewing distance and light exposure data. This includes time-series analyses for working distance, biologically relevant light metrics, and spectral characteristics. The tasks are standardized through the open-source R package LightLogR. By leveraging a modular approach, the tutorial supports researchers in building flexible and robust pipelines that accommodate diverse experimental paradigms and measurement systems.