The new man and the new world the influence of Renaissance humanism on the explorers of the Italian era of discovery

In contemporary research, microsaccade detection is typically performed using the calibrated gaze-velocity signal acquired from a video-based eye tracker. To generate this signal, the pupil and corneal reflection (CR) signals are subtracted from each other and a differentiation filter is applied, both of which may prevent small microsaccades from being detected due to signal distortion and noise amplification. We propose a new algorithm where microsaccades are detected directly from uncalibrated pupil-, and CR signals. It is based on detrending followed by windowed correlation between pupil and CR signals. The proposed algorithm outperforms the most commonly used algorithm in the field (Engbert & Kliegl, 2003), in particular for small amplitude microsaccades that are difficult to see in the velocity signal even with the naked eye. We argue that it is advantageous to consider the most basic output of the eye tracker, i.e. pupil-, and CR signals, when detecting small microsaccades

Searching with and against each other: Spatiotemporal coordination of visual search behavior in collaborative and competitive settings

Although in real life people frequently perform visual search together, in lab experiments this social dimension is typically left out. Here, we investigate individual, collaborative and competitive visual search with visualization of search partners' gaze. Participants were instructed to search a grid of Gabor patches while being eye tracked. For collaboration and competition, searchers were shown in real time at which element the paired searcher was looking. To promote collaboration or competition, points were rewarded or deducted for correct or incorrect answers. Early in collaboration trials, searchers rarely fixated the same elements. Reaction times of couples were roughly halved compared with individual search, although error rates did not increase. This indicates searchers formed an efficient collaboration strategy. Overlap, the proportion of dwells that landed on hexagons that the other searcher had already looked at, was lower than expected from simulated overlap of two searchers who are blind to the behavior of their partner. The proportion of overlapping dwells correlated positively with ratings of the quality of collaboration. During competition, overlap increased earlier in time, indicating that competitors divided space less efficiently. Analysis of the entropy of the dwell locations and scan paths revealed that in the competition condition, a less fixed looking pattern was exhibited than in the collaborate and individual search conditions. We conclude that participants can efficiently search together when provided only with information about their partner's gaze position by dividing up the search space. Competing search exhibited more random gaze patterns, potentially reflecting increased interaction between searchers in this condition

Influence of Hemianopic Visual Field Loss on Visual Motor Control

Background: Homonymous hemianopia (HH) is an anisotropic visual impairment characterized by the binocular inability to see one side of the visual field. Patients with HH often misperceive visual space. Here we investigated how HH affects visual motor control. Methods and Findings: Seven patients with complete HH and no neglect or cognitive decline and seven gender- and age-matched controls viewed displays in which a target moved randomly along the horizontal or the vertical axis. They used a joystick to control the target movement to keep it at the center of the screen. We found that the mean deviation of the target position from the center of the screen along the horizontal axis was biased toward the blind side for five out of seven HH patients. More importantly, while the normal vision controls showed more precise control and larger response amplitudes when the target moved along the horizontal rather than the vertical axis, the control performance of the HH patients was not different between these two target motion experimental conditions. Conclusions: Compared with normal vision controls, HH affected patients' control performance when the target moved horizontally (i.e., along the axis of their visual impairment) rather than vertically. We conclude that hemianopia affects the use of visual information for online control of a moving target specific to the axis of visual impairment. The implications of the findings for driving in hemianopic patients are discussed

Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)

The advent of foundation models signals a new era in artificial intelligence. The Segment Anything Model (SAM) is the first foundation model for image segmentation. In this study, we evaluate SAM's ability to segment features from eye images recorded in virtual reality setups. The increasing requirement for annotated eye-image datasets presents a significant opportunity for SAM to redefine the landscape of data annotation in gaze estimation. Our investigation centers on SAM's zero-shot learning abilities and the effectiveness of prompts like bounding boxes or point clicks. Our results are consistent with studies in other domains, demonstrating that SAM's segmentation effectiveness can be on-par with specialized models depending on the feature, with prompts improving its performance, evidenced by an IoU of 93.34% for pupil segmentation in one dataset. Foundation models like SAM could revolutionize gaze estimation by enabling quick and easy image segmentation, reducing reliance on specialized models and extensive manual annotation.Comment: 14 pages, 8 figures, 1 table, submitted to ETRA 2024: ACM Symposium on Eye Tracking Research & Application

Precise localization of corneal reflections in eye images using deep learning trained on synthetic data

We present a deep learning method for accurately localizing the center of a single corneal reflection (CR) in an eye image. Unlike previous approaches, we use a convolutional neural network (CNN) that was trained solely using simulated data. Using only simulated data has the benefit of completely sidestepping the time-consuming process of manual annotation that is required for supervised training on real eye images. To systematically evaluate the accuracy of our method, we first tested it on images with simulated CRs placed on different backgrounds and embedded in varying levels of noise. Second, we tested the method on high-quality videos captured from real eyes. Our method outperformed state-of-the-art algorithmic methods on real eye images with a 35% reduction in terms of spatial precision, and performed on par with state-of-the-art on simulated images in terms of spatial accuracy.We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem which is one of the important common roadblocks in the development of deep learning models for gaze estimation. Due to the superior CR center localization and ease of application, our method has the potential to improve the accuracy and precision of CR-based eye trackersComment: Published in Behavioural Research Method

What’s bothering developers in code review?

The practice of code review is widely adopted in industry and hasbeen studied to an increasing degree in the research community.However, the developer experience of code review has receivedlimited attention. Here, we report on initial results from a mixed-method exploratory study of the developer experience

LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images

Deep learning has bolstered gaze estimation techniques, but real-world deployment has been impeded by inadequate training datasets. This problem is exacerbated by both hardware-induced variations in eye images and inherent biological differences across the recorded participants, leading to both feature and pixel-level variance that hinders the generalizability of models trained on specific datasets. While synthetic datasets can be a solution, their creation is both time and resource-intensive. To address this problem, we present a framework called Light Eyes or "LEyes" which, unlike conventional photorealistic methods, only models key image features required for video-based eye tracking using simple light distributions. LEyes facilitates easy configuration for training neural networks across diverse gaze-estimation tasks. We demonstrate that models trained using LEyes are consistently on-par or outperform other state-of-the-art algorithms in terms of pupil and CR localization across well-known datasets. In addition, a LEyes trained model outperforms the industry standard eye tracker using significantly more cost-effective hardware. Going forward, we are confident that LEyes will revolutionize synthetic data generation for gaze estimation models, and lead to significant improvements of the next generation video-based eye trackers.Comment: 32 pages, 8 figure

Noise-robust fixation detection in eye movement data: Identification by two-means clustering (I2MC)

Eye-tracking research in infants and older children has gained a lot of momentum over the last decades. Although eye-tracking research in these participant groups has become easier with the advance of the remote eye-tracker, this often comes at the cost of poorer data quality than in research with well-trained adults (Hessels, Andersson, Hooge, Nyström, & Kemner Infancy, 20, 601–633, 2015; Wass, Forssman, & Leppänen Infancy, 19, 427–460, 2014). Current fixation detection algorithms are not built for data from infants and young children. As a result, some researchers have even turned to hand correction of fixation detections (Saez de Urabain, Johnson, & Smith Behavior Research Methods, 47, 53–72, 2015). Here we introduce a fixation detection algorithm—identification by two-means clustering (I2MC)—built specifically for data across a wide range of noise levels and when periods of data loss may occur. We evaluated the I2MC algorithm against seven state-of-the-art event detection algorithms, and report that the I2MC algorithm’s output is the most robust to high noise and data loss levels. The algorithm is automatic, works offline, and is suitable for eye-tracking data recorded with remote or tower-mounted eye-trackers using static stimuli. In addition to application of the I2MC algorithm in eye-tracking research with infants, school children, and certain patient groups, the I2MC algorithm also may be useful when the noise and data loss levels are markedly different between trials, participants, or time points (e.g., longitudinal research)