Eye Tracking during reading provides information about how readers process English texts when English is their second language (L2). We use unsupervised learning to derive reader profiles from eye tracking and comprehension data in MECO L2 dataset. For each participant, we compute eight features: skipping, regression in, refixation, reading rate, progressive duration, rereading duration, lookback duration, and comprehension accuracy. After standardization of these calculated features, a k-means clustering is deployed with and without reading rate to separate the influence of speed from other aspects of reading behavior. A gradient-boosted tree model serves as a surrogate classifier to interpret the resulting three profiles, which differ in speed, regression rate, and accuracy. We discuss how this profiling pipeline assists in studying individual differences in L2 reading and relates cluster-level patterns to existing reading style typologies.

This study integrates Bundesen’s Theory of Visual Attention (TVA) with task-evoked pupillary responses (TEPR) to examine how typog- raphy affects letter recognition. Twenty-one participants completed a whole-report task with Danish letters in three fonts (Cambria, Garamond, Roboto)× two styles (Regular, Italic) across eight expo- sure durations (10–200 ms). Bayesian posterior analysis revealed that italic processing-speed penalties scale with the degree of structural redesign: Garamond’s script-derived italic imposed the largest cost (Δ= 27.0 elem/s, (Δ > 0)= 1.00), Cambria’s glyph substitutions a moderate cost (Δ= 16.8, = 1.00), and Roboto’s oblique the smallest (Δ= 4.5, = 0.82, CI spanning zero). Pupillary metrics showed no significant font effects; only exposure duration drove dilation, though dPPD revealed a significant Font× Style interaction. This dissociation font-sensitive TVA parameters but font-insensitive pupillary am- plitude suggests typography modulates encoding efficiency rather than cognitive effort, although pupillometric

While Generative AI models like Large Language Models (LLMs) are capable of generating extensive text, their efficacy in producing readable content for human participants in experimental settings remains to be evaluated. Further, eye-tracking technology is increas- ingly utilized to study cognition and behavior, yet its application to readers’ cognitive processes when exposed to AI-generated versus human-authored texts remains unexplored. This study investigates how text generated by LLMs influences reading by analyzing gaze patterns. The study collects gaze data from 13 participants as they read AI- generated and human-authored passages. A comparative analysis is conducted within subjects to assess gaze patterns between authors and between text types based on the robust two-means clustering (I2MC) algorithm to identify fixations. In addition, pupil dilation and reading speed were examined. Our findings reveal significant differences in fixation character- istics not only between authors but also between AI-generated and human-authored texts.

While adaptive reading interfaces are capable of providing flexible typographical adjustments in real-time, readers are challenged to keep track of the context. This paper aims to contribute by introducing context preservation, which enables readers to resume reading faster after applying typographical adjustments, using eye-tracking. Typography adjustments are applied through so-called interventions, and the reading application currently has four intervention designs: Popup, Undo, Notification, and Gradual. To explore how much text is required to resume reading quickly, context-preservation functionality was applied and evaluated on 22 participants through within-subjects experiment design. Our findings reveal significant differences in reading-resume time (RRT) between interventions. Furthermore, context-preservation in a gradual intervention mode is the fastest and most liked intervention design by the participants.

Social signal extraction from the facial analysis is a popular research area in human-robot interaction. However, recognition of emotional signals from Traumatic Brain Injured (TBI) patients with the help of robots and non-intrusive sensors is yet to be explored. Existing robots have limited abilities to automatically identify human emotions and respond accordingly. Their interaction with TBI patients could be even more challenging and complex due to unique, unusual and diverse ways of expressing their emotions. To tackle the disparity in a TBI patient's Facial Expressions (FEs), a specialized deep-trained model for automatic detection of TBI patients' emotions and FE (TBI-FER model) is designed, for robot-assisted rehabilitation activities. In addition, the Pepper robot's built-in model for FE is investigated on TBI patients as well as on healthy people. Variance in their emotional expressions is determined by comparative studies. It is observed that the customized trained system is highly essential for the deployment of Pepper robot as a Socially Assistive Robot (SAR).

Rehabilitation after traumatic brain injury (TBI) is very critical as it is largely unpredictable depending upon the nature of the injury. Rehabilitation process and recovery time also varies, as it takes months and years, depending upon the assessment of treatment, mental and physical conditions and strategies. Due to non-cooperative behaviour of patients, and increase in negative emotional expressions it is very beneficial to evaluate these expressions in a contactless way, and perform a rehabilitation physiotherapy, cognitive or other behavioral activities when the patient is in a positive mood. In this paper we have analyzed the methods for facial features extraction for TBI patients to determine optimal time to have aforementioned rehabilitation process on the basis of positive and negative facial expressions. We have employed a deep learning architecture based on convolutional neural network and long short term memory on RGB and thermal data that were collected in challenging scenarios from real patients. It automatically identifies the patient's facial expressions, and inform experts or trainers that “it is the time” to start rehabilitation session.