No abstract

Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eyetracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.

SIGNIFICANCE This article evaluates the standardized Greek version of the International Reading Speed Texts (IReST) set, which enriches interlanguage comparisons and international clinical studies of reading performance. Moreover, it investigates how specific textual and subject-related characteristics modulate the variability of reading speed across texts and readers. PURPOSE The purpose of this study was to develop a standardized Greek version of the IReST set and investigate how specific textual and subject-related factors modulate the variability of reading speed across texts and readers. METHODS The English IReST texts were translated to Greek and matched for length, content, and linguistic difficulty. The Greek IReSTs were presented at a distance of 40 cm and size of 1 M to assess reading speeds of 25 normally sighted native speakers (age range, 18 to 35 years). The participants read the texts aloud while reading time was measured by stopwatch. Reading performance included measurement of reading speed in three units of analysis. Reading efficiency was assessed using a word-level oral reading task. Statistical analysis included evaluation of subject- and text-related variability, as well as correlations between reading speed and specific textual and subject-related factors. RESULTS The average reading speed between texts was 208 ± 24 words/min, 450 ± 24 syllables/min, and 1049 ± 105 characters/min. Differences between readers accounted for the 76.6%, whereas differences across texts accounted for the 23.4% of the total variability of reading speed. Word length (in syllables per word) and median word frequency showed a statistically significant contribution to the variability of reading speed (r = 0.95 and 0.70, respectively). Reading speed was also statistically correlated with word reading efficiency (r = 0.68). CONCLUSIONS The addition of the Greek version in the IReST language pack is expected to be a valuable tool for clinical practice and research, enriching interlanguage comparisons and international studies of reading performance.

No abstract

In the last years, many studies have been investigating emotional arousal and valence. Most of them have focused on the use of physiological signals such as EEG or EMG, cardiovascular measures or skin conductance. However, eye related features have proven to be very helpful and easy to use metrics, especially pupil size and blink activity. The aim of this study is to predict emotional arousal and valence levels which are induced during emotionally charged situations from eye related features. For this reason, we performed an experimental study where the participants watched emotion-eliciting videos and self-assessed their emotions, while their eye movements were being recorded. In this work, several classifiers such as KNN, SVM, Naive Bayes, Trees and Ensemble methods were trained and tested. Finally, emotional arousal and valence levels were predicted with 85 and 91% efficiency, respectively.

No abstract

Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.

No abstract