A Critical Review of Recent Advances in Deep Learning and Machine Learning Models for Cognitive Load Assessment Using Eye-Tracking Technology

Mehshan Ahmed Khan, Houshyar Asadi, Li Zhang, Eric Tatt Wei Ho, Mohammad Reza Chalak Qazani, Hussain Mohammed Dipu Kabir, Lei Wei, Samson Yu, Alan Wee-Chung Liew, Hailing Zhou, Saeid Nahavandi, Chee Peng Lim

Cognitive load is a critical factor that influences learning and performance. In recent years, eye-tracking technologies have emerged as a promising method for detecting and measuring cognitive load in real-time during learning activities. This paper presents a comprehensive review of the state-of-the-art machine learning (ML) and deep learning (DL) methods utilizing eye-tracking technology for cognitive load assessment. We systematically selected and analyzed 27 studies using the PRISMA protocol, focusing on the methodologies, data, and tasks employed. The reviewed studies leverage a variety of eye-tracking features, such as pupil size, fixations, saccades, blink rate, and eye gaze, to classify cognitive load. Key contributions of this review include identifying specific eye movement patterns associated with cognitive load and exploring multimodal approaches that combine eye-tracking data with other physiological measures to enhance model accuracy and generalizability. By tracking changes in eye movements, researchers can gain insights into the cognitive processes underlying learning activities and identify strategies for improving instructional designs. ML and DL models have become increasingly popular for cognitive load classification based on eye-tracking metrics. These algorithms are capable of learning complex patterns in data and identifying subtle changes in eye movements that are indicative of changes in cognitive load. Therefore, this review focuses on how ML/DL models are used to study cognitive processes using eye-tracking technology, along with relevant variables. The review involved an extensive examination of electronic repositories and a thorough exploration of references from papers that met the inclusion criteria. After removing duplicates and irrelevant papers, the analysis focused on journal articles that presented well-executed studies involving eye-tracking and physiological signals. Specifically, the studies needed to analyze healthy individuals both at rest and during cognitive load. Eye-tracking offers a rich source of data, including fixations, pupil size, saccades, blinks, and eye-gaze, which can provide valuable insights into attentional processes during cognitive tasks. Several studies focus on the use of DL algorithms for automated detection and classification of cognitive load levels using eye-tracking features. While the potential of Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) in accurately classifying cognitive load levels is emphasized, it is crucial to acknowledge their limitations. These models heavily rely on the quality and quantity of training data, as well as the generalizability of the learned patterns to different populations and learning contexts. We discuss the challenges posed by these gaps and emphasize the need for privacy-preserving technologies and robust legal frameworks to protect individuals' privacy as the adoption of eye-tracking technology grows. Additionally, the interpretability of DL models poses challenges, as their decision-making processes are often perceived as black boxes. Thus, it is important for future research to address these limitations and develop more robust and interpretable ML/DL models for cognitive load classification. It is worth noting that eye-tracking data are subject to potential bias as the related experiments are often conducted under controlled conditions with a small number of participants. Although eye-tracking technology has been the primary focus of analysis in many studies, researchers are increasingly exploring the benefits of combining eye-tracking data with other physiological signals. By integrating eye-tracking data with other physiological signals, effective classification models can be established to enhance the understanding of human cognitive processes. However, further research is needed to understand the generalizability and effectiveness of these approaches in different contexts and larger datasets.