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1. A Preliminary Investigation into Learning Behaviors in Complex Environments for Human-in-the-Loop Cyber-Physical Systems

   https://doi.org/10.1177/1071181321651222  

   Luster, Maya S.; Pitts, Brandon J. (September 2021, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   The field of Cyber-Physical Systems (CPS) is increasingly recognizing the importance of integrating Human Factors for Human-in-the-loop CPS (HiLCPS) developments. This is because psychological, physiological, and behavioral characteristics of humans can be used to predict human-machine interactions. The goal of this pilot study is to collect initial data to determine whether driving and eye tracking metrics can provide evidence of learning for a CPS project. Six participants performed a series of 12 repeated obstacle avoidance tasks in manual driving. Lane deviations and fixation-related eye data were recorded for each trial. Overall, participants displayed either conservation/safe or aggressive/risky in their lateral position with respect to the obstacle during successive trials. Also, eye tracking metrics were not significantly affected by trial number, but observational trends suggest their potential for aiding in understanding adjustments humans make in learning. Results can inform predictive modeling algorithms that can anticipate and mitigate potential problems in real-time. 

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2. Microsaccades in applied environments: Real-world applications of fixational eye movement measurements

   https://doi.org/10.16910/jemr.12.6.15  

   Alexander, Robert G; Macknik, Stephen L; Martinez-Conde, Susana (June 2020, Journal of Eye Movement Research)

   Across a wide variety of research environments, the recording of microsaccades and other fixational eye movements has provided insight and solutions into practical problems. Here we review the literature on fixational eye movements—especially microsaccades—in applied and ecologically-valid scenarios. Recent technical advances allow noninvasive fixational eye movement recordings in real-world contexts, while observers perform a variety of tasks. Thus, fixational eye movement measures have been obtained in a host of real-world scenarios, such as in connection with driver fatigue, vestibular sensory deprivation in astronauts, and elite athletic training, among others. Here we present the state of the art in the practical applications of fixational eye movement research, examine its potential future uses, and discuss the benefits of including microsaccade measures in existing eye movement detection technologies. Current evidence supports the inclusion of fixational eye movement measures in real-world contexts, as part of the development of new or improved oculomotor assessment tools. The real-world applications of fixational eye movement measurements will only grow larger and wider as affordable high-speed and high-spatial resolution eye trackers become increasingly prevalent. 

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3. Toward Explainable Artificial Intelligence for Early Anticipation of Traffic Accidents

   https://doi.org/10.1177/03611981221076121  

   Karim, Muhammad Monjurul; Li, Yu; Qin, Ruwen (June 2022, Transportation Research Record: Journal of the Transportation Research Board)

   Traffic accident anticipation is a vital function of Automated Driving Systems (ADS) in providing a safety-guaranteed driving experience. An accident anticipation model aims to predict accidents promptly and accurately before they occur. Existing Artificial Intelligence (AI) models of accident anticipation lack a human-interpretable explanation of their decision making. Although these models perform well, they remain a black-box to the ADS users who find it to difficult to trust them. To this end, this paper presents a gated recurrent unit (GRU) network that learns spatio-temporal relational features for the early anticipation of traffic accidents from dashcam video data. A post-hoc attention mechanism named Grad-CAM (Gradient-weighted Class Activation Map) is integrated into the network to generate saliency maps as the visual explanation of the accident anticipation decision. An eye tracker captures human eye fixation points for generating human attention maps. The explainability of network-generated saliency maps is evaluated in comparison to human attention maps. Qualitative and quantitative results on a public crash data set confirm that the proposed explainable network can anticipate an accident on average 4.57 s before it occurs, with 94.02% average precision. Various post-hoc attention-based XAI methods are then evaluated and compared. This confirms that the Grad-CAM chosen by this study can generate high-quality, human-interpretable saliency maps (with 1.23 Normalized Scanpath Saliency) for explaining the crash anticipation decision. Importantly, results confirm that the proposed AI model, with a human-inspired design, can outperform humans in accident anticipation. 

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4. Predicting children’s math skills from task-based and resting-state functional brain connectivity

   https://doi.org/10.1093/cercor/bhab476  

   Lynn, Andrew; Wilkey, Eric D; Price, Gavin R (December 2021, Cerebral Cortex)

   Abstract A critical goal of cognitive neuroscience is to predict behavior from neural structure and function, thereby providing crucial insights into who might benefit from clinical and/or educational interventions. Across development, the strength of functional connectivity among a distributed set of brain regions is associated with children’s math skills. Therefore, in the present study we use connectome-based predictive modeling to investigate whether functional connectivity during numerical processing and at rest “predicts” children’s math skills (N = 31, Mage = 9.21 years, 14 Female). Overall, we found that functional connectivity during symbolic number comparison and rest, but not during nonsymbolic number comparison, predicts children’s math skills. Each task revealed a largely distinct set of predictive connections distributed across canonical brain networks and major brain lobes. Most of these predictive connections were negatively correlated with children’s math skills so that weaker connectivity predicted better math skills. Notably, these predictive connections were largely nonoverlapping across task states, suggesting children’s math abilities may depend on state-dependent patterns of network segregation and/or regional specialization. Furthermore, the current predictive modeling approach moves beyond brain–behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills. 

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5. Can the E-Z Reader Model Predict Eye Movements Over Code? Towards a Model of Eye Movements Over Source Code

   https://doi.org/10.1145/3379156.3391983  

   Madi, Naser Al; Peterson, Cole S.; Sharif, Bonita; Maletic, Jonathan (May 2020, ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications)

   null (Ed.)

   Studies of eye movements during source code reading have supported the idea that reading source code differs fundamentally from reading natural text. The paper analyzed an existing data set of natural language and source code eye movement data using the E-Z reader model of eye movement control. The results show that the E-Z reader model can be used with natural text and with source code where it provides good predictions of eye movement duration. This result is confirmed by comparing model predictions to eye-movement data from this experiment and calculating the correlation score for each metric. Finally, it was found that gaze duration is influenced by token frequency in code and in natural text. The frequency effect is less pronounced on first fixation duration and single fixation duration. An eye movement control model for source code reading may open the door for tools in education and the industry to enhance program comprehension. 

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