Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed network–"StressNet"–features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal network for reconstructing the ISTI ( Initial Systolic Time Interval : a measure of change in cardiac sympathetic activity that is considered to be a quantitative index of stress in humans). The reconstructed ISTI signal is fed into a stress-detection model to detect and classify the individual’s stress state (i.e. stress or no stress). A detailed evaluation demonstrates that Stress-Net achieves estimated the ISTI signal with 95% accuracy and detect stress with average precision of 0.842.

Software engineering involves writing new code or editing existing code. Recent efforts have investigated the neural processes associated with reading and comprehending code --- however, we lack a thorough understanding of the human cognitive processes underlying code writing. While prose reading and writing have been studied thoroughly, that same scrutiny has not been applied to code writing. In this paper, we leverage functional brain imaging to investigate neural representations of code writing in comparison to prose writing. We present the first human study in which participants wrote code and prose while undergoing a functional magnetic resonance imaging (fMRI) brain scan, making use of a full-sized fMRI-safe QWERTY keyboard.

Code review is a critical step in modern software quality assurance, yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code,but no consensus understanding has emerged. Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behavioral, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias.

Data structures permeate many aspects of software engineering, but their associated human cognitive processes are not thoroughly understood. We leverage medical imaging and insights from the psychological notion of spatial ability to decode the neural representations of several fundamental data structures and their manipulations. In a human study involving 76 participants, we examine list, array, tree, and mental rotation tasks using both functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI). We find a nuanced relationship: data structure and spatial operations use the same focal regions of the brain but to different degrees. They are related but distinct neural tasks. In addition, more difficult computer science problems induce higher cognitive load than do problems of pure spatial reasoning. Finally, while fNIRS is less expensive and more permissive, there are some computing-relevant brain regions that only fMRI can reach.

Subjective judgments in software engineering tasks are of critical importance but can be difficult to study with conventional means. Medical imaging techniques hold the promise of relating cognition to physical activities and brain structures. In a controlled experiment involving 29 participants, we examine code comprehension, code review and prose review using functional magnetic resonance imaging. We find that the neural representations of programming languages vs. natural languages are distinct. We can classify which task a participant is undertaking based solely on brain activity (balanced accuracy 79%, p <; 0.001). Further, we find that the same set of brain regions distinguish between code and prose (near-perfect correlation, r = 0.99, p <; 0.001). Finally, we find that task distinctions are modulated by expertise, such that greater skill predicts a less differentiated neural representation (r = -0.44, p = 0.016) indicating that more skilled participants treat code and prose more similarly at a neural activation level.