Assessment of saccadic eye movements in healthy subjects using consumer-grade mobile devices

Assessing eye movement features may provide insight into neurological health, inform diagnoses, and guide clinical intervention. The potential to utilize saccadic eye movement latency is especially promising as a clinical biomarker in identifying and treating neurodegenerative disease. Artificial intelligence and deep learning technology have improved the feasibility of eye-tracking methodology and scalability in research studies. Tablet and smartphone-based tracking equipment have been shown to provide quantitative data of comparable accuracy to more costly, special-built equipment while reducing cost and complexity in experimental procedures. Establishing an efficient and accurate measurement tool to aid the detection and tracking of diseases may benefit the development of comprehensive treatment and monitoring strategies. This study, therefore, seeks to examine oculomotor function through saccade latency and error rate in healthy adults with respect to age, demonstrating a mobile device’s efficacy in assessing subtle eye movements and establishing a dataset upon which to guide further investigation

How to improve learning from video, using an eye tracker

The initial trigger of this research about learning from video was the availability of log files from users of video material. Video modality is seen as attractive as it is associated with the relaxed mood of watching TV. The experiments in this research have the goal to gain more insight in viewing patterns of students when viewing video. Students received an awareness instruction about the use of possible alternative viewing behaviors to see whether this would enhance their learning effects. We found that: - the learning effects of students with a narrow viewing repertoire were less than the learning effects of students with a broad viewing repertoire or strategic viewers. - students with some basic knowledge of the topics covered in the videos benefited most from the use of possible alternative viewing behaviors and students with low prior knowledge benefited the least. - the knowledge gain of students with low prior knowledge disappeared after a few weeks; knowledge construction seems worse when doing two things at the same time. - media players could offer more options to help students with their search for the content they want to view again. - there was no correlation between pervasive personality traits and viewing behavior of students. The right use of video in higher education will lead to students and teachers that are more aware of their learning and teaching behavior, to better videos, to enhanced media players, and, finally, to higher learning effects that let users improve their learning from video

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

The psychophysiology primer : A guide to methods and a broad review with a focus on human-computer interaction

Publisher Copyright: © 2016 B. Cowley, M. Filetti, K. Lukander.Peer reviewe

Engineering data compendium. Human perception and performance, volume 3

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design of military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by system designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is Volume 3, containing sections on Human Language Processing, Operator Motion Control, Effects of Environmental Stressors, Display Interfaces, and Control Interfaces (Real/Virtual)

Activity in area V3A predicts positions of moving objects

No description supplie

SMART WEARABLES: ADVANCING MYOPIA RESEARCH THROUGH QUANTIFICATION OF THE VISUAL ENVIRONMENT

Myopia development has been attributed to eyeball elongation, but its driving force is not fully understood. Previous research suggests lack of time spent outdoors with exposure to high light levels or time spent on near-work as potential environmental risk factors. Although light levels are quantifiable with wearables, near-work relies solely on questionnaires for data collection and there remains a risk of subjective bias. Studies spanning decades identified that eye growth is optically guided. This proposal received further support from recent findings of larger changes in the thickness of the eye’s choroidal layer after short-term optical interventions compared with daily eye-length changes attributed to myopia. Most of these studies used a monocular optical appliance to manipulate potential myogenic factors, which may introduce confounders by disrupting the natural functionality of the visual system. This thesis reports on improvements in systems for characterising the visual dioptric space and its application to myopia studies. Understanding the driving forces of myopia will prevent related vision loss. Study I: An eye-tracker was developed and validated that incorporated time-of-flight (ToF) technology to obtain spatial information of the wearer’s field of view. By matching gaze data with point cloud data, the distance to the point of regard (DtPoR) is determined. Result: DtPoR can be measured continuously with clinically relevant accuracy to estimate near-work objectively. Study II: Near-work was measured with diary entries and compared with DtPoR estimations. Diversity of the dioptric landscape presented to the retina was assessed during near-work. Results: Objective and subjective measures of near-work were not found to highly correlate. Ecologically valid dioptric landscape during near-work decreases by up to -1.5 D towards the periphery of a 50˚ visual field. Study III: Choroid thickness changes were evaluated after exposure (approximately 30min) to a controlled, dioptrically diverse landscape with a global, sensitivity enhanced model. Result: No choroid thickness changes were found within the measuring field of approximately 45˚. Discussion The developed device could support future research to resolve disagreement between objective and subjective data of near-work and contribute to a better understanding of the ecological valid dioptric landscape. Proposed choroid layer thickness model might support short-term myopia-control research

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions

Inferring implicit relevance from physiological signals

Ongoing growth in data availability and consumption has meant users are increasingly faced with the challenge of distilling relevant information from an abundance of noise. Overcoming this information overload can be particularly difficult in situations such as intelligence analysis, which involves subjectivity, ambiguity, or risky social implications. Highly automated solutions are often inadequate, therefore new methods are needed for augmenting existing analysis techniques to support user decision making. This project investigated the potential for deep learning to infer the occurrence of implicit relevance assessments from users' biometrics. Internal cognitive processes manifest involuntarily within physiological signals, and are often accompanied by 'gut feelings' of intuition. Quantifying unconscious mental processes during relevance appraisal may be a useful tool during decision making by offering an element of objectivity to an inherently subjective situation. Advances in wearable or non-contact sensors have made recording these signals more accessible, whilst advances in artificial intelligence and deep learning have enhanced the discovery of latent patterns within complex data. Together, these techniques might make it possible to transform tacit knowledge into codified knowledge which can be shared. A series of user studies recorded eye gaze movements, pupillary responses, electrodermal activity, heart rate variability, and skin temperature data from participants as they completed a binary relevance assessment task. Participants were asked to explicitly identify which of 40 short-text documents were relevant to an assigned topic. Investigations found this physiological data to contain detectable cues corresponding with relevance judgements. Random forests and artificial neural networks trained on features derived from the signals were able to produce inferences with moderate correlations with the participants' explicit relevance decisions. Several deep learning algorithms trained on the entire physiological time series data were generally unable to surpass the performance of feature-based methods, and instead produced inferences with low correlations with participants' explicit personal truths. Overall, pupillary responses, eye gaze movements, and electrodermal activity offered the most discriminative power, with additional physiological data providing diminishing or adverse returns. Finally, a conceptual design for a decision support system is used to discuss social implications and practicalities of quantifying implicit relevance using deep learning techniques. Potential benefits included assisting with introspection and collaborative assessment, however quantifying intrinsically unknowable concepts using personal data and abstruse artificial intelligence techniques were argued to pose incommensurate risks and challenges. Deep learning techniques therefore have the potential for inferring implicit relevance in information-rich environments, but are not yet fit for purpose. Several avenues worthy of further research are outlined