A Novel Framework for the Analysis of Eye Movements during Visual Search for Knowledge Gathering

Visual search in unilateral spatial neglect: The effects of distractors on a dynamic visual search task

The objective of this study was to examine visual scanning performance in patients with Unilateral Spatial Neglect (USN) in a visual search task. Thirty-one right hemisphere stroke patients with USN were recruited. They performed a dynamic visual search task with two conditions, with and without distractors, while eye movements were monitored with an eye-tracker. The main goal of the task was to select target stimuli that appeared from the top of the screen and moved vertically downward. Target detection and visual scanning percentage were assessed over two hemispaces (right, left) on two conditions (distractor, no distractor). Most Scanned Regions (MSR) were calculated to analyze the areas of the screen where most points of fixation were directed to. Higher target detection rate and visual scanning percentages were found on the right hemispace on both conditions. From the MSRs we found that participants with a center of attention further to the right of the screen also presented smaller overall MSRs. Right hemisphere stroke patients with USN presented not only a significant rightward bias but reduced overall search areas, implying hyperattention does not only restrict search on the horizontal (right-left) axis but the vertical axis (top-bottom) too.

Modeling eye movement patterns to characterize perceptual skill in image-based diagnostic reasoning processes

Experts have a remarkable capability of locating, perceptually organizing, identifying, and categorizing objects in images specific to their domains of expertise. In this article, we present a hierarchical probabilistic framework to discover the stereotypical and idiosyncratic viewing behaviors exhibited with expertise-specific groups. Through these patterned eye movement behaviors we are able to elicit the domain-specific knowledge and perceptual skills from the subjects whose eye movements are recorded during diagnostic reasoning processes on medical images. Analyzing experts' eye movement patterns provides us insight into cognitive strategies exploited to solve complex perceptual reasoning tasks. An experiment was conducted to collect both eye movement and verbal narrative data from three groups of subjects with different levels or no medical training (eleven board-certified dermatologists, four dermatologists in training and thirteen undergraduates) while they were examining and describing 50 photographic dermatological images. We use a hidden Markov model to describe each subject's eye movement sequence combined with hierarchical stochastic processes to capture and differentiate the discovered eye movement patterns shared by multiple subjects within and among the three groups. Independent experts' annotations of diagnostic conceptual units of thought in the transcribed verbal narratives are time-aligned with discovered eye movement patterns to help interpret the patterns' meanings. By mapping eye movement patterns to thought units, we uncover the relationships between visual and linguistic elements of their reasoning and perceptual processes, and show the manner in which these subjects varied their behaviors while parsing the images. We also show that inferred eye movement patterns characterize groups of similar temporal and spatial properties, and specify a subset of distinctive eye movement patterns which are commonly exhibited across multiple images. Based on the combinations of the occurrences of these eye movement patterns, we are able to categorize the images from the perspective of experts' viewing strategies in a novel way. In each category, images share similar lesion distributions and configurations. Our results show that modeling with multi-modal data, representative of physicians' diagnostic viewing behaviors and thought processes, is feasible and informative to gain insights into physicians' cognitive strategies, as well as medical image understanding.