Hidden Markov modeling of eye movements with image information leads to better discovery of regions of interest

Conference Theme: Integrating Psychological, Philosophical, Linguistic, Computational and Neural PerspectivesPoster Session 2: no. 56Hidden Markov models (HMM) can describe the spatial and temporal characteristics of eye-tracking recordings in cognitive tasks. Here, we introduce a new HMM approach. We developed HMMs based on fixation locations and we also used image information as an input feature. We demonstrate the benefits of the newly proposed model in a face recognition study wherein an HMM was developed for every subject. Discovery of regions of interest on facial stimuli is improved compared to earlier approaches. Moreover, clustering of the newly developed HMMs lead to very distinct groups. The newly developed approach also allows reconstructing image information at fixation.postprin

Exploring Cognitive States: Methods for Detecting Physiological Temporal Fingerprints

Cognitive state detection and its relationship to observable physiologically telemetry has been utilized for many human-machine and human-cybernetic applications. This paper aims at understanding and addressing if there are unique psychophysiological patterns over time, a physiological temporal fingerprint, that is associated with specific cognitive states. This preliminary work involves commercial airline pilots completing experimental benchmark task inductions of three cognitive states: 1) Channelized Attention (CA); 2) High Workload (HW); and 3) Low Workload (LW). We approach this objective by modeling these "fingerprints" through the use of Hidden Markov Models and Entropy analysis to evaluate if the transitions over time are complex or rhythmic/predictable by nature. Our results indicate that cognitive states do have unique complexity of physiological sequences that are statistically different from other cognitive states. More specifically, CA has a significantly higher temporal psychophysiological complexity than HW and LW in EEG and ECG telemetry signals. With regards to respiration telemetry, CA has a lower temporal psychophysiological complexity than HW and LW. Through our preliminary work, addressing this unique underpinning can inform whether these underlying dynamics can be utilized to understand how humans transition between cognitive states and for improved detection of cognitive states

Representing and Inferring Visual Perceptual Skills in Dermatological Image Understanding

Experts have a remarkable capability of locating, perceptually organizing, identifying, and categorizing objects in images specific to their domains of expertise. Eliciting and representing their visual strategies and some aspects of domain knowledge will benefit a wide range of studies and applications. For example, image understanding may be improved through active learning frameworks by transferring human domain knowledge into image-based computational procedures, intelligent user interfaces enhanced by inferring dynamic informational needs in real time, and cognitive processing analyzed via unveiling the engaged underlying cognitive processes. An eye tracking experiment was conducted to collect both eye movement and verbal narrative data from three groups of subjects with different medical training levels or no medical training in order to study perceptual skill. Each subject examined and described 50 photographical dermatological images. One group comprised 11 board-certified dermatologists (attendings), another group was 4 dermatologists in training (residents), and the third group 13 novices (undergraduate students with no medical training). We develop a novel hierarchical probabilistic framework to discover the stereotypical and idiosyncratic viewing behaviors exhibited by the three expertise-specific groups. A hidden Markov model is used to describe each subject\u27s eye movement sequence combined with hierarchical stochastic processes to capture and differentiate the discovered eye movement patterns shared by multiple subjects\u27 eye movement sequences within and among the three expertise-specific groups. Through these patterned eye movement behaviors we are able to elicit some aspects of the domain-specific knowledge and perceptual skill from the subjects whose eye movements are recorded during diagnostic reasoning processes on medical images. Analyzing experts\u27 eye movement patterns provides us insight into cognitive strategies exploited to solve complex perceptual reasoning tasks. Independent experts\u27 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\u27 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

Scanpath modeling and classification with Hidden Markov Models

How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed that scanpath, i.e., the sequence of eye movements made by an observer exploring a visual stimulus, can be used to infer observer-related (e.g., task at hand) and stimuli-related (e.g., image semantic category) information. However, eye movements are complex signals and many of these studies rely on limited gaze descriptors and bespoke datasets. Here, we provide a turnkey method for scanpath modeling and classification. This method relies on variational hidden Markov models (HMMs) and discriminant analysis (DA). HMMs encapsulate the dynamic and individualistic dimensions of gaze behavior, allowing DA to capture systematic patterns diagnostic of a given class of observers and/or stimuli. We test our approach on two very different datasets. Firstly, we use fixations recorded while viewing 800 static natural scene images, and infer an observer-related characteristic: the task at hand. We achieve an average of 55.9% correct classification rate (chance = 33%). We show that correct classification rates positively correlate with the number of salient regions present in the stimuli. Secondly, we use eye positions recorded while viewing 15 conversational videos, and infer a stimulus-related characteristic: the presence or absence of original soundtrack. We achieve an average 81.2% correct classification rate (chance = 50%). HMMs allow to integrate bottom-up, top-down, and oculomotor influences into a single model of gaze behavior. This synergistic approach between behavior and machine learning will open new avenues for simple quantification of gazing behavior. We release SMAC with HMM, a Matlab toolbox freely available to the community under an open-source license agreement.published_or_final_versio

Utilizing the Capabilities Offered by Eye-Tracking to Foster Novices' Comprehension of Business Porcess Models

Business process models constitute fundamental artifacts for enterprise architectures as well as for the engineering of processes and information systems. However, less experienced stakeholders (i.e., novices) face a wide range of issues when trying to read and comprehend these models. In particular, process model comprehension not only requires knowledge on process modeling notations, but also skills to visually and correctly interpret the models. In this context, many unresolved issues concerning the factors hindering process model comprehension exist and, hence, the identification of these factors becomes crucial. Using eye-tracking as an instrument, this paper presents the results obtained of a study, in which we analyzed eye-movements of novices and experts, while comprehending process models expressed in terms of the Business Process Model and Notation (BPMN) 2.0. Further, recorded eye-movements are visualized as scan paths to analyze the applied comprehension strategies. We learned that experts comprehend process models more effectively than novices. In addition, we observed particular patterns for eye-movements (e.g., back-and-forth saccade jumps) as well as different strategies of novices and experts in comprehending process models

Learning from Teacher's Eye Movement: Expertise, Subject Matter and Video Modeling

How teachers' eye movements can be used to understand and improve education is the central focus of the present paper. Three empirical studies were carried out to understand the nature of teachers' eye movements in natural settings and how they might be used to promote learning. The studies explored 1) the relationship between teacher expertise and eye movement in the course of teaching, 2) how individual differences and the demands of different subjects affect teachers' eye movement during literacy and mathematics instruction, 3) whether including an expert's eye movement and hand information in instructional videos can promote learning. Each study looked at the nature and use of teacher eye movements from a different angle but collectively converge on contributions to answering the question: what can we learn from teachers' eye movements? The paper also contains an independent methodology chapter dedicated to reviewing and comparing methods of representing eye movements in order to determine a suitable statistical procedure for representing the richness of current and similar eye tracking data. Results show that there are considerable differences between expert and novice teachers' eye movement in a real teaching situation, replicating similar patterns revealed by past studies on expertise and gaze behavior in athletics and other fields. This paper also identified the mix of person-specific and subject-specific eye movement patterns that occur when the same teacher teaches different topics to the same children. The final study reports evidence that eye movement can be useful in teaching; by showing increased learning when learners saw an expert model's eye movement in a video modeling example. The implications of these studies regarding teacher education and instruction are discussed.PHDEducation & PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145853/1/yizhenh_1.pd

Brand search

Consumers frequently buy the products they find most easily. This has forced manufacturers and retailers to invest in package design, shelf layouts, and expensive advertising campaigns to facilitate findability of their products. Surprisingly, there is no research in marketing that investigates how consumers localize products, which we call brand search. This dissertation investigates the brand search process and develops a statistical model that describes the eye movements of consumers while they are searching for a specific product. The proposed model uncovers the search strategies of consumers and suggests which marketing tools manufacturers and retailers may use to influence this process.

Brand Search.

Consumers frequently buy the products they find most easily. This has forced manufacturers and retailers to invest in package design, shelf layouts, and expensive advertising campaigns to facilitate findability of their products. Surprisingly, there is no research in marketing that investigates how consumers localize products, which we call brand search. This dissertation investigates the brand search process and develops a statistical model that describes the eye movements of consumers while they are searching for a specific product. The proposed model uncovers the search strategies of consumers and suggests which marketing tools manufacturers and retailers may use to influence this process.

Assessment of Visual Literacy – Contributions of Eye Tracking

Visual Literacy (VL) is defined as a set of competencies to understand and express oneself through visual imagery. An expansive model, the Common European Framework of Reference for Visual Literacy (CEFR-VL) (Wagner & Schönau, 2016), comprises 16 sub-competencies, including abilities such as analyzing, judging, experimenting with or aesthetically experiencing images. To empirically assess VL sub-competencies different visual tasks were presented to VL experts and novices. Problem-solving behavior and cognitive strategies involved in visual logical reasoning (Paper 1), Visual Search (Paper 2), and judgments of visual abstraction (Paper 3) were investigated. Eye tracking in combination with innovative statistical methods were used to uncover latent variables during task performance and to assess the possible effects of differences in expertise level. Furthermore, the relationship between students' self-reported visual abilities and their performance on VL assessment tasks is systematically explored. Results show how effects of perceptual skills of VL experts are less pronounced and more nuanced than implied by VL models. The comprehension of visual logical models does not seem to depend much on VL, as experts and novices did not differ in their solution strategies and eye movement indicators (Paper 1). In contrast, the visual search task on artworks revealed how experts were able to detect target regions with higher efficiency than novices revealed by higher precision of fixations on target regions. Furthermore, latent image features were detected by experts with more certainty (Paper 2). The assessment of perceived level of visual abstraction revealed how, contrary to our expectations, experts did not outperform novices but despite that were able to detect nuanced level of abstraction compared to student groups. Distribution of fixations indicate how attention is directed towards more ambiguous images (Paper 3). Students can be classified based on different levels of visual logical comprehension (Paper 1), on self-reported visual skills, and the time spent on the tasks (Paper 2, Paper 3). Self-reported visual art abilities of students (e.g., imagination) influences the visual search and the judgment of visual abstraction. Taken together the results show how VL skills are not determined solely by the number of correct responses, but rather by how visual tasks are solved and deconstructed; for example, experts are able to focus on less salient image regions during visual search and demonstrate a more nuanced interpretation of visual abstraction. Low-level perceptual abilities of experts and novices differ marginally, which is consistent with research on art expertise. Assessment of VL remains challenging, but new empirical methods are proposed to uncover the underlying components of VL