Gaze Transition Entropy

Krejtz, Krzysztof; Duchowski, Andrew T.; Szmidt, Tomasz; Krejtz, Izabela; Perilli, Fernando González; Pires, Ana Cristina; Vilaro, Anna; Villalobos, Natalia

doi:10.1145/2834121

Cited by 108 publications

(85 citation statements)

References 19 publications

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“…WMC) provide unique indices of ADHD and offer physiological insight regarding cognitive resources underlying WMC, an important cognitive construct responsible for behavioral inhibition and attention monitoring. Moreover, they are consistent with previous investigations finding that adults with ADHD demonstrate similar broad visual attention patterns as adults without ADHD but different scan patterns (Krejtz et al 2015) and different pupillometry metrics as a function of visual cue type (Michalek and Roche 2017).…”

Section: Resultssupporting

confidence: 92%

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Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

Michalek

Jayawardena

Jayarathna

2019

Computational Models for Biomedical Reasoning and Problem Solving

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ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.

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Section: Resultssupporting

confidence: 92%

“…dings of . Krejtz et al (2015) who suggest that while adults wit . 10 • •1ar fixation s to salient visual cues when compared to adults without had s1rru .…”

Section: Visual Analysismentioning

confidence: 99%

Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

Michalek

Jayawardena

Jayarathna

2019

Computational Models for Biomedical Reasoning and Problem Solving

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“…The set of AOIs can be represented as

, and the gaze switching process can be described as a stochastic process

. In [ 44 ], the Markov property has been tested. Once the stochastic process is modeled as a Markov process, we obtain the transition matrix

and the stationary or equilibrium probability

.…”

Section: Methodsmentioning

confidence: 99%

“…Besag and Mondal [ 43 ] verified the feasibility of modeling gaze transition as a first-order Markov process. According to modeling eye movement transitions between areas of interest (AOIs) as a Markov chain, Krejtz et al [ 44 , 45 ] calculated stationary entropy

and transition entropy

to measure the complexity of the Markov process. Raptis et al [ 46 ] divided the images into three AOIs and used the gaze transition entropy proposed by Krejtz et al [ 44 ] as a tool to quantify differences on visual search patterns among individuals within visual pattern recognition tasks of varying complexity.…”

Section: Introductionmentioning

confidence: 99%

Gaze Information Channel in Cognitive Comprehension of Poster Reading

Hao

Sbert

2019

Entropy

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Today, eye trackers are extensively used in studying human cognition. However, it is hard to analyze and interpret eye movement data from the cognitive comprehension perspective of poster reading. To find quantitative links between eye movements and cognitive comprehension, we tracked observers’ eye movement for reading scientific poster publications. We model in this paper eye tracking fixation sequences between content-dependent Areas of Interests (AOIs) as a Markov chain. Furthermore, we use the fact that a Markov chain is a special case of information or communication channel. Then, the gaze transition can be modeled as a discrete information channel, the gaze information channel. Next, some traditional eye tracking metrics, together with the gaze entropy and mutual information of the gaze information channel are calculated to quantify cognitive comprehension for every participant. The analysis of the results demonstrate that the gaze entropy and mutual information from individual gaze information channel are related to participants’ individual differences. This is the first study that eye tracking technology has been used to assess the cognitive comprehension of poster reading. The present work provides insights into human cognitive comprehension by using the novel gaze information channel methodology.

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“…Visual entropy is a measure to quantify complexity or randomness of visual scanning between AOIs. It is calculated using the following equation (Allsop & Gray, 2014;Ellis & Stark, 1986;Krejtz et al, 2015;Schieber & Gilland, 2008), based upon Shannon's entropy (Shannon & Weaver, 1998):…”

Section: Visual Entropymentioning

confidence: 99%

Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasks

Jeong

Kang

Liu

2019

Hum Ftrs & Erg Mfg Svc

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Performing secondary tasks (or non‐driving‐related tasks) while driving on curved roads may be risky and unsafe. The purpose of this study was to explore whether driving safety in situations involving curved roads and secondary tasks can be evaluated using multiple measures of eye movement. We adopted Markov‐based transition algorithms (i.e., transition/stationary probabilities, entropy) to quantify drivers’ dynamic eye movement patterns, in addition to typical static visual measures, such as frequency and duration of glances. The algorithms were evaluated with data from an experiment (Jeong & Liu, 2019) involving multiple road curvatures and stimulus‐response secondary task types. Drivers were more likely to scan only a few areas of interest with a long duration in sharper curves. Total head‐down glance time was longer in less sharp curves in the experiment, but the probability of head‐down glances was higher in sharper curves over the long run. The number of reliable transitions between areas of interest varied with the secondary task type. The visual scanning patterns for visually undemanding tasks were as random as those for visually demanding tasks. Markov‐based measures of dynamic eye movements provided insights to better understand drivers’ underlying mental processes and scanning strategies, compared with typical static measures. The presented methods and results can be useful for in‐vehicle systems design and for further analysis of visual scanning patterns in the transportation domain.

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Gaze Transition Entropy

Cited by 108 publications

References 19 publications

Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

Gaze Information Channel in Cognitive Comprehension of Poster Reading

Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasks

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