Detecting Personality Traits Using Eye-Tracking Data

Berkovsky, Shlomo; Taib, Ronnie; Koprinska, Irena; Wang, Eileen; Zeng, Yucheng; Li, Jingjie; Kleitman, Sabina

doi:10.1145/3290605.3300451

Cited by 97 publications

(61 citation statements)

References 56 publications

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“…Classification approaches are important for real‐world applications as well as for real‐time analysis of cognitive processes, for example, to study timings or causal relations, especially in variable experimental settings. Examples of such applications range from the robust detection of fixations and saccades (Startsev, Agtzidis, & Dorr, 2019) to the detection of user confusion (Sims, 2020) or personality traits (Berkovsky et al., 2019). Recently, machine learning on a single‐trial basis has also been applied to the differentiation of internal and external attention.…”

Section: Introductionmentioning

confidence: 99%

How Reliably Do Eye Parameters Indicate Internal Versus External Attentional Focus?

et al. 2021

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Eye behavior is increasingly used as an indicator of internal versus external focus of attention both in research and application. However, available findings are partly inconsistent, which might be attributed to the different nature of the employed types of internal and external cognition tasks. The present study, therefore, investigated how consistently different eye parameters respond to internal versus external attentional focus across three task modalities: numerical, verbal, and visuo‐spatial. Three eye parameters robustly differentiated between internal and external attentional focus across all tasks. Blinks, pupil diameter variance, and fixation disparity variance were consistently increased during internally directed attention. We also observed substantial attentional focus effects on other parameters (pupil diameter, fixation disparity, saccades, and microsaccades), but they were moderated by task type. Single‐trial analysis of our data using machine learning techniques further confirmed our results: Classifying the focus of attention by means of eye tracking works well across participants, but generalizing across tasks proves to be challenging. Based on the effects of task type on eye parameters, we discuss what eye parameters are best suited as indicators of internal versus external attentional focus in different settings.

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Section: Introductionmentioning

confidence: 99%

How Reliably Do Eye Parameters Indicate Internal Versus External Attentional Focus?

et al. 2021

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“…Robert McCrae and Paul Costa developed this theory based on research published by others, such as Gordon Allport, Raymond Cattel, and Hans Eysenck [ 90 ]. Personality traits are easy to detect and can, for example, be detected from online activities, such as Facebook use or eye-tracking [ 91 , 92 ]. This class also includes the subclasses of age, gender, and locus of control (LoC).…”

Section: Resultsmentioning

confidence: 99%

An Ontological Framework to Facilitate Early Detection of ‘Radicalization’ (OFEDR)—A Three World Perspective

Wendelberg

2021

J. Imaging

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This paper presents an ontology that involves using information from various sources from different disciplines and combining it in order to predict whether a given person is in a radicalization process. The purpose of the ontology is to improve the early detection of radicalization in persons, thereby contributing to increasing the extent to which the unwanted escalation of radicalization processes can be prevented. The ontology combines findings related to existential anxiety that are related to political radicalization with well-known criminal profiles or radicalization findings. The software Protégé, delivered by the technical field at Stanford University, including the SPARQL tab, is used to develop and test the ontology. The testing, which involved five models, showed that the ontology could detect individuals according to “risk profiles” for subjects based on existential anxiety. SPARQL queries showed an average detection probability of 5% including only a risk population and 2% on a whole test population. Testing by using machine learning algorithms proved that inclusion of less than four variables in each model produced unreliable results. This suggest that the Ontology Framework to Facilitate Early Detection of 'Radicalization' (OFEDR) ontology risk model should consist of at least four variables to reach a certain level of reliability. Analysis shows that use of a probability based on an estimated risk of terrorism may produce a gap between the number of subjects who actually have early signs of radicalization and those found by using probability estimates for extremely rare events. It is reasoned that an ontology exists as a world three object in the real world.

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“…Inferencing mental states through physiological measurements is a challenging task in general, given that a certain physiological state could be suggestive of different types of mental states [80]. Hence, many recent works in HCI have turned to machine learning for a better mapping of physiological signatures to psychological states [9,47,63,80]. Likewise, the Rethinking Eye-blink framework does include a machine learning classifier (2D LSTM) to benefit from its automatic feature learning capability.…”

Section: Discussionmentioning

confidence: 99%

Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking

Cho

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Detecting Personality Traits Using Eye-Tracking Data

Cited by 97 publications

References 56 publications

How Reliably Do Eye Parameters Indicate Internal Versus External Attentional Focus?

How Reliably Do Eye Parameters Indicate Internal Versus External Attentional Focus?

An Ontological Framework to Facilitate Early Detection of ‘Radicalization’ (OFEDR)—A Three World Perspective

Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking

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