DiVA

Khaled, Kassem,; Salah, Jailan; Abdrabou, Yasmeen; Morsy, Mahesty; El-Gendy, Reem; Abdelrahman, Yomna; Abdennadher, Slim

doi:10.1145/3152832.3152836

Cited by 16 publications

(7 citation statements)

References 17 publications

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“…Demberg (2013) have also recently reported changes in pupillometry due to linguistically induced cognitive load (e.g., comprehending syntactically demanding sentences). Other recent work has also examined user state related changes in pupil diameter in lab-settings such as variations in valence and arousal (Kassem et al, 2017) and interest in real-time (Jacob et al, 2018).…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

“…In driving, some researchers have suggested that pupil-based measurements are highly relevant for assessment of drowsiness (Maccora et al, 2018). However, detection of pupil diameter in real-world settings with rapidly changing and uncontrollable variations in luminance is a critical confounding factor in the utility of pupillometry in driving (Kassem et al, 2017).…”

Section: Research Applicability In Real-world Settingsmentioning

confidence: 99%

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A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

241

181

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As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

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Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

Section: Research Applicability In Real-world Settingsmentioning

confidence: 99%

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

241

181

View full text Add to dashboard Cite

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“…The behavioral and psychophysiological measures may reflect different internal states related to the specific task that the user is accomplishing. For instance, the electrodermal activity or visual behaviour can reveal different internal states, such as variations in the level of stress [9,10], affective state (i.e., arousal and valence) [11], or mental workload [12,13]. A reductionist approach could lead to the misunderstanding of data, causing a failure to support users when needed, or implementing adaptive features when they are not necessary.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Implicit and Explicit Measures of Mental Workload in Working Situations: Implications for Industry 4.0

et al. 2020

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Nowadays, in the context of Industry 4.0, advanced working environments aim at achieving a high degree of human–machine collaboration. This phenomenon occurs, on the one hand, through the correct interpretation of operators’ data by machines that can adapt their functioning to support workers, and on the other hand, by ensuring the transparency of the actions of the system itself. This study used an ad hoc system that allowed the co-registration of a set of participants’ implicit and explicit (I/E) data in two experimental conditions that varied in the level of mental workload (MWL). Findings showed that the majority of the considered I/E measures were able to discriminate the different task-related mental demands and some implicit measures were capable of predicting task performance in both tasks. Moreover, self-reported measures showed that participants were aware of such differences in MWL. Finally, the paradigm’s ecology highlights that task and environmental features may affect the reliability of the various I/E measures. Thus, these factors have to be considered in the design and development of advanced adaptive systems within the industrial context.

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“…Changes in these measures have been shown to correlate with diferent levels of cognitive load [15,41]. However, physiological measures depend on many factors, including other aspects of the user's cognitive state such as anxiety [7], arousal [21], the user's physical activity [33], and environmental variables such as light [32]. Hence, researchers should draw attention to the study conditions and user's state.…”

Section: Pupillometry and Cognitive Loadmentioning

confidence: 99%

Think Harder! Investigating the Effect of Password Strength on Cognitive Load during Password Creation

Abdrabou

Abdelrahman

Khamis

et al. 2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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Strict password policies can frustrate users, reduce their productivity, and lead them to write their passwords down. This paper investigates the relation between password creation and cognitive load inferred from eye pupil diameter. We use a wearable eye tracker to monitor the user's pupil size while creating passwords with diferent strengths. To assess how creating passwords of diferent strength (namely weak and strong) infuences users' cognitive load, we conducted a lab study (N = 15). We asked the participants to create and enter 6 weak and 6 strong passwords. The results showed that passwords with diferent strengths afect the pupil diameter, thereby giving an indication of the user's cognitive state. Our initial investigation shows the potential for new applications in the feld of cognition-aware user interfaces. For example, future systems can use our results to determine whether the user created a strong password based on their gaze behavior, without the need to reveal the characteristics of the password. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); • Security and privacy → Human and societal aspects of security and privacy.

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DiVA

Cited by 16 publications

References 17 publications

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Assessment of Implicit and Explicit Measures of Mental Workload in Working Situations: Implications for Industry 4.0

Think Harder! Investigating the Effect of Password Strength on Cognitive Load during Password Creation

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