Demonstrator for extracting cognitive load from pupil dilation for attention management services

Gollan, Benedikt; Haslgrübler, Michael; Ferscha, Alois

doi:10.1145/2968219.2968550

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…For example, Ahlstrom and Friedman-Berg (2016) found that average pupil size increased when controllers used a static storm forecast tool compared to when controllers used a dynamic forecast tool [23]. For the unobtrusive measurement of the pupillary responses, we employed a cognitive load algorithm that automatically models and subtracts size changes due to the pupillary light response based on empirical models of the pupillary light response and camera-based brightness measures, thereby, providing a measure of cognitive load free of this source of pupil size variation [22,[24][25][26]. Here, we did not find any effects of our manipulation of task performance (see Section 3.3).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Figure 8 shows an example of how this works. Here, one can see how the pupil size of a participant changed during the MATB-II's subtasks, as well as the calculated change in cognitive load, and the calculated light changes that were modelled via the Pupillary-Light-Response (PLR) model [25,26]. This model predicts the pupillary light reflex behavior to brightness via an individually trained empirical model.…”

Section: Analyses Of Pupillary Responses In the Multi-attribute Task ...mentioning

confidence: 99%

“…For example, it is known that even subjectively perceived lightness can prompt a light-elicited pupillary response [65,66]. There is arguably room for such illusory lightness in articulated monitor displays [67] such as the ones used in the MATB-II that is currently not ruled out by the automatic measurement and subtraction of the objective light-elicited pupillary response [25,26], so that it is possible that the corresponding artifacts in the pupil size measures could have watered down the true influence of cognitive load on pupil size in the present study's MATB-II, too.…”

Section: Pupillary Cognitive Load or Workload Responsesmentioning

confidence: 99%

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Studying the Role of Visuospatial Attention in the Multi-Attribute Task Battery II

Gugerell,

Gollan,

Stolte

et al. 2024

Applied Sciences

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Task batteries mimicking user tasks are of high heuristic value. Supposedly, they measure individual human aptitude regarding the task in question. However, less is often known about the underlying mechanisms or functions that account for task performance in such complex batteries. This is also true of the Multi-Attribute Task Battery (MATB-II). The MATB-II is a computer display task. It aims to measure human control operations on a flight console. Using the MATB-II and a visual-search task measure of spatial attention, we tested if capture of spatial attention in a bottom-up or top-down way predicted performance in the MATB-II. This is important to understand for questions such as how to implement warning signals on visual displays in human–computer interaction and for what to practice during training of operating with such displays. To measure visuospatial attention, we used both classical task-performance measures (i.e., reaction times and accuracy) as well as novel unobtrusive real-time pupillometry. The latter was done as pupil size covaries with task demands. A large number of analyses showed that: (1) Top-down attention measured before and after the MATB-II was positively correlated. (2) Test-retest reliability was also given for bottom-up attention, but to a smaller degree. As expected, the two spatial attention measures were also negatively correlated with one another. However, (3) neither of the visuospatial attention measures was significantly correlated with overall MATB-II performance, nor with (4) any of the MATB-II subtask performance measures. The latter was true even if the subtask required visuospatial attention (as in the system monitoring task of the MATB-II). (5) Neither did pupillometry predict MATB-II performance, nor performance in any of the MATB-II’s subtasks. Yet, (6) pupil size discriminated between different stages of subtask performance in system monitoring. This finding indicated that temporal segregation of pupil size measures is necessary for their correct interpretation, and that caution is advised regarding average pupil-size measures of task demands across tasks and time points within tasks. Finally, we observed surprising effects of workload (or cognitive load) manipulation on MATB-II performance itself, namely, better performance under high- rather than low-workload conditions. The latter findings imply that the MATB-II itself poses a number of questions about its underlying rationale, besides allowing occasional usage in more applied research.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Analyses Of Pupillary Responses In the Multi-attribute Task ...mentioning

confidence: 99%

Section: Pupillary Cognitive Load or Workload Responsesmentioning

confidence: 99%

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Studying the Role of Visuospatial Attention in the Multi-Attribute Task Battery II

Gugerell,

Gollan,

Stolte

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…With respect to situational factors, they include cognitive effort and user performance. The most reliable way to track individual cognitive load of the user is the analysis of physiological parameters, such as pupil dilation, eye activity, Galvanic skin response, cerebral activity, body temperature and heart rate variability [38]- [40]. The measurement is done by means of proximal and distal measurement techniques.…”

Section: A the Measure Modulementioning

confidence: 99%

“…Perception -Landoldt's rings tests [43] -Ishihara test [44] -Audiometer Cognition -see review in [45] Action -Functional-Independence-Measure questionnaire [46] -Fleishman factors [47] -Purdue Pegboard test [48] Knowledge -Work experience -Specific expertise related to the system SITUATIONAL Cognitive effort -Eye activity [38], [39], [40], [41] -Brain activity [38], [39] -Heart rate variability (HRV) [38], [39] -Galvanic skin response (GSR) [38], [39] Performance -…”

Section: Constitutional Information Processingmentioning

confidence: 99%