Psycho-physiological measures for assessing cognitive load

Haapalainen, Eija; Kim, SeungJun; Forlizzi, Jodi; Dey, Anind K.

doi:10.1145/1864349.1864395

Cited by 276 publications

(209 citation statements)

References 39 publications

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“…Previous studies have used skin conductance in detecting emotions [15] or differentiating between stress and cognitive load conditions [16], and a few ones have found relations between GSR features and mental workload [17,25]. Some others have tried but did not obtain satisfactory results for detecting cognitive load from GSR [7,11].…”

Section: Introductionmentioning

confidence: 99%

GSR and Blink Features for Cognitive Load Classification

Nourbakhsh

Wang

Chen

2013

Lecture Notes in Computer Science

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Abstract.A system capable of monitoring its user's mental workload can evaluate the suitability of its interface and interactions for user's current cognitive status and properly change them when necessary. Galvanic skin response (GSR) and eye blinks are cognitive load measures which can be captured conveniently and at low cost. The present study has assessed multiple features of these two signals in classification of cognitive workload level. The experiment included arithmetic tasks with four difficulty levels and two types of machine learning algorithms have been applied for classification. Obtained results show that the studied features of blink and GSR can reasonably discriminate workload levels and combining features of the two modalities improves the accuracy of cognitive load classification. We have achieved around 75% for binary classification and more than 50% for four-class classification.

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Section: Introductionmentioning

confidence: 99%

GSR and Blink Features for Cognitive Load Classification

Nourbakhsh

Wang

Chen

2013

Lecture Notes in Computer Science

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“…Additionally, validity could be improved by combining pupillometry with other physiological measures (e.g., Haapalainen et al, 2010;Just, Carpenter & Miyake, 2003;Kahneman et al, 1969;Satterthwaite et al, 2007;Van der Molen et al, 1989). For example, Haapalainen et al (2010) used an electrocardiogram (ECG)-enabled armband, a remote eye tracker, and a wireless electroencephalogram (EEG) headset, to collect various physiological signals simultaneously.…”

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confidence: 99%

Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Marquart

Winter

2015

PeerJ Computer Science

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Pupillometry is a promising method for assessing mental workload and could be helpful in the optimization of systems that involve human-computer interaction. The present study focuses on replicating the studies by Ahern (1978) and Klingner (2010), which found that for three levels of difficulty of mental multiplications, the more difficult multiplications yielded larger dilations of the pupil. Using a remote eye tracker, our research expands upon these two previous studies by statistically testing for each 1.5 s interval of the calculation period (1) the mean absolute pupil diameter (MPD), (2) the mean pupil diameter change (MPDC) with respect to the pupil diameter during the pre-stimulus accommodation period, and (3) the mean pupil diameter change rate (MPDCR). An additional novelty of our research is that we compared the pupil diameter measures with a self-report measure of workload, the NASA Task Load Index (NASA-TLX), and with the mean blink rate (MBR). The results showed that the findings of Ahern and Klingner were replicated, and that the MPD and MPDC discriminated just as well between the lowest and highest difficulty levels as did the NASA-TLX. The MBR, on the other hand, did not differentiate between the difficulty levels. Moderate to strong correlations were found between the MPDC and the proportion of incorrect responses, indicating that the MPDC was higher for participants with a poorer performance. For practical applications, validity could be improved by combining pupillometry with other physiological techniques.

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“…Previous research has linked eye-related features to cognitive, mental and memory load [31], [35] as well as emotional aspects, such as valence or negative affect [32], [34]. Brain-related Measurements.…”

Section: Biometric Datamentioning

confidence: 99%

Leveraging Biometric Data to Boost Software Developer Productivity

Fritz

Müller

2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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Producing great software requires great productive developers. Yet, what does it really mean for an individual developer to be productive, and what can we do to best help developers to be productive? To answer these questions, research has traditionally focused on measuring a developer's output and therefore suffered from two drawbacks: the measures can only be calculated after a developer finished her work and these measures do not account for individual differences between developers. The recent advances in biometric sensor technology offer new opportunities to measure a developer's cognitive and emotional states in real-time and thus allow us to know more about what an individual developer is currently experiencing and what might foster or impede the developer's productivity. Results from recent research studies demonstrate the potential that biometric data has to accurately predict aspects of a developer's work, such as perceived task and code difficulty, progress and interruptibility of a developer. This opens up new opportunities for better supporting developers in their work and, for instance, prevent bugs from entering the code, reduce costly interruptions, and foster a better and more productive work day. Our vision is that biometric sensing will be integrated into a developer's work and that biometrics can be Abstract-Producing great software requires great productive developers. Yet, what does it really mean for an individual developer to be productive, and what can we do to best help developers to be productive? To answer these questions, research has traditionally focused on measuring a developer's output and therefore suffered from two drawbacks: the measures can only be calculated after a developer finished her work and these measures do not account for individual differences between developers.The recent advances in biometric sensor technology offer new opportunities to measure a developer's cognitive and emotional states in real-time and thus allow us to know more about what an individual developer is currently experiencing and what might foster or impede the developer's productivity. Results from recent research studies demonstrate the potential that biometric data has to accurately predict aspects of a developer's work, such as perceived task and code difficulty, progress and interruptibility of a developer. This opens up new opportunities for better supporting developers in their work and, for instance, prevent bugs from entering the code, reduce costly interruptions, and foster a better and more productive work day. Our vision is that biometric sensing will be integrated into a developer's work and that biometrics can be used to boost the productivity of each individual developer.

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Psycho-physiological measures for assessing cognitive load

Cited by 276 publications

References 39 publications

GSR and Blink Features for Cognitive Load Classification

GSR and Blink Features for Cognitive Load Classification

Workload assessment for mental arithmetic tasks using the task-evoked pupillary response

Leveraging Biometric Data to Boost Software Developer Productivity

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