The influence of task demand and learning on the psychophysiological response

Fairclough, Stephen H.; Venables, Louise; Tattersall, Andy

doi:10.1016/j.ijpsycho.2004.11.003

Cited by 245 publications

(168 citation statements)

References 57 publications

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“…To date, only a few studies [7][8][9][10] have attempted to tackle this problem using ECG recordings. For one, Henning et al [8] show that reduced mental load is associated with slower heart rate (HR) [7].…”

Section: Iintroductionmentioning

confidence: 99%

Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task

et al. 2017

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Abstract-We designed and conducted an experiment using a repetitive task to investigate associations between mental workload, performance, and Heart Rate Variability (HRV) features across repetitions. According to the literature, we define mental workload as the interaction between a person and a task that causes task demands to exceed the person's capacity to deliver. Mental workload was triggered by the use of a highlypaced video game repeated over time. Before engaging with the task, each subject was assessed in controlled condition (i.e., relaxing period) for a short time. Short term HRV features variations between the baseline (i.e., control situation) and each repetitive gaming session (i.e., mental task) were explored. The results show that HRV dynamics diminish with repetitions, while performance increases. Importantly, this suggests that HRV features can be well correlated with performance. Overall, this study advances the use of HRV analysis in the behavioral sciences at large, allowing the design of flexible neurophysiological lab-based experiments. Thus, it also opens the way to future autonomic behavioral neuroscience research.

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Section: Iintroductionmentioning

confidence: 99%

Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task

et al. 2017

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“…Scholars have found physiological markers of mental effort in EEG (Fairclough, Venable, & Tattersall, 2005;Wang, Gwizdka, & Chaovalitwongse, 2016;Buettner, 2017b), fMRI (Lim et al, 2010;Gwizdka, 2013b), fNIR (Sassaroli et al, 2008;Herff et al, 2014), EDA (Wilson, 2002;Boucsein, 2012;Buettner, 2017b), HR (Vogt, Hagemann, & Kastner, 2006;Brookhuis & de Waard, 2010), facial action (Buettner, 2017b(Buettner, , 2018, fEMG (Stone & Wei, 2011;Ekman, Friesen, & Hager, 2002), PET (Kramer, 1990;Just, Carpenter, & Miyake, 2003), MEG (Tanaka, Ishii, & Watanabe, 2015;Ishii, Tanaka, & Watanabe, 2016), and various eye-tracking measures (e.g., Rayner, 1998;Buettner, 2013Buettner, , 2017bGwizdka, 2016). In particular, they have found that mental effort is associated with a lot of eye-related characteristics, such as a user's pupil diameter (Hess & Polt, 1964;Beatty, 1982;Buettner, 2017b), eye-blink duration and blink rate (Fairclough et al, 2005;Marshall, 2007), eye saccade speed (Porter et al, 2010;Buettner, 2013Buettner, , 2017b, and the number of eye gaze fixations (Rayner, 1998;Buettner, 2013Buettner, , 2017b.…”

Section: Mental Effort In Is Researchmentioning

confidence: 99%

“…In particular, they have found that mental effort is associated with a lot of eye-related characteristics, such as a user's pupil diameter (Hess & Polt, 1964;Beatty, 1982;Buettner, 2017b), eye-blink duration and blink rate (Fairclough et al, 2005;Marshall, 2007), eye saccade speed (Porter et al, 2010;Buettner, 2013Buettner, , 2017b, and the number of eye gaze fixations (Rayner, 1998;Buettner, 2013Buettner, , 2017b.…”

Section: Mental Effort In Is Researchmentioning

confidence: 99%

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

Buettner¹,

Sauer²,

Maier³

et al. 2018

THCI

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Abstract:We propose a method to predict user performance based on eye-tracking. The method uses eye-tracking-based pupillometry to capture pupil diameter data and calculates-based on a Random Forest algorithm-user performance expectations. We conducted a large-scale experimental evaluation (125 participants aged from 21 to 61 years) and found promising results that pave the way for a dynamic real-time adaption of IT to a user's mental effort and expected user performance. We have already achieved a good classification accuracy of user performance after only 40 seconds (5% of the mean total trial time that our participants took to complete our experiment). The non-invasive contact-free method can be applied cost-efficiently both in research and practical environments.

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“…1 Wearable sensor technology developed by CSEM SA and used in this study decreasing the gap between the laboratory and the eventual application (Guzik and Malik 2016). We capture cardiac measures that have been shown to differentiate taskloads (De Rivecourt et al 2008;Fairclough et al 2005;Splawn and Miller 2013) and respiration rates (Backs et al 2000;Brookings et al 1996;Wu et al 2011;Yao et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Understanding the human performance envelope using electrophysiological measures from wearable technology

Nixon

Charles

2017

Cogn Tech Work

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In this article, we capture electrophysiological measures from a new wearable technology to understand the human performance envelope. Using the NASA MultiAttribute Task Battery (MATB II), participants completed tasks associated with flight control which included communication, tracking and system and resource monitoring. Electrophysiological measures relating to cardiac activity and respiration were taken using the new wearable technology. Our results show significant differences in both heart rate and respiration rate in response to different taskloads and that higher taskloads were associated with higher mental workload. Frequency measures of heart rate variability discriminated different task types but not taskloads. This finding may be related to differences in task complexity being more important than the number events which we have used to manipulate taskload. We suggest that this new generation of wearable sensors could be used to inform operator locus in a human performance envelope, indicating when assistance by the aircraft or another crew member may be necessary to maintain safe and efficient performance.

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The influence of task demand and learning on the psychophysiological response

Cited by 245 publications

References 57 publications

Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task

Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

Understanding the human performance envelope using electrophysiological measures from wearable technology

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