The effects of anxiety, depressive, and obsessive-compulsive subclinical symptoms on performance monitoring

Lee, Kar Fye Alvin; Fox, Allison M.; Notebaert, Lies

doi:10.1016/j.ijpsycho.2020.09.009

Cited by 4 publications

(1 citation statement)

References 67 publications

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“…One argument is that putative biomarkers of a particular psychological state should have a certain level of specificity [ 82 , 132 ]. That is, HRV should be more strongly correlated with cognitive fatigue than with stress or mental workload.…”

Section: Issues and Implicationsmentioning

confidence: 99%

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Lee

Gan

Christopoulos

2021

Sensors

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Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.

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Section: Issues and Implicationsmentioning

confidence: 99%

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Lee

Gan

Christopoulos

2021

Sensors

Self Cite

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Biomarkers of mental disorders: Psychophysiological measures as indicators of mechanisms, risk, and outcome prediction

Riesel

Endraß

Weinberg

2021

International Journal of Psychophysiology

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A systematic review and meta-analysis of altered electrophysiological markers of performance monitoring in Obsessive-Compulsive Disorder (OCD), Gilles de la Tourette Syndrome (GTS), Attention-Deficit/Hyperactivity disorder (ADHD) and Autism

Bellato

Norman

Idrees

et al. 2021

Neuroscience & Biobehavioral Reviews

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Altered performance monitoring is implicated in obsessive-compulsive disorder (OCD), Gilles de la Tourette syndrome (GTS), attention-deficit/hyperactivity disorder (ADHD) and autism. We conducted a systematic review and meta-analysis of electrophysiological correlates of performance monitoring (error-related negativity, ERN; error positivity, Pe; feedback-related negativity, FRN; feedback-P3) in individuals with OCD, GTS, ADHD or autism compared to control participants, or associations between correlates and symptoms/traits of these conditions. Meta-analyses on 97 studies (5890 participants) showed increased ERN in OCD (Hedge's g=0.

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The effects of anxiety, depressive, and obsessive-compulsive subclinical symptoms on performance monitoring

Cited by 4 publications

References 67 publications

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Biomarkers of mental disorders: Psychophysiological measures as indicators of mechanisms, risk, and outcome prediction

A systematic review and meta-analysis of altered electrophysiological markers of performance monitoring in Obsessive-Compulsive Disorder (OCD), Gilles de la Tourette Syndrome (GTS), Attention-Deficit/Hyperactivity disorder (ADHD) and Autism

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