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

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.

Lowering the Sampling Rate: Heart Rate Response during Cognitive Fatigue.

Cognitive fatigue is a mental state characterised by feelings of tiredness and impaired cognitive functioning due to sustained cognitive demands. Frequency-domain heart rate variability (HRV) features have been found to vary as a function of cognitive fatigue. However, it has yet to be determined whether HRV features derived from electrocardiogram data with a low sampling rate would remain sensitive to cognitive fatigue. Bridging this research gap is important as it has substantial implications for designing more energy-efficient and less memory-hungry wearables to monitor cognitive fatigue. This study aimed to examine (1) the level of agreement between frequency-domain HRV features derived from lower and higher sampling rates, and (2) whether frequency-domain HRV features derived from lower sampling rates could predict cognitive fatigue. Participants (N = 53) were put through a cognitively fatiguing 2-back task for 20 min whilst their electrocardiograms were recorded. Results revealed that frequency-domain HRV features derived from sampling rate as low as 125 Hz remained almost perfectly in agreement with features derived from the original sampling rate at 2000 Hz. Furthermore, frequency domain features, such as normalised low-frequency power, normalised high-frequency power, and the ratio of low- to high-frequency power varied as a function of increasing cognitive fatigue during the task across all sampling rates. In conclusion, it appears that sampling at 125 Hz is more than adequate for frequency-domain feature extraction to index cognitive fatigue. These findings have significant implications for the design of low-cost wearables for detecting cognitive fatigue.

Effects of fun-seeking and external locus of control on smoking behaviour: a cross-sectional analysis on a cohort of working men in Singapore.

OBJECTIVES: We examined the combined effects of behavioural inhibition and behavioural activation, on one hand, and locus of control, on the other hand, on different categories of smoking behaviour (non-smoking, ex-smoking, occasional smoking, daily smoking). DESIGN: This study adopted a cross-sectional design. Participants completed questionnaires regarding demographics, smoking patterns, behavioural inhibition/behavioural activation systems and locus of control. SETTING: The study was conducted across four companies from the transportation, cooling plant and education sectors in Singapore. PARTICIPANTS: Three hundred sixty-nine male working adults were included in the final sample. RESULTS: Corroborating previous research, a logistic regression model examining behavioural inhibition/behavioural activation systems revealed that the fun-seeking aspect of behavioural activation was a unique predictor in distinguishing non-smokers from daily smokers (OR=1.24, p=0.012). By contrast, in a separate model examining locus of control, external locus of control was found to be a unique predictor in distinguishing non-smokers from daily smokers (OR=1.13, p<0.001). In addition, a third model combining both behavioural inhibition/behavioural activation systems and locus of control found that only external locus of control remained a significant predictor (OR=1.12, p<0.001). Further analyses revealed a mediating effect of external locus of control on the relationship between fun-seeking and smoking behaviour. That is, the increase in the odds of daily smoking due to fun-seeking was explained by external locus of control (direct pathway OR=1.20, p=0.058; indirect pathway OR=1.04, p<0.050). CONCLUSIONS: Overall, fun-seeking through its influence on external locus of control indirectly affects daily smoking behaviour, suggesting a more complex relationship than shown in previous research.

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

Dysfunctional performance monitoring has been proposed as a potential neurocognitive biomarker of various internalising psychopathological symptoms, such as anxiety, depressive, and obsessive-compulsive symptoms. Given the overlapping neurophysiological findings and high rates of comorbidity amongst these internalising symptoms, an important research gap pertains to the specificity of performance monitoring to each of these symptoms. The aim of the present study was to examine the effects of anxiety, depressive, and obsessive-compulsive symptoms on performance monitoring in healthy adults. The sample consisted of 50 participants, with ages ranging from 18 to 33 years (M = 22.82, SD = 3.84). The Depression, Anxiety, and Stress Scales-21 and the Obsessive Compulsive Inventory-Revised were administered to assess the various internalising symptoms. An arrow flanker task was administered to elicit error responses whilst electrophysiological data were recorded from the scalp. Performance monitoring was indexed by the error-related negativity and correct response negativity. Bivariate correlations revealed that the three internalising symptoms were not associated with error-related negativity or correct response negativity amplitudes. However, a regression model revealed that greater levels of anxiety symptoms were uniquely associated with larger error-related negativity amplitude after controlling for depressive symptoms. In addition, greater levels of depressive symptoms were uniquely associated with smaller error-related negativity amplitude. Another regression model revealed that greater levels of depressive symptoms were uniquely associated with smaller correct response negativity amplitude after controlling for anxiety symptoms. These findings suggest that performance monitoring differentially associates with anxiety and depressive symptoms amongst healthy adults, providing some evidence of specificity for each respective symptoms.