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Dissociation between mental fatigue and motivational state during prolonged mental activity.

Gergelyfi M, Jacob B, Olivier E, Zénon A - Front Behav Neurosci (2015)

Bottom Line: An influential hypothesis states that MF does not arise from a disruption of overused neural processes but, rather, is caused by a progressive decrease in motivation-related task engagement.Finally, alterations of the motivational state through monetary incentives failed to compensate the effects of MF.These findings indicate that MF in healthy subjects is not caused by an alteration of task engagement but is likely to be the consequence of a decrease in the efficiency, or availability, of cognitive resources.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience, Université Catholique de Louvain Brussels, Belgium.

ABSTRACT
Mental fatigue (MF) is commonly observed following prolonged cognitive activity and can have major repercussions on the daily life of patients as well as healthy individuals. Despite its important impact, the cognitive processes involved in MF remain largely unknown. An influential hypothesis states that MF does not arise from a disruption of overused neural processes but, rather, is caused by a progressive decrease in motivation-related task engagement. Here, to test this hypothesis, we measured various neural, autonomic, psychometric and behavioral signatures of MF and motivation (EEG, ECG, pupil size, eye blinks, Skin conductance responses (SCRs), questionnaires and performance in a working memory (WM) task) in healthy volunteers, while MF was induced by Sudoku tasks performed for 120 min. Moreover extrinsic motivation was manipulated by using different levels of monetary reward. We found that, during the course of the experiment, the participants' subjective feeling of fatigue increased and their performance worsened while their blink rate and heart rate variability (HRV) increased. Conversely, reward-induced EEG, pupillometric and skin conductance signal changes, regarded as indicators of task engagement, remained constant during the experiment, and failed to correlate with the indices of MF. In addition, MF did not affect a simple reaction time task, despite the strong influence of extrinsic motivation on this task. Finally, alterations of the motivational state through monetary incentives failed to compensate the effects of MF. These findings indicate that MF in healthy subjects is not caused by an alteration of task engagement but is likely to be the consequence of a decrease in the efficiency, or availability, of cognitive resources.

No MeSH data available.


Related in: MedlinePlus

Physiological measures. (A) The heart rate variability (HRV) was assessed by Poincaré plot analysis which represents each inter-beat interval (IBI) against the IBI that precedes it (IBIt vs. IBIt−1). This analysis results in two statistics: the short-term HRV (SD1 in red) is the standard deviation perpendicular to the line of symmetry while the long-term HRV (SD2 in green) is computed along the line of symmetry of the plot. (B) Example pupil size recording during the WM task. The pupil response was computed as the trialwise peak-to-peak difference. Lower and upper peaks of the pupil response in an example trial (trial n°14) are represented by black arrows. (C) Example skin conductance signal obtained during the WM task. The standard deviation of the amplitude of SCRs was computed for different frequency bands. The raw SCRs are shown in black while SCRs filtered in the low (<0.1 Hz) or high frequency band (>0.1 Hz) are represented in blue and red, respectively.
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Figure 2: Physiological measures. (A) The heart rate variability (HRV) was assessed by Poincaré plot analysis which represents each inter-beat interval (IBI) against the IBI that precedes it (IBIt vs. IBIt−1). This analysis results in two statistics: the short-term HRV (SD1 in red) is the standard deviation perpendicular to the line of symmetry while the long-term HRV (SD2 in green) is computed along the line of symmetry of the plot. (B) Example pupil size recording during the WM task. The pupil response was computed as the trialwise peak-to-peak difference. Lower and upper peaks of the pupil response in an example trial (trial n°14) are represented by black arrows. (C) Example skin conductance signal obtained during the WM task. The standard deviation of the amplitude of SCRs was computed for different frequency bands. The raw SCRs are shown in black while SCRs filtered in the low (<0.1 Hz) or high frequency band (>0.1 Hz) are represented in blue and red, respectively.

Mentions: The same subjects as for the EEG analyses were excluded. ECG signals were preprocessed similarly to the EEG signals. The segmentation of the ECG signals also resulted in 30 90-s long epochs for every subject. Thereafter, a finite impulse response band-pass filter with cut-off frequencies 10–20 Hz was used. The R peaks of the ECG signals were detected offline and the inter-beat interval (IBI) between the consecutive R deflections was computed. To assess the HRV, we used the non-linear Poincaré plot analysis (Piskorski and Guzik, 2007), which consists in representing each IBI against the IBI that precedes it (IBIt vs. IBIt−1) (see Figure 2A).


Dissociation between mental fatigue and motivational state during prolonged mental activity.

Gergelyfi M, Jacob B, Olivier E, Zénon A - Front Behav Neurosci (2015)

Physiological measures. (A) The heart rate variability (HRV) was assessed by Poincaré plot analysis which represents each inter-beat interval (IBI) against the IBI that precedes it (IBIt vs. IBIt−1). This analysis results in two statistics: the short-term HRV (SD1 in red) is the standard deviation perpendicular to the line of symmetry while the long-term HRV (SD2 in green) is computed along the line of symmetry of the plot. (B) Example pupil size recording during the WM task. The pupil response was computed as the trialwise peak-to-peak difference. Lower and upper peaks of the pupil response in an example trial (trial n°14) are represented by black arrows. (C) Example skin conductance signal obtained during the WM task. The standard deviation of the amplitude of SCRs was computed for different frequency bands. The raw SCRs are shown in black while SCRs filtered in the low (<0.1 Hz) or high frequency band (>0.1 Hz) are represented in blue and red, respectively.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4499755&req=5

Figure 2: Physiological measures. (A) The heart rate variability (HRV) was assessed by Poincaré plot analysis which represents each inter-beat interval (IBI) against the IBI that precedes it (IBIt vs. IBIt−1). This analysis results in two statistics: the short-term HRV (SD1 in red) is the standard deviation perpendicular to the line of symmetry while the long-term HRV (SD2 in green) is computed along the line of symmetry of the plot. (B) Example pupil size recording during the WM task. The pupil response was computed as the trialwise peak-to-peak difference. Lower and upper peaks of the pupil response in an example trial (trial n°14) are represented by black arrows. (C) Example skin conductance signal obtained during the WM task. The standard deviation of the amplitude of SCRs was computed for different frequency bands. The raw SCRs are shown in black while SCRs filtered in the low (<0.1 Hz) or high frequency band (>0.1 Hz) are represented in blue and red, respectively.
Mentions: The same subjects as for the EEG analyses were excluded. ECG signals were preprocessed similarly to the EEG signals. The segmentation of the ECG signals also resulted in 30 90-s long epochs for every subject. Thereafter, a finite impulse response band-pass filter with cut-off frequencies 10–20 Hz was used. The R peaks of the ECG signals were detected offline and the inter-beat interval (IBI) between the consecutive R deflections was computed. To assess the HRV, we used the non-linear Poincaré plot analysis (Piskorski and Guzik, 2007), which consists in representing each IBI against the IBI that precedes it (IBIt vs. IBIt−1) (see Figure 2A).

Bottom Line: An influential hypothesis states that MF does not arise from a disruption of overused neural processes but, rather, is caused by a progressive decrease in motivation-related task engagement.Finally, alterations of the motivational state through monetary incentives failed to compensate the effects of MF.These findings indicate that MF in healthy subjects is not caused by an alteration of task engagement but is likely to be the consequence of a decrease in the efficiency, or availability, of cognitive resources.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience, Université Catholique de Louvain Brussels, Belgium.

ABSTRACT
Mental fatigue (MF) is commonly observed following prolonged cognitive activity and can have major repercussions on the daily life of patients as well as healthy individuals. Despite its important impact, the cognitive processes involved in MF remain largely unknown. An influential hypothesis states that MF does not arise from a disruption of overused neural processes but, rather, is caused by a progressive decrease in motivation-related task engagement. Here, to test this hypothesis, we measured various neural, autonomic, psychometric and behavioral signatures of MF and motivation (EEG, ECG, pupil size, eye blinks, Skin conductance responses (SCRs), questionnaires and performance in a working memory (WM) task) in healthy volunteers, while MF was induced by Sudoku tasks performed for 120 min. Moreover extrinsic motivation was manipulated by using different levels of monetary reward. We found that, during the course of the experiment, the participants' subjective feeling of fatigue increased and their performance worsened while their blink rate and heart rate variability (HRV) increased. Conversely, reward-induced EEG, pupillometric and skin conductance signal changes, regarded as indicators of task engagement, remained constant during the experiment, and failed to correlate with the indices of MF. In addition, MF did not affect a simple reaction time task, despite the strong influence of extrinsic motivation on this task. Finally, alterations of the motivational state through monetary incentives failed to compensate the effects of MF. These findings indicate that MF in healthy subjects is not caused by an alteration of task engagement but is likely to be the consequence of a decrease in the efficiency, or availability, of cognitive resources.

No MeSH data available.


Related in: MedlinePlus