We explored changes in multiscale brain signal complexity and power‐law scaling exponents of electroencephalogram (EEG) frequency spectra across several distinct global states of consciousness induced in the natural physiological context of the human sleep cycle. We specifically aimed to link EEG complexity to a statistically unified representation of the neural power spectrum. Further, by utilizing surrogate‐based tests of nonlinearity we also examined whether any of the sleep stage‐dependent changes in entropy were separable from the linear stochastic effects contained in the power spectrum. Our results indicate that changes of brain signal entropy throughout the sleep cycle are strongly time‐scale dependent. Slow wave sleep was characterized by reduced entropy at short time scales and increased entropy at long time scales. Temporal signal complexity (at short time scales) and the slope of EEG power spectra appear, to a large extent, to capture a common phenomenon of neuronal noise, putatively reflecting cortical balance between excitation and inhibition. Nonlinear dynamical properties of brain signals accounted for a smaller portion of entropy changes, especially in stage 2 sleep.
Sustained attention is an essential behavior in life, but often leads to performance decrements with time. Computational accounts of sustained attention suggest this is due to brief disruptions in goal-directed processing, or microlapses. Decreases in gamma spectral power are a potential candidate for indexing microlapses and discriminating between low and high performers in sustained attention tasks, while increases in beta, alpha, and theta power are expected to exhibit compensatory effort to offset fatigue. The current study tests these hypotheses in a 10-minute Psychomotor Vigilance Test, a context that eliminates confounds with measuring gamma frequencies. 34 participants ( Mage = 22.60; SDage = 4.08) volunteered in the study. Results suggested frontal gamma power declined with time-on-task, indicating reduction in central cognition. Beta power increased with time-on-task, suggesting compensatory effort; however, alpha and theta power did not increase. Additionally, gamma power discriminated between low and high performers, potentially suggesting motivational differences between the groups.
A number of recent studies have documented rapid changes in behavioural sensory acuity induced by aversive learning in the olfactory and auditory modalities. The effect of aversive learning on the discrimination of low-level features in the visual system of humans remains unclear. Here, we used a psychophysical staircase procedure to estimate discrimination thresholds for oriented grating stimuli, before and after differential aversive learning. We discovered that when a target grating orientation was conditioned with an aversive loud noise, it subsequently led to an improvement of discrimination acuity in nearly all subjects. However, no such change was observed in a control group conditioned to an orientation shifted by ±90° from the target. Our findings cannot be explained by contextual learning or sensitisation factors. The results converge with those reported in the olfactory modality and provide further evidence that early sensory systems can be rapidly modified by recently experienced reinforcement histories.
Recently, the gamma band ( γ; 70-100 Hz) has been implicated in sustained attention decay across a vigil consistent with computational models of fatigue. Frontal γ indexing centrally controlled sustained attention and parietal γ linked to gated sensory processes declined across a 10-minute vigilance task, a pattern observed for faster but not slower performers. The anatomical distribution of γ activity indicates neural communication, or connectivity, within the fronto-parietal network. We used Granger Prediction to evaluate fatigue effects on network γ connectivity. Results showed stronger directional connectivity for frontal→parietal versus parietal→frontal over time, indicating that top-down control of attention largely remained intact. However, parietal→frontal early γ connectivity increased with time, suggesting a network shift to enhanced sensory-directed processes after only 8 minutes. This pattern of connectivity was mirrored by fast but not slower performers. Our findings provide new directions for computational accounts of fatigue mechanisms and highlight the importance of individual differences.
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