Spontaneous oscillatory activity in episodic timing: an EEG replication study and its limitations
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Episodic timing refers to the one-shot, automatic encoding of temporal information in the brain, in the absence of attention to time. A previous magnetoencephalography (MEG) study showed that the relative burst time of spontaneous alpha oscillations (α) during quiet wakefulness was a selective predictor of retrospective duration estimation. This observation was interpreted as α embodying the “ticks” of an internal contextual clock. Herein, we replicate and extend these findings using electroencephalography (EEG), assess robustness to time-on-task effects, and test the generalizability in virtual reality (VR) environments. In three EEG experiments, 147 participants underwent 4-minute eyes-open resting-state recordings followed by an unexpected retrospective duration estimation task. Experiment 1 tested participants before any tasks, Experiment 2 after 90 minutes of timing tasks, and Experiment 3 in VR environments of different sizes. We successfully replicated the original MEG findings in Experiment 1 but did not in Experiment 2. We explain the lack of replication through time-on-task effects (changes in α power and topography) and contextual changes yielding a cognitive strategy based on temporal expectation (supported by a fast passage-of-time). In Experiment 3, we did not find the expected duration underestimation in VR, and did not replicate the correlation between α bursts and retrospective time estimates. Overall, EEG captures the α burst marker of episodic timing, its reliability depends critically on experimental context. Our findings highlight the importance of controlling experimental context when using α bursts as a neural marker of episodic timing.
Significance Statement
How does the brain automatically keep track of time during everyday experiences? This study investigates alpha brain activity as a marker of contextual changes during quiet wakefulness. We successfully replicated the original findings using EEG, which is more widespread than MEG, but found some limitations. This neural marker is sensitive to mental fatigue and experimental context, with participants adopting temporal expectation strategies that alter the relation between alpha and temporal estimation. Virtual reality environments also affected behavior in a way that suggested prospective timing which the marker is known not to capture. As alterations in timing affect numerous neurological and psychiatric conditions, establishing a robust neural marker of experiential time has important implications for both basic neuroscience and clinical applications.
