Lucas Jeay-Bizot

Decades of research have shown that early ramping activity is a very reliable antecedent of self-initiated movement. However, the dominant paradigm has been to only analyze data epochs culminating in movement, which discounts how often slow ramping might occur when no movement follows. To address this, we introduced a matched control condition that did not culminate in movement. We recorded electroencephalography data and compared these two conditions using a variety of powerful machine-learning classifiers. Although early ramping activity was present, it was not useful in predicting movement. Instead, classification accuracy rose abruptly about 100 ms before movement. By contrast, the traditional approach reproduced the spurious impression of early predictability. Our results substantially constrain the interpretation that early EEG antecedents, including the RP, reflect a commitment to act, instead indicating that EEG-detectable evidence consistent with such a commitment emerges closer to movement onset, in line with the timing of conscious intention and the capacity for late movement inhibition. More broadly, our results show that a reliable antecedent is not necessarily a good predictor, underscoring the need for proper control conditions in time series analyses.

Decades of research have shown that early ramping activity is a very reliable antecedent of self-initiated movement. However, the dominant paradigm has been to only analyze data epochs culminating in movement, which discounts how often slow ramping might occur when no movement follows. To address this, we introduced a matched control condition that did not culminate in movement. We recorded electroencephalography data and compared these two conditions using a variety of powerful machine-learning classifiers. Although early ramping activity was present, it did not predict movement. Instead, classification accuracy rose abruptly about 100 ms before movement. By contrast, the traditional approach reproduced the spurious impression of early predictability. Our results resolve a long-standing controversy, showing that the neural commitment to act is a late-stage event consistent with the timing of conscious intention and the ability to inhibit movement. More broadly, our results show that a reliable antecedent is not necessarily a good predictor, underscoring the need for proper control conditions in time series analyses.

Decades of research have shown that early ramping activity is a very reliable antecedent of self-initiated movement. However, the dominant paradigm has been to only analyze data epochs culminating in movement, which discounts how often slow ramping might occur when no movement follows. To address this, we introduced a matched control condition that did not culminate in movement. We recorded electroencephalography data and compared these two conditions using a variety of powerful machine-learning classifiers. Although early ramping activity was present, it was not useful in predicting movement. Instead, classification accuracy rose abruptly about 100 ms before movement. By contrast, the traditional approach reproduced the spurious impression of early predictability. Our results substantially constrain the interpretation that early EEG antecedents, including the RP, reflect a commitment to act, instead indicating that EEG-detectable evidence consistent with such a commitment emerges closer to movement onset, in line with the timing of conscious intention and the capacity for late movement inhibition. More broadly, our results show that a reliable antecedent is not necessarily a good predictor, underscoring the need for proper control conditions in time series analyses.

Respiratory processes are increasingly implicated in shaping neural activity and behavior. Recent studies have reported a coupling between respiratory phase and the cortical readiness potential (RP), suggesting that breathing may modulate the neural processes preceding voluntary action. Here, using electroencephalography recordings in humans, we re-examine this claim using the original dataset and a new independent dataset, as well as in simulated data with no coupling. We show that the reported association arises from a confound: both RP amplitude and respiratory phase are coupled to movement onset. The original analysis does not control for this dependency, leading to a spurious effect that is also observed in simulated data. When trials are instead grouped by respiratory phase at the time of movement, thereby controlling for this confound, the apparent coupling disappears. Across datasets, Bayesian analyses provide evidence for the absence of an effect under natural breathing conditions. These findings indicate that respiratory phase does not directly modulate RP amplitude during spontaneous behavior. More broadly, they reveal a shortcoming of phase-amplitude coupling analyses applied to epoched data with slowly varying signals, and highlight the importance of controlling for shared dependencies when interpreting physiological–neural relationships.

Correlations in science typically matter as indications of underlying causal structures. In two recent studies, a direct correlation between respiration and the cortical readiness potential (RP) is claimed, with support for causation suggested by the use of terms such as ‘impact’ and ‘modulate’. Following these papers, the notion of a causal relation between respiration and the RP has been repeated in the literature. However, to support the claim that respiratory phase is directly correlated with RP amplitude, all reasonable potential confounds must first be ruled out. We show that a simple confound suffices to explain the observed coupling between respiration phase and the RP: RP amplitude is also coupled with movement onset, which, as these studies rightly reveal, tends to coincide with breathing phase. To test for direct correlation, we must fix one of the variables and test its effect on the other. Grouping RP amplitudes according to respiratory phase (thereby fixing the respiration-behavior coupling), we show that evidence for an RP-respiration coupling disappears. We show this to be true in the original dataset, in a new independent dataset we collected, and in a simulated dataset expressly engineered to have no direct coupling.

Experimental research on self-initiated movement has for decades relied on the same methodology: the data epochs subjected to analysis are only those culminating in a movement. This approach reliably reveals early ramping activity starting one second or more before movement, but discounts the prior probability of slow ramping. We trained powerful machine-learning classifiers in a sliding window to discriminate between EEG data epochs that either do or do not culminate in a self-initiated movement. We found early ramping signals, but they were not predictive of impending movement. Instead, the ability to correctly classify movement from no-movement epochs emerged abruptly ~100 ms before action onset, while, using the traditional approach, we replicated the spurious finding of early predictability. Our results resolve a long-standing controversy, showing the neural commitment to act is a late-stage process that aligns with results on the timing of subjective experience and the ability to inhibit self-initiated movement.