Aaron Schurger

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.

The unfolding argument in the neuroscience of consciousness posits that causal structure cannot account for consciousness because any recurrent neural network (RNN) can be “unfolded” into a functionally equivalent feedforward neural network (FNN) with identical input–output behavior. Subsequent debate has focused on dynamical properties and philosophy of science critiques. We examine a boundary condition on the unfolding argument for RNN systems with rapid plasticity in their connection weights. We demonstrate through rigorous mathematical proofs that rapid plasticity negates the functional equivalence between the RNN and the FNN. Our proofs address history-dependent plasticity, dynamical systems analysis, information-theoretic considerations, perturbational stability, complexity growth, and resource limitations. We demonstrate that neuronal systems that possess properties such as plasticity, history dependence, and complex temporal information encoding have features that cannot be captured by a static FNN. We show that plasticity is a concrete instance of lenient dependency between behavioral and internal observables, restoring empirical testability to theories that incorporate plasticity on perception-relevant timescales. Our results do not establish that recurrence, plasticity, or process are necessary for consciousness; they establish that the unfolding argument does not preclude empirical investigation of whether these properties matter.

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.

The scope of unconscious processing has long been, and still remains, a hotly debated issue. This is driven in part by the current diversity of methods to manipulate and measure perceptual consciousness. Here, we provide ten recommendations and nine outstanding issues about designing experimental paradigms, analyzing data, and reporting the results of studies on unconscious processing. These were formed through dialogue among a group of researchers representing a range of theoretical backgrounds. We acknowledge that some of these recommendations naturally do not align with some existing approaches and are likely to change following theoretical and methodological development. Nevertheless, we hold that at this stage of the field they are instrumental in evoking a much-needed discussion about the norms of studying unconscious processes and helping researchers make more informed decisions when designing experiments. In the long run, we aim for this paper and future discussions around the outstanding issues to lead to a more convergent corpus of knowledge about the extent – and limits – of unconscious processing.

The scope of unconscious processing has long been, and still remains, a hotly debated issue. This is driven in part by the current diversity of methods to manipulate and measure perceptual consciousness. We provide here ten recommendations and nine outstanding issues about designing experimental paradigms, analyzing data, and reporting the results of studies on unconscious processing. These were formed through dialogue among a group of researchers representing a range of theoretical backgrounds. We acknowledge that some of these recommendations naturally do not align with some existing approaches and are likely to change following theoretical and methodological development. Nevertheless, we hold that at this stage of the field they are instrumental in evoking a much-needed discussion about the norms of studying unconscious processes and helping researchers make more informed decisions when designing experiments. In the long run, we aim for this paper and future discussions around the outstanding issues to lead to a more convergent corpus of knowledge about the extent – and limits – of unconscious processing.