Transient Attention Gates Access Consciousness: Coupling N2pc and P3 Latencies Using Dynamic Time Warping.

The N2pc and P3 event-related potentials (ERPs), used to index selective attention and access to working memory and conscious awareness, respectively, have been important tools in cognitive sciences. Although it is likely that these two components and the underlying cognitive processes are temporally and functionally linked, such links have not yet been convincingly demonstrated. Adopting a novel methodological approach based on dynamic time warping (DTW), we provide evidence that the N2pc and P3 ERP components are temporally linked. We analyzed data from an experiment where 23 participants (16 women) monitored bilateral rapid serial streams of letters and digits in order to report a target digit indicated by a shape cue, separately for trials with correct responses and trials where a temporally proximal distractor was reported instead (distractor intrusion). DTW analyses revealed that N2pc and P3 latencies were correlated in time, both when the target or a distractor was reported. Notably, this link was weaker on distractor intrusion trials. This N2pc-P3 association is discussed with respect to the relationship between attention and access consciousness. Our results demonstrate that our novel method provides a valuable approach for assessing temporal links between two cognitive processes and their underlying modulating factors. This method allows to establish links and their modulator for any two time-series across all domains of the field (general-purpose MATLAB functions and a Python module are provided alongside this paper).

I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data.

Machine learning has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With these powerful techniques comes the danger of overfitting of hyperparameters which can render results invalid. We refer to this problem as 'overhyping' and show that it is pernicious despite commonly used precautions. Overhyping occurs when analysis decisions are made after observing analysis outcomes and can produce results that are partially or even completely spurious. It is commonly assumed that cross-validation is an effective protection against overfitting or overhyping, but this is not actually true. In this article, we show that spurious results can be obtained on random data by modifying hyperparameters in seemingly innocuous ways, despite the use of cross-validation. We recommend a number of techniques for limiting overhyping, such as lock boxes, blind analyses, pre-registrations, and nested cross-validation. These techniques, are common in other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques in the neurosciences.