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

Summary

The N2pc and P3 brain signals, which index selective attention and conscious awareness respectively, are temporally linked. In an experiment with 23 participants monitoring rapid letter and digit streams, dynamic time warping analysis showed that the latencies of these two signals correlated in time, both when participants correctly reported a target digit and when they mistakenly reported a nearby distractor. The link was weaker on distractor intrusion trials. The findings clarify the relationship between attention and access consciousness, and the novel method offers a general approach for assessing temporal links between any two time-series processes.

Study at a glance

Abstract

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).