Cite this paper as
Benjamin Nguyen & Michael J. Spivey (2023). Temporary disruption in language processing reflected as multiscale temporal discoordination in a recurrent network. Journal of Multiscale Neuroscience 2(1),
By juxtaposing time series analyses of activity measured from a fully recurrent network undergoing disrupted processing and of activity measured from a continuous meta-cognitive report of disruption in real-time language comprehension, we present an opportunity to compare the temporal statistics of the state-space trajectories inherent to both systems. Both the recurrent network and the human language comprehension process appear to exhibit long-range temporal correlations and low entropy when processing is undisrupted and coordinated. However, when processing is disrupted and discoordinated, they both exhibit more short-range temporal correlations and higher entropy. We conclude that by measuring human language comprehension in a dense-sampling manner similar to how we analyze the networks, and analyzing the resulting data stream with nonlinear time series analysis techniques, we can obtain more insight into the temporal character of these discoordination phases than by simply marking the points in time at which they peak.
Keywords: Psycholinguistics, Sentence Processing, Mouse-tracking, Recurrent Networks, Recurrence Quantification Analysis, Multiscale Systems, Nonlinear Dynamics, Time Series
Conflict of Interest
The authors declare no conflict of interest
This article belongs to the Special Issue
Prof Michael J Spivey, Author of "The Continuity of Mind".
Department of Cognitive and Information Sciences,
University of California, Merced, USA
Copyright: © 2023 The Author(s). Published by Neural Press.
This is an open access article distributed under the terms and conditions of the CC BY 4.0 license.
Temporary disruption in language processing reflected
as multiscale temporal discoordination in a recurrent network