The time course of individual face recognition: A pattern analysis of ERP signals

Abstract

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition.

Localization of vectors–patterns in the problems of parametric classification with the purpose of increasing its accuracy

“…When such problems are solved for face recognition [1,2], identification of objects by their text, graphic or video images [3][4][5][6][7][8], for medical [9,10] or technical diagnostics [11,12], the named optimization criterion is a priority. Its achieving depends on the selected method of classification, caused often by specific subject area of the studies Thus, for instance, nonparametric methods are mainly used in pharmacological applications [13] while in the tasks of trouble-shooting in the series-connected units of chemical engineering systems they use parametric methods [14].…”

“…In [1] a number of UN-factors are described, that have a defining impact on the experimental data set quality, including measurements imprecision, lack of conditions for direct observations of the object, incompleteness and ambiguity of the knowledge related to the subject area and the task at hand, unaccounted for (hidden) parameters impact, lack of…”

“…expert knowledge about the subject area or inability to formalize them, as well as uncertainty caused by input feature space dimensionality (redundancy and noise) [1,2].…”

Decoding eye-of-origin outside of awareness

“…Recently, studies using electro-and magneto-encephalography (EEG and MEG) have shown that both simple (Cichy et al, 2015;Ramkumar et al, 2013;Wardle et al, 2016) and more complex (Carlson et al, 2013(Carlson et al, , 2011Cichy et al, 2014;Coggan et al, 2016;Nemrodov et al, 2016) image properties can be decoded from the pattern of electromagnetic activity evoked by a visual stimulus. One study investigating orientation decoding (Cichy et al, 2015) has suggested that any information encoded in cortical columns should produce distinct spatial patterns of electrical activity that can be recovered using machine learning algorithms (multivariate pattern classifiers).…”

Improvement of Accuracy of Parametric Classification in the Space of N×2 Factors-Attributes on the Basis of Preliminary Obtained Linear Discriminant Function

“…A wide range of applications of these methods shows that one can't do without them when solving problems from completely different subject areas. The methods are used for image recognition [1][2][3][4][5], medical diagnostics [6,7], technical diagnostics [8][9][10][11][12]. Principal differences in approaches for solving specific problems are related to the choice of methods and the feasibility of the chosen alternative.…”