Using principal component analysis to characterize eye movement fixation patterns during face viewing

Abstract

Human faces contain dozens of visual features, but viewers preferentially fixate just two of them: the eyes and the mouth. Face-viewing behavior is usually studied by manually drawing regions of interest (ROIs) on the eyes, mouth, and other facial features. ROI analyses are problematic as they require arbitrary experimenter decisions about the location and number of ROIs, and they discard data because all fixations within each ROI are treated identically and fixations outside of any ROI are ignored. We introduce a data-driven method that uses principal component analysis (PCA) to characterize human face-viewing behavior. All fixations are entered into a PCA, and the resulting eigenimages provide a quantitative measure of variability in face-viewing behavior. In fixation data from 41 participants viewing four face exemplars under three stimulus and task conditions, the first principal component (PC1) separated the eye and mouth regions of the face. PC1 scores varied widely across participants, revealing large individual differences in preference for eye or mouth fixation, and PC1 scores varied by condition, revealing the importance of behavioral task in determining fixation location. Linear mixed effects modeling of the PC1 scores demonstrated that task condition accounted for 41% of the variance, individual differences accounted for 28% of the variance, and stimulus exemplar for less than 1% of the variance. Fixation eigenimages provide a useful tool for investigating the relative importance of the different factors that drive human face-viewing behavior.

Gaze Patterns in Children With Autism Spectrum Disorder to Emotional Faces: Scanpath and Similarity

“…Unlike prior research that analyzes gaze behavior using statistical features across distinct facial regions, this study examines fixation locations and discovers that children with ASD exhibit larger vertical gaze shift amplitudes than horizontal ones. This finding is consistent with the results of Wegner et al [48]. They find that ASD children and TD children exhibit significant individual differences in their tendency to gaze at the eyes or mouth, confirming that the distribution of gaze on the upper and lower face is the largest difference between the two groups.…”

Face-Information Sampling in Super-Recognizers

“…To capture the major differences in gaze patterns across participants, we conducted principal components analysis (PCA) on trial-level gaze patterns. PCA is data driven, in the sense that it does not rely on experimenter input in the form of predefined regions of interest and so is well suited to exploratory analysis of eye-tracking data (see Varela et al, 2018; Wegner-Clemens et al, 2019). To conduct this analysis, we first normalized and then resized the gaze maps—from 891 × 656 pixels to 209 × 182 pixels—and converted each to a single vector.…”

Evidence of inverted gravity‐driven variation in predictive sensorimotor function

“…In other words, each component will combine contributions from multiple variables within m to capture an aspect of the data that is orthogonal to the rest of the data and therefore qualitatively different in how it should be interpreted. As such, it is useful as a means of providing insights about data obtained in a range of different fields, for example economics, biology, engineering or psychology, particularly when one has an understanding of what is measured by individual variables, but a bigger picture about how they come together remains elusive (Jolliffe & Cadima, 2016; Wegner‐Clemens, Rennig, Magnotti, & Beauchamp, 2019). In the present case, we set n m = 32, restricting our matrix to just a selective explorative subset of what might have been possible in an unconstrained data‐driven approach.…”