Tracking cortical representations of facial attractiveness using time-resolved representational similarity analysis

Abstract

When we see a face, we rapidly form an impression of its attractiveness. Here, we investigated how rapidly representations of facial attractiveness emerge in the human brain. In an EEG experiment, participants viewed 100 face photographs and rated them for their attractiveness. Using time-resolved representational similarity analysis on the EEG data, we reveal representations of facial attractiveness after 150–200 ms of cortical processing. Interestingly, we show that these representations are related to individual participants’ personal attractiveness judgments, suggesting that already early perceptual representations of facial attractiveness convey idiosyncratic attractiveness preferences. Further, we show that these early representations are genuinely related to attractiveness, as they are neither explained by other high-level face attributes, such as face sex or age, nor by features extracted by an artificial deep neural network model of face processing. Together, our results demonstrate early, individually specific, and genuine representations of facial attractiveness, which may underlie fast attractiveness judgments.

Modelling brain representations of abstract concepts

“…Specifically, for each searchlight neighborhood, we computed a partial correlation between the neural RDM and the predictor RDM which was controlled for the control RDMs. This procedure ensured that if the control RDMs predicted the same portion of variance in the neural RDM as the predictor RDM, the correlation would disappear (for similar approaches, see [69][70][71]). All other aspects of the analysis remained identical to the previous searchlight analysis.…”

Semantic scene-object consistency modulates N300/400 EEG components, but does not automatically facilitate object representations

“…Second, to track the impact of consistent or inconsistent objects on scenes representations, we performed decoding analyses to discriminate between the eight scene categories separately for consistent and inconsistent conditions at each time point from -100 ms to 1800 ms relative to the onset of the scene (-1100 ms to 800 ms relative to the onset of the object). For all decoding analyses, we adopted two approaches: standard timeseries decoding (Boring et al, 2020;Kaiser and Nyga, 2020), using data from a sliding time window, and cumulative decoding (Ramkumar et al, 2013;Kaiser et al, 2020a), using aggregated data from all elapsed time points. The two approaches are detailed in the following paragraphs.…”

“…Considering excessive data dimensionality may harm classification, we adopted principal component analysis (PCA) to reduce the dimensionality of the data (Grootswagers et al 2017;Kaiser and Nyga 2020;Kaiser, Hä berle, et al 2020). For each classification, a PCA was performed on all data from the training set, and the PCA solution was projected onto data from the testing set.…”

Characterizing dynamic neural representations of scene attractiveness

“…Much less is known about when aesthetic experiences arise dynamically across the cortical processing cascade. Much of the M/EEG literature addressing this question has focused on the perception of face attractiveness (Carbon et al, 2018; Kaiser & Nyga, 2020; Schacht et al, 2008; Werheid et al, 2007; Zhang & Deng, 2012), with many studies highlighting that a face’s attractiveness can impact early and fundamental stages of the face processing hierarchy. In our own work, we have further highlighted that such early representations of face attractiveness are partly explained by personal preferences, rather than only by attractiveness judgments that are shared among a large group of observers (Kaiser & Nyga, 2020), suggesting that even the perceptual correlates of attractiveness are shaped in personally idiosyncratic ways.…”

“…Much of the M/EEG literature addressing this question has focused on the perception of face attractiveness (Carbon et al, 2018; Kaiser & Nyga, 2020; Schacht et al, 2008; Werheid et al, 2007; Zhang & Deng, 2012), with many studies highlighting that a face’s attractiveness can impact early and fundamental stages of the face processing hierarchy. In our own work, we have further highlighted that such early representations of face attractiveness are partly explained by personal preferences, rather than only by attractiveness judgments that are shared among a large group of observers (Kaiser & Nyga, 2020), suggesting that even the perceptual correlates of attractiveness are shaped in personally idiosyncratic ways. Unlike the rich literature on face attractiveness, only few studies have looked at the time-resolved neural correlates of aesthetic judgments for other stimuli, such as abstract patterns (Höfel & Jacobsen, 2007; Jacobsen & Höfel, 2003) and various types of artworks (Cela-Conde et al, 2004; de Tommaso et al, 2007; Strijbosch et al, 2021).…”