Understanding the semiotic affordances of human facial expressions using data-driven methods

Jonas Nölle, Oliver G. B. Garrod, Chaona Chen, Meng Liu, Robin A. A. Ince, Philippe G. Schyns and Rachael E. Jack

Human facial expressions are a powerful tool for social communication. In addition to the salient morphology of the human face (Tomasello et al., 2007; Vick et al., 2007), its powerful capacity to generate many complex dynamic face movement patterns enables it to convey myriad nuanced social messages. Though used frequently for everyday interactions, facial expressions have mainly been studied as displays of affect in nonhuman primates (Van Hooff, 1972; Waller & Micheletta, 2013) and humans (Ekman, 1994) and less so as a pragmatic tool for social communication. Thus, while multimodal accounts of communication continue to gain importance (e.g., Perniss, 2018; Holler & Levinson, 2019), including in discussions of language origins (Zlatev et al., 2017; Fröhlich et al., 2019), the contribution of facial expressions remains understudied. One major challenge to this endeavour is the sheer number and complexity of human facial expressions (Jack et al., 2018).
Here we showcase a novel data-driven psychophysical method that can navigate the complexity of facial expressions (Yu et al., 2012), and an information-theoretic framework (Ince et al., 2017) that can characterize the social information they convey. Specifically, we agnostically generate dynamic facial expressions, each composed of a random subset of individual facial movements called Action Units (AUs, e.g., Nose Wrinkler, Upper Lid Raiser; Ekman & Friesen, 1978), and ask participants to categorize them according to a given set of social messages – e.g., basic emotions such as ‘happy’ or ‘fear’ (Jack et al., 2012) or pragmatic messages such as ‘interested’ or ‘bored’ (Chen et al., 2015). We then build a statistical relationship between the dynamic face movements presented on each trial and the participant’s responses. This produces a precise, quantitative model of the dynamic face movement patterns that convey each social message to the participant. Analyses of the resulting facial expression models can reveal, for example, syntactical combinations of face movements (Jack et al., 2014), cross-cultural commonalities, variances, and accents that can facilitate or hinder cross-cultural communication (Jack & Schyns, 2017).
Using this data-driven method, we have examined human facial expressions as a tool for social communication and present new work addressing two key questions: Firstly, do facial expressions possess a similar iconic potential to manual gestures that can ground symbolic communication (Fay et al., 2014)? Does eye squinting express ‘small size’ akin to manual ‘pinching’ gestures (Woodin et al., 2020)? Our results suggest that they do – for example, facial movements that originally evolved to control sensory input (Susskind et al., 2008), such as narrowing the eyes in facial expressions of disgust, can be exapted to ground more abstract social signals for communication. Secondly, we examine the pragmatic role of specific face movements such as eyebrow raising and eye widening in supporting speech comprehension including emphasis, intonation, and modulating semantic content. Together, our results show specifically how human facial expressions serve a pragmatic function in social interaction, highlighting their central importance in theories of multimodal language origins. We anticipate that our data-driven method will facilitate further knowledge developments in this domain.


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