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Compared with the typical population, people with autism spectrum disorder often have difficulty recognising facial expressions of emotion. However, there also exists substantial variation in expression recognition within the typical population. It is important to understand this variation because difficulty in expression recognition is associated with negative social outcomes. One approach to understanding variation in expression recognition in the typical and autistic population is to measure ‘autistic traits’, which are personality traits relating to autism in the typical population, and their association with facial expression recognition. Autistic traits can be measured in three domains: social skills, social communication, and attention to detail. Our previous research suggests that expression recognition difficulty is primarily related to the two social domains of autistic traits, and minimally with attention to detail. This finding suggests that variation in these domains might also underlie difficulty in expression recognition in autistic people. Previous research further suggests that where associations between autistic traits and expression recognition do exist, these associations are mediated by a personality trait, alexithymia, which describes difficulty recognising one’s own emotions. This finding suggests that difficulty recognising expressions in autistic and typical people should not be fully attributed to autistic traits per se, but should be understood to substantially reflect co-occuring alexithymia. However, previous research is limited in that it has tended to use stereotypical, posed depictions of facial expressions which are often perceived as faked by observers. Evidence suggests observers respond differently to facial expressions which are perceived as posed versus those perceived as genuine. For example, genuine smiles may be associated with a stronger experience of social reward, and elicit different facial mimicry responses, in typical observers. Genuine and posed expressions also differ in physical characteristics such as symmetry. Finally, interpretation of posed expressions requires acceptance of a social façade, where interpretation of genuine expressions does not. Based on the differences between genuine and posed expressions, it is not clear whether the above findings for posed expressions would be observed in the context of naturalistic facial expression stimuli perceived as reflecting genuine emotion. We sought to investigate this by measuring associations between autistic traits, alexithymia, and facial expression recognition for both posed and naturalistic expressions in the typical population.

Trustworthiness is a primary dimension of facial first impressions (Oosterhof & Todorov, 2008; Sutherland et al., 2013) and we commonly make decisions about who to trust based on how trustworthy their face looks. While impressions of facial trustworthiness are powerful, they can be inaccurate, and so, adults also consider their social partners’ trait trustworthiness (i.e. how trustworthily they actually behave) when choosing who to trust. In fact, research using economic trust games suggest that adults’ trust in another person is usually the product of an interaction between how trustworthy that person’s face looks and how trustworthily they behave (Chang, Doll, van ’t Wout, Frank, & Sanfey, 2010; Hooper et al., 2018). However, it is not yet known whether a similar interaction exists in children’s trust in others. Therefore, this study will investigate the extent to which children’s trust behavior is influenced by their social partners’ facial trustworthiness and trait trustworthiness or whether their trust decisions are based instead on an interaction between these two trust cues, as they are in adults. We will use a repeated-interactions economic trust game where participants will indicate how much of their endowed assets they would like to invest with partners, who vary both in the trustworthiness of their facial appearance and their behavior – some partners will reciprocate participants’ investment behavior (fair behavior), whereas other partners will not (unfair behavior). This will allow us to determine whether children show adult-like impressions of trustworthiness, or whether trust impressions and behavior develop across childhood.

Romina Palermo (University of Western Australia, UWA), Linda Jeffery (UWA) and Brad Duchaine (Dartmouth) were awarded an Australian Research Council (ARC) Discovery Project award to investigate why face identification skills improve over childhood (DP140101743). Participants were 203 children aged 5 to 10 years, tested with a longitudinal design over three years. Here, we list the tests and measures that were included in the test battery, and note which papers the data has been used for.

Historically, our understanding of emotion perception has centred around the human face (Ekman, 1989, 1992b; Ekman & Oster, 1979), with a large body of research across psychology and neuroscience dedicated to understanding emotions inferred from faces (Adolphs, 2002; Barrett et al., 2019; Fernández-Dols & Russell, 2017). Accordingly, there are many databases of disembodied faces shown in isolation, on a blank background without any other context and most research has investigated how people respond to the face alone. However, when we see another person in real life, their face is a small part of our perception of them. For instance, we see how they position their body as well as the scene surrounding them, which may include other people. The fact that this information is missing is important as context plays a major role in the interpretation of faces (Aviezer et al., 2011; Barrett et al., 2011; Deffler et al., 2015; Kret et al., 2013). Fortunately, more research into the effects of stimuli external to the face (herein described as context) on inferred emotional states has emerged in recent years. The need now is to synthesise this evidence. The accumulating evidence almost invariably demonstrates that context influences our perception of emotional faces and that faces influence our perception of emotional contexts (Kret et al., 2013; Lecker et al., 2020). We refer to these as face-context interaction effects. The bidirectional nature of face-context interaction effects evidences that our visual perception of emotion is neither static (i.e., it is not determined by a specific set of cues in a face or a scene) nor is it solely reliant on a single visual category alone. Rather, the available emotional information from all visual cues is used to construct our emotion perception. As new and/or more information is made available to us, we update our prediction of (and confidence in) the likely emotional state of the person (Saxe & Houlihan, 2017). Therefore, emotion perception is contextual — our perception is constructed from the information available across the whole scene, including faces, bodies, the visual scene and other people. For the purposes of this review, context will only incorporate visual details, with other domains (e.g., auditory or learned affective processes) falling outside of its scope. Visual context is the most extensively researched modality of emotion perception which makes visual context a good starting point for a meta-analysis. In addition, visual context is comparable across the same modality to faces, limiting potential confounding variables. Faces influence the perception of emotion in bodies and visual scenes, and vice versa (Lecker et al., 2020; Reschke & Walle, 2021). However, little is known about precisely how much each influences the other, and what factors moderate the size of these effects. Investigating how much faces and context influence each other will provide a better understanding of the unique and shared contributions of each in visual emotion perception. To the best of our knowledge, there have only been two previous reviews of face-context interactions in emotion perception, one by Wieser and Brosch (2012) and another by Gendron et al. (2013). Although these reviews are highly cited, they are narrative reviews that provide a general summary of the literature at the time, without using a systematic search nor explicit and stringent inclusion/exclusion criteria. In addition, by nature of being narrative reviews, they also do not statistically evidence the relationship between faces and context. The proposed review will address these gaps by: • Updating the evidence to include the past decade. Our initial title search indicates this update will more than double the research pool. Nearly 60% of all PsycInfo titles that met our search criteria (Appendix A) were published post-2012. • Extracting data for meta-analysis. Extracting data from each publication and calculating effect sizes will allow for more accurate estimates of the true effects of face-context interactions. • Coding for variables that are theorised to moderate the size of face-context interaction effects. This review will also provide evidence for current debates in emotion perception theory. At present, there is considerable theoretical debate about whether emotion perception is best explained by the common view or constructionist theory. The long standing common view of emotion perception suggests that facial expressions provide universal and standard signals which allow us to interpret and understand the emotion another person is experiencing (Ekman, 1989, 1992b). In contrast, the constructionist view of emotion posits that our perception of emotion is predictive. That is, the emotion we perceive is a prediction of the most likely emotion from an array of various possible emotions based on the available information and our previous experience (Barrett, 2017). The constructionist view seems to align most with face-context interactions. As more information is available, that information is incorporated into our working perception of the emotional experience of others. If the face and context information match emotionally, there would be fewer feasible emotional states and so we would likely be more accurate and quicker in making our judgement. If the two information sources do not match emotionally, there would be more feasible emotional states, and we would therefore be less accurate and slower in our judgement (Barrett, 2017). Investigating face-context interactions provides an opportunity to better understand the relationship between faces and context, as well as to explore if their interaction provides evidence in support of the constructionist view of emotion perception.

Trustworthiness is a primary dimension of facial first impressions and we are able to form an impression about how trustworthy someone is after just 33 ms of exposure. Our first impressions can strongly influence social interaction, and can even impact financial and court decisions. These face-based trust impressions are thought to emerge early – even seven month old infants are sensitive to variations in facial trustworthiness, and trust impressions mature across childhood, reflecting adult-like patterns by 10 to 13 years. Recently, Swe et al., (2020) showed that electrophysical cortical activity is triggered by variation in visually presented facial trustworthiness cues in adults, using the fast periodic visual stimulation (FPVS) electroencephalogram (EEG) technique. FPVS is an objective measure that provides high signal to noise ratio data and allows trust impressions to be examined implicitly and non-verbally. These characteristics make it a valuable method of examining early neural activity underlying children’s trust impressions from faces. Therefore, this study will investigate whether eight to nine year old children can percieve trustworthiness implicitly by examining their neural discrimination response to facial trustworthiness. Specifically, we will use an oddball paradigm where trustworthy faces will be shown at a base frequency of 6 Hz, followed by an oddball untrustworthy face at 1 Hz (and vice versa for the trustworthy oddball face trials; following Swe et al 2020). If children’s visual cortex is sensitive to the change in facial trustworthiness, there will be an increase in amplitude of neural activity at the oddball frequency (1 Hz), and its harmonics. We will include both an upright and inverted face stimuli condition to control for low-level information. We will also collect adults’ neural discrimination responses to facial trustworthiness, and compare child and adult groups’ responses to examine any developmental trends.

There has been contention in the previous research regarding the association between face processing abilities (e.g. face detection, face perception, face memory and expression recognition) and cognitive abilities (i.e. intelligence). Some would contend that face processing abilities are unique and distinct from other cognitive abilities (Shakeshaft & Plomin, 2015; Wilmer et al., 2010). Whilst others have found moderate associations between these abilities (Connolly et al., 2019; Gignac et al., 2016; Hildebrandt et al., 2011; Schlegel et al., 2019). It would be greatly beneficial to explore the shared variance between face processing abilities and cognitive abilities, not only in improving our understanding of these abilities, but also the possibility of conceptualising face processing abilities within the contemporary models of cognitive ability, such as the Cattell-Horn-Carroll (CHC) model (Meyer et al., 2021). The majority of the recent research on the association between cognitive abilities and face identity processing has centred around face memory. Face memory is the ability to recognise a face that has previously been seen, a vital skill for socialisation (Oruc et al., 2019). Some studies have found a moderate, positive association between short-term face memory and other cognitive abilities, including general intelligence (Gignac et al., 2016; Hildebrandt et al., 2011; Shakeshaft & Plomin, 2015). However, other studies have failed to report a significant association (Davis et al., 2011; Palermo, Connor, Davis, Irons, & McKone, 2013; Peterson & Miller, 2012). The mixed results reported in the literature may be due to several factors, including small and range restricted samples (e.g., Davis et al., 2011; Peterson & Miller, 2012). Additionally, several studies measured cognitive abilities with only one or two subtests, which would only be described as ‘poor’ to ‘fair’ intelligence measurement, according to Gignac and Bate’s (2017) guidelines. Therefore, it is important to conduct a study that includes a large, representative sample with a balanced number of good quality cognitive abilities measures across the four core dimensions of cognitive ability: crystallised intelligence, visual/fluid intelligence, memory span, and processing speed. In comparison to face memory, there has been very minimal research conducted on face detection and cognitive abilities. Face detection is the process of locating a face within a visual scene. This ability is an essential prerequisite for higher-order face-specific processes, such as face memory (Garrido et al., 2008; Verhallen et al., 2017). To the authors’ knowledge, only one study has investigated the association between intelligence and face detection (Vigen et al., 1982). They found a moderate, positive correlation (r = .25) between the WAIS-R and a commonly used face detection task, the Mooney task. Therefore, it remains unknown the degree to which individual differences in face detection arise from a process specific to faces versus individual differences in visual processing more generally. On the one hand, there is neurophysiological evidence to suggest that face detection may be, at least to some degree, unique. For example, studies have shown that detection of a face in the visual space activates the highly face-selective fusiform face area, compared to when a face is not detected (Andrews & Schluppeck, 2004; Kriegeskorte et al., 2007; McKeeff & Tong, 2007). Whilst on the other hand face detection tasks are remarkably similar to tasks of visual closure, and an association between face detection and visual closure has been established (Walker et al., 2022). Therefore, an important aspect of this current study is to examine the association between face detection, other face processing abilities, specific cognitive abilities, and general cognitive ability. Face perception is considered a higher-level process than face detection (Verhallen et al., 2017). It’s the ability to either discriminated or individualised a person’s identity when viewing faces (Oruc et al., 2019). The ability to tell faces apart or individualise a face is important for day-to-day life. For example, being able to scan faces within a crowd and see each face as being distinct from each other. Previous research has shown a positive association between face perception and cognitive abilities (Connolly et al., 2019; Hildebrandt et al., 2011). However, only a small amount of empirical research has correlated individual differences in face perception with other cognitive ability dimensions, whether general or narrow in nature. This study aims to provide further evidence of the association between face perception and general cognitive ability, and specific cognitive abilities. Another very important face processing ability, especially for socialisation, is expression recognition, also known as face emotion recognition. The ability to accurately and efficiently recognise facial expressions is essential for effective interpersonal interaction and social cognition (Fox & Zougkou, 2011). Empirically, individual differences in expression recognition have been linked to cognitive abilities (Borod et al., 2000; Connolly et al., 2019). Schlegel et al. (2019) conducted a meta-analysis on the association between expression recognition and cognitive abilities. They found that general cognitive ability is positively correlated with expression recognition (r = .19). They also found that specific cognitive abilities (crystallized, fluid, spatial, memory, information processing speed and efficiency) also positively correlated with expression recognition to a similar extent. Schlegel et al. (2019) concluded that the effect size reported in the meta-analysis may be on the lower end of the true associations, due to psychometric limitations and limited complexity of the measures of expression recognition ability. Therefore, it is important that the association between expression recognition and cognitive abilities be examined when the measures have good psychometric properties and follow all steps outlined in recent test development guidelines (e.g. DeVellis, 2017). In doing so, the true association between expression recognition and cognitive abilities could be observed. It's also important to investigate the inter-association between the face processing abilities. Verhallen et al. (2017) and McCaffery et al. (2018) found highly significant correlations between their measures of face detection, face perception, and face memory. The correlations between measures differed in magnitude with shared variance ranging between 4% and 23%. Both studies concluded that a general ability of face processing may exist and underlies performance with a wide range of face tasks. Similarly, research into expression recognition and the other face processing abilities has seen considerable correlations between measures (Gignac et al., 2020; Hildebrandt et al., 2015; Palermo et al., 2013). Therefore, this study will aim to investigate the unique construct related variance associated with each of the four face processing abilities. One final aspect of this study is to investigate the shared variance between trait-autism and face processing abilities. To the authors’ knowledge, no individual difference study has yet examined all four dimensions of face processing (face detection, face perception, face memory and expression recognition) and trait-autism, simultaneously. There is evidence to suggest that higher trait-autism is associated with poor face processing abilities (Bothe et al., 2019; Davis et al., 2017; Halliday et al., 2014). Autistic-like traits (also known as the Broader Autism Phenotype) refers to milder forms of traits seen in neuro-typical individuals that are associated with the Autism Spectrum Disorder (ASD), such as communication deficits, restricted activities and poorer executive functioning (Deisinger, 2015). Specifically, previous research suggests that nonverbal communication, one dimension of trait-autism, is most likely to be associated with face processing ability compared to the other dimensions. For example, Bothe et al. (2019) found that nonverbal communication was negatively associated with discriminating and labelling emotions (r = -.24 & r = -.29, respectively). Davis et al. (2017) and Lewis et al. (2018) found that nonverbal communication and social aspects of trait-autism are negatively associated with face identity recognition. Therefore, it is predicted that the communication dimension of trait-autism, and potentially the social dimensions of trait-autism, is more likely to be negatively associated with face processing abilities. In conclusion, this study will provide the opportunity to determine the unique effects associated with each of the four face processing abilities, with respect to the dimensions of the trait-autism and cognitive abilities, in neuro-typical adults. In addition to enhancing our understanding of face processing, trait-autism, and cognitive abilities; the observation of one or more unique effects would suggest the possible benefits of experimental manipulation (i.e., face processing training may help ameliorate certain trait-autism behaviours). It will also be noted that cognitive abilities may be expected to associate positively with face processing ability, but unlikely with the trait-autism, thus, controlling for the effects of cognitive abilities on face processing abilities may increase the observed effects between face processing abilities and trait-autism (i.e., a suppressor effect).