Autism models

Due to the fact that autism is a multi-faceted condition, with affected individuals exhibiting various difficulties and to varying degrees, many models of autism have been developed. For example, models centered on difficulties in theory of mind (ToM) posit that the social challenges experienced by autistic individuals are rooted in their limited ability to recognize mental states (e.g., beliefs, desires, thoughts, and intentions) in themselves and/or others [8, 27]. However, there is yet to be one clear model for autism stemming from ToM. This is due in part to the fact that ToM difficulties cannot account for all characteristics of autism, and to several studies showing that some autistic individuals pass various ToM tests [28–31]. Despite conflicting results from ToM testing, the extant research supports difficulties with knowing mental states in the self and in others as a core component of autism. Recently, difficulties with ToM have also been found in individuals with the BAP [24, 32, 33].

Another model for autism is the weak central coherence theory (WCC) [34], which posits that autistic individuals do not integrate pieces of information into an organized whole, unlike neurotypicals (NTs) who can do so readily. Past research suggests WCC may also impact individuals with BAP traits [11, 19]. WCC can, for example, account for why autistic individuals have difficulty understanding the subtleties of language. WCC argues that the failure to connect context with meaning is part of the reason why autistic individuals have a difficult time understanding the subtler aspects of communication such as “reading between the lines”. While early WCC described autism as a cognitive dysfunction characterized by a weakness in global processing, it has since evolved to recognize that autism is a cognitive style characterized by superior local processing [31]. The current model also proposes that when given explicit instructions, autistic individuals can process globally to find whole meaning in pieces of information [34, 35], however, this ability does not always extend to social skills [34, 36]. Recent work has also reported that individuals with BAP do not apply contextual information as readily as NT when attempting to recognize facial emotions [11].

A third model, social motivation theory (SMT) [37], proposes that the diminished experience of reward during social interactions causes autistic individuals to not attend to socially salient stimuli. Autistic individuals have different reward processing compared to NTs [38, 39], particularly in the case of social interactions [40–42]. There is evidence to suggest that, relative to NTs, autistic individuals have different amygdala responses to faces and eye contact [41, 43, 44], and that such responses result in reduced association between social interaction and positive reward [45]. Research also suggests that another contributing factor is oxytocin: autistic individuals have been found to have lower blood levels of oxytocin [46], and lower oxytocin levels were associated with reduced sensitivity to social cues [47]. Similarly, individuals high in autistic traits have been found to have increased social anhedonia [48, 49], reduced fixations to socially relevant stimuli [50, 51], and atypical social reward processing relative to NT [42]. Additionally, administration of oxytocin nasal sprays to individuals high in autistic traits has been found to improve their facial emotion recognition [52] and, relative to NT, atypical event-related potential responses to emotional faces [53].

SMT posits that the combined result of the above differences (i.e., reward processing, attention to socially salient stimuli, oxytocin) is low motivation to interact with others. This in turn leads to difficulties in social orienting, seeking and enjoying friendships, and engaging in behaviours that maintain social relationships, all of which have cascading effects on social development. Under SMT, autistic individuals might have the ability to recognize social cues, but do not prioritize them, are not spontaneously motivated by them, and do not recognize their usefulness in reaching relevant goals (e.g., understanding others’ thoughts, reactions, or intentions, predicting behaviour, fostering friendships, etc.). Indeed, previous research has shown that when social cues prove to be a useful tool for completing a task and thus motivation is high, autistic children will attend to those cues in a similar fashion to NT children [54].

In the current study, we draw on the central tenets of these three models—ToM, WCC, and SMT—to guide our inquiry into the potential effects of autistic traits on the processing of social situations.

Autistic traits and navigating social relationships

The challenges in detecting the thoughts and emotions of others put autistic and high autistic trait individuals at a disadvantage when it comes to responding to different social situations. Autistic individuals have been shown to score lower than NTs in measures that assess ToM [27], cf. [29–31, 55, 56], while both autistic and high trait individuals have been shown to have difficulty reading emotions [20, 57, 58]. Additionally, they may simultaneously broadcast inaccurate or misleading signals of their own. Autistic individuals and those with the BAP can have intense topics of interest [1, 12], and can let these topics dominate their conversations with others [1, 59, 60]. This enthusiasm for a preferred topic may come across as arrogant, or condescending towards others attempting to present their opinions [61–63], and it can be perceived by NTs as bragging about their particular knowledge [61]. Additionally, they tend to disengage when non-preferred topics are being discussed, and thus may appear to be self-centered, disinterested in others, or unable to demonstrate humility or show respect to others.

Overall, these difficulties may pose barriers to building harmonious relationships for individuals with autistic traits (formally diagnosed or exhibiting the BAP). Outside of their own interpersonal relationships, however, there is also reason to surmise that individuals with autistic traits may have difficulties in decoding and inferring the social dynamics between other individuals, a topic that we turn to below.

Social hierarchy and the ability of individuals with autistic traits to infer status relations

According to the Dominance-Prestige account [64], a prevailing theory of social status, humans possess two independent yet co-existing pathways for gaining social status: prestige and dominance. Under the dominance pathway, high status results from forceful tactics, such as aggressive or intimidating behaviour. These tactics are typically adopted to accumulate and control resources through the induction of fear (for review see [65–67]). Accordingly, subordinates succumb to and monitor the aggressor in order to avoid being on the receiving end of intimidating or violent behaviour, and to safeguard what resources they might currently possess. In short, status is taken and preserved by the domineering individual [68].

How might autistic traits affect one’s ability to infer and recognize dominance-submission asymmetries? Given that individuals with autistic traits have greater difficulty in reading social cues, they may be far less likely to recognize that a message is being sent, which messages are meant for whom, what is being implied, and perhaps most detrimental of all, the signs of imminent danger. Moreover, a person who does not appear to react with fear to cues of intimidation might also be seen as undermining the dominant-styled individual’s authority, and could therefore unknowingly invite additional and escalated dominant tactics. For these reasons, individuals high in autistic traits may experience challenges in the presence of dominant individuals.

In the second avenue to status, prestige, high status is achieved by displaying valuable knowledge, expertise, or skills [64], or for directing their efforts towards maximizing the overall well-being of the group [69]. In this model, lower status individuals track the success of the respected individuals in order to obtain knowledge or learn skills. Leadership through shared knowledge enhances the feelings of fairness in the group, which in turn minimizes conflict. It also tends to coordinate group members’ skills or knowledge for maximum effect. In prestige-based relations, individuals confer high status upon knowledgeable and skilled group members, who have incentives to display prosociality to further augment their status and group-wide cooperation [69]. In such a setting, displays of humility and respect for others are key components for being respected within the group [70], as is working with the group to enhance overall well-being. Individuals high in autistic traits may not recognize when or why others are being deferred to, the interactions that promote group-wide cooperation, and what behaviours might disrupt group goals or harmony, all of which could lead to their own falling in social standing within the group.

The current study

Through the lens of the Dominance-Prestige theory of social hierarchy, the current study examines the ability of individuals with varying levels of autistic traits to perceive existing status hierarchies as observers. Understanding what college-aged individuals with autistic traits can glean from social interactions could inform career counselling for those with social skills difficulties, and would be timely and beneficial for a group whose next phase in life is entering the workforce.

Here, guided by the aforementioned reasoning, we examine whether individuals with autistic traits may have an attenuated ability to accurately infer relative dominance and prestige relationships in a group context. The current study examines the “reading the room” skills of university students who vary on autistic traits, and is modeled after studies reported in Foulsham et al. [71] and Cheng et al. [65]. In Cheng et al. [65], individuals were filmed while engaging in a group task, after which they were asked to rate fellow group members on their social influence, prestige, and dominance. A second group of participants then viewed the video recorded clips of the original group, and rated the onscreen individuals on the same domains. Cheng et al. [65] found that the status, prestige, and dominance ratings from the original group members converged with the ratings supplied by the participants who watched their interactions via video. This demonstrated that outside observers can accurately assess the status, prestige, and dominance of others, even when those others are only on a subset of video footage. In Foulsham et al. [71], participants viewed the same video tape interactions as in Cheng et al. [65], and revealed that participants’ fixation count and dwell times on the high status people in the videos were greater than for that on the medium status individuals, who in turn were fixated on more frequently and for longer periods of time than the low status individuals.

The present research has two primary goals. First, we recruited participants with varying levels of autistic traits to determine if they differ in their ability to interpret verbal and social cues (such as speech content, as well as facial expressions, tone of voice, and eye contact) as an outside observer, and to accordingly assess the status conferred to people by their fellow group members. This extends prior work such as that reported in Foulsham et al. [71] and Cheng et al. [65].

Our participants were recruited from a university population and their autistic trait levels were measured by the 10-item Autism quotient (AQ-10) [72]. We did not screen for clinical levels of autism and, as such, we could not determine whether or not any of our participants had a formal diagnosis of autism. However, the AQ-10 has been used in previous research to identify high trait individuals [73–76], and because we were interested in the effects of autistic traits on “reading the room” skills, the AQ-10 was used to identify the high and low trait individuals among our participants. Given that autistic individuals and those with BAP traits may have difficulty decoding social cues [1, 24, 60], we predict that relative to people low in autistic traits, individuals high in autistic traits will be compromised in their ability to discriminate between prestige-styled or dominance-styled tactics employed to attain status, and that their assessment of the relative status conferred to people by their fellow group members will differ from the assessment made by individuals low in autistic traits.

All three models of autism, theory of mind (ToM), weak central coherence (WCC), and social motivation theory (SMT), support these predictions. According to ToM, high autistic trait individuals will not be able to infer the intentions behind tactics meant to either intimidate or persuade fellow group members. Additionally, they should not be able to deduce the group status, prestige, and dominance conferred onto an individual because they have limited “I think she thinks” abilities. According to WCC, individuals with autistic traits will have difficulty extrapolating a person’s social standing because they will not piece together relevant information (e.g., getting talked over or cut-off, group members laughing at their jokes, etc.), and measure such information against the context of all six video clips. In terms of SMT, even if individuals with autistic traits are capable of recognizing social cues, they might not be motivated to attend to these cues, or prioritize them in a manner that helps deduce a person’s group standing.

Second, the current study will test whether individuals high in autistic traits differ from low trait individuals in the way that they observe dynamic social interactions, vis-a-vis their eye movement behaviour. Because the extant research in this area is mixed for adolescents and adults [77–79], cf. [80], we make no firm predictions regarding whether or not young adults high in autistic traits will behave differently from low trait individuals in the way that they visually monitor the social interactions. However, it follows that if individuals high in autistic traits rate people in a video differently than low trait individuals on social status, prestige, and/or dominance, then those higher in autistic traits may also look at those individuals differently. Thus, we assessed how individuals are looked at with regard to their body, head, and eyes. Indeed, looks to the eye regions of those in the videos are of particular interest. Although research shows that autistic children make less eye contact [45, 81, 82], results become mixed as the age of participants increases [44, 83, 84]. Nevertheless, the general weight of empirical evidence supports the prediction that high autistic trait individuals will look less to the eyes than low autistic trait individuals do.

Participants

Using the Fabs package [85] in RStudio [86], it was determined that 41 participants were needed to achieve a power of .80 at a significance level of .05. The nature of recruiting from the Human Subject Pool at the University of British Columbia resulted in our exceeding this target, with 64 participants taking part in exchange for extra credit. The study was approved by the university’s Behavioural Research Ethics Board.

Participants’ individual 10-item Autism Quotient (AQ-10) [72] scores, and the mean AQ-10 score (M = 4.94, SD = 2.57) for all responses to the survey, were calculated. No participants received an AQ-10 score of 0 or 10. Results from 10 individuals were not used in the analysis because they did not complete the AQ-10, or because their eyes could not be calibrated properly, leaving 54 participants in total. As an individual who scores 6 or higher on the AQ-10 is referred for a full diagnostic assessment [72], this became our high scorers group (HAQ). Twenty-five (16 women, 8 men, 1 other) of our participants met this cut-off with scores of either 6 (n = 1), 7 (n = 14), 8 (n = 7), or 9 (n = 3). Twenty-nine (21 women, 8 men) had low scores (LAQ) of either 1 (n = 5), and 2 (n = 7), 3 (n = 8), 4 (n = 8), or 5 (n = 1). In the absence of a diagnosis, however, we take a conservative position and assume that our study is composed of two groups of neurotypical participants with “high traits” and “low traits”. Note that although our participants were not screened for autism, the AQ-10 scores for this group of high-scorers (N = 25; 46%) met the cut-off for a referral for a full diagnostic assessment.

Stimuli

The stimuli were the same as the videos used by Foulsham et al. [71] and Cheng et al. [65]. These videos were created using unconcealed video cameras while six unacquainted undergraduates completed a group decision-making task called the Lost on the Moon task [87]. Each group was given a list of 15 items (e.g., signal flare, oxygen tank, pistol), and asked, “Which items would your group need to survive if marooned on the moon?” The groups were then seated around a table (three participants on each side), and given 20 minutes to discuss and rank the items. To incentivize active engagement, participants were told a monetary bonus would be given to the group whose rankings were closest to the correct answer.

There were four sets of videos, each containing six 20-second clips featuring three participants (“targets”) in the group sitting on one side of the table (Fig 1). These video clips were selected because they captured moments of debate, negotiation, persuasion, or conflict that led to a group decision. In short, the clips featured instances where tactics of dominance or prestige were most actively utilized.

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Fig 1. Participants’ view of targets in the video clips.

Note. Example of the view of targets presented to participants (only the schematic is presented here to protect the privacy of the targets). After viewing the clips, and before completing the Dominance and Prestige Peer Rating Scales [88] for each target, participants were given a screenshot from the videos that identified the targets as A, B, or C. Adapted from “Gaze Allocation in a Dynamic Situation: Effects of Social Status and Speaking,” by Foulsham, et al., 2010. Cognition, 117(3), p. 319–331.

https://doi.org/10.1371/journal.pone.0282310.g001

The videos were digital film files with dimensions of 1024 by 768 pixels, and a frame rate of 30 fps. Because the aspect ratio of the original film was 16:9, the clips were presented onscreen with a black border above and below their image. Sound was played through wired headphones.

The choice of video set for each participant was randomized. To increase the difficulty of this status-inference task, the order of the 6 video clips watched was also randomized (i.e., not in chronological order) for a given participant.

Measures

Equipment

Stimuli were controlled by the SensoMotoric Instruments (SMI) Experiment Center 3.4 and displayed on the SMI-issued 22” Dell color monitor. Eye tracking was done with the SMI iViewX 2.8 Red to synchronize data collection with the videos being played through Experiment Center. Pupil data was taken from the left eye at 500 Hz, and fixations were defined using velocity (30°/s) and acceleration thresholds (8000°/s). While the SMI equipment does not use a head rest to standardize participants’ distance from the screen, its program provides clear, onscreen cueing to ensure participants are seated within a target range to the sensors. The average distance from the screen for participants was 53 cm. The DPRRS was completed on a Dell Latitude E6530 laptop with a 15.6” display.

Procedure

All participants provided written consent prior to the start of the experiment. Immediately following that, the experiment began with on-screen instructions that explained the format of the study (e.g., a series of videos will be followed by questionnaires). Per the original studies, participants were instructed to watch the videos and “think about who you would want to work with on a subsequent task.” This directive was part of the instructions given to observers of the video clips in order to maximize the feeling of being in the room with the targets. Before each clip was shown, a 5-point eye calibration was performed. The volume was adjusted according to participant preference.

After watching all six clips, participants then completed the DPRRS on the laptop. As a memory prompt for the questionnaires, participants were given a printed screen shot of the targets they had watched, with each target identified as “A”, “B”, or “C”. The items within each questionnaire were randomized, as was the order of the targets (e.g., some participants answered randomized questions related to target A first, others answered randomized questions related to target C first, etc.).

Data analysis

The R packages afex [90], rstatix [91], and emmeans [92] were used for analysis and all follow-up tests. Figures were created with ggplot 2 [93]. For all anovas, where violations of sphericity occurred, Greenhouse-Geiser adjustments to the degrees of freedom were made.

Participants rated each person (“target”) in the video on three domains: status, prestige, and dominance. From these ratings, we then calculated the overall score for each domain to determine the relative rank (‘high’, ‘medium’, or ‘low’) of each target. Where a given participant’s overall score for a domain resulted in two targets with the same domain score, the participant’s AQ-10 group (high scorers vs. low scorers on the AQ-10) consensus was used to resolve those ties. As each participant’s AQ-10 score was used for group assignment, we reasoned that it was consistent to apply group membership to resolve ties. Note, however, that use of group assignment to resolve ties was necessary for less than 4% of the ratings.

Using participant domain rankings, fixation data were organized such that if a participant had given a specific target the highest status rating, for example, all fixations to that target were marked as fixations to the “high” status target. The same was done for the low and medium ranked targets. Proportions to each area of interest (AOI) were calculated per participant, per trial. This was done for all participants across all four video batches.

Next analyzed were the fixations to the target in the video clips. An AOI was created for each target. Due to the fact that targets often moved during the video clips, dynamic, rather than fixed, full body AOIs (head, eyes, and torso AOIs combined into one) were created to follow each target’s movements. All full body AOIs in each video clip were measured as a percentage (M = 10.2, SD = .42) of the total onscreen image, and were not significantly different across all targets in all video clips, F(2, 6) = .43. That is, each target’s full body AOI occupied a similar percentage of the onscreen image. A fixation that landed within a full body AOI of a target was assigned to that target. Fixations to the three targets’ full body AOIs in each video clip amounted to 82% of the fixations. Given that the full body AOIs amounted to roughly 30% (M = 30.5, SD = 2.53) of the total screen image, the data suggest the targets attracted more attention than the background objects.

Ratings

Table 1 presents the social influence (status), prestige, and dominance ratings made by HAQ and LAQ. Statistical results for each domain are outlined below.

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Table 1. LAQ and HAQ participants’ status-relevant ratings of targets.

https://doi.org/10.1371/journal.pone.0282310.t001

Do autistic traits predict differences in general viewing patterns?

Fig 2 presents the fixation data for the AQ-10 groups as a function of AOI and target status. Fixation results for each domain are outlined below. All background fixations were removed from this analysis, and the resulting proportion data represents the proportion of fixations to each AOI region out of the total number of fixations to the people in the videos, calculated separately for the low and high AQ-10 groups.

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Fig 2. Proportions of fixations to body, head, and eyes.

Note. Proportions of fixations to the areas of interest (AOIs) for Status (green triangles), Prestige (orange circles), and Dominance (purple squares) by AQ-10 group.

https://doi.org/10.1371/journal.pone.0282310.g002

The average number of fixations for each 20 second video clip was 46.82 fixations (SD = 9.86). A Welch’s t-test was conducted to compare the effect of AQ-10 scores on the number of fixations and found no significant effect, t(50) = 0.66, p = .500, d = .18, between low scorers (M = 47.7, SD = 9.29) and high scorers (M = 45.9, SD = 10.54). These results indicate that the high and low AQ scorers (HAQ and LAQ) made a similar number of fixations while viewing the clips.

Fixations to eyes.

Table 2 presents the fixation data to the eye AOIs for the AQ-10 groups as a function of target domain ratings.

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Table 2. Fixations to the eyes of low, medium, and high ranked targets.

https://doi.org/10.1371/journal.pone.0282310.t002

To discern if HAQ (M = 0.031, SEM = 0.005) and LAQ (M = 0.034, SEM = 0.005) looked at the eyes of the targets differently, we conducted a Welch’s t-test between the two groups and found no significant difference: t(160) = 0.41, p < 1.