Outcomes of real-world social interaction for autistic adults paired with autistic compared to typically developing partners

Abstract

English

Differences in social communication and interaction styles between autistic and typically developing have been studied in isolation and not in the context of real-world social interaction. The current study addresses this “blind spot” by examining whether real-world social interaction quality for autistic adults differs when interacting with typically developing relative to autistic partners. Participants (67 autism spectrum disorder, 58 typically developing) were assigned to one of three dyadic partnerships (autism–autism: n = 22; typically developing–typically developing: n = 23; autism–typically developing: n = 25; 55 complete dyads, 15 partial dyads) in which they completed a 5-min unstructured conversation with an unfamiliar person and then assessed the quality of the interaction and their impressions of their partner. Although autistic adults were rated as more awkward, less attractive, and less socially warm than typically developing adults by both typically developing and autistic partners, only typically developing adults expressed greater interest in future interactions with typically developing relative to autistic partners. In contrast, autistic participants trended toward an interaction preference for other autistic adults and reported disclosing more about themselves to autistic compared to typically developing partners. These results suggest that social affiliation may increase for autistic adults when partnered with other autistic people, and support reframing social interaction difficulties in autism as a relational rather than an individual impairment.

Keywords

adults autism spectrum disorder double empathy first impressions social interaction

Introduction

Poor functional and interpersonal outcomes for autistic adults are common (Magiati et al., 2014; Seltzer et al., 2004; for review, see Levy & Perry, 2011). Autistic adults, including those without intellectual disability, often struggle to develop satisfying personal relationships, maintain employment commensurate with their abilities, and achieve high quality of life (Howlin et al., 2004; Jennes-Coussens et al., 2006; Orsmond et al., 2004; Taylor et al., 2015). Treatment programs seeking to improve these outcomes through social skills training have been somewhat effective at increasing social knowledge and improving social cognitive task performance (Bishop-Fitzpatrick et al., 2014; Gates et al., 2017; Koegel et al., 2016), but effects tend to be small (Bishop-Fitzpatrick et al., 2014; Gates et al., 2017; Palmen et al., 2010; White et al., 2007) and generally fail to translate to real-world outcomes (e.g. increased rates of employment, friendships; Bottema-Beutel et al., 2018; Rao et al., 2008; Turner-Brown et al., 2008).

There has not yet been a systematic evaluation of why the social benefits of these programs rarely extend to life outcomes, but one possibility is that they often are developed based upon a research literature that has largely studied social interaction difficulties in autism spectrum disorder (ASD) without consideration of the social context in which interactive behavior occurs. Indeed, social interaction (i.e. the dynamic interchange by two or more people) has even been called a “blind spot” in the autism literature (De Jaegher, 2013, p. 14), with existing studies of social interaction in autism being limited in a number of ways. First, many are simply a count of the number and type of interactions autistic people have, primarily relying on informant reports (e.g. Billstedt et al., 2005; Howlin et al., 2004). Although such information is useful, they do not address factors contributing to interaction quality, nor do they provide insight about how autistic people themselves perceive and respond to others within an interaction context. Second, many studies have focused on social interaction in autistic children (e.g. Bauminger et al., 2008; Macintosh & Dissanayake, 2006) whose social expectations, demands, and goals differ from those of adults (e.g. pursuing adult friendships and romantic relationships, interacting with co-workers and employers, etc.). Finally, and perhaps most importantly, studies of social interaction in autism have overwhelmingly focused on the abilities and behaviors of the autistic person rather than on interaction itself. Social interaction by definition consists of more than one person (Sasson et al., 2017), and involves a dynamic interplay between two partners in which each person influences and is influenced by the verbal and non-verbal behavior of their social partner (De Jaegher, 2013; Hehman et al., 2017). Thus, although individual characteristics and abilities have been extensively studied in the autism literature, little work has examined relational factors, or the processes by which social difficulties in autism may result not simply from individual characteristics, but also from a lack of fit between those characteristics and a particular social environment (Hutchison, 1995; Milton, 2013; Sasson et al., 2017). From this perspective, social disability is not “of the individual” but rather of a social context (Oliver, 1999) which may be ameliorated not just by a focus on the individual but through social accommodation and environmental modification. For autistic adults, this necessitates understanding social difficulties in terms of relational dynamics, including the role of the social partner within the context of real-world social interactions.

A recent theoretical framework for investigating relational contributions to social difficulties in autism is the double empathy problem (DEP; Milton, 2012). DEP posits a communication gap between autistic and typically developing (TD) people in which differences in social expression and understanding present barriers for cross-diagnostic interaction and connection. Empirical support for the DEP comes not only from the countless studies demonstrating that autistic adults often struggle to infer the thoughts and emotions of TD adults (Mathersul et al., 2013; Sasson et al., 2011; Uljarevic & Hamilton, 2013), but more recent evidence showing that TD adults have similar difficulty interpreting emotional and mentalizing social cues conveyed by autistic adults (Alkhaldi et al., 2019; Edey et al., 2016; Sheppard et al., 2016) and anticipate more social difficulty interacting with autistic adults relative to other TD adults (Gernsbacher et al., 2017). Furthermore, TD adults tend to form more negative first impressions of autistic adults than TD controls, and these negative impressions are associated with a reluctance to pursue social interaction with them (Morrison et al., 2019a; Sasson et al., 2017; Sasson & Morrison, 2019). These negative impressions are likely driven by different social presentation styles of autistic adults that are perceived by TD raters as atypical and less natural (Faso et al., 2015; Hubbard et al., 2017; Sasson et al., 2017). Ultimately, such judgments by TD individuals may reduce social opportunities for autistic adults and affect social interaction quality when they do occur (Edey et al., 2016; Sheppard et al., 2016).

Although these studies suggest important relational contributors to social outcomes in autism, each was conducted in laboratory settings using experimental stimuli like video recordings and none examined actual real-world social interaction between autistic and TD people. Laboratory studies offer strong experimental control but cannot capture the dynamic processes occurring when two individuals interact. Several recent studies have begun to examine real-time interactions between autistic and TD individuals (Stevanovic et al., 2017; Usher et al., 2015, 2018), and in some respects, interactions between the two dyad types (i.e. ASD–TD and TD–TD) seem similar. TD–TD and ASD–TD dyads did not differ in how much partners liked one another after an unstructured conversation (Usher et al., 2018) or in patterns of complementary social warmth and dominance (Stevanovic et al., 2017). Usher et al. (2018), however, found a trend toward autistic adolescents being more accurate in predicting how their TD partners would like them, suggesting that TD individuals may have particular difficulty evaluating their autistic partners’ perceptions. Stevanovic et al. (2017) also found evidence for differing patterns in how dyad types respond to affiliative and dominance behaviors over the course of a conversation, which is consistent with the DEP and may suggest that conversations between autistic and TD adults may differ in quality and outcome (e.g. forming a friendship, being hired for a job) from TD–TD conversations.

Importantly, these studies did not compare how social dynamics may differ within ASD–ASD dyads relative to ASD–TD dyads, and other recent research suggests potentially unique benefits to social interaction quality when autistic adults interact with other autistic people. Autistic adults self-reported that they would be more socially competent when having an interaction with another autistic individual compared to a TD individual (Gernsbacher et al., 2017), which suggests that each partner’s diagnosis and knowledge of their partner’s diagnosis may influence social behaviors during an interaction. In addition, within the general population, similarity in levels of social autistic traits, such as social reticence, has been found to predict positive social relationship outcomes (Faso et al., 2016; Wainer, Block, Donnellan, & Ingersoll, 2013). Not only were TD friends’ self-reported autistic traits moderately correlated, but college roommates matched on social autistic traits were more satisfied with their relationship quality compared to roommates mismatched on the same traits (Faso et al., 2016; Wainer et al., 2013). These results suggest that when partners match on their social autistic traits—either both high or both low—positive relationship development is more likely to occur. Such mechanisms may also be present in autistic adults’ interactions, where social interaction may be predicated on an autistic adult’s degree of fit with their partner’s traits and behaviors.

The current study examined whether real-time social interaction quality and impression formation differs for autistic adults when interacting with other autistic adults compared to TD adults. Three groups of unfamiliar dyads (ASD–ASD, ASD–TD, and TD–TD) completed an unstructured interaction and afterward evaluated aspects of the quality of the interaction (e.g. how much they disclosed about themselves), their impressions of their partner, their interest in future interaction with their partner, and their closeness to their partner after the interaction. By doing so, this study sought to replicate prior first impressions work in autism and extend beyond it by testing how TD adults form first impressions of autistic adults in real-time interaction. We predicted that TD adults would rate lower interaction quality, lower closeness to their partner, and form less favorable impressions of autistic relative to TD partners. This study was also designed to offer a direct empirical test of the DEP by comparing social interaction outcomes for autistic adults when having autistic relative to TD conversation partners. If social impairment is intrinsic to the autistic person, social interaction between two autistic individuals should be poorer than between an autistic and TD person given that the exchange would consist of twice as many people with social impairment. In contrast, consistent with a DEP framework, we predicted that autistic partners would evaluate interactions with other autistic adults more positively than those with TD partners. Such findings would suggest that social interaction difficulties in autism are not an absolute or inherent characteristic of the individual, but rather are a relational characteristic that can vary depending on the degree of fit between the person and the social environment.

Methods

Participants

Three compositions of conversational dyads were assessed: TD–TD, ASD–ASD, and ASD–TD. The Actor–Partner Interdependence Model (APIM) Power Analysis Shiny Application (Ackerman et al., 2016) specified that 132 participants comprising 66 dyads total (22 dyads of each type) were needed to detect medium-size effects (Cohen’s d for actor and partner effects of 0.50) with 80% power assuming no correlation between actor and partner variables and a moderate (0.50) correlation between the errors of the outcome variables. All participants were males between the ages of 18 and 45. Participation was restricted to males because of the disproportionate male ratio in autism (Fombonne, 2009), and because including females would introduce complicating gender dynamics, be underpowered to detect gender differences in social interaction processes, and dilute effects for males. All autistic participants scored above the clinical threshold for ASD on the Autism Diagnostic Observation Schedule (ADOS-II; Lord et al., 2012), and only autistic participants with intelligence quotient (IQ) scores over 90 on the Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999) were included to aid in matching TD and ASD groups on IQ. Both the ADOS-II and WASI were completed in a separate clinical intake session prior to the dyadic interaction. TD participants had no current ASD diagnosis or any history of psychiatric illness, developmental disabilities, or an autistic first degree relative. In total, 140 individuals participated (69 ASD, 71 TD) in 22 ASD–ASD dyads, 23 TD–TD dyads, and 25 ASD–TD dyads. However, 15 individual participants were excluded from analyses for not meeting inclusion criteria: 1 autistic participant was dropped from an ASD–ASD dyad, 8 (1 autistic, 7 TD) participants were dropped from ASD–TD dyads, and 6 TD participants were dropped from TD–TD dyads. Thus, the final sample consisted of 67 autistic adults and 58 TD adults, comprising 55 dyads with complete data from both partners, and 15 dyads with data from only one partner.

Diagnostic and dyad groups were recruited with the intention of matching them on demographics and general intelligence, and this was largely successful (see Table 1). Race did not differ between diagnostic groups (χ²(3) = 0.87, p = 0.83) or dyad types (χ²(6) = 1.27, p = 0.97). IQ as estimated by the Wide Range Achievement Test Third Edition (WRAT-3; Wilkinson, 1993) also did not differ between diagnostic groups (p = 0.58) or dyad types (p = 0.17), but age did (ps < 0.01 for both diagnostic groups and dyad types), with the TD participants being younger than the autistic participants, and the TD–TD dyads being younger than the other two dyad types.

Table 1.

Demographic characteristics and scores on predictors and covariates for individuals in combinations of diagnostic and dyad groups.

	Dyad groups									Diagnostic groups
	ASD–ASD		TD–TD			ASD–TD				ASD		TD
	(n = 43)		(n = 40)			(n = 42)				(n = 67)		(n = 58)
Race
White	36		33			34				56		47
Black	2		2			2				3		3
Asian	2		1			2				3		2
Other	2		4			4				4		6
				ASD				TD
	M	SD	M		SD	M	SD	M	SD	M	SD	M	SD
Age	22.67	3.62	20.62		3.43	25.10	4.47	21.33	2.50	23.51	4.07	20.84	3.17
WRAT-3 IQ	111.88	7.12	110.78		7.91	108.67	10.72	108.00	9.34	110.77	8.58	109.91	8.39

ASD: autism spectrum disorder; TD: typically developing; WRAT-3: Wide Range Achievement Test Third Edition; SD: standard deviation.

Forty-three individuals participated in the ASD–ASD dyad group, but the number of participants for the race category only sum to 42 because 1 individual in an ASD–ASD dyad declined to answer the race question.

Procedure

Interested autistic and TD participants were contacted to determine scheduling availability and assess demographic characteristics used for matching. Recruited participants were entered into a database, and the three dyad types were created by assigning pairs of individuals that were approximately matched on race, age, scheduling availability, and preferred location of participation.

After the informed consent process, participants completed an unstructured measure of dyadic interaction previously developed to assess interactions within TD populations (Berry & Hansen, 1996) but recently used in autism as well (Usher et al., 2018). Participants were seated opposite from each other and told that they have 5 min to talk to one another, with the goal of getting to know each other better. Because the conversation was unstructured, they were given no specific topics to discuss with their partner. They were instructed to talk for the full 5 min. The experimenter then began recording the interaction and after 5 min told the participants to stop. Interactions were video-recorded. Because WASI performance was only available for autistic participants, following the interaction, the experimenter administered the WRAT-3 (Wilkinson, 1993), a brief measure of verbal intelligence, separately to each participant to allow comparison of estimated IQ between diagnostic groups and dyad types. Participants also completed additional measures on Qualtrics Survey software at separate computer stations administered in a counterbalanced order. In total, participation lasted between 60 and 90 min. After completing the measures, participants were asked if they were previously familiar with their partner. Only in one dyad did the two partners acknowledge seeing each other before, but both stated they had never spoken. In two other dyads, one partner expressed knowing the other, but this knowledge was not reciprocated by the other partner. For these reasons, data from these dyads were retained in analyses.

Measures

Evaluation of the interaction

The Social Interaction Evaluation Measure (Berry & Hansen, 1996) is a self-report measure completed by both partners after the interaction. Participants answer 11 questions about the interaction on an eight-point Likert-type scale, where higher values indicate more positive perceptions of the interaction. This scale measures interaction quality (e.g. enjoyment of the interaction), disclosure (e.g. how much did your partner disclose in the conversation), engagement (e.g. how much did your partner influence the conversation), and intimacy (e.g. to what extent was the interaction intimate). The items had strong internal consistency (ASD α = 0.75, TD α = 0.84). Because the 11 items are related (Berry & Hansen, 1996) and have previously been averaged to produce an overall interaction quality composite score (Heerey & Kring, 2007), the average score was computed and used in analyses.

In addition, the Inclusion of Other in the Self (IOS Scale; Aron et al., 1997) and the Subjective Closeness Index (SCI; Berscheid et al., 1989) were used to measure closeness between partners. In the IOS, participants indicate how close they feel to their partner by selecting one of seven overlapping circles representing the self and the partner (Aron et al., 1997). The SCI determines closeness by asking participants to rate on a seven-point scale: (a) how their relationship with their partner compares to their other relationships, and (b) to compare this relationship with what he or she knows about closeness of other people’s relationships (Berscheid et al., 1989). A composite closeness score was formed by averaging the raw scores of both measures together (Aron et al., 1997). This closeness composite has demonstrated strong psychometric properties (Aron et al., 1997) and was also strong in this study (ASD α = 0.78, TD α = 0.77).

Evaluation of the partner

The First Impression Scale for Autism (Sasson et al., 2017) has been used in previous work examining how others form first impressions of autistic adults. Participants rate their partner on 10 first impression statements. Six items assess first impressions of different traits (e.g. awkwardness, attractiveness, trustworthiness, dominance, likeability, and intelligence). The last four items are ratings of behavioral intentions to interact with their partner in the future (e.g. I would hang out with this person in my free time). All items are rated on a four-point scale in which higher ratings indicate more positive first impressions. Questions from the original scale were rephrased for the live-interaction nature of this study (e.g. from “This person is awkward” to “My partner is awkward”). Because the behavioral intent items assess participant social interest in their partner and were highly correlated in past work (Sasson et al., 2017; Sasson & Morrison, 2019), a composite metric average of the four items was used in analyses and showed fair internal consistency here (ASD α = 0.63; TD α = 0.60), suggesting shared measurement of an underlying construct. As expected, the six trait items showed lower internal consistency (ASD α = 0.37, TD α = 0.50), as each item is intended to measure distinct characteristics of partners. Therefore, each of the six traits was analyzed separately.

Finally, the International Personality Item Pool—Interpersonal Circumplex (IPIP-IPC; Markey & Markey, 2009) was used to assess warmth and dominance behaviors toward the partner. The IPIP-IPC was chosen to obtain a broader metric of the personality domains specifically related to social behavior (Markey & Markey, 2007). Participants answer 32 questions to create an index of interpersonal dominance (e.g. my partner demands attention) and warmth (e.g. my partner is interested in people). This scale has strong psychometric properties and closely aligns with the Big Five personality traits of agreeableness and extraversion, but reliability was lower in our sample than previously reported (ASD α = 0.60, TD α = 0.48).

Analysis plan

Normality, skew, and kurtosis were first examined for all measures, and all were in acceptable ranges for analyses. Prior to primary analyses, correlations between partners’ outcomes were also examined to assess general patterns in the data. The APIM was used (Kenny et al., 2006) to test the specific aims for this study. The APIM specified in this study estimates three types of effects (see Figure 1). First, our APIM estimates actor effects, or the effect of the individual’s own characteristics (e.g. diagnosis) on the individual’s own outcome (e.g. interaction quality). Second, our APIM estimates partner effects, or the effect of the partner’s characteristics on the individual’s outcome. Finally, our APIM estimates interaction effects, allowing for examination of how an individual’s characteristics are related to his own outcomes depending on his partner’s characteristics.

Figure 1.

Actor–Partner Interdependence Model of social abilities predicting social outcomes. A-paths represent the actor effects (e.g. effect of individual’s diagnosis on the individual’s outcomes) and P-paths represent the partner effects (e.g. effect of the partner’s diagnosis on the individual’s outcomes). The interaction term represents the effect of the individual’s characteristics on the individual’s outcome depending on the partner’s characteristics.

To estimate actor, partner, and interaction effects, multilevel modeling was run using Restricted Maximum Likelihood (REML) estimation in SPSS Version 25. Actor WRAT-3 IQ, race, and age were entered in the model as covariates, and the effect of the actor’s diagnosis, partner’s diagnosis, and the interaction of diagnoses were entered into the models as fixed effects. In addition to fixed effects of interest, random effects were specified to account for variation in individuals across dyads using compound symmetry covariance structure. All analyses were run separately for each social interaction outcome: social interaction quality, closeness, first impressions formed (i.e. each of the six traits and averaged behavioral intent composite), and interpersonal circumplex traits (i.e. warmth and dominance behaviors). For all analyses, continuous predictors were grand mean centered, categorical predictors were effects coded, and missing data were removed casewise. Interactions were followed up with simple slopes. Due to the number of tests that were performed, an adjusted alpha threshold of 0.01 was specified a priori for all fixed effects. Significant interaction terms were then followed up with the alpha of 0.05.

Results

Means and standard deviations for outcomes and zero-order correlations between outcomes are displayed in Tables 2 and 3. Outcome variables were generally related. First impression traits, behavioral intentions, and interaction quality and closeness were minimally to strongly related for both autistic and TD groups. For TD adults, ratings of warmth, desire to hang out with, and interaction quality were strongly correlated with most other outcomes. For autistic adults, these relationships were less strong, and fewer were statistically significant.

Table 2.

Means and standard deviations of actor outcome measures for diagnostic and dyad groups.

	ASD–ASD dyads		TD–TD dyads		ASD–TD dyads
	Autistic ratings of autistic partners (n = 43)		TD ratings of TD partners (n = 40)		Autistic (n = 24) ratings of TD partners (n = 18)		TD (n = 18) ratings of autistic partners (n = 24)
	M	SD	M	SD	M	SD	M	SD
Awkward_R	2.76	0.69	3.38	0.67	3.39	0.66	2.72	0.58
Attractive	2.33	0.72	2.65	0.58	2.70	0.82	2.22	0.65
Trust	3.17	0.44	3.20	0.46	3.26	0.45	3.17	0.38
Aggressive/dominant	1.83	0.58	1.75	0.71	1.52	0.51	1.52	0.51
Likable	3.21	0.47	3.30	0.46	3.39	0.50	3.28	0.58
Smart	3.24	0.66	3.23	0.62	3.26	0.62	3.11	0.76
Live near	3.21	0.72	3.27	0.64	2.74	0.86	3.00	0.77
Hangout	2.95	0.49	2.85	0.58	2.78	0.78	2.33	0.59
Sit near	3.24	0.73	3.33	0.66	3.22	0.74	3.17	0.71
Conversation	3.07	0.60	3.15	0.53	3.09	0.60	2.94	0.54
Behavioral intent	3.12	0.40	3.15	0.43	2.96	0.59	2.86	0.35
IPC warmth	−0.27	1.00	0.20	0.65	0.32	0.84	−0.22	0.59
IPC dominance	−0.16	0.96	0.10	1.02	0.02	0.86	0.12	1.18
Interaction quality	5.71	0.87	5.62	0.89	5.77	0.71	5.31	0.92
Closeness	3.14	1.11	2.57	0.84	2.99	1.15	2.19	0.79

ASD: autism spectrum disorder; TD: typically developing; IPC: Interpersonal Circumplex; SD: standard deviation.

Outcomes are actor evaluations of the interaction and partner. Awkward scored such that higher scores indicate less awkwardness.

Table 3.

Correlations between social interaction outcomes.

	Awkward_R	Attractive	Trustworthy	Aggressive/dominant	Likable	Smart	Live near	Hangout	Sit near	Conversation	Behavioral intent	IPC warmth	IPC dominance	Interaction quality	Closeness
Awkward_R	1	0.350**	0.12	0.194	0.154	0.155	0.112	0.441**	0.158	0.284*	0.358**	0.422**	0.232	0.384**	0.328*
Attractive	0.102	1	0.02	0.184	0.237	0.224	0.216	0.237	0.239	0.177	0.323*	0.364**	0.033	0.243	0.032
Trustworthy	0.16	0.141	1	−0.281*	0.305*	0.419**	0.053	0.282*	0.357**	0.450**	0.407**	0.242	−0.198	0.390**	0.042
Aggressive/dominant	−0.2	0.118	−0.019	1	−0.093	−0.227	−0.179	−0.153	−0.081	−0.420**	−0.292*	0.101	0.343**	0.095	0.057
Likable	0.152	0.24	0.400**	−0.381**	1	0.470**	0.452**	0.410**	0.386**	0.232	0.557**	0.486**	−0.15	0.405**	0.289*
Smart	0.046	0.188	0.328**	−0.194	0.438**	1	0.151	0.525**	0.236	0.446**	0.486**	0.337**	−0.239	0.482**	0.197
Live near	−0.332**	−0.189	−0.122	0.071	−0.082	0.062	1	0.261*	0.151	0.097	0.588**	0.264*	−0.181	0.058	0.261*
Hangout	0.095	−0.219	−0.037	−0.174	0.202	0.215	0.17	1	0.249	0.599**	0.758**	0.492**	−0.089	0.656**	0.545**
Sit near	0.014	0.103	−0.005	−0.201	0.207	0.209	0.451**	0.232	1	0.370**	0.661**	0.086	−0.058	0.234	0.103
Conversation	0.216	−0.107	0.24	−0.162	0.251*	0.315**	0.153	0.492**	0.353**	1	0.719**	0.212	−0.288*	0.505**	0.282*
Behavioral intent	−0.038	−0.145	0.009	−0.153	0.188	0.274*	0.698**	0.637**	0.758**	0.670**	1	0.387**	−0.219	0.515**	0.434**
IPC warmth	0.171	0.074	0.319**	−0.335**	0.299*	0.319**	−0.142	0.062	0.041	0.373**	0.093	1	0.19	0.404**	0.270*
IPC dominance	0.262*	0.2	−0.034	0.04	−0.142	−0.072	−0.311*	0.064	−0.127	0.02	−0.153	−0.004	1	−0.009	−0.026
Interaction quality	0.236	0.183	0.269*	−0.219	0.381**	0.369**	−0.117	0.333**	0.173	0.328**	0.226	0.344**	0.077	1	0.517**
Closeness	−0.066	0.216	−0.006	0.052	0.022	0.235	0.07	0.449**	0.213	0.286*	0.345**	0.005	0.18	0.305*	1

IPC: Interpersonal Circumplex; TD: typically developing.

TD correlations are above the diagonal.

Outcomes are actor ratings of the partner and interaction. Awkward was reverse scored.

p < 0.05; **p < 0.01.

APIM models were then run for each of the 11 outcome variables looking at effects of actor, partner, and interaction effects of diagnosis. Regression coefficients are displayed in Table 4. To test primary hypotheses that outcomes would differ depending on dyad diagnostic combinations, we examined the interaction effects. There was an effect of the interaction of partners’ diagnoses on behavioral intent ratings (b = 0.28, β = 0.31, SE = 0.09, p = 0.004), indicating that interest in future interactions depended upon dyad diagnostic composition. Whereas TD participants endorsed higher intentions to interact with TD relative to autistic partners (b = –0.16, β = –0.36, SE = 0.06, p = 0.01), autistic participants did not show this preference and trended toward higher intentions to interact with autistic compared to TD partners (b = 0.11, β = 0.26, SE = 0.06, p = 0.052). There was also a significant actor effect for closeness (b = 0.28, β = 0.27, SE = 0.10, p = 0.004), such that autistic participants reported feeling closer to their partners after the interaction regardless of their diagnosis than did TD participants. Finally, there were also significant partner effects. Autistic partners were rated as more awkward, less attractive, and less warm than TD partners (ps < 0.001) both by TD and autistic participants. No other actor effects (ps > 0.27), partner effects (ps > 0.38), or interactions of partners’ diagnoses (ps > 0.03) were significant.

Table 4.

Effect of diagnosis on social interaction outcomes.

		Intercept	Actor diagnosis	Partner diagnosis	WRAT-3	Race—African American	Race—Asian	Race—other	Age	Actor diagnosis × partner diagnosis	χ ²	R ²
Closeness	b	2.94	0.28**	−0.06	−0.02	0.09	0.22	0.03	0.05	0.21	28.08	0.19
	SE	0.18	0.10	0.09	0.01	0.36	0.38	0.28	0.02	0.11
	p	<0.001	<0.001	0.52	0.04	0.80	0.57	0.91	0.03	0.05
Interaction quality	b	5.78	0.09	−0.07	−0.01	0.20	−0.45	0.54	0.04	0.11	20.32	0.15
	SE	0.15	0.08	0.08	0.01	0.29	0.31	0.24	0.02	0.09
	p	<0.001	0.28	0.38	0.32	0.50	0.15	0.03	0.05	0.19
Awkward_R	b	3.10	<0.001	−0.33**	−0.01	0.06	−0.10	0.12	<0.001	0.01	34.61	0.23
	SE	0.11	0.07	0.06	0.01	0.20	0.22	0.18	0.02	0.06
	p	<0.001	0.97	<0.001	0.13	0.78	0.64	0.50	0.91	0.86
Attractiveness	b	2.57	<0.001	−0.20**	0.01	0.11	0.13	−0.11	0.02	0.02	12.53	0.09
	SE	0.13	0.07	0.07	0.01	0.27	0.28	0.21	0.02	0.08
	p	<0.001	0.96	<0.001	0.31	0.68	0.66	0.60	0.38	0.84
Trustworthy	b	3.30	0.02	−0.03	<0.001	0.25	−0.05	−0.07	−0.01	−0.02	5.47	0.05
	SE	0.08	0.05	0.04	<0.001	0.15	0.16	0.13	0.01	0.05
	p	<0.001	0.63	0.44	0.49	0.11	0.76	0.61	0.64	0.66
Aggressive/dominant	b	1.85	0.01	0.02	−0.01	−0.06	0.40	−0.11	<0.001	0.12	15.5	0.11
	SE	0.09	0.06	0.06	0.01	0.17	0.19	0.16	0.02	0.05
	p	<0.001	0.81	0.71	0.27	0.73	0.04	0.47	0.95	0.03
Likable	b	3.34	−0.01	−0.04	<0.001	0.17	−0.32	0.23	0.01	−0.03	11.26	0.08
	SE	0.08	0.05	0.05	0.01	0.14	0.16	0.13	0.01	0.04
	p	<0.001	0.88	0.40	0.48	0.26	0.05	0.08	0.42	0.48
Smart	b	3.30	−0.02	−0.01	−0.01	−0.06	−0.05	0.26	0.05	0.08	15.44	0.11
	SE	0.12	0.06	0.06	0.01	0.22	0.24	0.18	0.02	0.07
	p	<0.001	0.72	0.91	0.19	0.80	0.83	0.16	<0.001	0.20
Behavioral intent	b	3.02	−0.01	−0.02	<0.001	0.19	−0.35	0.19	0.02	0.14**	17.85	0.13
	SE	0.08	0.04	0.04	<0.001	0.16	0.17	0.13	0.01	0.05
	p	<0.001	0.83	0.61	0.72	0.25	0.05	0.14	0.03	<0.001
IPC warmth	b	0.15	−0.04	−0.21**	0.01	0.34	−0.19	0.04	0.04	0.01	18.75	0.15
	SE	0.14	0.08	0.08	0.01	0.27	0.29	0.22	0.02	0.08
	p	0.29	0.61	0.01	0.47	0.22	0.52	0.86	0.04	0.92
IPC dominance	b	0.18	−0.07	−0.06	−0.02	−0.12	0.16	0.18	<0.001	−0.04	8.13	0.06
	SE	0.18	0.10	0.09	0.01	0.35	0.37	0.29	0.03	0.10
	p	0.32	0.47	0.55	0.09	0.74	0.67	0.54	0.95	0.67

IPC: Interpersonal Circumplex; WRAT-3: Wide Range Achievement Test Third Edition; TD: typically developing.

Awkward was reverse scored. Race was effects coded with white as the reference group. Diagnosis was effects coded with TD as the reference group. The R² value is pseudo R², the proportion of variance accounted for in the model. This is evaluated for significant using the Chi-Square values comparison between full and empty models.

p < 0.01.

Post hoc testing

Because this study focuses on predictors of social interaction quality in autism and two of the included measures—the interaction quality and closeness scales—evaluated perceptions of the interaction rather than just of the interaction partner’s qualities, these measures were followed up with more detailed analyses. The Social Interaction Measure’s subscales (Quality, Disclosure, and Engagement) were assessed with separate models. There was a significant interaction for the disclosure item (b = 0.31, β = 0.27, SE = 0.11, p = 0.007), such that autistic adults felt there was more disclosure between partners when they interacted with an autistic rather than a TD partner (b = 0.36, β = 0.31, SE = 0.15, p = 0.02), while TD adults did not differ on disclosure depending on their partners’ diagnoses (b = –0.26, β = –0.22, SE = 0.16, p = 0.10). No other interaction effects (ps > 0.36), actor effects (ps > 0.13), nor partner effects (ps > 0.29) were significant for disclosure, quality, or engagement.

To assess whether partners demonstrated agreement on perceptions of interaction quality, disclosure, engagement, and closeness in the closeness composite and Social Interaction Measure subscales, a discrepancy score was computed for each dyad by subtracting the partner’s score from the actor’s score and taking the absolute value of that difference. Average scores for each dyad type are presented in Table 5. Although the pattern of means suggested that the TD–TD dyads showed the most similarity (i.e. difference scores closest to zero), testing differences between dyad types with a three-way analysis of variance (ANOVA) suggested no differences in agreement between dyad types (ps > 0.40).

Table 5.

Discrepancy scores for differences on Social Interaction Measure subscales and closeness.

	TD–TD		ASD–ASD		ASD–TD		F (2, 55)	p
	M	SD	M	SD	M	SD	F (2, 55)	p
Social interaction quality measure composite	1.06	0.64	1.02	0.93	0.77	0.70	0.71	0.50
Interaction quality score	1.29	1.02	1.41	0.94	1.33	0.93	0.07	0.93
Disclosure	1.12	0.94	1.57	1.68	1.06	0.92	0.94	0.40
Engagement	0.97	0.78	1.38	1.32	1.29	1.25	0.63	0.54
Closeness	1.05	0.77	0.87	0.85	1.22	0.95	0.79	0.46

ASD: autism spectrum disorder; TD: typically developing; SD: standard deviation.

Discrepancy scores were calculated as the absolute value of the difference between the actor and partner’s scores on each item.

Discussion

Despite a large research literature devoted to understanding the social characteristics of autism, most have studied individual abilities in isolated contexts and very few have examined social interaction of autistic people within real-world environments. This is particularly true in adult populations. The current study was designed to address this “blind spot” within autism research (De Jaegher, 2013, p. 14) by moving beyond inferring social interaction differences in autism from informant reports (Billstedt et al., 2005; Howlin et al., 2004), performance on standardized social cognitive tasks (Morrison et al., 2019b), or previous studies from our group in which participants passively view and evaluate autistic adults from video clips (DeBrabander et al., 2019; Morrison et al., 2019a; Sasson et al., 2017; Sasson & Morrison, 2019). Here, we examined autistic sociability within a more ecologically valid context. A total of 67 autistic adults and 58 TD participants completed a real-world 5-min unstructured interaction across three dyad types in which unfamiliar partners were either both autistic, both TD, or one autistic and one TD. After the interaction, participants evaluated their partner’s traits, their interest in future interaction with their partner, indices of the quality of the interaction, and how close they felt to their partner. In this way, this study examined social interaction in autism from a relational perspective, with a particular focus on whether the combination of partners’ diagnoses impacted interaction quality and impression formation. Autistic participants were evaluated less favorably on some characteristics by both TD and autistic partners, but autistic participants did not share the TD preference for TD over autistic partners, and in contrast to traditional conceptualizations of autistic sociability, reported feeling closer to their partners than did TD adults, and disclosed more about themselves to autistic partners relative to TD partners. These findings provide some empirical support for the DEP of autism (Milton, 2012) and suggest that traditionally defined social interaction impairments in autism are not solely of the individual, but rather are more contextual and may emerge to a greater degree during interaction between autistic and TD individuals who are mismatched on social and communication styles.

In line with previous findings using video stimuli (e.g. Sasson et al., 2017), autistic adults were not judged to be less intelligent or trustworthy than their TD counterparts, but were perceived to be more awkward, less attractive, and less warm. Given that similar impressions have now been found across several independent samples and different methodological approaches (i.e. video clips and face to face interactions; Hubbard et al., 2017; Sasson et al., 2017), these results suggest that some aspects of negative trait assessment of autistic adults are relatively pervasive across evaluative contexts. Importantly, however, autistic adults here were not rated as less likable than TD adults, which has been consistently found in studies using video stimuli (e.g. Sasson et al., 2017). Although an explicit controlled comparison of impression formation between video and face-to-face methodologies using a single sample would be needed to more conclusively demonstrate changes in likability judgments across contexts, this finding may suggest that direct face-to-face interaction mitigates certain negative evaluations that form during indirect observation of autistic adults. Such an interpretation is consistent with previous work indicating that experience with autistic people improves impressions and decreases stigma toward autism (Gillespie-Lynch et al., 2015; Sasson & Morrison, 2019), and suggests that some aspects of TD impression formation may improve for autistic adults when given the chance for personal interaction—an opportunity often denied to them when negative judgments made from afar reduce the chances for subsequent social interaction (Sasson et al., 2017).

Less favorable trait assessments of autistic adults were not limited to those made by TD partners. Autistic partners made them as well, suggesting that the characteristics driving negative impression formation of autistic adults are similarly perceived and evaluated by autistic and TD individuals. This finding is inconsistent with conceptualizations of autistic adults as being less sensitive to perceiving and interpreting social cues (Klin et al., 1999; Mathersul et al., 2013; Sasson, Pinkham, Carpenter, & Belger, 2011; Uljarevic & Hamilton, 2013). Autistic adults in this study detected social presentation differences between autistic and TD adults and formed similar trait inferences about them. Thus, despite autism being associated with poorer performance on many social cognitive assessments (Morrison et al., 2019b), such differences do not appear to affect person evaluation in the real-world interactive context tested here. One potential explanation for this finding is that judgments like awkwardness, attractiveness, and warmth are highly salient aspects of person evaluation and the expectations and norms for evaluating these specific traits do not differ between autistic and TD adults. It also suggests that, contrary to our hypothesis, simply being autistic and therefore presumably having increased familiarity with autism and its associated characteristics does not translate to more favorable trait assessments of other autistic adults. Consequently, it does not appear that the less favorable impressions of autistic individuals found across multiple studies (DeBrabander et al., 2019; Grossman, 2015; Grossman et al., 2018; Morrison et al., 2019a; Sasson et al., 2017; Sasson & Morrison, 2019) are driven primarily by a lack of familiarity with autism itself. This may in part be due to the diversity within the spectrum of autism diagnoses, whereby matching on diagnosis alone does not necessitate matching on social cognitive ability and social communication styles that may make detecting social cues easier or harder.

In contrast, judgments about interest in future social interaction did differ based upon diagnostic alignment between interaction partners. Replicating prior work (Sasson et al., 2017; Sasson & Morrison, 2019), TD adults rated a stronger desire for future interaction with other TD adults compared to autistic adults. Thus, the current study finds little evidence that direct contact between autistic and TD individuals, at least in a brief “get to know you” context examined here, improves social prospects for autistic adults among TD individuals. Results for autistic adults, however, were quite different. They did not share the TD preference for future interaction with TD individuals, and in fact trended toward greater interest in future interaction with other autistic adults. In other words, despite mirroring the TD pattern to evaluate autistic adults less favorably on several characteristics, such impressions did not reduce their interest in future interaction with autistic adults as they appear to do for TD adults. These results are therefore consistent with the DEP model (Milton, 2012) and suggest that autistic adults may form more positive connections with, and develop more social interest for, other autistic adults.

It may be the case that the criteria autistic adults use to decide whether or not to interact with others differs from those used by TD adults. Whereas awkwardness and warmth were correlated with intentions for future interaction ratings for TD adults, these relationships were smaller in magnitude for autistic adults, and many did not reach statistical significance. That is, evaluative judgments such as awkwardness and attractiveness may have a stronger bearing on TD adults’ social inclinations, whereas autistic adults may rely to a lesser degree on these same judgments and instead base social decisions on other factors. Attractiveness and awkwardness are socially defined constructs, and autistic adults may place less value on awkwardness and warmth as relevant to their social interest, or are better able to relate to other autistic adults in ways not captured by the trait evaluations measured in this study. Alternatively, the direction of effect could be in the opposite direction: social inclinations may affect and inform ratings of awkwardness or attractiveness for TD but not autistic adults. It is also possible that the traits assessed here are interpreted differently between the two groups and evoke different biases and beliefs.

Regardless, the heightened social interest in autistic partners expressed by autistic adults relative to their TD counterparts is inconsistent with theories of reduced social motivation in autism (Chevallier et al., 2012) and suggests a broader conceptualization of social motivation that is not just individually defined but also influenced by social factors like relational dynamics and partner compatibility. This interpretation is also supported by the finding that autistic adults felt closer to their conversational partners than did TD adults. What is driving this effect is not entirely clear, but it may be the case that autistic adults’ perceptions of closeness may in part be driven by differences in the experiences they have had with unfamiliar peers. For example, if autistic adults are typically excluded from interactions, a brief encounter may feel more intimate compared to TD adults who may not feel the same level of closeness as quickly, as short conversations may be more common to their daily experiences. This interpretation, however, is speculative and requires further investigation.

Importantly, evidence of greater partner compatibility between two autistic adults was also seen in aspects of their behavior within the interaction. Post hoc tests showed autistic adults reported that their interactions had higher levels of self-disclosure when they interacted with other autistic adults compared to TD adults, which suggests that autistic adults either felt more open and comfortable disclosing personal information to other autistic adults, or developed greater conversational connection through shared interests, experiences, or sensibilities that resulted in higher self-disclosure ratings (Carrington et al., 2003; Kuo et al., 2013). Because autistic individuals and those with autistic traits are more likely to endorse specific interests (e.g. video games; Anthony et al., 2013; Morrison et al., 2018), heightened self-disclosure about personal interests within ASD–ASD dyads may have facilitated social connection and influenced the greater desire for future interaction between autistic partners. Anecdotally, conversations in many ASD–ASD dyads did veer toward shared interests, which may have contributed to increased disclosure and affiliation. Future studies are encouraged to examine whether and how conversational content between unfamiliar autistic partners differs from that between autistic and TD partners.

Despite autistic adults being evaluated less favorably overall, perceptions of the quality of the conversation did not differ for autistic and TD partners, suggesting autistic adults are perceived by both TD and autistic partners as participating in the same level of meaningful and high quality conversations as TD adults. This finding suggests that negative trait evaluation of autistic adults does not necessarily translate to lower perceptions of conversation quality, and may indicate that both autistic and TD partners are capable of separating their judgments of personal characteristics from their assessment of conversation quality. Such a discrepancy also suggests that less favorable impressions of autistic adults do not invariably result in lower quality interactions. This interpretation aligns with prior work indicating that the negative impressions formed about autistic adults are driven by their social presentation differences rather by the content of their conversation (Sasson et al., 2017).

This study has several limitations. First, the study was underpowered to detect small effects of interest. Although APIM analyses can be performed with incomplete data for dyads, having complete data from only 55 dyads may have made small effects especially difficult to detect. In addition, although the sample size was based upon prior work, these prior studies used different measures and different populations (e.g. broad autism phenotype and adolescents; Faso et al., 2016; Usher et al., 2018). Therefore, the estimated effect size used in the power analysis may have been overestimated, making the required sample size too small to detect small effects. For this reason, null effects should also be interpreted with caution, and we encourage replication of this study with larger samples. Second, given the complexity in matching participants within and between dyads and the potential complicating effect of gender on interaction dynamics, the sample used here was all male and mostly white. Study findings may be expected to differ in more diverse samples and with females, both in cross-sex and cross-race interactions and with interactions between two female partners. Furthermore, the social interactions examined here were limited to “get to know you” conversations between two unfamiliar people, and results may have differed if generated within other social contexts in which interaction occurs for autistic adults (e.g. educational and professional settings, dating, etc.). Future work in these areas and with more diverse samples is needed to determine the generalizability of the findings reported here. Third, autistic adults in this study also were quite intellectually capable, as participants were recruited from either a college campus or a training program for computer skills and software development to match TD adults from a university setting. Successfully functioning in these environments requires some ability to interact with others and function relatively independently, and thus these recruitment sources may have resulted in a sample that is already more socially and intellectually skilled than some autistic populations. Whether results reported here would extend to less cognitively able or more functionally impaired individuals is unclear. Fourth, although the interaction quality and closeness measures showed strong psychometric properties, the IPIP-IPC and behavioral intention score on the first impression scale showed poorer properties than in past work (Morrison et al., 2019a; Sasson et al., 2017a), and relationships between traits and behavioral intentions were not as strongly related relative to previous studies (Sasson et al., 2017). Thus, some measures may perform differently in face-to-face contexts compared to use in forming evaluations from videos.

Finally, diagnostic disclosure and knowledge of autism are related to first impressions and behavioral intentions (Morrison et al., 2019a; Sasson & Morrison, 2019) and may have influenced results here. To mimic how spontaneous interactions may occur in the real-world, participants were not explicitly told about the diagnostic status of their partner but autistic participants chose to disclose their diagnosis or indirectly referenced it by mentioning the training program for autistic adults where they worked or attended in 10 of the dyads. In addition, TD adults in this study may have had more prior knowledge about and experience with autism than many TD samples because they were recruited predominately from psychology courses at a university with one of the highest proportions of autistic adults on a college campus in the United States (Hoffman, 2016). Thus, the effects reported here may reflect a “best case scenario” for TD impressions toward autistic adults. Future work should determine the role of diagnostic disclosure and whether TD knowledge of autism is related to improved behaviors and social interaction outcomes within a conversation with an autistic person. In addition, the findings reported here focused on comparing the social outcomes of differing dyad types rather than on examining any aspects of the conversations themselves. Future work that compares the conversational content and social dynamics between ASD–ASD dyads and ASD–TD dyads may provide greater insight into whether and how autistic adults interact differently with each other than with a TD partner.

Despite these limitations, the current study provides greater insight into factors affecting real-world social interaction for autistic adults. The relational framework used here not only provided a novel and more ecologically valid way of examining social interaction for autistic adults, but also for the first time allowed for an examination of how the diagnostic composition of interacting social partners affects the quality of social interaction for autistic adults. Results showed that autistic adults were rated more negatively on some traits, and TD partners reported lower interest in future interaction with them. However, autistic adults disclosed more to other autistic adults and had more positive interest in interacting with them than did TD partners. These findings suggest that autistic sociability is not an individual characteristic but a relational one in which social outcomes are dependent on the fit between the person and the social environment. In this way, the results reported here are consistent with conceptualizations of a “double empathy problem” (Milton, 2012), and suggest that the social experiences of autistic adults may benefit when around others with similar interests, backgrounds, and perspectives. At the same time, results provide further evidence that TD individuals are less positively inclined toward autistic people relative to other TD adults, and efforts to promote inclusion of autistic people in predominantly TD environments may be aided by increasing understanding and acceptance of autistic differences among TD adults.

Footnotes

Acknowledgements

We thank the program engagement team at the nonPareil Institute for their support of this project. The first author would like to thank Mariel Peterson for supporting this project.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Texas Higher Education Coordinating Board’s Autism Grant Program and by the UT Dallas School of Graduate Studies Small Grant Award for dissertation research.

ORCID iDs

Kerrianne E Morrison

Desiree R Jones

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