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Theory of mind in naturalistic conversations between autistic
and typically developing children and adolescents
Diana Alkire1,2, Kathryn A. McNaughton1,2, Heather A. Yarger1,2, Deena Shariq1,2, Elizabeth
Redcay1,2
1Neuroscience and Cognitive Science Program, University of Maryland, College Park
2Department of Psychology, University of Maryland, College Park
Abstract
Successful social interactions are assumed to depend on theory of mind (ToM)—the ability
to represent others’ mental states—yet most studies of the relation between ToM and social-
interactive success rely on non-interactive tasks that do not adequately capture the
spontaneous
engagement of ToM, a crucial component of everyday social interactions. We addressed this
gap by establishing a novel observational rating scale to measure the spontaneous use of
ToM (or lack thereof) within naturalistic conversations (
conversational ToM
; cToM). In 50
age- and gender-matched dyads of autistic (AUT) and typically developing (TD) youth aged
8–16 (three dyad types: AUT-TD, TD-TD, AUT-AUT), we assessed cToM during 5-minute,
unstructured conversations. We found that ratings on the cToM Negative scale, reflecting ToM-
related violations of neurotypical conversational norms, were negatively associated with two
forms of non-interactive ToM: visual-affective and spontaneous. In contrast, the cToM Positive
scale, reflecting explicit mental state language and perspective-taking, was not associated with
non-interactive ToM. Furthermore, autistic youth were rated higher than TD youth on cToM
Negative, but the groups were rated similarly on cToM Positive. Together, these findings provide
insight into multiple aspects of ToM in conversation and reveal a nuanced picture of the relative
strengths and difficulties among autistic youth.
Introduction
Everyday life is saturated with social interactions, and leveraging these interactions to form
high-quality relationships is critical for mental and physical wellbeing (Hedley et al., 2018;
Holt-Lunstad et al., 2010). It is widely assumed that social-interactive success depends on
theory of mind (ToM)—the ability to represent the mental states of others and oneself—
an assumption that drives the influential “mindblindness” account of social impairment in
autism (Baron-Cohen et al., 1985). While empirical findings indicate broad, distal relations
between ToM and social outcomes (Bosacki & Astington, 1999; Caputi et al., 2017; Devine
& Hughes, 2013; Peterson et al., 2016), the specific mechanisms driving these relations are
Correspondence: Diana Alkire, Department of Psychology, University of Maryland, College Park, MD 20742,
diana.r.alkire@gmail.com.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
HHS Public Access
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unclear. That is, how does ToM ability or propensity impact real-world behaviors that in turn
influence social-interactive success?
Answering this question with a useful degree of specificity is hampered by the discrepancy
between standard ways of measuring ToM in the laboratory and the nature of everyday
social interactions. Standard ToM tasks range widely in format, but they share a common
feature: the subject plays the role of an observer rather than a participant in a social
exchange (Begeer et al., 2010; Schilbach et al., 2013). As such, the behaviors directly
elicited by observational tasks—labeling mental states, or verbally predicting or explaining
a character’s actions, based on passive observation—do not easily map onto the behaviors
that typically occur throughout everyday social interactions. Furthermore, most laboratory
tasks include explicit prompts to make ToM inferences, for example, asking participants to
reason about a fictional character’s motivations in a social vignette (e.g., Happé, 1994). Such
prompts rarely occur in everyday interactions; thus, performance on these standard tasks
may not correspond with the tendency to represent the mental states of one’s real-world
interaction partners.
Reliance on explicit, non-interactive measures is especially problematic when it comes to
characterizing ToM abilities in autism. Autistic individuals who pass first-order false belief
tasks nevertheless struggle with everyday mindreading (Frith, 1994; Peterson et al., 2009),
a finding that has prompted the development of several more advanced ToM measures such
as the Strange Stories (Happé, 1994) and the Reading the Mind in the Eyes Test (Baron-
Cohen et al., 2001). Yet, even performance on advanced ToM tasks is not consistently
related to social impairment in autism (Alkire et al., 2021; Barendse et al., 2018; Usher
et al., 2015), suggesting that autistic individuals may solve explicit ToM tasks through
compensatory strategies (i.e., verbal or domain-general reasoning) that do not serve them
in naturalistic, interactive contexts (Happé, 1995; Scheeren et al., 2013). Furthermore, the
unstructured nature of everyday social situations (as opposed to highly structured laboratory
tasks) appears to exacerbate ToM difficulties in autism (Ponnet et al., 2008; Roeyers et
al., 2001). Thus, to more accurately characterize real-world ToM and its role in interactive
success, particularly for autistic individuals, there is a need for more direct measures of the
application of ToM within unstructured social interactions.
Naturalistic conversation is an ideal unstructured format in which to measure applied ToM.
Conversation becomes an increasingly prominent feature of peer interactions during middle
childhood to adolescence, an important period for social development in general (Raffaelli
& Duckett, 1989; Rubin et al., 2007). Crucially, successfully navigating a conversation
inherently requires ToM (Roth & Spekman, 1984; Sperber & Wilson, 1987). As such,
the application of ToM within conversation, hereafter termed
conversational ToM
(cToM),
represents a key aspect of interactive social cognition and, potentially, its impairment in
autism.
cToM is apparent in a variety of communicative acts. While some of these acts are overt
indications of ToM, such as explicitly referring to a conversation partner’s mental states,
other speech acts
imply
that the speaker has or has not taken the listener’s perspective into
account. Failing to engage in this perspective-taking can result in statements that are under-
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or over-informative, irrelevant, ambiguous, or disorganized (Grice, 1975), at least from a
neurotypical perspective. Conversely, speakers engage in cToM when they clarify potential
ambiguities, distinguish between common and privileged ground (i.e., information that is
or is not mutually shared; Clark & Marshall, 1981; Raczaszek-Leonardi et al., 2014), and
repair misunderstandings (Schegloff, 2004). These properties of everyday conversation offer
a promising avenue for investigating how ToM supports social interaction. However, as we
review below, there is a dearth of measures specifically designed to capture cToM.
Behaviors pertaining to cToM are primarily assessed within the discipline of pragmatic
language, which concerns the use of language in social contexts and the ability to infer
meanings that depart from the literal meaning of an utterance. Pragmatic impairment has
been associated with poor social outcomes (Mok et al., 2014; Whitehouse et al., 2009)
and is common in autism (Kalandadze et al., 2018; Ying Sng et al., 2018). However, two
characteristics of the pragmatics literature limit its applicability to studying ToM in real-time
social interactions. First, the broad category of pragmatics includes a range of heterogeneous
skills that vary in the extent to which they depend on ToM. Several authors have proposed a
theoretical division between aspects that require taking the perspective of one’s conversation
partner and those that do not, instead depending solely on structural language (i.e.,
vocabulary and syntax) and contextual reasoning from the listener’s egocentric perspective
(Andrés-Roqueta & Katsos, 2017; Kissine, 2012; O’Neill, 2012). However, many existing
measures of pragmatic ability conflate these heterogeneous aspects (Matthews et al., 2018).
A second limitation of previous pragmatics studies is that, much like ToM studies, they often
employ non-interactive measures. Because these measures may simply reflect knowledge of
social norms (e.g., Carrow-Woolfolk, 1999), and because pragmatic language use is highly
context dependent (Ying Sng et al., 2018), standard laboratory tasks may not adequately
capture one’s true capacity to use ToM within everyday conversations.
Given the limited ecological validity of task-based measures of pragmatic competence,
observational measures of natural language samples are better suited to identifying behaviors
that reflect real-world ToM. For example, the Pragmatic Rating Scale (PRS; Landa et
al., 1992) has been used to characterize pragmatic difficulties in autism that are observed
during interactions with a clinician or experimenter (Greenslade et al., 2019; Lam & Yeung,
2012; Paul et al., 2009) and during free-play among preschoolers (Bauminger-Zviely et
al., 2013). However, because the PRS and similar rating scales are designed to quantify
only pragmatic deficits, they are ill-suited to measuring positive indicators of cToM such
as clarifying ambiguities or explicit perspective-taking. Other observational studies have
quantified discrete ToM-relevant features of conversation, such as the number of relevant
and appropriately informative statements (Capps et al., 1998; Nadig et al., 2010; Tager-
Flusberg & Anderson, 1991). Though focused on limited aspects of discourse, these studies
establish the feasibility of analyzing naturalistic conversation for behavioral indicators of
cToM.
We argued above that non-interactive ToM tasks are limited in their capacity to
measure individuals’ ability and propensity to apply ToM during real-world interactions.
Nevertheless, understanding how performance on established tasks relates to cToM-related
pragmatic abilities could shed light on the cognitive underpinnings of these abilities.
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ToM is multifaceted, and different non-interactive tasks likely tap into several distinct
underlying abilities (Altschuler et al., 2018; Hayward & Homer, 2017; Schaafsma et al.,
2015; Warnell & Redcay, 2019). As measuring every facet of ToM is beyond the scope of
a single study, here we focus on two aspects that are likely instrumental to conversation.
Face-to-face interactions in particular require sensitivity to multimodal cues from one’s
interaction partner, including facial expressions conveying affective information relevant
to the conversation (e.g., interest or confusion). Tasks measuring
visual-affective ToM
(vaToM), the ability to infer emotional states from facial expressions, are widespread
in the general ToM literature and show positive correlations with social functioning in
autism (Trevisan & Birmingham, 2016), yet their connection with conversational skills is
underexplored. vaToM tasks provide participants with a set of emotion words from which
to select the most appropriate label for each facial expression. However, as mentioned
above, real-world social interaction lacks such explicit prompts to reason about mental
states. In contrast,
spontaneous ToM
(sToM), the propensity to attribute mental states in
the absence of explicit prompts1, is commonly measured using social animation tasks in
which participants are asked to describe scenes depicting interactions between geometric
shapes; responses are then coded for the degree of spontaneous mental state attribution
(Abell et al., 2000; Castelli et al., 2002; Klin, 2000). sToM may be another key mechanism
for cToM, allowing one to actively make inferences or predictions about a conversation
partner’s mental states to guide and adjust one’s own communication.
The present study aims to demonstrate the instrumental role of ToM in social interaction
through observational coding of unstructured, face-to-face conversations among autistic and
typically developing (TD) children and adolescents. Guided by theoretical arguments (e.g.,
the “double empathy problem”; Milton, 2012) and empirical work suggesting that dyadic
interactions differ depending on the characteristics of each member, particularly autism
status (Crompton et al., 2020; Morrison et al., 2019), our study includes dyads that were
either matched (both TD or both autistic) or mismatched (one TD, one autistic).
Within this ecologically valid context, we established a novel coding framework for cToM
that builds on prior work by focusing on ToM-relevant behaviors rather than general
pragmatic competence, and by considering both positive and negative indicators of cToM
rather than a deficit-only approach (Clark & Adams, 2020; McCrimmon & Montgomery,
2014). After examining differences between autistic and TD participants on cToM, we
examined cToM in relation to the two aforementioned facets of ToM (measured with
non-interactive tasks) that are especially relevant to conversation: vaToM and sToM. We
hypothesized that both vaToM and sToM would show positive associations with the
tendency to apply ToM in conversation (cToM Positive) and negative associations with ToM-
related violations of conversational norms (cToM Negative) (Hypothesis 1). Finally, in line
with the widespread assumption that ToM plays an instrumental role in social interactions,
1This distinction between explicit and spontaneous
tasks
should not be confused with the distinction between explicit and implicit
cognitive processes
that is common in the ToM literature (e.g., Frith & Frith, 2008). Throughout this paper, we highlight measures
(sToM and cToM) that are spontaneous in that they do not explicitly prompt participants to engage in ToM; we make no claim about
whether the cognitive processes participants may spontaneously engage are explicit or implicit in the sense of the two-systems account
of ToM (Apperly & Butterfill, 2009).
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we hypothesized that cToM Positive would positively predict interaction success, as rated by
interaction partners, and cToM Negative would negatively predict success (Hypothesis 2).
Methods
Participants
The present study was part of a larger ongoing study approved by the University
of Maryland’s Institutional Review Board (approval number 733144). Participants were
recruited largely based on participation in previous studies in our lab. Additional autistic
participants were recruited through the Interactive Autism Network, Simons Foundation
Powering Autism Research (SPARK), Facebook advertisements, and flyering at local events.
We appreciate obtaining access to recruit participants through SPARK research match
on SFARI Base. Additional TD participants were recruited through the University of
Maryland’s Infant and Child Studies database. Autism diagnoses were confirmed using
the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; Lord et al.,
2012) administered by a research-reliable clinician. All participants were native English
speakers with a full-scale IQ of at least 80 on the Kaufman Brief Intelligence Test, 2nd
edition (KBIT-2; Kaufman & Kaufman, 2004). TD participants had no diagnosis of any
neurological or psychiatric disorders, or first-degree relatives with autism or schizophrenia.
Additional diagnoses of the autistic participants are reported in the Supplementary
Materials.
The sample size was based on a power analysis using the Actor–Partner Interdependence
Model (APIM) Power Analysis Shiny Application (Ackerman et al., n.d.), which indicated
that a sample of 50 dyads provides 80% power to detect effects with standardized beta
weights of at least 0.3. See Supplementary Materials for additional details.
Dyad members were matched on gender and age (within 1 year or grade level) and
were arranged into three dyad types: AUT-TD (one autistic, one TD participant), TD-TD,
and AUT-AUT. The final sample of 50 dyads included 25 TD-TD (8 female dyads), 18
AUT-TD (4 female dyads), and 7 AUT-AUT (1 female dyad)2; across dyad types, the
sample comprises 68 (20 female) TD and 32 (6 female) AUT individuals, for a total of
100 individuals. Each participant was included in only one dyad. The average age across
the whole sample was 13.34 years, with a range of 8.72–16.91 and a standard deviation
of 1.85 years. Participants’ gender was assessed via parent report: parents were asked to
select between the options of “male” or “female” to indicate their child’s gender. Race
and ethnicity distributions for the whole sample are as follows: 2% Asian, 11% Black or
African American, 65% White, 11% more than one race, 11% not reported; 9% Hispanic or
Latino, 78% not Hispanic or Latino, 13% not reported. Additional participant demographics,
including indicators of socioeconomic status, are reported in Supplementary Table 1.
2We had intended to recruit a balanced number of dyads across the three types; however, data collection was halted indefinitely
by the COVID-19 pandemic. While we were sufficiently powered to test the hypotheses presented in this paper, the limited sample–
particularly of AUT-AUT dyads–precluded us from testing the effect of (or interactions with) dyad type as a main research question.
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Face-to-face Peer Interaction Task
The unstructured conversations took place within a peer interaction paradigm developed
by Usher et al. (2015, 2018). After separately providing informed written consent along
with their caregivers, participants were seated across from each other in a behavioral testing
room in which three video cameras recorded the interaction from multiple angles. The
experimenter told participants they would engage in simple activities together for about 20
minutes, then said, “Before I explain your task, why don’t you get to know each other? I’ll
be back in about five minutes.” This five-minute “Get to Know You” period was followed
by two semi-structured activities. Given that the present study focused on unstructured
conversation, only the Get to Know You portion was coded for cToM. Following these
activities, participants were placed in separate rooms, where they individually completed
questionnaires and additional behavioral tasks.
cToM Rating Scale
Conversations were rated along two scales, Positive and Negative, based on whether
behaviors reflected the presence (Positive) or absence (Negative) of ToM. Categories of
behaviors considered for the cToM rating are described in Supplementary Table 3; where
applicable, real examples from the observed sample are provided. Among other categories,
the Positive scale included explicit references to the partner’s mental state, whereas the
Negative scale included violations of neurotypical conversational norms (e.g., over- or
under-informative statements). The Positive and Negative scales were 6 points each (0 to 5),
with higher numbers representing higher frequencies of behaviors.
The Get to Know You task was coded for cToM by a team of three trained research
assistants supervised by the first author. Coders were masked to participant diagnosis except
for three participants who self-disclosed their autism diagnosis and one who self-disclosed
his lack of autism diagnosis during the conversation task. Furthermore, due to concern that
coders may be biased by participants’ physical appearance or mannerisms such that they
could speculate on diagnosis or form a negative impression (Sasson et al., 2017), coding
was based on extracted audio files rather than video. Prior to coding, conversations were
transcribed from the videos using the Computerized Language Analysis (CLAN) program
(MacWhinney, 2000). Transcripts assisted coding by indicating meaningful nonverbal
gestures (e.g., nodding the head yes or no, shaking hands, etc.), which provided additional
information by which raters could interpret the conversation, and by highlighting mental
state words, which were taken into consideration for the explicit ToM category. All
transcripts were checked for accuracy by a second research assistant or the first author.
Additional details on the coding system, training and coding procedures, and interrater
reliability are reported in the Supplementary Materials. Reliability coefficients for the final
dataset were as follows: Positive scale, Krippendorff’s alpha = 0.81; Negative scale, Finn’s
r
= 0.943. Coefficients of 0.7 or above are considered acceptable for both Krippendorff’s
alpha and Finn’s
r
(Hayes & Krippendorff, 2007; Heyman et al., 2014).
3Finn’s
r
was used to accommodate the skewed distribution of the Negative scale; see Supplementary Materials.
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Control variables
For both hypotheses, analyses included the following variables (in italics) as covariates of no
interest.
Verbal IQ
, consistently linked to performance on ToM tasks (Ronald et al., 2006;
Scheeren et al., 2013), was measured via the KBIT-2 Verbal Intelligence score.
Executive
functioning
(EF), also known to correlate with ToM (Jones et al., 2018; Lecce et al., 2017)
and pragmatic abilities (Matthews et al., 2018), was measured via the Global Executive
Composite on the Behavior Rating Inventory of Executive Function, 2nd ed. (Gioia et al.,
2015). Higher scores on this measure correspond to greater impairment.
Age, gender
, and
autism diagnosis
are also associated with variability in ToM skills (Bal et al., 2013; Kirkland
et al., 2013). Finally, given that cToM scores were derived from speech, we controlled for
the amount of speech produced by each individual to ensure that any effects of interest
were specific to cToM and not simply driven by how much an individual talked.
Language
productivity
was operationalized as the total number of words per minute (Scott & Windsor,
2000) spoken by each child during the 5-minute interaction, as calculated by CLAN. For
Hypothesis 1 only, this total was averaged together with the average word count of responses
to the sToM task (described below) to create a language productivity composite.
Hypothesis 1: Non-interactive ToM predicts cToM
After the peer interaction, participants individually completed two non-interactive ToM
tasks: vaToM and sToM.
vaToM was assessed using the Face task from the Cambridge Mindreading Face-Voice
Battery for Children (Golan et al., 2015). Children viewed videos of actors expressing
complex emotions (emotions involving cognitive states) and chose the appropriate label
from among four choices. The task consisted of 27 items representing nine emotional
concepts: unfriendly, disappointed, embarrassed, jealous, loving, nervous, bothered, amused,
and undecided. The measure of interest was accuracy (% correct). See Supplementary
Materials for details on stimulus presentation and task administration.
sToM was assessed using the Frith-Happé Triangles task (Abell et al., 2000). Children
viewed four short animations in which two triangles move in a manner that suggests a
ToM-related interaction. After viewing each video, children were asked to describe the
cartoon while their verbal responses were audio-recorded. Responses were coded for the
frequency of unique and appropriate internal state attributions (mental states, intentions, and
emotions) following guidelines adapted from Rice and Redcay (2015); see Supplementary
Materials. Responses were independently coded by two trained research assistants, with
24% of cases double-coded and discussed with the coding supervisor (a third research
assistant) during weekly meetings until consensus was reached. Interrater reliability was
excellent (Krippendorff’s alpha = 0.85). Scores for each of the three items (excluding a
practice item) were averaged for an overall sToM score.
Analysis strategy—Due to the dyadic data structure and thus the possibility of non-
independence among variables, actor-partner interdependence models (APIM; Kenny et al.,
2006) were used to estimate the effects of interest (vaToM and sToM) on cToM. The APIM
simultaneously estimates actor effects (e.g., the effect of Person 1’s vaToM on Person 1’s
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cToM) and partner effects (e.g., the effect of Person 1’s vaToM on Person 2’s cToM); see
Figure 1. We ran separate models with each of the cToM scales as outcomes. Each model
included actor and partner effects of the non-interactive ToM variables, diagnosis (AUT or
TD), verbal IQ, EF, and the language productivity composite; and age (averaged between
dyad members) and gender as dyad-level covariates. The hypothesized effects were the
actor effects of each of the non-interactive ToM variables. Additionally, we ran reduced
versions of these models without partner effects of the individual-level predictors. Results
and interpretations for the reduced models were similar to the full models and are reported
in the Supplementary Materials, along with details on the modeling approach used for
each cToM scale. Because the Negative scale had a highly skewed distribution, leading to
violations of the assumptions of linear models, for this model we instead applied generalized
estimating equations, which make no distributional assumptions and are recommended for
estimating APIMs with non-Gaussian outcomes (Loeys et al., 2014; Loeys & Molenberghs,
2013). All APIM analyses (both hypotheses) were conducted using SPSS Version 26; the
data and syntax used for these analyses are available via the Open Science Framework.
Hypothesis 2: cToM predicts interaction success
Following the peer interaction, participants individually completed six items from the Social
Interaction Evaluation Measure (Berry et al., 1996) assessing interaction quality, that is,
how much the participant enjoyed the interaction and would like to interact again (see
Supplementary Materials for specific items). The summed total of responses to these items
from each participant’s partner served as the operational definition of interaction success.
We predicted that each cToM scale would have a significant effect on interaction success
(positive effect for Positive, negative effect for Negative).
Analysis strategy—For each of the cToM scales, an APIM was estimated using linear
mixed effects modeling in SPSS, with actor and partner cToM as the predictors and
interaction success as the outcome. Covariates included actor and partner effects of verbal
IQ, language productivity, and EF, as well as dyad-level age, gender, and dyad type (TD-TD,
AUT-TD, AUT-AUT). We included dyad type rather than individual diagnosis as a covariate
based on dyadic research suggesting that perceptions may depend on the match or mismatch
between partners’ diagnosis rather than an individual’s diagnosis alone (Morrison et al.,
2019).
We also conducted a planned follow-up analysis examining the effect of vaToM and sToM
on interaction success (see Supplementary Materials).
Community Involvement Statement
Community members were not involved in the design, implementation, or interpretation of
the study.
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Results
Preliminary Analyses
Descriptive statistics, group comparisons, and zero-order correlations among all variables
are shown in Supplementary Tables 4–7. Notably, Positive and Negative scales were
uncorrelated (
r
= .00). Figure 2 depicts the distributions of and group differences in each
cToM scale. The Negative scale showed a marked group difference in the expected direction
based on previous literature, with autistic participants rated higher (meaning they displayed
more frequent negative cToM behaviors;
t
(35.14) = 3.25,
p
= 0.003). In contrast, TD and
autistic participants showed very similar distributions on the Positive scale (
t
(59.18) = 0.02,
p
= 0.98).
We also examined the frequency of each category of behavior within the cToM coding
system. Figure 3 depicts these within-category scores and their averages across the full
sample and for each group separately. The most frequent category on the Positive scale
was explicit ToM, followed by distinguishing common versus privileged ground. The other
Positive categories were relatively infrequent, with only a handful of participants scoring 1
or above. Categories on the Negative scale were more comparable in their frequency, with
most participants showing no or very few instances across categories (as reflected in the
skewed distribution of the overall Negative score).
Autistic and TD participants showed comparable rates across all Positive categories. On the
Negative scale, higher rates within each category were represented almost exclusively by
a minority of autistic participants. On average, autistic participants were more likely than
TD participants to violate maxims of quantity, relevance, and manner and to interrupt their
partners in a manner suggesting they were either not attending to their partner’s mental state
or were attending but struggled to integrate this information into their responses.
The distributions of the other key variables in Hypotheses 1 and 2 are plotted by diagnostic
group in Figure 4. Groups did not significantly differ on sToM (
t
(45.83) = −0.22,
p
= 0.83),
but accuracy on the vaToM task was significantly lower in the autistic group (
t
(42.21) =
−3.72,
p
< 0.001). Groups did not significantly differ on interaction quality (participant’s
own rating;
t
(50.23) = 0.63,
p
= 0.53) or interaction success (partner rating;
t
(55.51) = 0.56,
p
= 0.58). Interaction success did not significantly differ across dyad types (
F
(2,97) = 1.07,
p
= 0.35; AUT-TD vs. TD-TD:
t
(80.65) = −0.44,
p
= 0.66; AUT-TD vs. AUT-AUT:
t
(17.19)
= 1.17,
p
= 0.26; AUT-AUT vs. TD-TD:
t
(16.94) = 0.95,
p
= 0.36). The distributions of all
additional variables are plotted by group in Supplementary Figure 1.
Hypothesis 1 Results
For cToM Negative, there was a significant negative actor effect4 of vaToM (
B
= −0.24,
SE
= 0.09,
p
= 0.01) and a significant negative actor effect of sToM (
B
= −0.30,
SE
= 0.13,
p
= 0.02), supporting our hypothesis that non-interactive ToM predicts cToM (Figure 5).
However, this hypothesis was not supported for cToM Positive, for which there were no
4That is, there was a negative effect of an individual’s own vaToM on their own cToM Negative score; see Figure 1.
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significant actor effects of either vaToM or sToM. Unstandardized regression coefficients for
all effects are reported in Table 1.
Hypothesis 2 Results
To test the hypothesis that cToM predicts interaction success, we estimated linear mixed
effects models with each of the cToM scales as predictors. Contrary to our hypothesis,
there were no significant effects of either cToM scale predicting interaction success
(Supplementary Table 11).
Discussion
This study introduced the cToM coding system, a novel approach to measuring the use
of ToM in social interaction that capitalizes on properties of naturalistic conversation
that inherently involve ToM. We found partial support for our hypotheses in that cToM
(Negative) was correlated with non-interactive ToM; however, cToM was not related
to interactive success. We also demonstrated dissociations between cToM Positive and
Negative scales, and this divergence is relevant to our understanding of cToM in autism.
Divergence between cToM Positive and Negative scales
The Positive and Negative cToM scales—reflecting the presence or absence, respectively, of
ToM-related behaviors—were decidedly uncorrelated (
r
= 0.00). Given that they appear to
capture orthogonal dimensions, it is not surprising that we found divergent results for the
two scales in Hypothesis 1, as well as in the between-group comparison.
Comparing TD and autistic participants on cToM revealed a few noteworthy patterns,
especially considering the prominent mindblindness theory of autism. As expected, at the
group level the autistic participants were rated significantly higher than TD participants on
the Negative scale (reflecting more frequent negative cToM behaviors). Thus, the Negative
scale seems to capture ToM-relevant pragmatic difficulties that have been previously
documented in the autism literature, particularly violations of Gricean maxims (Landa,
2000; Paul et al., 2009; Ying Sng et al., 2018). At the same time, autistic and TD participants
showed highly similar distributions on the Positive scale, and this was also true at the
category level (Figure 3). Consistent with previous findings (reviewed in Bang et al.,
2013), autistic participants used mental state language (“explicit ToM” in our categorization)
to the same extent as TD participants. Furthermore, while explicit ToM was the most
frequent category within the Positive scale, the autistic participants’ scores do not solely
reflect superficial knowledge of mental state concepts; they were also comparable to TD
participants on the second-most frequent category of distinguishing between common
and privileged ground. Behaviors coded in this category include preemptively sharing
information that would plausibly be unknown to one’s partner, or asking whether one’s
partner shares certain knowledge, both of which reflect an active awareness that others
can possess different knowledge states from oneself. Thus, our findings converge with
other evidence that autistic individuals’ ToM abilities are often underestimated (Heasman &
Gillespie, 2018), and that they are aware of common ground and modify their discourse in
response (De Marchena & Eigsti, 2016). Together with the lack of correlation between the
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Positive and Negative scales, this pattern of between-group difference and similarity implies
even those autistic individuals who violate certain neurotypical conversational norms may be
comparable to their TD peers on other aspects of cToM. As such, the abilities captured by
the cToM Positive scale could be recognized as an area of strength that could potentially be
leveraged in social skills interventions (McCrimmon & Montgomery, 2014).
The discrepancy between the Positive and Negative scales also raises the question of how
they may differ in their cognitive demands. Focusing on the dominant category of explicit
ToM, simply referencing a partner’s mental state does not necessarily require switching
between two perspectives (self vs. other) beyond the basic recognition that others have their
own mental states. Thus, it is possible that the behaviors captured by the Positive scale do
not reflect the degree of perspective-taking necessary for scoring low on the Negative scale.
cToM Negative relates to non-interactive ToM
Non-interactive ToM—specifically, visual-affective (vaToM) and spontaneous (sToM)—was
negatively associated with cToM Negative but not Positive. The association between vaToM
and cToM Negative suggests that children who struggle to identify complex emotions
based on facial expressions also tend to violate neurotypical conversational norms, such
as providing too little, too much, or irrelevant information, or expressing themselves
in a confusing manner. It is easy to imagine how this association might play out in
a conversation. For example, a child who does not pick up on her partner’s confused
expression would be unaware that her utterances come across as irrelevant or unclear (in
the absence of verbal feedback to this effect), and thus would not adjust her behavior. In
contrast, explicitly referencing a partner’s mental state could reflect a general (rather than
partner-specific) understanding of diverse mental states, as argued above, and thus might not
necessitate “checking in” with a partner to dynamically integrate feedback from their facial
expressions—hence the lack of association between vaToM and cToM Positive.
The negative association between sToM and cToM Negative is consistent with the
notion that successfully navigating a conversation, and social interaction more broadly,
involves spontaneous mental state attribution. Specifically, participants who described social
animations in more mentalistic terms were rated lower on cToM Negative, suggesting
that these children spontaneously attributed mental states to their partners during the
conversation, and that this allowed them to follow neurotypical conversational norms. The
lack of association between sToM and cToM Positive is somewhat surprising given that our
sToM measure was the frequency of mental state attributions within a verbal description, and
cToM Positive scores were largely driven by explicit mental state language. However, it is
important to consider that while the sToM task did not explicitly prompt children to attribute
mental states, it did require them to give verbal descriptions of the scenes. In contrast, most
mental state attributions made during a naturalistic conversation are almost certainly not
vocalized. Whereas cToM Negative may be related to the general tendency to make mental
state attributions (which may be unvocalized unless prompted), cToM Positive, specifically
the explicit ToM category, indexes the subset of mental state attributions that happen to be
vocalized during the conversation.
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Altogether, these results add to the relatively sparse literature linking diverse aspects of ToM
with conversational skills.
cToM does not predict partner-reported interaction success
Contrary to our hypothesis, neither cToM scale was a significant predictor of interaction
success, as measured by partner ratings of interaction quality. Two aspects of our
methodology must be considered when interpreting these null findings.
First, this study focused on the specific context of a single interaction with an unfamiliar
peer. This setup was meant to represent the type of real-world scenario that, if successful,
would lead to further interactions and eventually the formation of a long-term relationship.
Autistic individuals often find interacting with unfamiliar peers particularly challenging,
as they struggle to apply their social knowledge (including skills learned through explicit
training) to novel situations (Bauminger-Zviely et al., 2013; Usher et al., 2015). As
such, we expected our paradigm to be more effective at eliciting cToM difficulties than
would interacting with familiar individuals. However, it is possible that cToM plays a
more important role in the perceived quality of interactions between familiar compared
to unfamiliar partners. That is, as two people gain more experience with each other, one
might expect the other to make more accurate mental state attributions about oneself, and
thus failure to do so would be more striking. More generally, while the present study
suggests that cToM is not a significant factor in how individuals judge the quality of a single
interaction, the full impact of ToM on relationship quality likely plays out over repeated
interactions. Future studies could explore this possibility by following dyads longitudinally
and tracking their interaction patterns, including cToM, and relationship outcomes over time.
Second, our choice of partner self-report as the measure of interaction success may have
limited our ability to detect a true effect of cToM, as social desirability bias may have led
some children to underreport any dissatisfaction with the interaction. We do not believe
this was a major concern, as there is a decent spread of scores on this measure (Figure 4).
Furthermore, in a post hoc exploration of a subset of participants’ future partner preferences
(i.e., whether they would prefer the same or a different partner if they were to participate
in another session; see Supplementary Materials), which they completed at the end of
the session while the experimenter was out of the room, the extent to which participants
preferred the same partner correlated positively with their earlier report of interaction quality
(
r
= 0.42,
p
< 0.001). This suggests that our measure of interaction success is a reasonable
proxy for the participants’ motivation to interact with their partners again, a prerequisite for
long-term relationship formation. Nevertheless, future studies could incorporate alternative
measures of peer acceptance, such as classroom sociometric ratings, that may be stronger
indicators of real-world interaction success.
General limitations and future directions
A few additional caveats apply to our discussion of the overall study. First, while our sample
of 50 dyads allowed sufficient power to test our hypotheses, a larger sample would likely
produce more robust and reproducible results. Furthermore, the relatively low proportion
of autistic participants (32% of the total sample) likely limited the variability we observed
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in the cToM Negative scale, on which only a handful of autistic participants received
high ratings. This in turn may have limited our ability to detect the predicted effects.
The generalizability of our findings to the wider autistic population is also limited, as we
excluded individuals with IQs below 80 or who are minimally verbal, and our autistic
sample was 84% White. It is also possible that our findings would not generalize to age
groups outside of the middle childhood to adolescent range. Additionally, while there is
growing recognition that autistic females often differ from their male counterparts in their
experience and behavioral presentation of autism, including social behaviors (Dean et al.,
2017; Mandy & Lai, 2017; Wood-Downie et al., 2021), our sample was predominantly
male. Further research with larger and more balanced samples should assess whether gender
moderates the relations under study, and whether gender interacts with autism diagnosis
to predict cToM or interaction success. We also recognize the limitation that we assessed
gender via parent- and not self-report and provided only binary options. In line with growing
recognition of a broader spectrum of gender identities, particularly among the autistic
population (Corbett et al., 2022; Strang et al., 2020), future studies should include nonbinary
and other options for gender and pose this question to the participants themselves.
Future studies should also further explore AUT-AUT interactions, which constituted only a
small portion of our sample. Recent work has suggested that AUT-AUT interactions may
qualitatively differ from TD-TD or TD-AUT interactions, including features relevant to
cToM such as a generous assumption of common ground (Heasman & Gillespie, 2019),
and in ways that impact interaction success (Granieri et al., 2020; Morrison et al., 2019).
Such findings support the idea of the double empathy problem (Milton, 2012), in which
social interaction difficulties are attributed not solely to the autistic individual, but to a
breakdown in communication resulting from the different experiences and expectations of
autistic and neurotypical people (Crompton et al., 2021). Thus, rather than being a static
trait of an individual, cToM may be intrinsically tied to the dyadic context created between
an individual and a particular partner, and an important aspect of this context may be the
match or mismatch in autism status. As such, future studies of cToM should be sufficiently
powered to investigate the effect of dyad type (i.e., match or mismatch) on both cToM and
interaction success.
Other limitations relate to the cToM measure itself. Because coding was based on the
audio and transcripts of the conversations, we were unable to capture potentially relevant
features like facial expressions and eye gaze; future iterations of the cToM system could
be expanded to include the visual modality. Furthermore, any measure based on third-party
ratings has the limitation of not directly accessing an individual’s subjective experience.
We encourage future studies using the cToM coding system to collect self-report measures
immediately following the conversation to provide complementary information about how
people perceive their own use of ToM in conversation. Ratings about an individual’s cToM
from their conversation partner would be similarly valuable and may be more likely to
uncover associations between cToM and partner-rated interaction success.
Further research is also needed to validate and refine the cToM rating scales. Beyond
the above-discussed divergence between the Positive and Negative scales, there may be
heterogeneity within each scale in terms of the cognitive processes they reflect. Future
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research using a larger sample, and perhaps a slightly more structured interactive task
that elicits higher rates of the categories that were infrequent in the present study (e.g.,
misunderstandings, explicit perspective-taking, and irony comprehension), could employ
factor analysis to characterize the pattern of correlations among the categories. Uncovering
the latent dimensional structure of cToM could enable more precise assessments of its
relations with non-interactive ToM, interaction success, and other constructs.
Finally, our cToM coding system was developed without input from autistic perspectives
and thus is biased to reflect the norms of the dominant neurotypical culture; these norms
may not be as valued by autistic individuals. Future studies could involve autistic individuals
in adapting the cToM system to better capture how autistic individuals consider (or do not
consider) each other’s perspectives while interacting, and how important this is to their
perceptions of interaction quality.
Conclusion
We introduced the cToM coding system, addressing the need for interactive, ecologically
valid measures of ToM. While we did not find evidence that this novel measure improves on
standard ToM measures in predicting interaction success, the cToM is a useful framework
for characterizing the various ways in which ToM- related difficulties show up in naturalistic
conversations between TD and autistic individuals. Crucially, the divergence between the
Positive and Negative scales reveals the multidimensionality of ToM in conversation. This
finding is valuable in two respects. First, it adds to mounting evidence of divergence
between distinct components of ToM in general (Schaafsma et al., 2015; Warnell & Redcay,
2019). Second, it refines our understanding of ToM and pragmatic difficulties among
autistic individuals, as even those who struggle with ToM-related violations of neurotypical
conversational norms can nevertheless display typical levels of other forms of mental state
representation, such as those indexed by the cToM Positive scale. Furthermore, our finding
that cToM Negative relates to two forms of non-interactive ToM can inform future studies
of the more basic processes that support the application of ToM in social interactions.
Altogether, the present study provides a springboard for further investigation into the
mechanisms and consequences of ToM-related behavior within naturalistic conversations.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgements
We thank the members of the cToM coding team: Ryan Regars, Aliceann Trostle, Daniel Friedman-Brown,
and Ming Yuan; Katie Beverley, Hema Clarence, Diana Grant, Sarah Gray, Alex Kalomiris, Dahye Kang, and
Manasvinee Mayil Vahanan for their assistance with transcription and coding; Jacqueline Thomas, Alexandra
Hickey, Tina Nguyen, Micah Plotkin, Kathryn Bouvier-Weinberg, Matthew Kiely, Ryan Stadler, Aranje
Sripanjalingam, Dominic Smith-DiLeo, Avi Warshawsky, Maddie Reiter, Bess Bloomer, Miranda Sapoznik, Nicole
Chapman, and Aiste Cechaviciute for their assistance with data collection; Dr. Lauren Usher for conceiving of
the Get to Know You task; and Drs. Peter Carruthers, Jude Cassidy, and Edward Lemay for their advice on study
design, methods, and data analysis. Finally, we thank the participants and their families for making this research
possible.
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Funding
Research reported in this publication was supported by the National Institute of Mental Health of the National
Institutes of Health under Award number R01MH107441. The content is solely the responsibility of the authors and
does not necessarily represent the official views of the NIH.
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Figure 1.
Schematic of the actor-partner interdependence model.
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Figure 2.
Distributions of the cToM scales among the full sample (leftmost graphs) and by diagnostic
group. AUT = autistic, TD = typically developing
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Figure 3.
Category-level analysis of cToM Positive and Negative scales. Within each scale, sample-
wide averages are displayed (with individual data points superimposed) above boxplots
comparing the distributions within diagnostic group. On the Positive scale, “Following
maxims” refers to consistently following the maxims of quantity, relevance, and manner
throughout the conversation; each child was assigned a global score of either 1 (consistent
throughout), 0.5 (followed maxims for about half the conversation), or 0 (rarely followed
maxims).
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Figure 4.
Distributions plotted by diagnostic group for (A) non-interactive ToM and (B) interaction
quality (participant’s own rating) and interaction success (partner rating). Interaction success
is also plotted by dyad type. vaToM = visual-affective theory of mind. sToM = spontaneous
theory of mind. ***
p
< 0.001 ns = not significant (
p
> 0.1)
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Figure 5.
Hypothesis 1 results for cToM Negative. B = unstandardized beta coefficient. vaToM =
visual-affective ToM, sToM = spontaneous ToM. *
p
< 0.05 **
p
< 0.01 ns =
p
> 0.1
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Table 1.
Hypothesis 1: Parameter estimates of non-interactive ToM predicting cToM
cToM Negative cToM Positive
Effectsa B SE 95% CI
(lower, upper) P B SE 95% CI
(lower, upper) P
vaToM
Actor −0.24
**
0.09 −0.42, −0.07 0.01 −0.17 0.15 −0.46, 0.11 0.24
Partner 0.07 0.12 −0.17, 0.31 0.55 0.03 0.14 −0.25, 0.31 0.84
sToM
Actor −0.30
*
0.13 −0.56, −0.04 0.02 −0.01 0.18 −0.35, 0.34 0.98
Partner 0.02 0.12 −0.27, 0.22 0.84 −0.23 0.18 −0.58, 0.12 0.20
Verbal IQ
Actor −0.01 0.01 −0.03, 0.00 0.13 −0.00 0.01 −0.03, 0.02 0.69
Partner −0.01 0.01 −0.03, 0.00 0.10 −0.01 0.01 −0.04, 0.01 0.20
LangProd
Actor 0.75
**
0.16 0.43, 1.06 0.00 0.64
**
0.22 0.20, 1.08 0.00
Partner 0.03 0.17 −0.31, 0.37 0.86 0.20 0.22 −0.24, 0.64 0.36
EF
Actor 0.01 0.01 −.01, 0.03 0.22 0.00 0.01 −0.01, 0.02 0.61
Partner −0.01 0.01 −0.03, 0.01 0.56 0.01 0.01 −0.00, 0.03 0.13
Dx (AUT)
Actor 0.28 0.35 −0.40, 0.97 0.42 −0.48 0.54 −1.54, 0.58 0.37
Partner 0.74 0.55 −0.36, 1.83 0.18 −0.78 0.54 −1.84, 0.28 0.15
Dyad age −0.06 0.08 −0.22, 0.10 0.46 0.22
#
0.12 −0.02, 0.46 0.07
Dyad gender (F) 0.38 0.31 −0.22, 0.98 0.22 0.32 0.39 −0.45, 1.09 0.41
a
Hypothesized effects in bold
*p
< 0.05
** p
< 0.01
#p
< 0.1
B
= unstandardized beta coefficient,
SE
= standard error, CI = confidence interval, LangProd = language productivity composite (see Methods), EF
= executive functioning (higher scores correspond to greater impairment), Dx = diagnosis
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