Registered report: investigating a preference for certainty in conversation among autistic adults compared to dyslexic adults and the general population

Intolerance of uncertainty

A preference for certainty and explicit communication may link to the widely-researched cognitive trait, Intolerance of Uncertainty (IU). IU has been defined as a tendency to negatively evaluate uncertain situations and information (Shihata et al., 2016). We use the term ‘Intolerance of Uncertainty’ in line with previous research and intend to convey a value-neutral meaning in using it, as we recognise that high levels of IU may be an understandable, even adaptive, response where individuals have experienced mishaps in confusing situations. IU has mostly been investigated as a transdiagnostic construct that plays a central role in emotional disorders across the general population (see Shihata et al. (2016) for a review), but it also seems especially relevant to autism, with autistic children and adults showing significantly elevated levels of the trait compared to the general population (Hwang et al., 2020; Vasa et al., 2018). IU has been closely linked to anxiety in autistic people (Jenkinson, Milne & Thompson, 2020), and also relates to core features of autism, including social difficulties, sensory sensitivities, insistence on sameness and repetitive behaviours (Hwang et al., 2020; Vasa et al., 2018; Wigham et al., 2015).

A possible link between IU and communication in autistic people remains largely unexplored. However, there are reasons to believe that a link is plausible. First, inferential models of communication, such as Relevance Theory (Sperber & Wilson, 1986), propose that communication inherently involves uncertainty. Under Relevance Theory, language comprehension is not simply a process of ‘understanding what the words mean’, as there are often indeterminacies and ambiguities in uses of language. Instead, the words are used as evidence by the listener in supporting a hypothesis about what the speaker probably means in the context—that is, the listener infers the speaker’s intended meaning under conditions of uncertainty. Relevance Theory suggests that there is a gradient of uncertainty in communication. Sometimes, the listener can rely mostly on the explicit content of the utterance to compute the intended meaning. In other situations, there is a greater reliance on inferential processing to understand the speaker’s probable meaning by integrating the utterance with contextual cues and world knowledge. Compare for instance the utterances ‘No, let’s stay inside’ and ‘It’s quite cold today’ as responses to a suggestion to go outside. In the second example, the speaker communicates implicitly, leaving the listener to process the implicature that they would probably prefer to stay inside. In a previous study, we provided evidence for cognitive differences between autistic and non-autistic people in processing implicatures (Wilson & Bishop, 2020b). Crucially, it seemed that a cognitive preference for certainty and explicit communication, and not simply reduced ability, may account for some of the differences. Participants completed the Implicature Comprehension Test, which required individuals to listen to short conversational interchanges that are followed by a comprehension question to assess whether an implied meaning has been processed; test-takers responded with ‘yes’, ‘no’ or ‘don’t know’. Controlling for grammar/vocabulary ability, we found that autistic adults (N = 66) were 6.19, 95% CI [3.63–10.39], times more likely to select the ‘don’t know’ rather than the correct response compared to non-autistic people (N = 118), and also 2.56, 95% CI [1.76–3.77], times more likely to choose the ‘incorrect’ response (Wilson & Bishop, 2020b). Group differences were large, and performance on the test gave 76% sensitivity and specificity for differentiating between autistic and non-autistic groups. On the face of it, these results suggest that autistic people have difficulties inferring the gist of a speaker’s meaning, as predicted by the ‘central coherence’ theory. This theory proposes that autistic people may show less tendency than non-autistic people to process information at a global level (Frith, 1989).

However, in an alternative version of the test without a ‘don’t know’ response, autistic individuals showed high accuracy for items for which they had selected ‘don’t know’ first time round. This marked tendency to select ‘don’t know’ when given a chance, but to process the inference as intended when constrained by the task, suggested reduced confidence in the face of uncertain information and a preference for explicit communication. This could be due to possible difficulties around metacognition in autistic people, who may experience a mismatch between performance and confidence in their performance due to differences in self-monitoring. There is evidence that autistic people may show such a mismatch (Grainger, Williams & Lind, 2016; Nicholson et al., 2019) although there is some concern about the replicability of these results (Maras, Norris & Brewer, 2020). An alternative view would be that it is less an issue of metacognitive ‘ability’, and more about differences in personality/cognitive preference, with the well-replicated elevated levels of IU in autistic people (Hwang et al., 2020; Vasa et al., 2018) accounting for this apparent preference for explicit communication observed in our previous study. In the present study, we aim to replicate this finding with more refined methods. In an adapted version of the Implicature Comprehension Test, individuals will respond using a 4-point scale of ‘yes’, ‘maybe yes’, ‘maybe no’, and ‘no’, allowing us to capture accuracy and confidence in the same measure. We hypothesise that confidence is likely to be affected specifically in a pragmatic language task (i.e. where the individual needs to make flexible context-dependent inference about uncertain implied meanings), and not on tasks focused on more structural, codified aspects of language such as grammatical competence. As such, we present the Grammaticality Decision Test as a control task with a similar response format to the pragmatic task to test the specificity of any differences.

We propose that reduced confidence on the Implicature Comprehension Test will be a marker of IU in autistic people, and may be a more influential factor in the communication difficulties diagnostic of autism, as opposed to a ‘deficit’ in understanding social meanings. If this claim is borne out, it would have a couple of implications for psychological practice. First, it would suggest that interventions targeting IU may be useful for autistic people wanting support with communication challenges. Current interventions for communication focus on explicit instruction in social skills, and reviews suggest modest effectiveness although there are questions about the extent to which skills transfer to daily life (Gates, Kang & Lerner, 2017; Spain & Blainey, 2015). A focus on IU may be a useful alternative target. Existing cognitive interventions involve integrating psychoeducation and cognitive challenge techniques to target a client’s beliefs about (un)certainty, and these have shown some effectiveness for treating mental health difficulties and particularly anxiety in the general population (Shihata et al., 2016). It remains to be seen whether such interventions could be adapted to support autistic individuals with distressing communication experiences, although this is a promising possibility given that early studies suggest that such interventions may be feasible and acceptable for autistic groups (Rodgers et al., 2018, 2017). Second, if a cognitive preference for certainty is especially significant as an explanation for social difficulties, then it supports an autism-positive approach to intervention which focuses on awareness of cognitive differences across communities. In addition, if performance on the Implicature Comprehension Test is a sensitive marker of IU, that in itself might have clinical and research utility, since measurement of IU is currently limited to self- and informant-report questionnaires.

A remaining question is whether any differences observed on our tasks are specific to autism or might also be relevant to other neurodevelopmental diagnoses. This is certainly plausible in the light of dimensional models of neurodiversity, where features of autism, developmental language disorder, dyslexia, ADHD, etc., might show some overlap and exist as a continuum in the general population (Thapar, Cooper & Rutter, 2017). To test the specificity of any cognitive differences observed on our tests, we will compare performance by autistic people to both a dyslexic and a general population sample. As neurodevelopmental conditions are often co-occurring, we view these three groups as defined less by a specific diagnostic label but rather as varying along a communication continuum. As such, one group is defined by social communication differences potentially alongside co-occurring language/literacy impairments (the autism group), a second group by language/literacy impairments but no diagnosed social communication difficulties (the dyslexia group), and a final group without any communication, language or literacy related diagnosis. It is possible that dyslexic adults may show some difficulty on our pragmatic task (i.e. the Implicature Comprehension Test), as previous research has documented some limited evidence for pragmatic difficulties in dyslexic individuals (Cappelli et al., 2018; Cardillo et al., 2018; Griffiths, 2007). An alternative possibility is that adults with dyslexia will show greater difficulty with tasks focused more on structural language skills compared to pragmatics. For instance, a meta-analysis has found that dyslexic adults perform less well on language measures, such as vocabulary, speeded naming, verbal memory and phonological processing, than people without a diagnosis of dyslexia, with moderate to large effect sizes (Swanson & Hsieh, 2009). Given that there is no clear reason to support one of these possibilities over the other, we will take a more exploratory approach with the dyslexic group to examine how they compare with autistic adults.

In summary, we propose the following hypotheses:

1. Autistic adults will score lower on a pragmatic language task when responses are coded purely in terms of confidence (number of yes and no responses, regardless of polarity) than when responses are coded in terms of accuracy (with yes and maybe yes, and maybe no and no responses, combined according to polarity), compared to adults without any neurodevelopmental diagnosis, but will not show this same disparity between accuracy and confidence on a core language task.

2. The number of less confident responses (maybe responses) on the pragmatic language task, the score on the Intolerance of Uncertainty Scale, and self-reported social communication difficulties will significantly intercorrelate across the full sample.

Power calculation

We determined power to detect the three-way interaction described in Hypothesis 1 using simulations. We used data reported in Wilson & Bishop (2020b) to estimate the likely size of fixed and random effects in the mixed model described in “Data analysis” below. Using R package simr (Green & MacLeod, 2016) we ran 1,000 simulations with a sample size of 200 people (100 autistic, 100 non-autistic) and a significant three-way interaction was found in 9,830 simulations, indicating that power was over 98% to detect our effect of interest at an alpha level of 0.05. Effectively, this allows us to detect a significant difference where approximate Cohen’s d values in favour of the non-autistic group are 0.70 and 1.10 for the implicature accuracy and confidence variables and 0.20 for the grammar variables, as suggested by our previous data. Allowing for exclusion of up to 10% of participants during the outlier exclusion phase described in “Data analysis”, power remains very high (98% in a sample of 180). For hypothesis 2, a sample of 200 is powered at over 99% to detect a correlation of 0.3.

Participants

We will recruit individuals with autism, dyslexia, and no neurodevelopmental diagnosis. Based on the power calculation, we will recruit 100 autistic adults and 100 adults without a neurodevelopmental diagnosis in order to run the confirmatory analysis. In addition, we will aim to recruit 50 dyslexic adults as a clinical control group for exploratory analysis. All participants will meet the following eligibility criteria: (i) age of 18 years or over, (ii) native-level fluency in English, (iii) no history of acquired brain injury, (iv) no significant uncorrected sensory impairment, and (v) access to a computer with internet and audio.

Individuals will be recruited into three groups defined by communication and language/literacy problems. One group will be recruited on the basis of a clinical diagnosis of autism; participants will need to declare where, by whom and what label was used for their diagnosis on the Study Questionnaire. For inclusion, the diagnosis must have been made in a clinical service by appropriately trained individuals, such as clinical psychologists, psychiatrists or developmental paediatricians. We will recruit autistic individuals through Autistica, the research network for families and individuals with autism, as well as support groups arranged privately and by the National Autistic Society, and through social media. A second group will include individuals reporting dyslexia or specific reading difficulties. For inclusion in this group, individuals must score below the clinical threshold of six on the Autism Spectrum Quotient (AQ) and at six or above on the Reading Scale of the Adult Reading Questionnaire (ARQ); this latter score translates to over 1.5 SDs above the mean in individuals not self-reporting dyslexia in the original validation study (Snowling et al., 2012). Individuals will be recruited through charitable organisations such as the British Dyslexia Association and social media. Other neurodevelopmental diagnoses will be noted but will not be grounds for exclusion from these groups. A third group will have no neurodevelopmental diagnosis, and will be recruited via the online participant platform, Prolific (https://prolific.co). Individuals will be excluded from this third group if they score above threshold on either the AQ or ARQ (i.e. above six on either) and if they have ever been diagnosed with: a global or specific learning disability, attention deficit hyperactivity disorder, dyspraxia/developmental coordination disorder, a genetic variation (such as Down’s syndrome or Fragile-X) or a neurological condition (such as epilepsy).

Procedure

The study will be presented online using Gorilla, the online platform for behavioural experiments and surveys (https://gorilla.sc/). Individuals complete an online set of tasks and questionnaires in one sitting at a time and place of their choosing. After providing informed written consent to participate, individuals will complete a Study Questionnaire (please see the OSF link above) on which they will be asked to report on demographics and any neurodevelopmental diagnoses. Then they will complete questionnaires/tasks in two sections. The first section will include the experimental tasks required for the hypothesis-testing, and the second will include several brief measures for characterising the sample. The two experimental tasks will be randomised between participants, and all other measures will be administered in the order set out below.

Measures

Data analysis

Individuals will be excluded from the dataset if they have an outlying score for either (a) accuracy on the GDT or the positive control items of the ICT-2 (b) total number of timeouts across the ICT-2 and GDT. Outliers will be defined according to the method of Hoaglin & Iglewicz (1987): more than 2.2 times the interquartile range below the first quartile. In previous work, these criteria led to exclusion of approximately 5% of participants, and captured individuals scoring below approximately 50% on the GDT and 70% on the positive control items of the original version of the ICT (Wilson & Bishop, 2020a).

Data will be analysed in R (R Core Team, 2019). After exclusions, total scores on the two experimental tasks for the groups with autism and no neurodevelopmental diagnosis will be turned into long format, and each participant’s total will be coded for task (ICT-2 or GDT), group (autistic or no neurodevelopmental diagnosis), response (accuracy or confidence) and participant. We will run a mixed effects linear regression using the lme4 R package (Bates et al., 2015). The model will include three fixed effects (task, group and response) and the interactions between these, as well as a random effect (participant). The significance level of the three-way interaction will offer a test of Hypothesis 1. We will also compute correlations between confidence on the ICT-2, self-reported communication challenges on the CC-SR and total score on IUS-12 across the full sample; this will test Hypothesis 2 and represents a dimensional analysis of the relationship between communication difficulties and sensitivity to uncertainty. Table 1 shows a summary of our planned analyses, linking research questions, hypotheses, tests and power calculations.

In exploratory analysis, we will examine how the dyslexic group compares to the autistic group and the group without a neurodevelopmental diagnosis on the ICT-2 and GDT in terms of accuracy and confidence.