A comprehensive psychometric analysis of autism‐spectrum quotient factor models using two large samples: Model recommendations and the influence of divergent traits on total‐scale scores

English, Michael C. W.; Gignac, Gilles E.; Visser, Troy A. W.; Whitehouse, Andrew J. O.; Maybery, Murray T.

doi:10.1002/aur.2198

Cited by 55 publications

(72 citation statements)

References 59 publications

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“…The Autism Quotient, 86 a measure purporting to quantify autistic traits, includes subscales measuring distinct autistic traits that are weakly associated, suggesting the overall score of ''autistic traits'' lacks coherent meaning. 87 The term ''autistic traits'' is also rarely used to refer to autistic advantages or neutral charac-teristics, and therefore may offer a biased view of what constitutes ''autistic traits.'' A final example in this category is using ''typically developing'' or ''neurotypical'' to describe participants serving as a reference group.…”

Section: Concerns About Accuracymentioning

confidence: 99%

Avoiding Ableist Language: Suggestions for Autism Researchers

Bottema‐Beutel

Kapp²,

Lester

et al. 2021

Autism in Adulthood

656

329

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In this commentary, we describe how language used to communicate about autism within much of autism research can reflect and perpetuate ableist ideologies (i.e., beliefs and practices that discriminate against people with disabilities), whether or not researchers intend to have such effects. Drawing largely from autistic scholarship on this subject, along with research and theory from disability studies and discourse analysis, we define ableism and its realization in linguistic practices, provide a historical overview of ableist language used to describe autism, and review calls from autistic researchers and laypeople to adopt alternative ways of speaking and writing. Finally, we provide several specific avenues to aid autism researchers in reflecting on and adjusting their language choices.

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Section: Concerns About Accuracymentioning

confidence: 99%

Avoiding Ableist Language: Suggestions for Autism Researchers

Bottema‐Beutel

Kapp²,

Lester

et al. 2021

Autism in Adulthood

656

329

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“…Third, as we did not include ASD subjects, we did not cover the full spectrum but nonclinical phenotype expressions and cannot infer to brain structural changes seen in ASD. This is relevant not only given some inconsistencies of AQ factor structure across populations [49]. Neither the latent structure of autistic-like traits (tapped by the AQ) [64] nor that of neurobiological features [65] are clarified.…”

Section: Discussionmentioning

confidence: 99%

“…However, Koolschijn et al used the short version of the AQ (28 items), whereas we used the long version (50 items), making direct comparison between the studies limited. Furthermore, recent psychometric analysis of the AQ does not support the use of total-scale scores as there is strong evidence for divergence between the factors of the AQ [49].…”

Section: Discussionmentioning

confidence: 99%

Associations of subclinical autistic-like traits with brain structural variation using diffusion tensor imaging and voxel-based morphometry

et al. 2021

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Previous case-control studies of Autistic Spectrum Disorder (ASD) have identified altered brain structure such as altered frontal and temporal cortex volumes, or decreased fractional anisotropy (FA) within the inferior fronto-occipital fasciculus in patients. It remains unclear whether subclinical autistic-like traits might also be related to variation in these brain structures. In this study, we analysed MRI data of 250 psychiatrically healthy subjects phenotyped for subclinical autistic-like traits using the Autism Spectrum Quotient (AQ). For data analysis, we used voxel-based morphometry of T1-MRIs (Computational Anatomy Toolbox (CAT12)) and Tract-Based Spatial Statistics (TBSS) for diffusion tensor imaging (DTI) data. AQ attention switching subscale correlated negatively with FA values in the bilateral uncinate fasciculus as well as the bilateral inferior fronto-occipital fasciculus. Higher AQ attention switching subscale scores were associated with increased mean diffusivity (MD) and radial diffusivity (RD) values in the uncinate fasciculus, while axial diffusivity (AD) values within this tract show a negative correlation. AQ attention to detail subscale correlated positively with grey matter volume in the right pre-and postcentral gyrus. We demonstrate that individuals with higher levels of autism-spectrum-like features show decreased white matter integrity in tracts associated with higher-level visual processing and increased cortical volume in areas linked to movement sequencing and working memory. Our results resemble regional brain structure alterations found in individuals with ASD. This offers opportunities to further understand the aetiology and pathogenesis of the disorder and shows a subclinical continuum perspective.

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“…Firstly, AQ scores do not correspond perfectly to diagnoses, with many autistic people scoring below the cut-off (Ashwood et al, 2016;Bishop & Seltzer, 2012), and no clear relationship between AQ scores and standard clinical measures (Bishop & Seltzer, 2012). It has also been suggested that the construct measured by the AQ is not continuous, as assumed, but instead categorical (James et al, 2016), making it difficult to interpret relationships (or lack thereof) with the total AQ score (see also English et al, 2020). Similarly, Agelink…”

Section: Discussionmentioning

confidence: 99%

“…van Rentergem et al (2019) reported that some of the items on the AQ function differently between autistic and non-autistic samples. While alternative scoring algorithms have been proposed to mitigate some of these challenges (Agelink van Rentergem et al, 2019;English et al, 2020), we were unable to use these as we did not have access to item-level data from all participants. Yet, despite the conceptual and methodological issues associated with the AQ, some studies have nonetheless reported relationships between AQ scores and prior information use in different tasks (Powell et al, 2016;Skewes et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Prior information use and response caution in perceptual decision-making: No evidence for a relationship with autistic-like traits

Retzler¹,

Boehm²,

Cai³

et al. 2020

Preprint

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Interpreting the world around us requires integrating incoming sensory signals with prior information. Autistic individuals have been proposed to rely less on prior information and make more cautious responses than non-autistic individuals. Here we investigated whether these purported features of autistic perception vary as a function of autistic-like traits in the general population. We used a diffusion model framework, whereby decisions are modelled as noisy evidence accumulation processes towards one of two bounds. Within this framework, prior information can bias the starting point of the evidence accumulation process. Our pre-registered hypotheses were that higher autistic-like traits would relate to reduced starting point bias caused by prior information and increased response caution (wider boundary separation). 222 participants discriminated the direction of coherent motion stimuli as quickly and accurately as possible. Stimuli were preceded with a neutral cue (square) or a directional cue (arrow). 80% of the directional cues validly predicted the upcoming motion direction. We modelled accuracy and response time data using a hierarchical Bayesian model in which starting point varied with cue condition. We found no evidence for our hypotheses, with starting point bias and response caution seemingly unrelated to AQ scores. Alongside future research applying this paradigm to autistic individuals, our findings will help refine theories regarding the role of prior information and altered decision-making strategies in autistic perception. Our study also has implications for models of bias in perceptual decision-making, as the most plausible model was one that incorporated bias in both decision-making and sensory processing.

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A comprehensive psychometric analysis of autism‐spectrum quotient factor models using two large samples: Model recommendations and the influence of divergent traits on total‐scale scores

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 55 publications

References 59 publications

Avoiding Ableist Language: Suggestions for Autism Researchers

Avoiding Ableist Language: Suggestions for Autism Researchers

Associations of subclinical autistic-like traits with brain structural variation using diffusion tensor imaging and voxel-based morphometry

Prior information use and response caution in perceptual decision-making: No evidence for a relationship with autistic-like traits

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