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      Influence of autistic traits and communication role on eye contact behavior during face-to-face interaction

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          Abstract

          Eye contact is a central component in face-to-face interactions. It is important in structuring communicative exchanges and offers critical insights into others' interests and intentions. To better understand eye contact in face-to-face interactions, we applied a novel, non-intrusive deep-learning-based dual-camera system and investigated associations between eye contact and autistic traits as well as self-reported eye contact discomfort during a referential communication task, where participants and the experimenter had to guess, in turn, a word known by the other individual. Corroborating previous research, we found that participants’ eye gaze and mutual eye contact were inversely related to autistic traits. In addition, our findings revealed different behaviors depending on the role in the dyad: listening and guessing were associated with increased eye contact compared with describing words. In the listening and guessing condition, only a subgroup who reported eye contact discomfort had a lower amount of eye gaze and eye contact. When describing words, higher autistic traits were associated with reduced eye gaze and eye contact. Our data indicate that eye contact is inversely associated with autistic traits when describing words, and that eye gaze is modulated by the communicative role in a conversation.

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          The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians.

          Currently there are no brief, self-administered instruments for measuring the degree to which an adult with normal intelligence has the traits associated with the autistic spectrum. In this paper, we report on a new instrument to assess this: the Autism-Spectrum Quotient (AQ). Individuals score in the range 0-50. Four groups of subjects were assessed: Group 1: 58 adults with Asperger syndrome (AS) or high-functioning autism (HFA); Group 2: 174 randomly selected controls. Group 3: 840 students in Cambridge University; and Group 4: 16 winners of the UK Mathematics Olympiad. The adults with AS/HFA had a mean AQ score of 35.8 (SD = 6.5), significantly higher than Group 2 controls (M = 16.4, SD = 6.3). 80% of the adults with AS/HFA scored 32+, versus 2% of controls. Among the controls, men scored slightly but significantly higher than women. No women scored extremely highly (AQ score 34+) whereas 4% of men did so. Twice as many men (40%) as women (21%) scored at intermediate levels (AQ score 20+). Among the AS/HFA group, male and female scores did not differ significantly. The students in Cambridge University did not differ from the randomly selected control group, but scientists (including mathematicians) scored significantly higher than both humanities and social sciences students, confirming an earlier study that autistic conditions are associated with scientific skills. Within the sciences, mathematicians scored highest. This was replicated in Group 4, the Mathematics Olympiad winners scoring significantly higher than the male Cambridge humanities students. 6% of the student sample scored 32+ on the AQ. On interview, 11 out of 11 of these met three or more DSM-IV criteria for AS/HFA, and all were studying sciences/mathematics, and 7 of the 11 met threshold on these criteria. Test-retest and interrater reliability of the AQ was good. The AQ is thus a valuable instrument for rapidly quantifying where any given individual is situated on the continuum from autism to normality. Its potential for screening for autism spectrum conditions in adults of normal intelligence remains to be fully explored.
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            Some functions of gaze-direction in social interaction

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              Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction and software implementation.

              Homoskedasticity is an important assumption in ordinary least squares (OLS) regression. Although the estimator of the regression parameters in OLS regression is unbiased when the homoskedasticity assumption is violated, the estimator of the covariance matrix of the parameter estimates can be biased and inconsistent under heteroskedasticity, which can produce significance tests and confidence intervals that can be liberal or conservative. After a brief description of heteroskedasticity and its effects on inference in OLS regression, we discuss a family of heteroskedasticity-consistent standard error estimators for OLS regression and argue investigators should routinely use one of these estimators when conducting hypothesis tests using OLS regression. To facilitate the adoption of this recommendation, we provide easy-to-use SPSS and SAS macros to implement the procedures discussed here.
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                Author and article information

                Contributors
                max.thorsson@gu.se
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 April 2024
                8 April 2024
                2024
                : 14
                : 8162
                Affiliations
                [1 ]Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, University of Gothenburg, ( https://ror.org/01tm6cn81) Gothenburg, Sweden
                [2 ]Division of Cognition and Communication, Department of Applied Information Technology, University of Gothenburg, ( https://ror.org/01tm6cn81) Gothenburg, Sweden
                [3 ]Section of Speech and Language Pathology, Institute of Neuroscience and Physiology, University of Gothenburg, ( https://ror.org/01tm6cn81) Gothenburg, Sweden
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Athinoula A. Martinos Center for Biomedical Imaging, , Massachusetts General Hospital, Harvard Medical School, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0001-5030-7949
                Article
                58701
                10.1038/s41598-024-58701-8
                11001951
                38589489
                ca4ef0a4-b4ba-4f13-93e9-24ea0f134dad
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 December 2023
                : 2 April 2024
                Funding
                Funded by: Swedish Child Neuropsychiatry Science Foundation
                Funded by: Vetenskapsrådet (Swedish Research Council)
                Funded by: University of Gothenburg
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                face-to-face,deep learning,eye contact,gaze convergence,autism,human behaviour,behavioural methods

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