Influences of Social Learning in Individual Perception and Decision Making in People with Autism: A Computational Approach

Tanu Wadhera; Mufti Mahmud

doi:10.1007/978-3-031-15037-1_5

Influences of Social Learning in Individual Perception and Decision Making in People with Autism: A Computational Approach

Tanu Wadhera
, Mufti Mahmud^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Scopus citations

Abstract

The present paper proposes a computational approach to explore the influences of social learning on social cognition among individuals with Autism Spectrum Disorder (ASD) compared to the Typically Developing (TD) group. An experimental paradigm is designed to perceive and differentiate social cues related to real-time road and traffic light situations. The computational metrics such as sensitivity index (d^′ ), response bias (c) and detection accuracy (DA) are recorded and analysed using machine learning classifiers. The results revealed that cognitive level is attenuated in ASD (d^′= 0.427, c= - 0.0076 and DA= 51.67 % ) compared to TD (d^′= 1.42, c= - 0.0027 and DA= 80.33 % ) with an improvement considering social influence as key factor (S_f ) with best-fit quantitative value for ASD (S_f= 0.3197 ) when compared to TD (S_f= 0.3937 ). The automated classification with an accuracy of 96.2% supported the significance of the metrics in distinguishing ASD from TDs. The present findings revealed that social conformity and social influence imparted growth in ASD cognition.

Original language	English
Title of host publication	Brain Informatics - 15th International Conference, BI 2022, Proceedings
Editors	Mufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	50-61
Number of pages	12
ISBN (Print)	9783031150364
DOIs	https://doi.org/10.1007/978-3-031-15037-1_5
State	Published - 2022
Externally published	Yes
Event	15th International Conference on Brain Informatics, BI 2022 - Virtual, Online Duration: 15 Jul 2022 → 17 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13406 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Brain Informatics, BI 2022
City	Virtual, Online
Period	15/07/22 → 17/07/22

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Correlation coefficient
Machine learning
Social learning
Support Vector Machine (SVM)

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-15037-1_5

Cite this

Wadhera, T., & Mahmud, M. (2022). Influences of Social Learning in Individual Perception and Decision Making in People with Autism: A Computational Approach. In M. Mahmud, J. He, S. Vassanelli, A. van Zundert, & N. Zhong (Eds.), Brain Informatics - 15th International Conference, BI 2022, Proceedings (pp. 50-61). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13406 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-15037-1_5

Wadhera, Tanu ; Mahmud, Mufti. / Influences of Social Learning in Individual Perception and Decision Making in People with Autism : A Computational Approach. Brain Informatics - 15th International Conference, BI 2022, Proceedings. editor / Mufti Mahmud ; Jing He ; Stefano Vassanelli ; André van Zundert ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2022. pp. 50-61 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Influences of Social Learning in Individual Perception and Decision Making in People with Autism: A Computational Approach",

abstract = "The present paper proposes a computational approach to explore the influences of social learning on social cognition among individuals with Autism Spectrum Disorder (ASD) compared to the Typically Developing (TD) group. An experimental paradigm is designed to perceive and differentiate social cues related to real-time road and traffic light situations. The computational metrics such as sensitivity index (d′ ), response bias (c) and detection accuracy (DA) are recorded and analysed using machine learning classifiers. The results revealed that cognitive level is attenuated in ASD (d′= 0.427, c= - 0.0076 and DA= 51.67 \% ) compared to TD (d′= 1.42, c= - 0.0027 and DA= 80.33 \% ) with an improvement considering social influence as key factor (Sf ) with best-fit quantitative value for ASD (Sf= 0.3197 ) when compared to TD (Sf= 0.3937 ). The automated classification with an accuracy of 96.2\% supported the significance of the metrics in distinguishing ASD from TDs. The present findings revealed that social conformity and social influence imparted growth in ASD cognition.",

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author = "Tanu Wadhera and Mufti Mahmud",

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Wadhera, T & Mahmud, M 2022, Influences of Social Learning in Individual Perception and Decision Making in People with Autism: A Computational Approach. in M Mahmud, J He, S Vassanelli, A van Zundert & N Zhong (eds), Brain Informatics - 15th International Conference, BI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13406 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 50-61, 15th International Conference on Brain Informatics, BI 2022, Virtual, Online, 15/07/22. https://doi.org/10.1007/978-3-031-15037-1_5

Influences of Social Learning in Individual Perception and Decision Making in People with Autism: A Computational Approach. / Wadhera, Tanu; Mahmud, Mufti.
Brain Informatics - 15th International Conference, BI 2022, Proceedings. ed. / Mufti Mahmud; Jing He; Stefano Vassanelli; André van Zundert; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2022. p. 50-61 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13406 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The present paper proposes a computational approach to explore the influences of social learning on social cognition among individuals with Autism Spectrum Disorder (ASD) compared to the Typically Developing (TD) group. An experimental paradigm is designed to perceive and differentiate social cues related to real-time road and traffic light situations. The computational metrics such as sensitivity index (d′ ), response bias (c) and detection accuracy (DA) are recorded and analysed using machine learning classifiers. The results revealed that cognitive level is attenuated in ASD (d′= 0.427, c= - 0.0076 and DA= 51.67 % ) compared to TD (d′= 1.42, c= - 0.0027 and DA= 80.33 % ) with an improvement considering social influence as key factor (Sf ) with best-fit quantitative value for ASD (Sf= 0.3197 ) when compared to TD (Sf= 0.3937 ). The automated classification with an accuracy of 96.2% supported the significance of the metrics in distinguishing ASD from TDs. The present findings revealed that social conformity and social influence imparted growth in ASD cognition.

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Wadhera T, Mahmud M. Influences of Social Learning in Individual Perception and Decision Making in People with Autism: A Computational Approach. In Mahmud M, He J, Vassanelli S, van Zundert A, Zhong N, editors, Brain Informatics - 15th International Conference, BI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 50-61. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-15037-1_5