skip to main content
10.1145/3510458.3513007acmconferencesArticle/Chapter ViewAbstractPublication PagesicseConference Proceedingsconference-collections
research-article

Impact of animated objects on autistic and non-autistic users

Published:17 October 2022Publication History

ABSTRACT

Interface usability and accessibility are crucial for autistic people because of the challenges they may confront while navigating the Internet. Nevertheless, limited empirical evidence is available from investigations of software accessibility and usability for autistic users. The core aim of this work was to empirically investigate the effects of irrelevant animation in user interfaces (UI). We analysed the impact of animation on autistic and non-autistic individuals, as well as the impact of animated UI objects on the performance of both cohorts while conducting various task types. Our findings suggest that animation significantly affects task performance for both cohorts, with autistic users more severely affected: autistic individuals are distracted to a greater extent, exacerbating frustration and necessitating greater mental exertion. The results of our study will help practitioners to establish greater comprehension of the autistic population's particular traits, as well as provide a basis for UI design guidelines so that autistic people will find interfaces more usable and accessible.

There are legal guidelines requiring web sites to be accessible to everyone. Our research focuses on making software interfaces inclusive for autistic people. About 1% of the overall population are known to be on the autism spectrum, and many autistic users are very sensitive to visual overload in everyday life. They may also get distracted more easily, potentially making it harder to do typical tasks using software. However, there were very studies on the accessability of software interfaces for autistic people, in particular on whether and how animated (moving) images might impact them.

Our study compared how autistic and non-autistic users completed tasks such as writing emails, on-line shopping and search when there were images that rotated on the screen. We also experimented with different rotation speeds and image sizes. We found that on average, people have more trouble when there is a rotating image on the screen, but that autistic users struggled more.

References

  1. Mona Alzahrani, Alexandra L. Uitdenbogerd, and Maria Spichkova. 2021. Human-Computer Interaction: Influences on Autistic Users. Proceedings of the 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'21). Procedia Computer Science 192 (2021), 4691--4700.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Simon Baron-Cohen, Sally Wheelwright, Richard Skinner, Joanne Martin, and Emma Clubley. 2001. The autism-spectrum quotient (AQ): Evidence from asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J. Autism. Dev. Disord. 31, 1 (2001), 5--17.Google ScholarGoogle ScholarCross RefCross Ref
  3. Alberto Battocchi, Ayelet Ben-Sasson, Gianluca Esposito, Eynat Gal, Fabio Pianesi, Daniel Tomasini, Paola Venuti, Patrice Weiss, and Massimo Zancanaro. 2010. Collaborative puzzle game: a tabletop interface for fostering collaborative skills in children with autism spectrum disorders. J. of Assistive Technologies (2010).Google ScholarGoogle Scholar
  4. Talita Britto and E Pizzolato. 2016. Towards web accessibility guidelines of interaction and interface design for people with autism spectrum disorder. In International Conference on Advances in Computer-Human Interactions. 1--7.Google ScholarGoogle Scholar
  5. Traolach S Brugha, Nicola Spiers, John Bankart, Sally-Ann Cooper, Sally McManus, Fiona J Scott, Jane Smith, and Freya Tyrer. 2016. Epidemiology of autism in adults across age groups and ability levels. The British Journal of Psychiatry 209, 6 (2016), 498--503.Google ScholarGoogle ScholarCross RefCross Ref
  6. Moira Burke, Anthony Hornof, Erik Nilsen, and Nicholas Gorman. 2005. High-cost banner blindness: Ads increase perceived workload, hinder visual search, and are forgotten. ACM T. Comput-Hum. INT. 12, 4 (2005), 423--445.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Peter Chapman, Sanjeebhan Selvarajah, and Jane Webster. 1999. Engagement in multimedia training systems. In Proc. of the 32nd Annual Hawaii International Conference on Systems Sciences. IEEE, 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  8. Muller YM Cheung, Weiyin Hong, and James YL Thong. 2017. Effects of animation on attentional resources of online consumers. Journal of the Association for Information Systems 18, 8 (2017), 605--632.Google ScholarGoogle ScholarCross RefCross Ref
  9. Australian Human Rights Commission. 2014. World Wide Web Access: Disability Discrimination Act Advisory Notes. https://humanrights.gov.au/our-work/disability-rights/world-wide-web-access-disability-discrimination-act-advisory-notes-ver. Accessed: 24-May-2021.Google ScholarGoogle Scholar
  10. Doăa Çorlu, Şeyma Taşel, Semra Gülce Turan, Athanasios Gatos, and Asim Evren Yantaç. 2017. Involving autistics in user experience studies: A critical review. In Conference on Designing Interactive Systems. 43--55.Google ScholarGoogle Scholar
  11. Hannah Joy Deering. 2013. Opportunity for success: Website evaluation and scanning by students with autism spectrum disorders. (2013).Google ScholarGoogle Scholar
  12. Jacquiline den Houting. 2019. Neurodiversity: An insider's perspective.Google ScholarGoogle Scholar
  13. Fifth Edition et al. 2013. Diagnostic and statistical manual of mental disorders. Am Psychiatric Assoc 21 (2013).Google ScholarGoogle Scholar
  14. Sukru Eraslan, Victoria Yaneva, Yeliz Yesilada, and Simon Harper. 2019. Web users with autism: eye tracking evidence for differences. Behav. Inform. Technol. 38, 7 (2019), 678--700.Google ScholarGoogle ScholarCross RefCross Ref
  15. Ronald A Fisher and Frank Yates. 1938. Statistical tables: For biological, agricultural and medical research. Oliver and Boyd.Google ScholarGoogle Scholar
  16. Linet Frey. 2005. The nature of the suppression mechanism in reading: Insights from an L1-L2 comparison. In Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 27.Google ScholarGoogle Scholar
  17. Mark G Friedman and Diane Nelson Bryen. 2007. Web accessibility design recommendations for people with cognitive disabilities. Technology and disability 19, 4 (2007), 205--212.Google ScholarGoogle Scholar
  18. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. 2016. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990--2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 10053 (2016), 1545--1602.Google ScholarGoogle Scholar
  19. Tony Gentry, Joseph Wallace, Connie Kvarfordt, and Kathleen Lynch. 2010. Personal digital assistants as cognitive aids for high school students with autism: Results of a community-based trial. J. Vocat. Rehabil. 32, 2 (2010), 101--107.Google ScholarGoogle ScholarCross RefCross Ref
  20. Francesca Happé and Uta Frith. 2006. The weak coherence account: detail-focused cognitive style in autism spectrum disorders. Journal of autism and developmental disorders 36, 1 (2006), 5--25.Google ScholarGoogle ScholarCross RefCross Ref
  21. Jason Harrison, Ronald A Rensink, and Michiel Van De Panne. 2004. Obscuring length changes during animated motion. ACM Transactions on Graphics (TOG) 23, 3 (2004), 569--573.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Advances in psychology. Vol. 52. Elsevier, 139--183.Google ScholarGoogle Scholar
  23. Rosa A Hoekstra, Anna AE Vinkhuyzen, Sally Wheelwright, Meike Bartels, Dorret I Boomsma, Simon Baron-Cohen, Danielle Posthuma, and Sophie Van Der Sluis. 2011. The construction and validation of an abridged version of the autism-spectrum quotient (AQ-Short). Journal of autism and developmental disorders 41, 5 (2011), 589--596.Google ScholarGoogle ScholarCross RefCross Ref
  24. Weiyin Hong, James YL Thong, and Kar Yan Tam. 2007. How do Web users respond to non-banner-ads animation? The effects of task type and user experience. J. AM. SOC. INF. SCI. TEC. 58, 10 (2007), 1467--1482.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Daniel Kahneman. 1973. Attention and effort. Vol. 1063. Citeseer.Google ScholarGoogle Scholar
  26. Jonathan Lazar, Jinjuan Heidi Feng, and Harry Hochheiser. 2017. Research methods in human-computer interaction. Morgan Kaufmann.Google ScholarGoogle Scholar
  27. Khalid Majrashi. 2016. Cross-platform user experience. Ph. D. Dissertation. RMIT University.Google ScholarGoogle Scholar
  28. Laura Millen, Rob Edlin-White, and Sue Cobb. 2010. The development of educational collaborative virtual environments for children with autism. In Cambridge Workshop on Universal Access and Assistive Technology, Vol. 1. 7.Google ScholarGoogle Scholar
  29. Jakob Nielsen. 1996. Original Top ten mistakes in Web design. Unpublished article, available online at: http://www.useit.com/alertbox/9605a.html (1996).Google ScholarGoogle Scholar
  30. Julie Pallant. 2020. SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.Google ScholarGoogle Scholar
  31. Bertram O Ploog. 2010. Stimulus overselectivity four decades later: A review of the literature and its implications for current research in autism spectrum disorder. Journal of autism and developmental disorders 40, 11 (2010), 1332--1349.Google ScholarGoogle ScholarCross RefCross Ref
  32. Lumpapun Punchoojit and Nuttanont Hongwarittorrn. 2017. Usability Studies on Mobile User Interface Design Patterns: A Systematic Literature Review. Adv. Hum. Comput. Interact. 6787504 (2017), 1--22.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Cynthia Putnam and Lorna Chong. 2008. Software and technologies designed for people with autism: what do users want?. In Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility. 3--10.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Pei-Luen Patrick Rau, Qin Gao, and Jie Liu. 2007. The effect of rich web portal design and floating animations on visual search. International Journal of Human-Computer Interaction 22, 3 (2007), 195--216.Google ScholarGoogle ScholarCross RefCross Ref
  35. Roseann C Schaaf and Lucy Jane Miller. 2005. Occupational therapy using a sensory integrative approach for children with developmental disabilities. Mental retardation and developmental disabilities research reviews 11, 2 (2005), 143--148.Google ScholarGoogle Scholar
  36. Lisa Seeman and Michael Cooper. 2015. Cognitive accessibility user research. W3C First Public Working Draft 15 (2015).Google ScholarGoogle Scholar
  37. Karanya Sitdhisanguan, Nopporn Chotikakamthorn, Ajchara Dechaboon, and Patcharaporn Out. 2012. Using tangible user interfaces in computer-based training systems for low-functioning autistic children. Personal and Ubiquitous Computing 16, 2 (2012), 143--155.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Katherine Valencia, Cristian Rusu, Daniela Quiñones, and Erick Jamet. 2019. The Impact of Technology on People with Autism Spectrum Disorder: A Systematic Literature Review. Sensors 19, 20 (2019), 4485.Google ScholarGoogle ScholarCross RefCross Ref
  39. Yeliz Yesilada and Simon Harper (Eds.). 2019. Web Accessibility - A Foundation for Research, Second Edition. Springer.Google ScholarGoogle Scholar
  40. Ping Zhang. 2000. The Effects of Animation on Information Seeking Performance on the World Wide Web: Securing Attention or Interfering with Primary Tasks? J. Assoc. Inf. Syst. 1 (2000), 1.Google ScholarGoogle Scholar

Index Terms

  1. Impact of animated objects on autistic and non-autistic users

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        ICSE-SEIS '22: Proceedings of the 2022 ACM/IEEE 44th International Conference on Software Engineering: Software Engineering in Society
        May 2022
        195 pages
        ISBN:9781450392273
        DOI:10.1145/3510458

        Copyright © 2022 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 17 October 2022

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Upcoming Conference

        ICSE 2025

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader