• Adults with High-functioning Autism Process Web Pages With Similar Accuracy but Higher Cognitive Effort Compared to Controls

      Yaneva, Victoria; Ha, Le; Eraslan, Sukru; Yesilada, Yeliz (ACM, 2019-05-31)
      To accommodate the needs of web users with high-functioning autism, a designer's only option at present is to rely on guidelines that: i) have not been empirically evaluated and ii) do not account for the di erent levels of autism severity. Before designing effective interventions, we need to obtain an empirical understanding of the aspects that speci c user groups need support with. This has not yet been done for web users at the high ends of the autism spectrum, as often they appear to execute tasks effortlessly, without facing barriers related to their neurodiverse processing style. This paper investigates the accuracy and efficiency with which high-functioning web users with autism and a control group of neurotypical participants obtain information from web pages. Measures include answer correctness and a number of eye-tracking features. The results indicate similar levels of accuracy for the two groups at the expense of efficiency for the autism group, showing that the autism group invests more cognitive effort in order to achieve the same results as their neurotypical counterparts.
    • Autism detection based on eye movement sequences on the web: a scanpath trend analysis approach

      Eraslan, Sukru; Yesilada, Yeliz; Yaneva, Victoria; Harper, Simon; Duarte, Carlos; Drake, Ted; Hwang, Faustina; Lewis, Clayton (ACM, 2020-04-20)
      Autism diagnostic procedure is a subjective, challenging and expensive procedure and relies on behavioral, historical and parental report information. In our previous, we proposed a machine learning classifier to be used as a potential screening tool or used in conjunction with other diagnostic methods, thus aiding established diagnostic methods. The classifier uses eye movements of people on web pages but it only considers non-sequential data. It achieves the best accuracy by combining data from several web pages and it has varying levels of accuracy on different web pages. In this present paper, we investigate whether it is possible to detect autism based on eye-movement sequences and achieve stable accuracy across different web pages to be not dependent on specific web pages. We used Scanpath Trend Analysis (STA) which is designed for identifying a trending path of a group of users on a web page based on their eye movements. We first identify trending paths of people with autism and neurotypical people. To detect whether or not a person has autism, we calculate the similarity of his/her path to the trending paths of people with autism and neurotypical people. If the path is more similar to the trending path of neurotypical people, we classify the person as a neurotypical person. Otherwise, we classify her/him as a person with autism. We systematically evaluate our approach with an eye-tracking dataset of 15 verbal and highly-independent people with autism and 15 neurotypical people on six web pages. Our evaluation shows that the STA approach performs better on individual web pages and provides more stable accuracy across different pages.
    • Detecting high-functioning autism in adults using eye tracking and machine learning

      Yaneva, Victoria; Ha, Le An; Eraslan, Sukru; Yesilada, Yeliz; Mitkov, Ruslan (Institute of Electrical and Electronics Engineers (IEEE), 2020-04-30)
      The purpose of this study is to test whether visual processing differences between adults with and without highfunctioning autism captured through eye tracking can be used to detect autism. We record the eye movements of adult participants with and without autism while they look for information within web pages. We then use the recorded eye-tracking data to train machine learning classifiers to detect the condition. The data was collected as part of two separate studies involving a total of 71 unique participants (31 with autism and 40 control), which enabled the evaluation of the approach on two separate groups of participants, using different stimuli and tasks. We explore the effects of a number of gaze-based and other variables, showing that autism can be detected automatically with around 74% accuracy. These results confirm that eye-tracking data can be used for the automatic detection of high-functioning autism in adults and that visual processing differences between the two groups exist when processing web pages.
    • “Keep it simple!”: an eye-tracking study for exploring complexity and distinguishability of web pages for people with autism

      Eraslan, Sukru; Yesilada, Yeliz; Yaneva, Victoria; Ha, Le An (Springer Science and Business Media LLC, 2020-02-03)
      A major limitation of the international well-known standard web accessibility guidelines for people with cognitive disabilities is that they have not been empirically evaluated by using relevant user groups. Instead, they aim to anticipate issues that may arise following the diagnostic criteria. In this paper, we address this problem by empirically evaluating two of the most popular guidelines related to the visual complexity of web pages and the distinguishability of web-page elements. We conducted a comparative eye-tracking study with 19 verbal and highly independent people with autism and 19 neurotypical people on eight web pages with varying levels of visual complexity and distinguishability, with synthesis and browsing tasks. Our results show that people with autism have a higher number of fixations and make more transitions with synthesis tasks. When we consider the number of elements which are not related to given tasks, our analysis shows that they look at more irrelevant elements while completing the synthesis task on visually complex pages or on pages whose elements are not easily distinguishable. To the best of our knowledge, this is the first empirical behavioural study which evaluates these guidelines by showing that the high visual complexity of pages or the low distinguishability of page elements causes non-equivalent experience for people with autism.
    • Web users with autism: eye tracking evidence for differences

      Eraslan, Sukru; Yaneva, Victoria; Yesilada, Yeliz; Harper, Simon (Taylor and Francis, 2018-12-11)
      Anecdotal evidence suggests that people with autism may have different processing strategies when accessing the web. However, limited empirical evidence is available to support this. This paper presents an eye tracking study with 18 participants with high-functioning autism and 18 neurotypical participants to investigate the similarities and differences between these two groups in terms of how they search for information within web pages. According to our analysis, people with autism are likely to be less successful in completing their searching tasks. They also have a tendency to look at more elements on web pages and make more transitions between the elements in comparison to neurotypical people. In addition, they tend to make shorter but more frequent fixations on elements which are not directly related to a given search task. Therefore, this paper presents the first empirical study to investigate how people with autism differ from neurotypical people when they search for information within web pages based on an in-depth statistical analysis of their gaze patterns.