The Impact of Autism Spectrum Disorder on the Categorisation of External Representations Beate Grawemeyer (b.grawemeyer@bath.ac.uk) 1 , Hilary Johnson (h.johnson@bath.ac.uk) 1 , Mark Brosnan (m.j.brosnan@bath.ac.uk) 2 , Emma Ashwin (e.l.ashwin@bath.ac.uk) 2 , Laura Benton (l.j.benton@bath.ac.uk) 1 Human-Computer Interaction Group, Department of Computer Science, University of Bath Department of Psychology, University of Bath Bath, BA2 7AY, UK Abstract The knowledge structures and reasoning processes that underlie the use of external representations (ERs) in individuals with autism spectrum disorder (ASD) are not well understood. This paper compares the organisation of knowledge of ERs in young people with a diagnosis of ASD and an age-matched typically developing control group. ASD and non-ASD participants (twenty-eight in each group) were given an untimed ER card-sorting task. The ERs were based on representations used in educational software, for example graphs, charts, and text. Cluster analysis of the card sort task revealed similar clusters for both groups: maps, drawings, text, graphs and charts, and network and tree diagrams. However, comparison of the card sorts of the two different groups showed a difference in ‘basic level’ categories. While in the non-ASD group, maps and non-maps were the most distinctive category, analysis of the ASD cluster revealed, in addition, another ‘basic level’ category of textual representations. These results are discussed in relation to theories of information processing in autism. Our ultimate research aim is to develop educational software tailored to the specific needs of users with ASD. We wish to use our research results to inform requirements for the development of such educational software, in which ERs are able to support differences in information processing for individuals with ASD. Keywords: Autism Spectrum Disorder (ASD); external representations; categorisation; organisation. Introduction To investigate aspects of knowledge of external representations (ERs) which influence their usability, we examined the clustering of ERs by young people with and without autism spectrum disorder (ASD). As more children are diagnosed with ASD, there is a need to develop software (particularly the user interface) that takes into account the specific needs of individuals affected by ASD. Current estimates suggest that 1% of the population have a diagnosis of ASD, which includes autism and Asperger’s Syndrome (Baird et al., 2006). The characteristics that are associated with ASD include impairments in social reciprocity and language development, restricted (obsessional) interests, and repetitive behaviour (Diagnostic and Statistical Manual of Mental Disorders, 2000). In order to inform the design criteria for effective and efficient user interfaces for those with ASD, particularly their textual and visual aspects, we were interested to explore how the differences in cognitive abilities in individuals with ASD impacted upon the usability of ERs. Here, ERs are defined as representations, used in educational software and diagrammatic reasoning, including graphs, charts, text, drawings, maps, network diagrams and tree diagrams. Research has shown that ASD is related to an imbalance in cognitive abilities. For example, Minshew and Goldstein (2001) have shown that individuals with ASD have relatively weaker language skills. In contrast, other studies have reported that spatial cognition in ASD might be intact or even superior to that of individuals without ASD (Kamio and Toichi, 2000; Caron et al., 2004). This implies that spatial reasoning within ASD might be preferred, and of higher utility. Research indicates that individuals with ASD might represent information internally in different ways to those without ASD, in order to compensate for certain impaired brain areas (Jolliffe and Baron-Cohen, 2001). The difference in how information is internally represented might impact the way information is processed. For example, Mottron et al. (2006) describe how individuals with ASD may process and perceive visual information differently, based upon a different organisation of the visual regions of the brain. Matessa (2008) proposes a cognitive model in which the reduced declarative function associated with ASD is compensated by a ‘visual module’, where for example, mental imagery processing is used for sentence comprehension (e.g. Grandin, 1995; Kana et al., 2006). As described by Kunda and Goel (2008), the bias towards visual processing in individuals with ASD may explain differences in cognition. The above research in ASD might imply that weaker language skills and a deficit in text processing in ASD might be a result of a lack of declarative processing which is not fully compensated by visual processing. If this is the case, user interface design for ASD individuals needs to accommodate this difference. In particular, ERs need to be developed which support and enhance visual processing of text, implying that interfaces for individuals with ASD should be guided by specific requirements. These may differ to those aimed at individuals without ASD. This paper reports the results of an ER card sort study, focusing on a number of relevant issues: participants’
[1]
Michael Matessa,et al.
An ACT-R Representation of Information Processing in Autism
,
2008
.
[2]
Pablo Romero,et al.
A Cognitive Processing Perspective on Student Programmers' 'Graphicacy'
,
2004,
Diagrams.
[3]
M. Just,et al.
Sentence comprehension in autism: thinking in pictures with decreased functional connectivity.
,
2006,
Brain : a journal of neurology.
[4]
Richard Cox,et al.
The Mental Organisation of External Representations
,
2019,
Proceedings of EuroCogSci 03.
[5]
Ashok K. Goel,et al.
Thinking in Pictures: A Fresh Look at Cognition in Autism
,
2008
.
[6]
Eric Schopler,et al.
Learning and cognition in autism
,
1995
.
[7]
D. Levitin.
The foundations of cognitive psychology: Core readings
,
2005,
History & Philosophy of Psychology.
[8]
C. Rainville,et al.
Do high functioning persons with autism present superior spatial abilities?
,
2004,
Neuropsychologia.
[9]
Guus Schreiber,et al.
Knowledge Engineering and Management: The CommonKADS Methodology
,
1999
.
[10]
G. Baird,et al.
Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: the Special Needs and Autism Project (SNAP)
,
2006,
The Lancet.
[11]
B. Hubert,et al.
Enhanced Perceptual Functioning in Autism: An Update, and Eight Principles of Autistic Perception
,
2006,
Journal of autism and developmental disorders.
[12]
Richard Cox,et al.
Diagrams in the UK National School Curriculum
,
2008,
Diagrams.
[13]
M. Toichi,et al.
Dual access to semantics in autism: is pictorial access superior to verbal access?
,
2000,
Journal of child psychology and psychiatry, and allied disciplines.
[14]
Simon Baron-Cohen,et al.
A test of central coherence theory: Can adults with high-functioning autism or Asperger syndrome integrate fragments of an object?
,
2001,
Cognitive neuropsychiatry.
[15]
T. Grandin.
How people with autism think
,
1995
.
[16]
Janet B W Williams.
Diagnostic and Statistical Manual of Mental Disorders
,
2013
.
[17]
N. Minshew,et al.
The pattern of intact and impaired memory functions in autism.
,
2001,
Journal of child psychology and psychiatry, and allied disciplines.
[18]
N. Minshew,et al.
Abstract reasoning in autism: a dissociation between concept formation and concept identification.
,
2002,
Neuropsychology.