Introduction
Autism spectrum disorder (ASD) is a permanent neurodevelopmental affliction that affects over 0.6% of the population (Hill, 2004; Zeidan et al., 2022). Several cognitive models have been described to offer insight into characterising ASD in individuals. Among these are the weak central coherence, the theory of mind, and executive function (Lind & Williams, 2011).
Executive functioning (EF) in the context of autism is characterised by deficits in self-directed actions related to goal selection and the initiation, maintenance, and adjustment of activities to achieve those goals (Demetriou et al., 2019). These areas include restoring information, planning, managing emotions, adapting thinking, and organisation.
EF can be broken down into three primary domains: working memory (holding information to pursue and adapt goals), cognitive flexibility (adapting plans in response to changing contexts), and inhibitory control (maintaining focus and resisting distractions while pursuing goals).
These EF components support skills like self-regulation, organisation, planning, time management, self-management of time, self-organisation, self-restraint, motivation, and emotional regulation (Stark & Lindo, 2022).
Understanding these interconnections can help structure support programs for individuals with autism in educational settings such as private tutoring.
Rationale
The accompanying visual resource is directed towards new parents, particularly those with a bioinformatics or coding background. Before starting my tutoring business, I was a researcher in veterinary microbiology and bioinformatics, performing and analysing whole genome sequencing for many years. Many of my colleagues were young and at the age of finishing their PhDs and starting families. This presented the idea of an easily recognisable analogy between common errors in coding for machine learning/artificial intelligence and their children’s executive function deficits that might benefit them.
The use of “The Mitchells vs. the Machines” at the top of my poster exemplifies this. The human characters in the movie all present as people with autism, and the machines clearly represent the machine learning/AI angle. Hill (2004) highlights that universally, there is little evidence from experiments on executive function that correlate deficits with a low IQ. Instead, it has been demonstrated that they have been linked to the development of the prefrontal cortex in the brain – confirmed in research by Catani et al. (2016) and in review by Demetriou et al. (2019). Pellicano (2012) noted that language plays an essential role in a child’s working memory and the foundational development of executive function.
Strategies to assist deficits in EF
Two of the three proposed theoretical models illustrate that a failure to establish language (or in conjunction with attention) early in EF’s development will interfere with its evolution. Communication also suffers due to these language deficiencies, having a flow-on effect on higher-order cognitive abilities such as problem-solving, organisation and planning. This has been depicted in my poster by the issue of combatting syntax errors whilst programming for machine learning and or artificial intelligence. The photo of the knitted snake represents a ‘bash code’ incorporating PERL (knitting technique) and Python, two higher-order computer languages. It is important to note that PERL is not used in machine learning coding – hence, it would cause syntax errors.
Executive functioning in autistic people, and indeed in ASD itself, has long been reflected upon as a spectrum characterised by heterogeneity (Demetriou et al., 2019; Stark & Lindo, 2022). However, there is a degree of consensus concerning interventions for multiple models of EF, including fractionated, multifactorial and supervisory attentional systems and their processes, such as inhibitory control, working memory and set-shifting (Demetriou et al., 2019).
Cognitive remediation, such as chunking, allows complex tasks and information to be broken down into smaller, palatable chunks. It also improves working memory by reducing cognitive load and increasing the time information is held and manipulated within short-term memory (Li et al., 2017; Macoun et al., 2020). This newfound cognitive flexibility allows tasks to be approached in alternative ways, fostering the ability to adapt when required (Buttelmann & Karbach, 2017). This idea has been reflected in my visual resource with the deficit seen in Bender’s inability to find an alternative response and the solution of splitting commands. The benefits of such splitting are more elegantly described by Tan et al. (2021) in this paper about machine learning in drug discovery. This capacity to adapt is also crucial in another component of EF, self-regulation and in of itself is not enough to counter difficulties with social interactions and comorbidities such as stress and depression (Hill, 2004).
O’Haire (2017) reports a growing interest in interventional animal-assisted therapies to assist autistic people. The review found consistent social interaction and engagement improvements across a broad cross-section of children. A study by Tepper et al. (2022) found that the therapy dog held the participants’ focus, although they did not see an increase in the “positive behaviors” they were researching.
However, they did hypothesise that the dog had a calming influence and believed the intervention might encourage participants to improve social communication and reduce restrictive and repetitive behaviours. While these interventions are cost-prohibitive, potentially disruptive and ethically ambiguous for the therapy animal/s in a large school setting, they are well suited to a 1:1 tutoring session.
Even though neither of these studies included adults, I have found in my personal tutoring experience that having a pet (or even a video of my pets) in our sessions reduces stress, improves engagement and strengthens our bond. The biggest benefit of tutoring individuals with ASD is the heterogeneity encountered between them (Stark & Lindo 2022). Large classroom sizes make it especially difficult for primary, secondary or even tertiary teachers to offer the individual flexibility private tutoring can.
References
Buttelmann, F., & Karbach, J. (2017). Development and plasticity of cognitive flexibility in early and middle childhood. Frontiers in Psychology, 8, 1040.
Delage, H., Eigsti, I.-M., Stanford, E., & Durrleman, S. (2022). A Preliminary Examination of the Impact of Working Memory Training on Syntax and Processing Speed in Children with ASD. Journal of Autism and Developmental Disorders, 52(10), 4233–4251. https://doi.org/10.1007/s10803-021-05295-z
Demetriou, E. A., DeMayo, M. M., & Guastella, A. J. (2019). Executive Function in Autism Spectrum Disorder: History, Theoretical Models, Empirical Findings, and Potential as an Endophenotype [Review]. Frontiers in Psychiatry, 10. https://doi.org/10.3389/fpsyt.2019.00753
Hill, E. L. (2004). Evaluating the theory of executive dysfunction in autism. Developmental Review, 24(2), 189–233. https://doi.org/10.1016/j.dr.2004.01.001
Li, S., Hu, J., Li, C., Wang, Q., He, J., Wang, Y., & Yang, C. (2017). Chunking processing of spatial working memory in autism preschool children. Acta Psychologica Sinica, 49(5), 631. https://doi.org/10.3724/sp.j.1041.2017.00631
Lind, S. E., & Williams, D. M. (2011). Behavioural, biopsychosocial, and cognitive models of autism spectrum disorders. In International Handbook of Autism and Pervasive Developmental Disorders (pp. 99-114). New York, NY: Springer New York.
Macoun, S. J., Schneider, I., Bedir, B., Sheehan, J., & Sung, A. (2020). Pilot Study of an Attention and Executive Function Cognitive Intervention in Children with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 51(8), 2600–2610. https://doi.org/10.1007/s10803-020-04723-w
O’Haire, M. E. (2017). Research on animal-assisted intervention and autism spectrum disorder, 2012–2015. Applied Developmental Science, 21(3), 200–216. https://doi.org/10.1080/10888691.2016.1243988
Pellicano, E. (2012). The development of executive function in autism. Autism research and treatment, 2012.
Stark, M. D., & Lindo, E. J. (2022). Executive Functioning Supports for College Students with an Autism Spectrum Disorder. Review Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s40489-022-00311-z
Tan, J., Yang, J., Wu, S., Chen, G., & Zhao, J. (2021). A critical look at the current train/test split in machine learning. arXiv preprint arXiv:2106.04525.
Tepper, D. L., Landry, O., Howell, T. J., Stephens, D., Molina, J., & Bennett, P. C. (2022). Therapy dogs for children with autism spectrum disorder: Impacts of active versus passive dog engagement. Human-Animal Interaction Bulletin.
Zeidan, J., Fombonne, E., Scorah, J., Ibrahim, A., Durkin, M. S., Saxena, S., Yusuf, A., Shih, A., & Elsabbagh, M. (2022). Global prevalence of autism: A systematic review update. Autism Research, 15(5), 778-790. https://doi.org/10.1002/aur.2696
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