References - Cognitive Load, Sequencing and Predictability

Cognitive Load in Virtual Reality refers to the total volume of mental effort imposed on a user’s working memory while navigating an immersive environment. Unlike traditional two-dimensional interfaces, VR requires the simultaneous processing of spatial orientation, complex motor mechanics, and multi-sensory feedback. For neurodivergent learners, particularly those with executive function challenges, a standardised interaction paradigm often fails to account for the increased mental effort required to decode cluttered or high-density environments. When system designs assume a high baseline of cognitive flexibility, the resulting "information density" can lead to rapid cognitive overwhelm, causing the user to abandon the task entirely (Sweller, 1988; Newbutt et al., 2016).

Sequencing and predictability are essential for maintaining a manageable cognitive load. Sequencing involves the ability to organise and execute a series of actions in a specific order to reach a goal - a task that is often complicated in VR by non-linear environments and multi-step tutorials. Predictability ensures that the system responds consistently to user inputs, providing a sense of agency and safety. For users who rely on structure, a lack of predictability within an interface can trigger "system-induced anxiety," where the user becomes hesitant to interact for fear of an unexpected or jarring mechanical change. This barrier is often exacerbated when software requires users to remember instructions from previous screens without persistent visual scaffolding (Crompton et al., 2020).

The consequences of excessive cognitive demand are both psychological and physiological. Academic research into immersive technologies highlights that when neurodivergent individuals encounter unpredictable navigational challenges, there is a measurable increase in heart rate and stress-related cortisol levels. This physiological response can transition a learner from a state of engagement into a state of "sensory or cognitive saturation," leading to distress or total withdrawal from the experience. Effective inclusive design must therefore replace a uniform design philosophy with one that prioritises simplified information architecture, predictable feedback loops, and the ability to break down complex goals into discrete, repeatable steps (Mott et al., 2020).

Cognitive Load

"The paralysis that can occur when a user faces multiple simultaneous objectives - is a primary driver of task abandonment. Miller's Law suggests that working memory typically holds approximately seven discrete items, though this capacity diminishes significantly under sensory stress or for individuals with specific learning profiles."

• Crompton, C. J., Sharp, M., Axbey, H., Fletcher-Watson, S., Flynn, A. and Devenney, L. (2020). ‘Neurodevelopmental diversity in the workplace’. The Lancet Psychiatry, 7(9).
• Mott, M., Tang, J., Kane, S., Cutrell, E. and Ringel Morris, M. (2020). ‘Accessible VRChat: Barriers and Opportunities for People with Disabilities in Social Virtual Reality’. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
• Newbutt, N., Sung, C., Kuo, H. J. and Leahy, M. J. (2016). ‘The potential of virtual reality technology in supporting people with autism’. Journal of Intellectual Disability Research.
• Sweller, J. (1988). ‘Cognitive Load During Problem Solving: Effects on Learning’. Cognitive Science, 12(2).

Cognitive Load Theory

• Orru, G. and Longo, L., 2018, September. The evolution of cognitive load theory and the measurement of its intrinsic, extraneous and germane loads: a review. In International symposium on human mental workload: Models and applications (pp. 23-48). Cham: Springer International Publishing.
• Klepsch, M. and Seufert, T., 2020. Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. Instructional Science, 48(1), pp.45-77.

The assertion that working memory capacity diminishes under sensory stress and varies across specific learning profiles is supported by a robust body of peer-reviewed research in cognitive psychology, neuroscience, and educational theory. While George Miller’s 1956 paper, The Magical Number Seven, Plus or Minus Two, established the foundational concept of "chunking" and the limits of short-term memory, subsequent research has refined these parameters, particularly concerning cognitive load and neurodivergence.

1. The Evolution of Miller’s Law

Modern cognitive science has largely superseded the "seven items" rule with more nuanced findings. Nelson Cowan (2001) demonstrated that the capacity of "pure" working memory - when rehearsal and long-term memory cues are controlled - is actually closer to four discrete items. This reduction in the baseline capacity underscores the fragility of working memory, making it even more susceptible to disruption from external stressors.

2. Evidence for Diminishment Under Sensory Stress

The primary framework for understanding how sensory stress impacts memory is Cognitive Load Theory (CLT), developed by John Sweller (1988). CLT identifies three types of cognitive load:

• Intrinsic Load: The inherent difficulty of the task.
• Extraneous Load: The manner in which information is presented (including sensory "noise").
• Germane Load: The mental resources used for processing and automation. In Virtual Reality (VR), sensory stress functions as Extraneous Load. Peer-reviewed evidence (e.g., Moreno & Mayer, 2002) indicates that when a user is subjected to redundant or conflicting sensory input - such as excessive haptic feedback, "noisy" spatial audio, or high-intensity visual transitions - the brain must divert limited working memory resources to filter this "noise". This phenomenon, often referred to as the Split-Attention Effect or the Redundancy Principle, directly leads to the "paralysis" or task abandonment mentioned in your query. The sensory-rich nature of VR can trigger a "bottleneck" effect where the cognitive cost of maintaining presence in the environment consumes the bandwidth required for task execution.

3. Evidence for Specific Learning Profiles (SEN)

There is extensive peer-reviewed evidence regarding working memory deficits and variations within neurodivergent populations:

• Autism Spectrum Disorder (ASD): Research by Steele et al. (2007) and Joseph et al. (2005) suggests that while individuals with ASD may possess strong long-term memory, their working memory capacity - particularly for verbal and complex spatial information - is often significantly lower than neurotypical peers. Sensory processing sensitivities, common in ASD, exacerbate this; what a neurotypical user perceives as background detail may be processed as primary sensory data, causing rapid cognitive exhaustion.
• ADHD: Research by Rapport et al. (2008) identifies working memory deficits as a "core" impairment in ADHD. The inability to inhibit irrelevant stimuli means that the "seven discrete items" are quickly replaced by irrelevant environmental distractions, leading to the "paralysis" of multi-step sequencing.
• Working Memory and Learning Disabilities: Gathercole and Alloway (2008) provided comprehensive evidence that children with specific learning profiles (including Dyslexia and Dyscalculia) frequently fail at classroom tasks not because of a lack of intelligence, but because the "storage" and "processing" requirements of the task exceed their functional working memory capacity.

4. Application to the Inclusive UX Framework

In the context of the Openality Inclusive VR Framework, this evidence necessitates a shift from "complex menus" to "minimalist, sequential interaction patterns." As noted in the primary research at Ysgol y Deri, users sometimes became "stuck" when facing multi-step actions - a direct manifestation of working memory failure.

To mitigate this, the framework prioritises:

• Reduced Sequencing: Breaking tasks into single, atomic actions to prevent memory overflow.
• Multimodal Redundancy: Using sensory cues to support rather than distract (e.g., a visual highlight that persists to reduce the need for the user to remember where an object is).
• Sensory Customisation: Allowing users to dampen "extraneous" sensory input to preserve cognitive bandwidth for the core objective. The "paralysis" we describe is a documented psychological reality where the cost of processing the environment exceeds the capacity to act within it.

Cowan, N. (2001) 'The magical number 4 in short-term memory: a reconsideration of mental storage capacity', Behavioral and Brain Sciences, 24(1), pp. 87–114.

Gathercole, S.E. and Alloway, T.P. (2008) Working memory and learning: a practical guide for teachers. London: SAGE Publications.

Joseph, R.M., Steele, S.D., Gaillard, W.D., Nelson, T.E. and Tager-Flusberg, H. (2005) 'The neural basis of working memory in autism: an fMRI study', Neuropsychologia, 43(13), pp. 1847–1856.

Miller, G.A. (1956) 'The magical number seven, plus or minus two: some limits on our capacity for processing information', Psychological Review, 63(2), pp. 81–97.

Moreno, R. and Mayer, R.E. (2002) 'Learning science in virtual reality worlds: role of methods and media', Journal of Educational Psychology, 94(3), pp. 598–610.

Rapport, M.D., Alderson, R.M., Kofler, M.J., Sarver, D.E., Bolden, J. and Sims, V. (2008) 'Working memory deficits in boys with attention-deficit/hyperactivity disorder (ADHD): the contributing role of central executive processes and visual-spatial storage', Journal of Abnormal Child Psychology, 36(6), pp. 825–837.

Steele, S.D., Minshew, N.J., Luna, B. and Sweeney, J.A. (2007) 'Spatial working memory deficits in autism', Journal of Autism and Developmental Disorders, 37(4), pp. 605–612.

Sweller, J. (1988) 'Cognitive load during problem solving: effects on learning', Cognitive Science, 12(2), pp. 257–285.

Working Memory and Executive Functioning Deficits

Dehn, M.J. (2013) showed that, for individuals with low working memory ability and executive functioning deficits, the cognitive processing required to learn and apply multi-step strategies increases working memory load, potentially causing them to forget information. Therefore: -

• Avoid Complex Multi-Steps: Advises avoiding complex, multi-step long-term memory strategies for individuals with working memory impairments because learning and applying the strategy adds significantly to cognitive load.
• Reducing Cognitive Load: Suggests that keeping lists of information or procedural steps in view reduces working memory load, an intervention designed to minimize cognitive load during learning.

Lower Load Better Shaban, A et al. (2021) found that children's training performance and perceived experience were better in gamified activities that induced a lower cognitive load level, suggesting a need to carefully manage task complexity and the amount of information users must process.

Dehn, M.J., 2013. Supporting and strengthening working memory in the classroom to enhance executive functioning. In Handbook of executive functioning (pp. 495-507). New York, NY: Springer New York.

Shaban, A., Pearson, E. and Chang, V., 2021. Evaluation of user experience, cognitive load, and training performance of a gamified cognitive training application for children with learning disabilities. Frontiers in Computer Science, 3, p.617056.

Hick's Law

• Nugraha, W.A.P., 2024. The power of ux laws: enhancing user experience research and design processes.
• Yablonski, J., 2024. Laws of UX. " O'Reilly Media, Inc.".
• Proctor, R.W. and Schneider, D.W., 2018. Hick’s law for choice reaction time: A review. Quarterly Journal of Experimental Psychology, 71(6), pp.1281-1299.

Miller's Law

• Yablonski, J., 2024. Laws of UX. " O'Reilly Media, Inc.".