Cognitive Load in ADHD and Autism: Why Low-Cognitive-Load Software Matters

6 October 2026 • Guides

By Luke Henderson - NeuroMoney Founder

What is cognitive load theory?

Open an app to do one simple thing. Before you can do it, a banner asks about cookies, a pop-up announces a new feature, three badges want your attention, the dashboard shows twelve cluttered cards, and the button you actually need is hidden inside a random menu. The task has not become more complicated. The interface has. This is where cognitive load theory becomes useful. Cognitive load refers to the amount of mental effort being used to process information at a given moment. The theory, developed by psychologist John Sweller in the 1980s, began as a way to understand learning. Working memory, the small mental workspace we use to hold and manipulate information in the moment, is limited, so the way information is presented can either protect that capacity or waste it (Sweller, 2019). Cognitive load theory is used to separate necessary task difficulty from avoidable difficulty. Intrinsic cognitive load is the mental work that genuinely belongs to the task, such as comparing two insurance prices or understanding a bill. Extraneous cognitive load is the extra effort created by the way information is presented, such as unclear labels, clutter, repeated choices, or the need to remember details from the previous screen. Cognitive overload happens when the demands on attention and working memory exceed the resources available at that moment. Cognitive load theory was not created specifically for autism, ADHD, or user-interface design, but its central idea transfers well. Therefore if a system makes the brain spend energy on the interface instead of the goal, the system is adding unnecessary work that can easily break the flow of a task that should be simple.

Cognitive load in autism

Cognitive load in autism needs a little care because the research does not support a simple claim that autistic people have “less capacity”. Autistic cognitive profiles are varied, and laboratory findings change with the task at hand. One small study found lower efficiency and capacity of cognitive control (Mackie, 2016), while Jana Brinkert’s thesis found a much more mixed picture, including preliminary evidence of reduced distractibility or increased capacity on particular tasks (Brinkert, 2021). Cognitive control means the ability to direct mental resources towards a goal while managing competing information. In everyday software, the practical issue is that sensory input, uncertainty, unexpected changes, ambiguous instructions, and frequent task switching can all compete with the thing the user came to do. A page can therefore feel demanding even when the underlying task is simple. Good design should not assume that every autistic user needs the same thing; it should make the interface predictable and easier to control.

More like this: The ASD Tax

Cognitive load in ADHD

Cognitive load in ADHD often shows up around working memory, attention regulation, and executive function. Executive function is the group of mental skills used to plan, prioritise, inhibit distractions, switch tasks, and keep a goal active long enough to finish it. A meta-analysis of 38 studies found moderate working-memory differences in adults with ADHD, although performance varies from person to person and from task to task (Alderson et al., 2013). Mental load is related but slightly different, it refers to the background work of remembering / coordinating everything that needs attention in daily life. When that background load is already high, an app that adds five unnecessary decisions can be the thing that tips a manageable task over the edge. The answer here is not to demand more concentration but rather to stop spending concentration where the product does not need it.

More like this: The ADHD Tax

How software creates cognitive overload for users with ADHD and/or Autism

Software can create too much cognitive load long before it looks “complicated” to the team that built it. Anything that adds unnecessary complexity or confusion forces the user to shift their attention away from what they are trying to achieve and toward figuring out how the product works. In software, interface cognitive load includes the mental resources needed simply to understand and operate the system, so visual clutter and unnecessary demands on memory can add effort before the user ever reaches the task at hand. This is especially relevant for neurodivergent users because cognitive control is not an unlimited reserve. If an ADHD user is already using effort to resist distractions and keep the current step in working memory, a sudden context switch can pull attention away from the task. On the other hand, if an autistic user is already processing sensory detail, a change from the expected sequence can add another layer of complexity. None of this means ADHD or autistic users are incapable of handling complex software. Complexity is sometimes necessary. The problem is unnecessary complexity that sits on top of the real task. Imagine opening a finance app to check whether a bill has been paid. The user sees a promotional card, an investment prompt, five account tiles, three alerts, a chart, a chatbot, and a “smart insight” before they ever see the bill. They now have to scan, filter, remember the original goal, decide which information is relevant, and avoid tapping the wrong thing. That is extraneous cognitive load. When cognitive overload builds up, users can become stuck and disengaged, making it much harder to complete the task at hand. A user who closes the app may not lack motivation. The interface may simply have spent their attention before they reached the thing they came to do. Furthermore, it is important to note that cognitive-load difficulties are not limited to ADHD and autism; people with any neurodivergent conditions can experience them.

Low Cognitive Load User Interfaces

Low cognitive load software is characterised by how little unnecessary mental work it adds to a task while still keeping the information a user needs easy to find. A low cognitive load user interface (UI) is the visible layer people interact with. This does not necessarily mean an ultra-minimal interface. Hiding useful labels, context, or controls can sometimes increase load. Instead, low cognitive load interfaces tend to be low-noise and low-friction: they reduce distractions and unnecessary decisions, greatly reducing the amount of information a user has to keep in their head.

• Clear next steps. The purpose of a screen and its primary action are easy to identify, without several buttons competing for attention.
• Visible information and context. Relevant context stays visible where it is useful, reducing the need to remember another screen when moving around the software.
• Progressive disclosure. Information and controls needed for the current step are prioritised, while advanced options appear only when they become relevant.
• Predictable patterns. Familiar labels, consistent navigation, stable button placement, and consistent language make the interface easier to learn and return to, stopping stress when a known routine suddenly changes.
• Controlled sensory and visual noise. Motion, colour, badges, sounds, alerts, and decoration are used deliberately rather than competing continuously for attention.
• Context protection and easy recovery. Autosave, preserved form entries, clear error messages, undo options, and obvious routes back to the task make interruptions and mistakes less costly.

Taken together, these features reduce extraneous cognitive load and leave more cognitive control available for the work that actually matters. They can be particularly helpful for neurodivergent users, while also benefiting anyone whose available attention is reduced by tiredness, stress, interruptions or pain. Low cognitive load software is therefore not necessarily simplistic software. It can still be detailed and extremely powerful however the interface avoids making users spend mental effort on complexity that does not help them complete the task.

Low cognitive load design in NeuroMoney

NeuroMoney uses this low cognitive load approach to make financial management easier for neurodivergent adults, including (but not limited to) people with ADHD and autism. Information such as bills, subscriptions, planned purchases, and other money tasks is kept easier to see and return to, with an emphasis on clear structure and less reliance on memory. Instead of removing useful financial information, we aim to reduce the extra mental work involved in finding it and remembering it.

FAQ

Note: This article is educational and is not personalised financial or medical advice.