Review: Computer Simulation Can Supplement Cognitive Tests Used to Evaluate ADHD Symptoms

January 12, 2021

The accuracy and utility of cognitive tests used to diagnose ADHD may be improved by the use computational models, or computer simulations of normal brain processes that are compared to dysfunctional processes observed in patients, according to a new review published in Psychological Bulletin.¹ Cognitive tests can help identify selective attention, poor working memory, altered time perception, difficulties in maintaining attention, and impulsive behavior, but they don’t always capture the complexity of ADHD symptoms, according to the study authors. Computational models may help to fill this gap, they found.

Researchers reviewed 50 studies of cognitive tests for ADHD and determined how three common computational models (diffusion decision models, absolute accumulator models, and ex-Gaussian distribution models) could supplement them. They then offered guidance for testing and clinical practice that could help clinicians better characterize ADHD and any accompanying mental health diagnoses, improve treatment outcomes, and predict the longevity of ADHD symptoms.

Decision-making while driving is a scenario that helps illustrate the potential problem with cognitive tests. When a red light turns green, most drivers know they can continue driving, but not everyone presses the gas pedal at the same time. A cognitive test would show participants the same red-light green-light scenario to determine an average reaction time and use the deviations from that average to categorize the typical versus disordered behavior. Individuals with ADHD are typically found to be “slower” to start driving, a conclusion that doesn’t take into account factors like distraction, day-dreaming, or nervousness. Computer modeling could capture this broader distribution of reactions.

Nadja Ging-Jehli, lead author of the review, explained: “We can use computational modeling to think about the factors that generate the observed behavior. These factors will broaden our understanding of a disorder, acknowledging that there are different types of individuals who have different deficits that also call for different treatments.” The researchers proposed “using the entire distribution of the reaction times, taking into consideration the slowest and the fastest reaction times to distinguish between different types of ADHD.”²

The review also revealed a wide range of externally evident ADHD symptoms and more subtle characteristics that are difficult to detect with the most common testing methods. This led researches to conclude that a single task-based test was not sufficient for diagnosing ADHD accurately.

Ging-Jehli concluded “We need to account for the different types of drivers and we need to understand the different conditions to which we expose them. Based on only one observation, we cannot make conclusions about diagnosis and treatment options.”

Finally, cognitive testing and computational modeling should be seen as complements, not replacements for clinical interviews and questionnaires. Clinicians should administer a variety of tasks that gauge social and cognitive traits, and more consistency is needed across literature to guarantee that the same cognitive tasks are used to assess the appropriate cognitive concepts.

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