Active Ingredients in Digital Cognitive Interventions: Integrating Dismantling Designs with Mechanistic Neuroscience (Preprint)

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Abstract
BACKGROUND Digital cognitive interventions (DCIs) are increasingly used to address cognitive deficits across psychiatric, neurological, and aging populations. However, the field lacks consensus about which intervention components are causally responsible for cognitive and clinical benefits. This limits interpretation of negative trials, impedes optimization, and constrains personalization. Mechanistic dismantling trials can clarify whether specific DCI features (e.g., adaptive difficulty, reward schedules, feedback contingencies, task variability, specific cognitive target, and human support) are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable generalization. Further, dismantling designs hold potential to more precisely probe underlying disease mechanisms and identify specific neurocognitive processes responsible for clinical impairment in the target population. OBJECTIVE We argue that DCI research should shift from broad efficacy testing to mechanistic studies designed to identify “active ingredients” e.g. the minimal components required to engage a prespecified target and improve clinically meaningful outcomes. We propose adapting dismantling designs from psychotherapy research and integrating them with mechanistic neuroscience, cognitive theory, and high-resolution digital behavioral data. This approach aligns with the National Institute of Mental Health’s Experimental Therapeutics framework by explicitly linking: (1) target specification, (2) target engagement, and (3) downstream clinical and functional benefit. METHODS No Methods RESULTS No Results CONCLUSIONS Mechanistic dismantling trials can clarify whether specific DCI features (e.g., adaptive difficulty, reward schedules, feedback contingencies, task variability, specific cognitive target, and human support) are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable generalization. Further, dismantling designs hold potential to more precisely probe underlying disease mechanisms and identify specific neurocognitive processes responsible for clinical impairment in the target population.

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