Integrating computational modeling, neuroimaging, and neuromodulation to decompose perceptual decision-making in healthy aging: a review of methods, findings, and gaps

Perceptual decision-making (PDM) — the transformation of sensory input into behavioral choice — declines with healthy aging, leading to slower responses, altered accuracy, and negative impacts on quality of life. Recent advances in model-based cognitive neuroscience, particularly drift-diffusion models (DDMs), allow decomposition of PDM into latent components (e.g., evidence accumulation, decision caution, non-decision time) and enable identification of neural circuits associated with each component. This decomposition provides a foundation for targeted neuromodulatory interventions (e.g., transcranial magnetic stimulation) aimed at mitigating age-related cognitive decline. Although a substantial body of literature has successfully combined DDM with neuromodulation to enhance distinct PDM components in young adults, evidence in older populations remains critically limited. Due to age-related morphological, physiological, and connectivity changes, findings from young adults cannot be directly generalized to older adults. This review highlights this research gap and calls for systematic investigation at the intersection of cognitive modeling and neuromodulation to selectively improve distinct components of perceptual decision-making in aging.