Exploring Mathematical Psychology: A Systematic Review with Special Reference to Neurodiversity

Abstract

Mathematical psychology uses formal mathematical and computational models to study cognitive phenomena such as perceptual processes, decision making, memory, learning. The present systematic review is based on 47 peer-reviewed pertaining to the period 1950 to 2026 and extracted from  PsycINFO, PubMed, Web of Science, Scopus and Google Scholar databases, according to the PRISMA 2020 guidelines. The review combines two complementary streams of literature: (1) classical and contemporary mathematical psychology frameworks such as sequential sampling models, probabilistic graphical models, Bayesian inference, signal detection theory, and computational cognitive models; and (2) an emerging literature of mathematical and computational models of neurodiversity, including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), dyslexia, dyscalculia, and dyspraxia. Results show that fundamental cognitive processes like evidence accumulation, perceptual differentiation and memory retrieval can be consistently and formally described as differential equations, stochastic processes, and network-based processes. There are mechanistic, testable models of autism, ADHD, dyslexia and dyscalculia within predictive coding frameworks, connectome-based predictive modelling and dynamic systems approaches. These models together imply that mathematical psychology provides a principled and experimentally tractable route towards an understanding of typical and atypical cognition that is personalised on the basis of the strengths involved.