Abstract
Studying how people behave and understanding why their actions unfold differently aremajor goals of psychological science. However, most research relies on nomotheticapproaches that aggregate behavioural data into summary statistics, obscuringperson-specific patterns that afford person-specific understanding. The progressingproliferation of digital technologies such as smartphones, wearables, and computermediatedsystems now generates fine-grained, temporally ordered records of everydaybehaviour, creating unprecedented opportunities to study both systematic interindividualdifferences and patterns of intra-individual variation. Here, we proposeNatural Behaviour Processing (NBP) as a framework for analysing digital behaviouraldata using sequence modelling approaches adapted from natural languageprocessing. We argue that behaviour shares critical structural properties withlanguage: both involve a repository of possible actions (vocabulary), systematicpatterns governing how elements typically combine (grammar), and context-dependentmeaning (semantics). These structural parallels motivate applying sequence-basedmodels that have revolutionised language processing to the study of behaviour morebroadly. We review pioneering studies on diverse topics of research, including lifeoutcomes, decision-making, social media use, consumer behaviour, online education,and health, demonstrating that sequential approaches capture person-specific patternsthat are typically lost in aggregation. We discuss the challenges facing NBP adoption,including the tension between predictive modelling and theoretical understanding, theneed for large-scale curated behaviour datasets, and methodological trainingrequirements. The adoption of NBP could transform the way psychologyoperationalises, conceptualises, and ultimately understands human behaviour bysimultaneously accounting for person-specificity in real-world complexity whilemaintaining predictive performance at population levels.