AI-driven complex systems redefine cognitive science

Human behavior and cognition emerge from dynamic interactions across neural, psychological, social, and environmental levels, producing phenomena from simple actions such as picking up a dropped pen to complex processes such as learning, mood regulation, or symptom change. These processes are multi-scale and non-linear, unfolding from milliseconds (e.g., neural firing) to years (e.g., habit formation), forming interconnected, adaptive systems. In step with this reality, cognitive science is shifting toward complex systems thinking, which frames cognition as an emergent property of interacting components rather than a product of isolated causes.1 Concepts from complexity science—non-linear dynamical systems, attractor states, coupled networks, and critical transitions—clarify sensitivity to initial conditions and shifts between stable and unstable states, offering richer accounts than static, reductionist models.

 This shift is visible across domains, exemplified by psychiatry here: temporal-network and dynamical-systems approaches recast disorders as evolving configurations of mutually reinforcing symptoms that can exhibit attractors (e.g., persistent low mood), bifurcations, and critical transitions such as rapid onsets of suicidal crises.2 Hour-scale fluctuations in suicidal ideation underscore system instability and the need for time-sensitive, dynamic risk models.3 More broadly, the central question of cognitive science moves from “which factor causes outcome X?” to “how do elements interact within a complex system to generate X?”—a pivot enabled by high-dimensional, longitudinal data and methods that treat variability as signals rather than noise.4 Against this backdrop, advances in artificial intelligence (AI)/machine learning (ML) make it feasible to model high-dimensional, temporally embedded data and to simulate evolving trajectories at scale (Figure 1 illustrates how AI/ML technologies unify these domains to advance complex systems thinking).

Our integrating route is straightforward: we synthesize emerging developments and highlight how human cognition is increasingly modeled as an adaptive system, with AI acting either as a simulator—probing stability, controllability, and tipping points through simulated behavioral trajectories—or as an estimator—inferring latent states, couplings, and transition structures from heterogeneous, time-dependent data. In doing so, we offer a cross-domain framework—linking cognition, mental health, and education—that preserves domain-specific nuance while charting the AI-catalyzed shift from reductionism to complex dynamics.

AI as a catalyst for a new era of complexity

Below, we explore AI’s role in three domains to demonstrate how it starts to enable cognitive science to model complex, evolving systems. Throughout this discussion, “AI model” denotes any learning model—typically Deep Neural Network (DNN) based—that maps data to predictions or latent dynamics. “AI system” is the end-to-end application that embeds such models with data pipelines, interfaces, and governance. “AI agent” refers to a goal-directed component capable of planning and tool use.