Reframing Cognitive Science as a Complexity Science
Argues that cognitive science should shift from reductionist models to complexity science frameworks that better capture the emergent, adaptive, and multi-scale nature of cognition
Contends that traditional cognitive science—rooted in computationalism and modularity—fails to account for the dynamic, context-sensitive, and self-organizing properties of cognition
Proposes reframing cognition as a complex adaptive system, drawing on principles from physics, biology, and network science
Key Arguments
1. Cognition Is Multi-Scale and Emergent
Cognitive phenomena arise from interactions across scales—from neurons to social systems.
Concepts like nonlinearity, feedback, self-organization, and criticality help explain how cognition adapts and evolves
Cognitive systems resemble ecosystems more than machines
3. Limitations of Traditional Models
Symbolic and connectionist models often ignore environmental coupling and embodied dynamics
These models struggle to explain creativity, resilience, and context-dependent behavior
4. Toward a New Framework
Network-based models of cognition
Information-theoretic approaches to mental representation
Evolutionary and ecological perspectives on learning and adaptation
Implications for Cognitive Science
Theory-informed practice must embrace complexity: Practitioners should model cognition as adaptive, emergent, and context-sensitive
Interdisciplinary integration is essential: Cognitive science must draw from biology, physics, and systems theory—not just psychology and computer science
New metrics and methods are needed: Traditional experimental designs may miss emergent properties; complexity science encourages richer, multi-modal data