Conceptual Metaphor Theory

Conceptual metaphor theory (CMT), pioneered by Lakoff and Johnson, posits that our conceptual system is fundamentally metaphorical in nature. We understand abstract domains (e.g., time, ideas, emotions) in terms of concrete, embodied experiences (e.g., spatial orientations, physical objects). This mapping is not merely linguistic but shapes how we reason, categorize, and remember.

For machine memory systems, this implies that 'raw' data is insufficient. To build robust and interpretable knowledge graphs, AI agents must also encode the metaphorical mappings that underpin human (and potentially machine) cognition. For example, the metaphor TIME IS MONEY underscores a cultural emphasis on quantification and efficiency—a pattern an AI might recognize across economic and temporal reasoning tasks.

This theory connects deeply to Representation, Models & Semantics and Epistemic Debt. Ignoring metaphorical mappings can lead to brittle systems that fail to generalize or, worse, perpetuate biases embedded in language. Recognizing these patterns is a form of Tacit Knowledge—the inarticulable foundation of understanding—that we make explicit through this wiki.