Deconstructing analogy
This work addresses the computational challenge of analogy for cognitive science, offering insights into human cognitive strategies.
The paper deconstructs existing cognitive models of analogy by analyzing their simplification strategies for handling NP-hard complexity, and proposes a new model that better aligns with psychological evidence.
Analogy has been shown to be important in many key cognitive abilities, including learning, problem solving, creativity and language change. For cognitive models of analogy, the fundamental computational question is how its inherent complexity (its NP-hardness) is solved by the human cognitive system. Indeed, different models of analogical processing can be categorized by the simplification strategies they adopt to make this computational problem more tractable. In this paper, I deconstruct several of these models in terms of the simplification-strategies they use; a deconstruction that provides some interesting perspectives on the relative differences between them. Later, I consider whether any of these computational simplifications reflect the actual strategies used by people and sketch a new cognitive model that tries to present a closer fit to the psychological evidence.