generalike

Generalike is a term used to describe a form of generalization centered on cross-domain likeness. The word fuses general with like, signaling a focus on structural similarities that enable knowledge or reasoning to transfer across tasks rather than relying on surface features.

Though not a standard term in major reference works, generalike appears in a range of late-2010s to 2020s discussions across cognitive science, artificial intelligence, and design theory. Authors applying the term emphasize mechanisms that extract relations, roles, and invariances that persist across contexts.

In cognitive science, a generalike mechanism would support analogical reasoning by mapping relational structures from one domain to another, rather than matching objects by appearance alone.

In machine learning and AI, a generalike approach favors representations and learning objectives that preserve relational structure, enabling models to adapt to new tasks with minimal data by exploiting underlying likenesses.

Critics argue that the term is underspecified and overlaps with established notions such as generalization, transfer learning, and abstraction, which can lead to confusion without clear criteria or formalization.

See also: generalization, transfer learning, analogy, abstraction.