What you’ve articulated resonates strongly, but I suspect the distinction you’re pointing to (evidentiary norms in science vs the preformal heuristics guiding scientific practice) only scratches the surface of a far deeper structural misalignment. The deeper problem, as I see it, is that we lack a coherent theory of pre-paradigmatic cognition (i.e., a formal account of how conceptual priors/aesthetic heuristics/anticipatory abstractions function PRIOR to stabilization within institutions.)
So the core tension is between epistemology as a theory of justification or as an architecture ofgenerativity (the former governs what counts as knowledge; the latter shapes what becomes thinkable before it becomes provable). We’re still quite epistemically illiterate about the second (and this ignorance begins to show especially in fields like AI where the frontier progressively consists of ontologically ambigious objects such as gradients/embeddings/emergent capabilities and architectures that have no firm referents in either nature or math.) What you’re describing as scientists’ reliance on “hunches” is a form of cognitive structure beyond a simple heuristic in that it functions more like a proto-taxonomy for world-model constructions in domains where empirical anchoring is delayed or fundamentally soft.
As for the borrowing from analogical fields such as evolution/cognition/language, it’s useful because it serves as a generative constraint that allows high-dimensional exploration without epistemic collapse. The fact that such constraints don’t cleanly fit publication standards I think is less a defect of the constraints themselves but more a recognition of impoverished formal infrastructure for representing proto-theoretical cognition, which is why I believe the invocation of philosophy of science is suggestive but arguably not sufficiently radical. I would take it a step further and suggest a re-engineering of the “cognitive contract” between our discoveries and their validation. Our present pipeline (where insights shall pass through empirical formalism before they can be recognized as epistemically legitimate) appears to amputate fertile forms of abstractions. In domains where feedback is sparse/referents are emergent and the ontological status of the research object is itself under construction I think we shouldn’t conflate our methodological constraints with epistemic sufficiency. AI is a notable case here, but the same holds for climate modeling/synthetic biology /quantum foundations/consciousness studies, etc.
I therefore believe in this light that the role of philosophy shouldn’t be to “adjudicate from the sidelines” but instead to operate as a meta-generative layer that co-evolves with the science it seeks to clarify. Until we take this project seriously we’ll continue to be epistemically confused and treat perhaps the most important cognitive substrates of discovery as informal noise.
What you’ve articulated resonates strongly, but I suspect the distinction you’re pointing to (evidentiary norms in science vs the preformal heuristics guiding scientific practice) only scratches the surface of a far deeper structural misalignment. The deeper problem, as I see it, is that we lack a coherent theory of pre-paradigmatic cognition (i.e., a formal account of how conceptual priors/aesthetic heuristics/anticipatory abstractions function PRIOR to stabilization within institutions.)
So the core tension is between epistemology as a theory of justification or as an architecture of generativity (the former governs what counts as knowledge; the latter shapes what becomes thinkable before it becomes provable). We’re still quite epistemically illiterate about the second (and this ignorance begins to show especially in fields like AI where the frontier progressively consists of ontologically ambigious objects such as gradients/embeddings/emergent capabilities and architectures that have no firm referents in either nature or math.) What you’re describing as scientists’ reliance on “hunches” is a form of cognitive structure beyond a simple heuristic in that it functions more like a proto-taxonomy for world-model constructions in domains where empirical anchoring is delayed or fundamentally soft.
As for the borrowing from analogical fields such as evolution/cognition/language, it’s useful because it serves as a generative constraint that allows high-dimensional exploration without epistemic collapse. The fact that such constraints don’t cleanly fit publication standards I think is less a defect of the constraints themselves but more a recognition of impoverished formal infrastructure for representing proto-theoretical cognition, which is why I believe the invocation of philosophy of science is suggestive but arguably not sufficiently radical. I would take it a step further and suggest a re-engineering of the “cognitive contract” between our discoveries and their validation. Our present pipeline (where insights shall pass through empirical formalism before they can be recognized as epistemically legitimate) appears to amputate fertile forms of abstractions. In domains where feedback is sparse/referents are emergent and the ontological status of the research object is itself under construction I think we shouldn’t conflate our methodological constraints with epistemic sufficiency. AI is a notable case here, but the same holds for climate modeling/synthetic biology /quantum foundations/consciousness studies, etc.
I therefore believe in this light that the role of philosophy shouldn’t be to “adjudicate from the sidelines” but instead to operate as a meta-generative layer that co-evolves with the science it seeks to clarify. Until we take this project seriously we’ll continue to be epistemically confused and treat perhaps the most important cognitive substrates of discovery as informal noise.