Dress Rehearsals, Previews, and Encores: A New Account of Mental Representation
Bibliographic record
Abstract
Abstract One of the central debates in cognitive science is the dispute over the role of representation in cognition: on computational/representational accounts, representations are theoretically central; on dynamic systems approaches in which cognition is investigated as a particular sort of physical process, representations play either no role, or, at best, a derivative one. But these two perspectives lead to a deeply unsatisfying theoretical divide: accounts situated in the representational camp are plagued by the inscrutable problem of intentionality, while those hedging towards anti‐representationalism seem incapable of saying anything theoretically interesting about high‐level cognition. This unhelpful polarization is due in part, at least, to a muddy debate; while some take representationalism to be a commitment to the necessity of conceptual representations for cognition, representations the having of which require certain conceptual capacities, others do not. Recently, there has been a surge of work on non‐conceptual representation. This article aims to add to this movement by suggesting a particular cognitive mechanism for non‐conceptual representations, one that plays a pivotal role in making conceptual representations possible. One of the central consequences of this new view of representation is the possibility of a non‐question‐begging naturalistic account of intentionality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".