Bibliographic record
Abstract
Abstract The human mind is capable of entertaining an astounding range of thoughts. These thoughts are composed of concepts or ideas, which are the building blocks of thoughts. This book is about where all of these concepts come from and the psychological structures that ultimately account for their acquisition. We argue that the debate over the origins of concepts, known as the rationalism-empiricism debate, has been widely misunderstood—not just by its critics but also by researchers who have been active participants in the debate. Part I fundamentally rethinks the foundations of the debate. Part II defends a rationalist view of the origins of concepts according to which many concepts across many conceptual domains are either innate or acquired via rationalist learning mechanisms. Our case is built around seven distinct arguments, which together form a large-scale inference to the best explanation argument for our account. Part III then defends this account against the most important empiricist objections and alternatives. Finally, Part IV argues against an extreme but highly influential rationalist view—Jerry Fodor’s infamous view that it is impossible to learn new concepts and his related radical concept nativism, which holds that essentially all lexical concepts are innate. Throughout the book, our discussion blends philosophical and theoretical reflection with consideration of a broad range of empirical work drawn from many different disciplines studying the mind, providing a thorough update to the rationalism-empiricism debate in philosophy and cognitive science and a major new rationalist account of the origins of concepts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".