Introduction: Philosophy in and Philosophy of Cognitive Science, Part II
Bibliographic record
Abstract
In this second installment on the theme, Philosophy in and Philosophy of Cognitive Science, we have two papers, very different from one another and both very interesting, by William Bechtel and Pierre Jacob. In the first installment, the three authors all talked about the role of philosophical work in cognitive science. In contrast, in this installment, the two papers illustrate two ways in which this work is done. In the last installment we introduced a distinction between philosophy of cognitive science and philosophy in cognitive science. The former consists of philosophical reflections on cognitive science, whereas the latter consists of philosophical contributions to cognitive research. Bechtel’s paper is a good example of work of the former kind, and Jacob’s paper is a good example of work of the latter kind. Drawing on philosophical work on the nature of explanation in good science, Bechtel applies control theory, an important episode in the history of biochemistry, and an underexplored way of thinking about the relationship of mental function to brain function and structure to the specific situation of cognitive science and argues that we are in need of advances in all three areas. His paper is indeed the philosophy of science of cognitive science, just as he says. In contrast, Jacob in his paper does cognitive research. Specifically, his paper is a contribution to the cognitive neuroscience of mirror neurons. Accepting the data produced by experimental work on these neurons, he argues that the prevalent interpretation of these data is suspect. He then argues that a better interpretation, a rather surprising one, that the activity of mirror neurons is the result of concept-application, is available. Looking at Jacob’s paper as a whole and using the old Reichenbach/Popper distinction between the context of discovery and the context of justification, between generating hypotheses and testing them, the paper shows that much good work can be done on testing hypotheses and finding alternative hypotheses better supported by data without doing new experiments, without generating new data—and that researchers trained in philosophy can be very good at doing it. In short, these two papers are excellent illustrations of the two roles for philosophy in relation to cognitive science that we delineated in the first installment of this theme.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.026 |
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".