A case for using methods from natural science in advancing the field of cognitive neuroscience
Bibliographic record
Abstract
Cognitive neuroscience seeks to pinpoint the neural basis of cognitive function. Application of scientific methods can be credited for its advancement within the field of psychology. Past approaches such as phrenology, that linked bumps on the skull to mental capabilities, initially gained popularity, but the lack of experimental testing contributed to its demise. Research in neuropsychology and the use of the double dissociation experimental technique subsequently emerged. Objective measurements of behaviour following selective damage within the brain led to a paradigm shift. More recently, application of the subtraction technique, coupled with the emergence of cognitive neuroimaging tools, has allowed psychologists to isolate and measure specific functions such as language, vision, memory, and recognition of emotion. Importantly, these approaches enable reliable prediction of behaviours, given parameters of brain integrity, a key goal within the field of psychology.
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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 | 0.145 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.019 | 0.045 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.018 | 0.037 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".