The Neuropsychology and Psychophysiology of Human Intelligence
Bibliographic record
Abstract
Since the time of Sir Francis Galton and throughout the 20th century, numerous researchers interested in human intelligence have attempted to identify its biological bases. Some of these attempts have seemed promising at first but have either failed to replicate or have not been pursued for one reason or another; others have yielded a more consistent and reliable pattern of results. In this chapter, four approaches to the investigation of biological correlates of intelligence are described covering (1) anatomical or structural head size and brain volume estimates, (2) psychophysiological event-related potentials, (3) nerve conduction velocity, and (4) cerebral glucose metabolic rates. These are not the only approaches that have been taken to investigate the biology of intelligence, but their description should suffice to give an introduction to the area; coverage of such additional topics as biochemical factors and molecular genetic studies of quantitative trait loci may be found in Vernon (1993a, 1997). One of the most interesting findings from studies in these areas is the extent to which completely noncognitive measures correlate with performance on complex problem-solving and intelligence tests. Offsetting these encouraging empirical results, however, has been a relative dearth of sound theory development. A substantial amount is currently known about which biological and physiological measures and mechanisms correlate with intelligence, but the functional significance of many of these correlations largely remains unclear. One of the central challenges for scientists who pursue the search for the biological bases of human intelligence will be to integrate their findings into a cohesive explanatory framework that goes beyond the correlational and incorporates causational and theoretically compelling substance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".