Research Consortia and Large-Scale Data Repositories for Studying Intelligence
Bibliographic record
Abstract
The first neuroimaging studies of intelligence were done with positron emission tomography (PET) (Haier et al., 1988). PET was expensive and invasive but more researchers had access to neuroimaging when Magnetic Resonance Imaging (MRI) became widely available around the year 2000. The advent of advanced MRI methods enabled researchers to investigate localized (region-level) associations of brain measures and measures of intelligence in healthy individuals (Gray & Thompson, 2004; Luders, Narr, Thompson, & Toga, 2009). At the whole-brain level, MRI-based studies have reported a positive association ( r = .40 to .51) between some measures of intelligence and brain size (Andreasen et al., 1993; McDaniel, 2005). Several studies at the voxel and regional levels have also demonstrated a positive correlation of morphometry with intelligence in brain regions that are especially relevant to higher cognitive functions including frontal, temporal, parietal, hippocampus, and cerebellum (Andreasen et al., 1993; Burgaleta, Johnson, Waber, Colom, & Karama, 2014; Colom et al., 2009; Karama et al., 2011; Narr et al., 2007; Shaw et al., 2006). More recently, neuroimaging studies have revealed large-scale structural and functional brain networks as potential neural substrates of intelligence (see review by Jung & Haier, 2007 and Barbey et al., 2012; Barbey, Colom, Paul, & Grafman, 2014; Colom, Karama, Jung, & Haier, 2010; Khundrakpam et al., 2017; Li et al., 2009; Sripada, Angstadt, Rutherford, & Taxali, 2019).
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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 | 0.430 | 0.676 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.070 | 0.088 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.012 | 0.023 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.063 | 0.028 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".