Individual Differences in Reasoning and the Algorithmic/Intentional Level Distinction in Cognitive Science
Bibliographic record
Abstract
When a layperson thinks of individual differences in reasoning they think of IQ tests. It is quite natural that this is their primary associate, because IQ tests are among the most publicized products of psychological research. This association is not entirely inaccurate either, because intelligence – as measured using IQ-like instruments – is correlated with performance on a host of reasoning tasks (Ackerman, Kyllonen, and Roberts 1999; Carroll 1993; Hunt 1999; Lohman 2000; Lubinski 2004; Rips and Conrad 1983; Sternberg 1977, 1985). Nonetheless, a major theme of this chapter will be that certain very important classes of individual differences in thinking are ignored if only intelligence-related variance is the primary focus. A number of these ignored classes of individual differences are those relating to rational thought. In this chapter, I will argue that intelligence-related individual differences in thinking are largely the result of differences at the algorithmic level of cognitive control. Intelligence tests thus largely fail to tap processes at the intentional level of control. Because understanding rational behavior necessitates understanding processes operating at both levels, an exclusive focus on intelligence-related individual differences will tend to obscure important differences in human thinking. This argument obviously depends strongly on differentiating the algorithmic from the intentional level of analysis. Therefore, in the next section I will outline the sources that I rely on for this conceptual distinction and how I will utilize the distinction to provide a framework for thinking about individual differences in reasoning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".