Individual differences in semantic processing: Insights from the Calgary semantic decision project.
Bibliographic record
Abstract
Most previous studies of semantic processing have examined group-level data. We investigated the possibility that there might be individual differences in semantic decision performance even among the standard undergraduate population and that such differences might provide insights into semantic processing. We analyzed the Calgary Semantic Decision Project dataset, which includes concrete/abstract semantic decision responses to thousands of words and also a vocabulary measure for each of 312 participants. Results of our analyses showed that semantic decision responses had good reliability, and that the speed of those responses was related to individual differences as assessed by vocabulary scores and also by diffusion model parameters. That is, semantic decisions were faster for participants with higher vocabulary scores and for participants with steeper drift rates. Further, in their semantic decision responses high vocabulary participants showed more sensitivity to some lexical/semantic predictors and less sensitivity to others. For responses to both concrete and abstract words, high vocabulary participants were more sensitive to word concreteness and less sensitive to word frequency and age of acquisition. For concrete words, high vocabulary participants were also more sensitive to semantic neighborhood similarity. The results suggest that high vocabulary participants are able to more readily access semantic information and are better able to emphasize task-relevant dimensions. In sum, the results are consistent with a dynamic, multidimensional account of semantic processing. (PsycINFO Database Record
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".