feature correlations 1 Further Evidence for Feature Correlations in Semantic Memory
Bibliographic record
Abstract
The role of feature correlations in semantic memory is a central issue in conceptual representation. In two versions of the feature verification task, subjects were faster to verify that a feature ( ) is part of a concept (grapefruit) if it is strongly rather than weakly intercorrelated with the other features of that concept. Contrasting interactions between feature correlations and SOA were found when the concept versus the feature was presented first. An attractor network model of word meaning that naturally learns and uses feature correlations predicted those interactions. This research provides further evidence that semantic memory includes implicitly-learned statistical knowledge of feature relationships, in contrast to theories such as spreading activation networks, in which feature correlations play no role. To appear in Special Issue of Canadian Journal of Experimental Psychology on Visual Word Recognition (December, 1999). This work was supported by NSERC grant RGPIN155704 to the first author and NSERC postgraduate fellowships to the second and third authors. Part of this research formed RW's University of Western Ontario undergraduate thesis. The authors thank Michael Masson and Jeff Elman for helpful comments on earlier drafts. Correspondence concerning this article should be addressed to Ken McRae, Department of Psychology, Social Science Centre, University of Western Ontario, London, Ontario, N6A 5C2. email: mcrae@uwo.ca Our environment is highly structured. In the domain of language processing, for instance, there are numerous sources of structure to which people are sensitive. Some words occur together more often than chance within sentences, as do some letters and phonemes within words. In this article, we focus on the fact that some semantic f...
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".