On the Strength of Connections Between Localist Mental Modules as a Source of Frequency-of-Occurrence Effects
Bibliographic record
Abstract
Frequency-of-occurrence effects (e.g., effects of word frequency or familiarity) are widely thought to arise through differences in resting levels of activation in localist input-output modules. A different account posits that these effects at least partially reflect the strength of connections between various localist modules. Given that Arabic numerals appear more frequently than their alphabetic counterparts, we contrasted reaction times to stimuli in both formats in a naming/reading-aloud task and a parity-judgment task. The script effect (the difference between reaction times to Arabic and to alphabetic formats) was large in the parity-judgment task but absent in the naming/reading-aloud task. This script-by-task interaction follows naturally from the idea that at least part of the effect of frequency of occurrence of a printed word or digit (and other instances of familiarity) resides in the strength of connections between specialized localist input-output modules and a localist semantic module. This conclusion is likely applicable across a variety of domains.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".