The influence of semantic primes on the typing of word targets
Bibliographic record
Abstract
Abstract Typing is a complex activity involving identifying and encoding the to-be-typed word, planning, and executing the keystrokes. Different mechanisms have been proposed to explain how contextual information about meaning influences the processing of a target word (for example, semantic priming), and it is unclear how this information influences the typing output of the target word. When the interval between the prime and target is short, the prime is thought to automatically activate the target. With a longer interval, the facilitation may be more strategic. The influence of the prime on the output of the target may thus depend on the interval between the two. We found that at both short and long intervals, related semantic primes facilitated the speed of the first keystroke of the target word. However, there was no effect on how quickly the non-initial letters of the target were entered, suggesting that information from semantic primes influences the planning and initiation of typing, but not the execution of remaining keystrokes. Interestingly, the initial and non-initial keystrokes were faster when the interstimulus interval was long, suggesting participants encode the letters of the prime which could interfere with encoding and typing the target if the interval between them is short.
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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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".