Stroop-Like Serial Position Effects in Color Naming of Words and Nonwords
Bibliographic record
Abstract
Color-naming latencies to noncolor words and nonwords were faster when the onset or final phoneme of the displays corresponded to the onset or final phoneme of the color response. For example, for displays printed in red, the word rack and nonword rask, which share the initial onset phoneme with the response, led to faster naming than did the control word chap and nonword chup. Conversely, when the onset or final phoneme of the displays matched the onset or final phoneme of a conflicting color response (e.g., rack printed in blue), latencies were longer than to control items. Facilitation effects were stronger than interference effects, and the onset phoneme facilitation effect was augmented by coloring only the initial letter in the display. It is hypothesized that nonlexical processes that govern the translation of print to speech may be a source of facilitation in Stroop-like tasks, whereas lexical processes are more likely to contribute to interference.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".