On the association between intention and visual word identification.
Bibliographic record
Abstract
One of the most fundamental distinctions in cognitive psychology is between processing that is "controlled" and processing that is "automatic." The widely held automatic processing account of visual word identification asserts that, among other characteristics, the presentation of a well-formed letter string triggers sublexical, lexical, and semantic activation in the absence of any intention to do so. Instead, the role of intention is seen as independent of stimulus identification and as restricted to selection for action using the products of identification (e.g., braking in response to a sign saying "BRIDGE OUT"). We consider four paradigms with respect to the role of an intention-defined here as a "task set" indicating how to perform in the current situation-when identifying single well-formed letter strings. Contrary to the received automaticity view, the literature regarding each of these paradigms demonstrates that the relation between an intention and stimulus identification is constrained in multiple ways, many of which are not well understood at present. One thing is clear: There is no simple relation between an intention, in the form of a task set, and stimulus identification. Automatic processing of words, if this indeed ever occurs, certainly is not a system default. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".