On the joint effects of stimulus quality and word frequency in lexical decision: Conditions that promote staged versus cascaded processing.
Bibliographic record
Abstract
. Yap and Balota's (2007) Familiarity Discrimination account predicts additive effects of these two factors on mean RT and across the RT distribution because it assumes a staged normalization process that deals with the effect of low Stimulus Quality; a subsequent process produces the effect of Word Frequency. In contrast, O'Malley and Besner's (2008) context-dependent thresholding/cascading account predicts an interaction because the use of illegal foils eliminates the need for thresholding at the letter level normally used to protect against lexical capture (identifying a nonword as a word) in experiments where Stimulus Quality is a factor, and hence the system reverts to processes in cascade. Critically, the present experiment yielded an interaction in which low-frequency words were more impaired by low Stimulus Quality than were high-frequency words. These data are inconsistent with the Familiarity Discrimination account as currently constituted, but consistent with a context-specific cascaded account. Further discussion considers how the Familiarity account may be modified so as to accommodate these data. Most generally, these data add to the view that processing is highly malleable (context dependent) rather than the received view, especially in regard to computational accounts, in which interactive-activation dynamics dominate. (PsycInfo Database Record (c) 2022 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".