The effect of letter-case type on the semantic processing of words and sentences during attentive and mind-wandering states
Bibliographic record
Abstract
Abstract The task of finding a case type that, on average, enhances the processing of verbal material has yielded mixed results in the literature. This study tackled this issue with an eye to the issue of processing textual information on road signs and the additional consideration of readers’ attentive states. Participants (n = 104) completed three experiments, the first two of which made use of both short (i.e., attentive state) and long (i.e., nonattentive or mind-wandering state) inter-trial intervals (ITIs). Experiment I consisted of a living versus non-living category-decision task involving the presentation of single words. Experiment II consisted of a sensical versus nonsensical sentence-judgment task. Experiment III consisted of a recognition memory task for words presented during the category-decision task. No significant difference in letter-case-type effectiveness was found for either the semantic categorization of or memory for single words. On the other hand, sensical sentences were correctly judged more quickly in lower case (or, more precisely, sentence case with the first letter of the first word capitalized). Such results point to either a more fluent processing of or enhanced conceptual resonance for sentences presented in lower case.
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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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".