Shallow semantic processing of text: Evidence from eye movements
Bibliographic record
Abstract
Evidence for shallow semantic processing has depended on paradigms that required readers to explicitly report whether they noticed an anomalous noun phrase (NP) after reading text such as ‘Amanda was bouncing all over because she had taken too many tranquillizing sedatives in one day’. We replicated previous research by showing that readers frequently fail to report the anomaly, and that less-skilled readers have particular difficulty reporting locally anomalous NPs such as tranquillizing stimulants. In addition, we examined the time course of anomaly detection by monitoring readers’ eye movements for spontaneous disruptions when encountering the anomalous NPs. The eye fixation data provided evidence for on-line detection of anomalies; however, the detection was delayed. Readers who later reported the anomaly did not spend longer processing the anomalous NP when first encountering it; however, they did spend longer refixating it. Our results challenge orthodox models of comprehension that assume that semantic analysis is exhaustive and complete.
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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 | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".