Talking about what we see, again: further evidence for non-anticipatory eye movements in dynamic scenes during sentence comprehension
Bibliographic record
Abstract
We present two experiments involving true scenes (motion pictures of events), manipulating verb class, sentence semantic context, and scene motion context. Experiment 1 constituted a replication of [reference omitted, 2019]. Participants (N=32) were presented with sentences containing either a causative or a perception/psychological (experiencer) verb (e.g., Before making the desert, the cook will crack/examine the eggs that are in the bowl). Scene context varied according to the action performed by the agent (cook), moving towards the target object (eggs), away from it, or remaining neutral. Results were similar to those obtained by our previous study: a main effect of motion and no main effect of verb type. We obtained faster saccades to the target object in the causative sentences than in the experiencer sentences, but only in the towards motion condition. As in our previous study, we did not find anticipatory effects to target objects. In Experiment 2 (N=46), in addition to verb type (causative vs. experiencer) and agent motion (towards vs. neutral) we introduced a sentence context manipulation, with the first clause denoting either a semantically restrictive activity (e.g., In order to make the omelet...) or a non-restrictive one (e.g., After pouring the flour into the bowl,...). We predicted that the stronger context would enhance attention to properties of verbs making the potential referents of their objects more salient, thus driving anticipatory effects found in other studies. We found an effect of motion and semantic context, with restrictive sentences driving faster saccades to target objects. We also found a verb effect with causatives yielding faster saccades than experiencer verbs only in the towards condition, but no anticipatory effects. These results suggest that early linguistic and visual processes are largely independent, interacting at a later, conceptual stage. We propose that the two systems interact using a common propositional code.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".