Context Breeds False Memories for Indeterminate Sentences
Bibliographic record
Abstract
What are the roles of semantic and pragmatic processes in the interpretation of sentences in context? And how do we attain such interpretations when sentences are deemed indeterminate? Consider a sentence such as “Lisa began the book” which does not overtly express the activity that Lisa began doing with the book. Although it is believed that individuals compute a specified event to enrich the sentential representation – yielding, e.g., “began [reading] the book” – there is no evidence that a default event meaning is attained. Moreover, if indeterminate sentences are enriched, it is not clear where the information required to generate enriched interpretations come from. Experiment 1 showed that, in isolation, there is no default interpretation for indeterminate sentences. The experiment also showed that biasing contexts constrain event interpretations and improve plausibility judgments, suggesting that event representations for indeterminate sentences are generated by context. In Experiment 2, participants heard biasing discourse contexts and later falsely recognized foil sentences containing the biased events (“Lisa began reading the book”) at the same proportion and with the same confidence as the original indeterminate sentence (“Lisa began the book”). We suggest that indeterminate sentences trigger event-enriching inferences but only in sufficiently constraining contexts. We also suggest that indeterminate sentences create two memory traces, one for the proposition consistent with the denotational, compositional meaning, and another for the proposition that is enriched pragmatically over time.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".