A multilingual preregistered replication of the semantic mismatch effect on serial recall.
Bibliographic record
Abstract
Visual-verbal serial recall is disrupted when task-irrelevant background speech has to be ignored. Contrary to previous suggestion, it has recently been shown that the magnitude of disruption may be accentuated by the semantic properties of the irrelevant speech. Sentences ending with unexpected words that did not match the preceding semantic context were more disruptive than sentences ending with expected words. This particular instantiation of a deviation effect has been termed the semantic mismatch effect. To establish a new phenomenon, it is necessary to show that the effect can be independently replicated and does not depend on specific boundary conditions such as the language of the stimulus material. Here we report a preregistered replication of the semantic mismatch effect in which we examined the effect of unexpected words in 4 different languages (English, French, German, and Swedish) across 4 different laboratories. Participants performed a serial recall task while ignoring sentences with expected or unexpected words that were recorded using text-to-speech software. Independent of language, sentences ending with unexpected words were more disruptive than sentences ending with expected words. In line with previous results, there was no evidence of habituation of the semantic mismatch effect in the form of a decrease in disruption with repeated exposure to the occurrence of unexpected words. The successful replication and extension of the effect to different languages indicates the expression of a general and robust mechanism that reacts to violations of expectancies based on the semantic content of the irrelevant speech. (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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".