Language experience predicts semantic priming of lexical decision.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computational models of semantic memory have been successful in accounting for a wide range of cognitive phenomena, including word categorization, semantic priming, and release from proactive interference. Conventionally, the texts input to these models have been curated to represent the average individual's language experience. While this approach has proven successful for making predictions that generalize across individuals, it prevents consideration of situations in which individuals have divergent semantic representations. The use of a representative corpus prevents the generation of predictions specific to the language experience of an individual. While this limitation has been discussed in the literature, previous investigations have not yet validated such corpus-specific predictions. I present an approach to generate corpus-specific semantic representations using internet news sites as corpora. I then validate the semantic representations against subjects that read specific news sites. Results demonstrate that similarities between news sites are specific to the words under consideration and that news site-specific representations successfully predict differential priming effects in lexical decision as a function of news readership. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it