On the roles of form systematicity and sensorimotor effects in language processing.
Bibliographic record
Abstract
Grounded or embodied cognition research has employed body-object interaction (BOI; e.g., Pexman et al., 2019) ratings to investigate sensorimotor effects during language processing. We investigated relationships between BOI ratings and nonarbitrary statistical mappings between words' phonological forms and their syntactic category in English; i.e., form systematicity. In Study 1, principal components analysis revealed that BOI and form systematicity measures load on a common component, indicating they convey similar information about the probability of a word belonging to a particular syntactic category. In Studies 2, 3, and 4, form systematicity measures were stronger predictors of English Lexicon Project (ELP; Balota et al., 2007), Auditory English Lexicon Project (AELP; Goh et al., 2020), and English Crowdsourcing Project (ECP; Mandera et al., 2020) performance than BOI. In Study 5, BOI was a stronger predictor of performance from the Calgary Semantic Decision Project (CSDP; Pexman et al., 2017) than form systematicity. In Study 6, only form systematicity significantly predicted performance from the LinguaPix object naming megastudy (Krautz & Keuleers, 2022). Together, these results demonstrate that nonarbitrary statistical relationships in the form of mappings between ortho-phonological information and meaning are accessed automatically during language processing; i.e., even when syntactic category is not relevant to the task, and that sensorimotor simulation mechanisms are only strongly engaged when explicitly demanded by the task. We discuss the implications of these findings for proposals of embodied or grounded cognition and interpretations of neuroimaging data from word recognition tasks. (PsycInfo Database Record (c) 2023 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".