Quantifying Sensorimotor Experience: Body-Object Interaction Ratings for More Than 9,000 English Words
Bibliographic record
Abstract
Ratings of body object interaction (BOI) measure the ease with which the human body can interact with a word’s referent. Researchers have studied the effects of BOI in order to investigate the relationships between sensorimotor and cognitive processes. Such efforts could be improved, however, by the availability of more extensive BOI norms. In the present work, we collected BOI ratings for over 9,000 words. These new norms show good reliability and validity, and have extensive overlap with the words used in other lexical and semantic norms, and in available behavioral megastudies (e.g., the English Lexicon Project, Balota et al., 2007; the Calgary Semantic Decision Project, Pexman, Heard, Lloyd, & Yap, 2017). In analyses using the new BOI norms we found that high BOI words tended to be more concrete, more graspable, and more strongly associated with sensory, haptic, and visual experience than low BOI words. When we used the new norms to predict response latencies and accuracy data from behavioral megastudies we found that BOI was a stronger predictor of responses in the semantic decision task than in the lexical decision task. These findings are consistent with a dynamic, multi-dimensional account of lexical semantics. The norms described here should be useful for future research examining effects of sensorimotor experience on performance in tasks involving word stimuli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".