Communicating abstract meaning: concepts revealed in words and gestures
Bibliographic record
Abstract
Abstract words refer to concepts that cannot be directly experienced through our senses (e.g. truth , morality ). How we ground the meanings of abstract words is one of the deepest problems in cognitive science today. We investigated this question in an experiment in which 62 participants were asked to communicate the meanings of words (20 abstract nouns, e.g. impulse ; 10 concrete nouns, e.g. insect ) to a partner without using the words themselves (the taboo task). We analysed the speech and associated gestures that participants used to communicate the meaning of each word in the taboo task. Analysis of verbal and gestural data yielded a number of insights. When communicating about the meanings of abstract words, participants' speech referenced more people and introspections. In contrast, the meanings of concrete words were communicated by referencing more objects and entities. Gesture results showed that when participants spoke about abstract word meanings their speech was accompanied by more metaphorical and beat gestures, and speech about concrete word meanings was accompanied by more iconic gestures. Taken together, the results suggest that abstract meanings are best captured by a model that allows dynamic access to multiple representation systems. This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".