Abstract Concepts and Pictures of Real‐World Situations Activate One Another
Bibliographic record
Abstract
concepts typically are defined in terms of lacking physical or perceptual referents. We argue instead that they are not devoid of perceptual information because knowledge of real-world situations is an important component of learning and using many abstract concepts. Although the relationship between perceptual information and abstract concepts is less straightforward than for concrete concepts, situation-based perceptual knowledge is part of many abstract concepts. In Experiment 1, participants made lexical decisions to abstract words that were preceded by related and unrelated pictures of situations. For example, share was preceded by a picture of two girls sharing a cob of corn. When pictures were presented for 500 ms, latencies did not differ. However, when pictures were presented for 1,000 ms, decision latencies were significantly shorter for abstract words preceded by related versus unrelated pictures. Because the abstract concepts corresponded to the pictured situation as a whole, rather than a single concrete object or entity, the necessary relational processing takes time. In Experiment 2, on each trial, an abstract word was presented for 250 ms, immediately followed by a picture. Participants indicated whether or not the picture showed a normal situation. Decision latencies were significantly shorter for pictures preceded by related versus unrelated abstract words. Our experiments provide evidence that knowledge of events and situations is important for learning and using at least some types of abstract concepts. That is, abstract concepts are grounded in situations, but in a more complex manner than for concrete concepts. Although people's understanding of abstract concepts certainly includes knowledge gained from language describing situations and events for which those concepts are relevant, sensory and motor information experienced during real-life events is important as well.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".