Accessing embodied object representations from vision: A review.
Bibliographic record
Abstract
Theories of embodied cognition (EC) propose that object concepts are represented by reactivations of sensorimotor experiences of different objects. Abundant research from linguistic paradigms provides support for the notion that sensorimotor simulations are involved in cognitive tasks like comprehension. However, it is unclear whether object concepts, as accessed from the visual presentation of objects, are embodied. In the present article we review a large body of visual cognitive research that addresses 5 main predictions of the theory of EC. First, EC accounts predict that visual presentation of manipulable objects, but not nonmanipulable objects, should activate motor representations. Second, EC predicts that sensorimotor activity is necessary to perform visual-cognitive tasks such as object naming. Third, EC posits the existence of distinct neural ensembles that integrate information from action and vision. Fourth, EC predicts that relationships between visual and motor activity change throughout development. Fifth, EC predicts that the visual presentation of objects or actions should prime performance cross-modally. We summarize findings from neuroimaging, neuropsychology, neurophysiology, development, and behavioral paradigms. We show that while much of the research published so far demonstrates that there is a relationship between visual and motoric representations, there is no evidence supporting a strong form of EC. We conclude that sensorimotor simulations may not be required to perform visual cognitive tasks and highlight a number of directions for future research that could provide strong support for EC in visual cognitive paradigms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".