The Treachery of Images: Objects, Pictures, Words and the Role of Affordances in Similarity Judgements
Bibliographic record
Abstract
Categorization is a fundamental cognitive strategy employed to ease information processing and to aid memory formation. Past research on how humans categorize objects has used images of objects as experimental stimuli. Results suggest these stimuli are categorized based on abstract linguistic concepts. Concurrently, studies in the past 10 years have found differences in the processing of images as compared to real-world objects. One proposed explanation is that these results are due to differences in the affordances of images versus objects. Using a similarity judgement paradigm, we have explored the effect of affordances in a categorization task including words (object names), images, and objects. Consistent with previous research, we found significant differences in how participants made similarity judgements of images and objects. Moreover, we found that similarity judgments using object names were much more similar to the judgments of pictures than of objects. An exploratory cluster analysis opens the possibility of framing such differences as affordance driven. These results suggest a need for more ecologically valid categorization tasks, more conservative inferences when using images as stimuli in these tasks, and the need for further exploring the role of affordances in categorization.
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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 | 0.004 | 0.052 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| 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".