The impact of action similarity on visual object identification
Bibliographic record
Abstract
Previous research has shown that visual similarity influences visual object identification: participants tend to confuse objects that are visually similar rather than objects that are visually dissimilar. It has also been suggested that nonvisual information, for example information about how objects are used, can impact visual object identification. The visual identification of novel objects can be facilitated in some neurological patients by associating novel objects to the names of dissimilar objects. With healthy participants, making novel objects distinct by associating them to non-overlapping features can serve to make them more discriminable when asked to perform same / different judgements. In both cases, the associations are verbally based: novel objects are associated with verbal labels (object names or attributes). We evaluated the impact of visual similarity and action similarity on visual object identification using a learning paradigm where novel associations were formed between objects and actions. We taught participants to associate novel objects with novel actions, and taught them to identify these novel objects with non-word labels. Specific objects were paired with specific actions. Visually similar objects paired with similar actions were confused more often in memory than when these same objects were paired with dissimilar actions. Hence the actions associated with objects served to increase or decrease their separation in memory space, and influenced the ease with which these objects could be identified. These experiments ultimately demonstrated that when identifying stationary objects, the memory of how these object were used dramatically influenced the ability to identify these objects.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".