Task characteristics modulate the impact of action similarity on visual object identification
Bibliographic record
Abstract
We previously 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 to identify these novel objects with non-word labels. Specific objects were paired with specific actions, and findings revealed that visually similar objects paired with similar actions were confused more often in memory than when these same objects were paired with dissimilar actions. 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. In earlier experiments, this pairing process occurred in two steps: participants first visually identified actions performed on a cylinder, and when participants could correctly identify all actions, they visually identified stationary objects. However, this two-step process may not represent the way we learn about objects in the real world. In the present study, we varied task characteristics to more adequately represent how we learn about objects, and asked participants to concurrently focus on action and object information. We contrasted the performance of participants who completed the original experiment to those who were asked, during test trials, to visually identify objects and either (1) produce their associated action, or (2) visually identify their associated action. All three tasks produced similar patterns of results. An analysis of the effect sizes revealed that the impact of action similarity on visual object identification was strongest in the two-step process and weakest when participants were asked to visually identify objects and their associated actions. Because learning trials and the object-naming part of test trials were identical for all tasks, the findings suggest that characteristics of the action-testing task modulated the impact of action information on visual object identification.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".