Action and semantic attributes in object identification
Bibliographic record
Abstract
Previous research has shown that action and semantic attributes are processed in physiologically distinct streams (Milner, A.D. & Goodale, M.A., 2006). For example, visually guided movements, such as the action of a hammer hammering a nail, are processed predominantly in the dorsal stream, while colour is processed in the ventral stream. Furthermore, these two streams, ventral and dorsal, have been associated with the upper and lower visual fields respectively (Milner, A.D. & Goodale, M.A., 2006). The present investigation sought to evaluate the impact of attribute type and visual field on identification of novel objects. It is expected that matching the processing stream with its complementary visual field and attribute type (dorsal stream ? lower visual field ? action; ventral stream ? upper visual field ? semantic) will produce decidedly faster reaction times in identifying these novel objects as compared to mismatched presentations. Twenty-one university students learned names and attributes associated with six novel objects. Three objects were paired with action attributes (pull, twist, slide) and three were paired with semantic attributes (nice, weak, rare). A computer recall task was performed once participants were able to recall all six objects error free during randomized presentation. Data was collected from this computer recall task where the six novel objects were presented in pseudo-randomized order in the upper or lower visual fields. Recall errors and the time required to identify the object were recorded. This study investigated interactions between visual field, processing stream, and their associated attributes. Previous research has examined each of these variables separately, and interactions may prove useful in further understanding neurological disorders such as apraxia.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| 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".