Bibliographic record
Abstract
PURPOSE. Visual search studies with adults have demonstrated an asymmetry in that search for a feature-present target amidst feature-absent distracters (R among Ps) is faster and more efficient than the reverse search (P among Rs). Research with infants has suggested that they might exhibit a search asymmetry; however, these studies assessed asymmetry in seconds or minutes, whereas in adults it is assessed in milliseconds. Consequently, whether infants have similar visual search and selective attention mechanisms as in adults is not clear. Adler and Orprecio (2006) have recently demonstrated search performance in infants on the order of millisecond, similar to adults. The present study examined search asymmetry in infants using a visual search paradigm. METHODS. Three-month-olds' saccade latencies to a target in a visual search array were measured as they randomly viewed the following four arrays: feature-present (R among Ps), feature-absent (P among Rs), homogenous Rs and homogenous Ps. All four arrays were presented in set sizes of 1, 3, 5 and 8. RESULTS.Similar to findings with adults, the target in the feature-present array popped-out from amidst the distracters and the saccade latencies were unaffected by increasing set sizes. In contrast, the saccade latencies to the target in the feature-absent array and the homogenous arrays increased with increasing set-sizes. CONCLUSIONS. These findings indicate that infants exhibit a search asymmetry similar to that found with adults. This suggests that the same selective attention mechanisms are functioning in infants and adults. Supported by NSERC 503860 to SAA.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".