The Whole-Part Effect is Modulated by Spatial Cues
Bibliographic record
Abstract
In the Whole-Part Effect (WPE) we are better able to discriminate a face part (e.g., eyes, nose, or mouth) when the part is embedded in a face than when it is presented in isolation. The results of a recent study (Konar, VSS 2011) suggest that the magnitude of the WPE may depend on the presence of uninformative external features (e.g., neck, chin, ears, hair). The current experiments attempted to replicate this effect, and to determine if the WPE is correlated with the face inversion effect. A same-different task was used to measure the discriminability of eyes, noses, or mouths presented in isolation or within an uninformative facial context that did or did not include external features. In Experiment 1, the target part was indicated by a word cue ("eyes," "nose," or "mouth") that appeared at the top of the response screen on each trial. In Experiments 2 and 3, the cue was a word plus a short horizontal line displayed at the same height as the target part. Experiment 1 failed to find a significant WPE: response accuracy was the same for parts presented in isolation or within a full face. However, Experiments 2 and 3 found a significant WPE for upright but not inverted faces. Averaged across experiments, there was a small but significant effect of external face parts: the WPE was slightly larger when the stimuli contained a neck, chin, ears, and hair. Since the external features are uninformative, and the spatial cues are present on each trial, their influence on the WPE was unexpected. Finally, we failed to find a significant correlation between the WPE and the magnitude of the face inversion effect. Overall, our results suggest that the WPE is highly unstable, and suggest a role for spatial attention in modulating the strength of the effect. Meeting abstract presented at VSS 2013
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".