Reduced Interference Between Identity and Expression Processing With Dynamic Faces
Bibliographic record
Abstract
Facial motion cues facilitate identity and expression processing (Pilz et al., 2006). To explore the possible basis of this dynamic advantage, we used Garner’s selective attention paradigm (Garner, 1976) to determine whether adding dynamic cues alters the way that identity and expression processing interact, and whether this varies depending on the age of the viewer. Adults (ages 18-26), adolescents (ages 12-13), and children (ages 6-7) made speeded judgments of the expression (or identity) of static and dynamic faces while identity (or expression) was either held constant (baseline block) or varied (orthogonal block). Accuracy was high (>90%) for all three age groups. We calculated corrected Garner interference scores by determining the percent change from baseline RT seen in the orthogonal block. A 2 (Task: Expression, Identity) X 2 (Mode: Static, Dynamic) X 3 (Age Group: Adult, Adolescent, Child) ANOVA conducted on these scores revealed that interference was stronger in the Expression than in the Identity task, and with static compared to dynamic faces. However, follow-up tests conducted on the significant Task X Mode interaction showed that, while the addition of dynamic cues led to a significant reduction in Garner interference for both tasks, the effect was much more dramatic for the Expression task. Although young children took significantly longer than adolescents or adults to make their judgments, no age-related differences in Garner interference were observed. Reductions in interference after the introduction of dynamic cues might arise if viewers focus more selectively on specific facial features, or if they integrate multiple cues more effectively, when viewing moving as opposed to static faces. Both of these ideas hold merit and, indeed, it is possible that individual differences in processing style determine which strategy a viewer will adopt. These results highlight the importance of using naturalistic, dynamic stimuli in studies of face processing. 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".