How Dynamic Facial Cues, Stimulus Orientation and Processing Biases Influence Identity and Expression Interference
Bibliographic record
Abstract
Research using Garner’s selective attention paradigm suggests that, when we view static faces, the processing of facial identity interferes with the processing of expression, and vice versa (Ganel & Goshen-Gottstein, 2004). We recently replicated this result, but went on to show that interference is negligible when dynamic faces are viewed (Stoesz & Jakobson, submitted). This "dynamic advantage" could arise if, with the introduction of dynamic cues, viewers shift from using a global processing approach to focusing on local facial features (see Xiao et al., 2012). If this is the case, the advantage should be most apparent with upright stimuli, and in those with a global processing bias. To test these ideas, we assessed participants’ processing style using hierarchical stimuli, and then had them make speeded expression (or identity) judgements of static and dynamic faces presented in upright and inverted orientations while identity (or expression) was held constant (baseline block) or varied (orthogonal block). We calculated (a) corrected interference scores by determining the percent change from baseline RT seen in the orthogonal block; and (b) dynamic advantage scores by finding the difference between static and dynamic interference scores for each condition. As in our earlier work, interference was seen with static but not with dynamic stimuli, and the dynamic advantage was more evident with the expression than the identity task. However, planned comparisons revealed that the dynamic advantage seen during expression processing was eliminated after stimulus inversion for individuals showing a global (but not a local) processing bias. These results are consistent with the view that, when making expression judgments, global processors respond to the introduction of dynamic cues by switching to the use of a local processing strategy. Our findings highlight the importance of using dynamic displays and of considering individual differences when characterizing typical face processing mechanisms. 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".