Effect of context on the N170 for low spatial frequency filtered faces
Bibliographic record
Abstract
People typically rely on middle spatial frequencies (SF) for face recognition, and stimuli containing only low SFs can be difficult, or even impossible, to recognize (Gold et al., 1999). One explanation for this effect is that the horizontal information around the eyes/eyebrows that people rely on most for face recognition (Dakin & Watt, 2009; Sekuler et al., 2004) may not be the most informative for discrimination for low SF faces. Here we ask whether the processing of low SF filtered faces can be influenced by altering the context in which they are presented, or whether the stimulus drives processing strategy through bottom-up information. We measured N170s for low SF filtered faces presented in a 10AFC identification task. Participants were randomly assigned to one of two context conditions: face or texture. In the face condition, trials intermixed unfiltered faces with low SF filtered faces. In the texture condition, trials intermixed textures with low SF filtered faces. In both conditions, observers completed 200 trials of each stimulus types, for a total of 400 trials. Observers’ behavioural performance was similar for unfiltered faces and textures, and, as expected, unfiltered faces led to large N170s, while textures did not. For low SF filtered faces, performance was significantly reduced compared to that of both unfiltered faces and textures, but it did not vary significantly across conditions. In contrast, the EEG results for low SF filtered faces varied considerably across conditions: participants in the face condition showed strong N170, whereas those in the texture condition showed no significant N170 even though the stimuli were identical in the two conditions. Hence, the N170, but not response accuracy, was sensitive to stimulus context. These results suggest that subjects used different processes in the two conditions, even though performance was the same. Meeting abstract presented at VSS 2012
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".