Upright & inverted face recognition relies on the same, narrow band of spatial frequencies
Bibliographic record
Abstract
How does information processing differ between upright and inverted face recognition? Previous research from our lab suggests that, in the spatial domain, the differences in processing are quantitative rather than qualitative (Sekuler et al., 2004). Here we ask whether differences exist in the Fourier domain. Previous studies suggest that observers rely on frequency information centered around 9 cyc/face when recognizing upright faces (Nasanen 1999; Gold et al., 1998; Gold et al., 1999). Observers might be worse at inverted face recognition because they rely on a different, or broader range, of spatial frequencies for inverted face recognition than for upright face recognition. A recent study by Nakayama (VSS 2003) suggests that inverted face recognition might be less selective for frequency than upright face recognition, however spatial frequency tuning was not measured directly. We re-examined the issue using critical-band masking to measure spatial frequency tuning in a 10-alternative face recognition task, for both upright and inverted faces. In agreement with past results, upright face recognition relied on a narrow band channel, ∼1.4 octaves, centered at ∼8 cyc/face. However, despite the fact that observers required significantly more contrast to discriminate upside-down faces than upright faces, observers used a similar narrow band of spatial frequencies regardless of face orientation. Because our stimuli and task differed considerably from those of Nakayama, additional research is needed to elucidate the nature of spatial frequency selectivity under various conditions. Regardless, our results place strong constraints on how the strategies for upright and inverted face recognition might differ. We propose that the critical difference lies not in which frequencies are used, but in how information is used within a narrow band of frequencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".