Reducing the perceptual field of view does not cause the face inversion effect
Bibliographic record
Abstract
The face inversion effect (FIE) is defined as a disproportionate drop in recognition performance when faces, compared to other objects, are inverted in the picture plane. Recently, the FIE has been attributed to a shrinking perceptual field of view (PFV; i.e., the visual field area in which features can be processed in parallel and integrated in a perceptual whole) as a direct result of inversion (Van Belle et al., 2015). This offers an elegant and testable explanation to the FIE, since shrinking the PFV by inverting faces would limit face processing to fewer (or even one) features, instead of the whole face, as is normally the case when faces are upright. Recent research has shown inversion also reduces efficiency with which horizontal spatial information is processed (Pachai et al., 2013), though spatial frequency use appears unaffected (Royer et al., 2017). The goal of this study was to test whether PVF shrinking is causally linked to the FIE. To this end, we simulated the effect of PFV shrinking in ten participants by revealing faces through a small gaze-contingent Gaussian window. We then measured face processing strategies for upright, inverted, and windowed stimuli using spatial frequency (Willenbockel et al., 2010) and orientation (Duncan et al., 2017) bubbles (520 trials per condition). Size of the Gaussian window was determined on a subject basis to produce FIE-magnitude accuracy loss. For inverted vs. upright faces (i.e., FIE), there was a reduction in horizontal spatial information processing efficiency, but no difference in spatial frequency processing, replicating previous findings. For windowed vs. upright faces (i.e., PFV simulation), there was a reduction in horizontal processing efficiency, but it was not commensurate with the FIE. Furthermore, there was also a change in frequency tuning, which is not expected of the FIE. Thus PFV reduction does not cause the FIE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".