Behavioral evidence for a predominant and nonlateralized coarse-to-fine encoding for face categorization
Bibliographic record
Abstract
Influential models on visual perception assume that there is a precedence of low over high spatial frequencies (SFs) in the processing time course of the visual input, that is, a coarse-to-fine (CtF) encoding. Additionally, hemispheric asymmetries for strategies of SF processing have been shown. A CtF processing would be favored in the right hemisphere, whereas the reverse fine-to-coarse (FtC) processing would be favored in the left hemisphere. In the current article, we aimed to behaviorally investigate which temporal strategy, that is, CtF or FtC, each brain hemisphere performs to integrate SF information of human faces. To address this issue, we conducted a male–female categorization task using the divided visual field paradigm; CtF and FtC brief dynamic sequences of faces were presented in the left, right, and central visual fields. Results of the correct response time and the inverse efficiency score showed an overall advantage of CtF processing for face categorization, irrespective of the visual field of presentation. Error rate data also highlights the role of the right hemisphere in CtF processing. Here, we provide evidence at the behavioral level for a general and nonlateralized precedence of the default CtF strategy carried out by the visual system to encode faces, a complex stimulus with ecological value.
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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 | 0.001 |
| 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) | 0.003 | 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".