Developmental Trajectory of Face Processing Revealed by Integrative Dynamics
Bibliographic record
Abstract
Given their unique connectivity, a primary function of brain networks must be to transfer and integrate information. Therefore, the way in which information is integrated by individual nodes of the network may be an informative aspect of cognitive processing. Here we present a method inspired by telecommunications research that utilizes time-frequency fluctuations of neural activity to infer how information is integrated by individual nodes of the network. We use a queueing theoretical model to interpret empirical data in terms of information processing and integration. In particular, we demonstrate, in participants aged from 6 to 41 years, that the well-known face inversion phenomenon may be explained in terms of information integration. Our model suggests that inverted faces may be associated with shorter and more frequent neural integrative stages, indicating fractured processing and consistent with the notion that inverted faces are perceived by parts. Conversely, our model suggests that upright faces may be associated with a smaller number of sustained episodes of integration, indicating more involved processing, akin to holistic and configural processing. These differences in how upright and inverted faces are processed became more pronounced during development, indicating a gradual specialization for face perception. These effects were robustly expressed in the right fusiform gyrus (all groups), as well as right parahippocampal gyrus (children and adolescents only) and left inferior temporal cortex (adults only).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".