Revisiting the effective connectivity within the distributed cortical network for face perception
Bibliographic record
Abstract
The classical core system of face perception consists of the occipital face area (OFA), fusiform face area (FFA), and posterior superior temporal sulcus (STS). The functional interaction within this network, more specifically the effective connectivity, was first described by Fairhall and Ishai (2007) using functional magnetic resonance imaging and dynamic causal modeling. They proposed that the core system is hierarchically organized; information is processed in a parallel and predominantly feed-forward fashion from the OFA to downstream regions such as the FFA and STS, with no lateral connectivity, i.e., no connectivity between the two downstream regions (FFA and STS). Over a decade later, we conducted a conceptual replication of their model using four different functional magnetic resonance imaging data sets. The effective connectivity within the core system was assessed with contemporary versions of dynamic causal modeling. The resulting model of the core system of face perception was densely interconnected. Using hierarchical linear modeling, we identified several significant forward, backward, and lateral connections in the core system of face perception across the data sets. Face perception increased the forward connectivity from the OFA to the FFA and OFA to the STS and increased the inhibitory backward connectivity from the FFA to the OFA, as well as the lateral connectivity between the FFA and STS. Emotion perception increased forward connectivity between the OFA and STS and decreased the lateral connectivity between the FFA and STS. Face familiarity did not significantly alter these connections. Our results revise the 2007 model of the core system of face perception. We discuss the potential meaning of the resulting model parameters and propose that our revised model is a suitable working model for further studies assessing the functional interaction within the core system of face perception. Our work further emphasizes the general importance of conceptual replications.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".