Exploring the relationship between the N170 inversion effect and horizontal tuning
Bibliographic record
Abstract
Recent research suggests that faces contain the most information in the horizontal orientation band (Dakin & Watt, J Vis 2009), and the size of the behavioural face inversion effect (bFIE) is correlated with changes in horizontal tuning following inversion (Pachai et al., VSS 2011). Moreover, ERP studies have shown that 1) the N170 is delayed and sometimes increased in amplitude following inversion, 2) the N170 and bFIE are correlated (Jacques and Rossion, NeuroImage 2007), and 3) the N170 inversion effect decreases when horizontal information is scrambled (Jacques et al., VSS 2011). However, the question remains whether the N170 is associated with horizontal tuning, and how that association varies with face inversion. To answer these questions, observers completed a 10AFC identification task using filtered faces. In the full-face condition, faces contained information at all orientations. In the horizontal/vertical conditions, target face information was contained only in orientations within ±35 deg of horizontal/vertical; remaining orientations contained non-informative face information, so stimuli were face-like in all conditions. Initial results from 8 observers, show a bFIE only in the full-face and horizontal conditions. N170 latency, but not amplitude, depended on both orientation filtering and face orientation. Specifically, face inversion increased latency equivalently across filter conditions, whereas latency for upright faces depended on orientation filtering, with the shortest and longest latencies occurring in the full-face and vertical conditions, respectively. When we examined the relationship between the N170 inversion effect for full-faces and the change in behavioural horizontal tuning (horizontal – vertical) following inversion, we found a positive correlation for both latency (r=0.78) and amplitude (r=0.56). To date, our findings reinforce the notion that upright face identification is driven by increased efficiency in processing horizontal face information compared to vertical, and suggest an association between changes in this efficiency following inversion with changes in the N170. Meeting abstract presented at VSS 2012
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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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".