Behavioral face recognition performance correlates with an electrophysiological index of individual face discrimination obtained by fast periodic oddball stimulation
Bibliographic record
Abstract
The current study measured electrophysiological response to periodic oddball stimulation of faces (Liu-Shuang et al., 2013) from 34 participants, and correlated the magnitude of this visual response with face recognition performance as measured by the Cambridge Face Memory Test (CFMT, Duchaine & Nakayama, 2006). During a stimulation sequence, a face picture (A) was presented at the frequency of 6 Hz (F, 6 faces/second) for 60-seconds, with different oddball faces (B, C, D) being presented at every 5th cycle (F/5=1.2 Hz) (i.e., AAAABAAAACAAAAD....). In the electroencephalogram (EEG) recorded from only 4 trials of stimulation (60-seconds each), the generic visual responses associated with the general neural responsiveness to the periodic stimulation emerged at 6 Hz and harmonics (12 Hz, 18 Hz, etc.) in all 32 channels, with the largest responses over medial occipital locations. The specific responses at 1.2 Hz and harmonics (2.4 Hz, 3.6 Hz, etc.) specifically indexing individual face discrimination were also present, peaking over occipito-temporal locations (as in Liu-Shuang et al., 2013). The magnitude of the generic component at the medial occipital locations did not correlate with CFMT score (r=-0.27, p=0.14). However, the magnitude of the specific component at occipital-temporal locations showed a trend to be significantly correlated with the CFMT score (r=0.32, p=0.08). This correlation reached significance (r=0.40, p=0.02) when individual differences in the general neural responsiveness to periodic stimulation were taken into account by normalizing (i.e., dividing) the magnitude of the specific response with that of the generic response. Overall, these findings suggest that, without an explicit face discrimination task, the electrophysiological response elicited by the periodic oddball stimulation is able to provide a reliable neural index of individual differences in face recognition ability. Meeting abstract presented at VSS 2014
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 | 0.002 |
| 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".