Putting a Name to a Face: The Role of Name Labels in the Formation of Face Memories
Bibliographic record
Abstract
Although previous research in ERPs has focused on the conditions under which faces are recognized, less research has focused on the process by which face representations are acquired and maintained. In Experiment 1, participants were required to monitor for a target "Joe" face that was shown among a series of nontarget "Other" faces. At the halfway point, participants were instructed to switch targets from the Joe face to a previous nontarget face that is now labeled "Bob." The ERP analysis focused on the posterior N250 component known to index face familiarity and the P300 component associated with context updating and response decision. Results showed that, in the first half of the experiment, there was increase in N250 negativity to the target Joe face compared with the nontarget Bob and designated Other face. In the second half of the experiment, an enhanced N250 negativity was produced to the now-target Bob face compared with the Other face. Critically, the enhanced N250 negativity to the Joe face was maintained, although Joe was no longer the target. The P300 component followed a similar pattern of brain response, where the Joe face elicited a significantly larger P300 amplitude than the Other face and the Bob face. In the Bob half of the experiment, the Bob face elicited a reliably larger P300 than the Other faces, and the heightened P300 to the Joe face was sustained. In Experiment 2, we examined whether the increased N250 and P300 to Joe was because of simple naming effects. Participants were introduced to both Joe and Bob faces and names at the beginning of the experiment. In the first half of the experiment, participants monitored for the target Joe face and at the halfway point, they were instructed to switch targets to the Bob face. Findings show that N250 negativity significantly increased to the Joe face relative to the Bob and Other faces in the first half of the experiment and an enhanced N250 negativity was found for the target Bob face and the nontarget Joe face in the second half. An increased P300 amplitude was demonstrated to the target Joe and Bob faces in the first and second halves of the experiment, respectively. Importantly, the P300 amplitude elicited by the Joe face equaled the P300 amplitude to the Bob face, although it was no longer the target face. The findings from Experiments 1 and 2 suggest that the N250 component is not solely determined by name labeling, exposure, or task relevancy, but it is the combination of these factors that contribute to the acquisition of enduring face representations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".