Availability of semantic knowledge in familiar-only experiences for names.
Bibliographic record
Abstract
Situations in which the name of a person is perceived as familiar but does not trigger recall of pertinent semantic knowledge are common in daily life. In current connectionist models of person recognition, such "familiar-only" experiences reflect supra-threshold activation at person-identity nodes but subthreshold activation at nodes representing semantic knowledge. As knowledge is posited to be either present or absent according to a threshold, these models predict that no semantic knowledge should be observed in association with familiar-only experiences. In 4 experiments, we tested this prediction with fame judgments for names and a highly sensitive forced-choice occupation task. In Experiment 1, we demonstrated that familiar-only experiences for fame judgments are associated with above-chance performance on the semantic forced-choice occupation task. In Experiments 2 and 3, we replicated this finding and also revealed some metacognitive awareness of the availability of knowledge. In Experiment 4, we showed that graded familiarity judgments are highly correlated with the accuracy and confidence of corresponding occupation judgments. Overall, the current findings suggest that feelings of familiarity for names are not as clearly separable from semantic knowledge as the term "familiar-only" suggests. Although people may not recall a "piece" of pertinent knowledge when encountering a familiar name, this cannot be taken as evidence that no knowledge is available. These findings support the view that semantic retrieval in name recognition is better understood as a process that operates on graded evidence than as a process with discrete categorical states that only leaves an impression of familiarity when it fails.
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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 | 0.001 |
| 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) | 0.000 | 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".