True and False Recognition Memories of Odors Induce Distinct Neural Signatures
Bibliographic record
Abstract
Neural bases of human olfactory memory are poorly understood. Very few studies have examined neural substrates associated with correct odor recognition, and none has tackled neural networks associated with incorrect odor recognition. We investigated the neural basis of task performance during a yes-no odor recognition memory paradigm in young and elderly subjects using event-related functional magnetic resonance imaging. We explored four response categories: correct (Hit) and incorrect false alarm (FA) recognition, as well as correct (CR) and incorrect (Miss) rejection, and we characterized corresponding brain responses using multivariate analysis and linear regression analysis. We hypothesized that areas of the medial temporal lobe were differentially involved depending on the accuracy of odor recognition. In young adults, we found that significant activity in the hippocampus and the parahippocampal gyrus was associated with correct (true) recognition of odors, whereas the perirhinal cortex was associated with FAs and Misses. These findings are consistent with literature regarding hypothetical functional organization for memory processing. We also found that for correct recognition and rejection responses, the involvement of the hippocampus decreased when memory performances improved. In contrast to young individuals, elderly subjects were more prone to false memories and exhibited less specific activation patterns for the four response categories. Activation in the hippocampus and the parahippocampal gyrus was positively correlated with response bias scores for true and false recognition, demonstrating that conservative subjects produced an additional search effort leading to more activation of these two medial temporal lobe regions. These findings demonstrate that correct and incorrect recognition and rejection induce distinct neural signatures.
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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 | 0.000 | 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) | 0.001 | 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".