Knowing by heart: Visceral feedback shapes recognition memory judgments.
Bibliographic record
Abstract
Although theories of emotion have long noted the importance of afferent feedback from the autonomic nervous system in generating feelings, there is a growing appreciation that this feedback may also play a role in shaping cognitive experiences. At present, little is known about its functional role in memory judgments. In the current study, we examined whether afferent cardiovascular feedback shapes recognition-memory decisions and experiences when previously encountered faces are being discriminated from novel ones. To investigate this possibility, we capitalized on the natural variation in baroreceptor mediated cardiovascular feedback that is associated with the cardiac cycle, synchronizing the brief presentation of memory probes during retrieval with individual heartbeats. In Experiments 1 and 2, we found that faces presented during cardiac systole (i.e., when visceral feedback is maximal) were more likely endorsed as "old" than those presented during cardiac diastole (i.e., when afferent feedback is minimal). This pattern was present for targets and lures, and held for faces with fearful or neutral expressions. Combining this manipulation with a remember/know procedure, Experiment 3 showed that the influence of afferent cardiovascular feedback is specific to trials on which participants report a feeling of familiarity without successful recollection of pertinent contextual detail. By revealing an influence of baroreceptor mediated cardiovascular feedback on familiarity, the current findings identify the functional role of a specific autonomic channel, previously implicated in emotion, in feeling states that pertain to memory experience.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".