Episodic memory and recognition are influenced by cues’ sensory modality: comparing odours, music and faces using virtual reality
Bibliographic record
Abstract
Most everyday experiences are multisensory, and all senses can trigger the conscious re-experience of unique personal events embedded in their specific spatio-temporal context. Yet, little is known about how a cue's sensory modality influences episodic memory, and which step of this process is impacted. This study investigated recognition and episodic memory across olfactory, auditory and visual sensory modalities in a laboratory-ecological task using a non-immersive virtual reality device. At encoding, participants freely and actively explored unique and rich episodes in a three-room house where boxes delivered odours, musical pieces and pictures of face. At retrieval, participants were presented with modality-specific memory cues and were told to 1) recognise encoded cues among distractors and, 2) go to the room and select the box in which they encountered them at encoding. Memory performance and response times revealed that music and faces outperformed odours in recognition memory, but that odours and faces outperformed music in evoking encoding context. Interestingly, correct recognition of music and faces was accompanied by more profound inspirations than correct rejection. By directly comparing memory performance across sensory modalities, our study demonstrated that despite limited recognition, odours are powerful cues to evoke specific episodic memory retrieval.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".