Order-Selective Cells Tile Temporal Space and Predict Order Memory in Humans
Bibliographic record
Abstract
Remembering the temporal order of events is critical for episodic memory, allowing us to link individual events into sequences. While the medial temporal lobe and prefrontal cortex are essential for this process, the underlying neural mechanisms remain poorly understood. Here we characterized the representation of order information at the level of single neurons and field potentials recorded from human neurosurgical patients watching naturalistic videos of everyday events and later recalling the order and content of the events depicted. We found order-selective cells (OSCs) in the human hippocampus, amygdala, and orbitofrontal cortex that responded selectively to specific event orders, independent of event content or absolute time. OSCs exhibited transient theta phase precession following their preferred order during both memory encoding and retrieval, the strength of which predicted participants' order memory accuracy. During retrieval, OSC spike timing relative to theta varied with the relative position of their preferred event within the recalled event sequence, enabling selective retrieval of relevant events. These findings reveal a neural substrate for representing, encoding into and retrieving from memory absolute and relative ordinal relationships between discrete events. OSCs tile temporal space into discrete ordinal positions, thereby weaving episodic experiences into coherent temporal narratives.
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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 | 0.000 |
| 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".