The Primacy of Spatial Context in the Neural Representation of Events
Bibliographic record
Abstract
Some theories of episodic memory hypothesize that spatial context plays a fundamental role in episodic memory, acting as a scaffold on which episodes are constructed. A prediction based on this hypothesis is that spatial context should play a primary role in the neural representation of an event. To test this hypothesis in humans, male and female participants imagined events, composed of familiar locations, people, and objects, during an fMRI scan. We used multivoxel pattern analysis to determine the neural areas in which events could be discriminated based on each feature. We found that events could be discriminated according to their location in areas throughout the autobiographical memory network, including the parahippocampal cortex and posterior hippocampus, retrosplenial cortex, posterior cingulate cortex, precuneus, and medial prefrontal cortex. Events were also discriminable based on person and object features, but in fewer regions. Comparing classifier performance in regions involved in memory for scenes and events demonstrated that the location of an event was more accurately classified than the person or object involved. These results support theories that suggest that spatial context is a prominent defining feature of episodic memory. SIGNIFICANCE STATEMENT Remembered and imagined events are complex, consisting of many elements, including people, objects, and locations. In this study, we sought to determine how these types of elements differentially contribute to how the brain represents an event. Participants imagined events consisting of familiar locations, people, and objects (e.g., kitchen, mom, umbrella) while their brain activity was recorded with fMRI. We found that the neural patterns of activity in brain regions associated with spatial and episodic memory could distinguish events based on their location, and to some extent, based on the people and objects involved. These results suggest that the spatial context of an event plays an important role in how an event is represented in the brain.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".