Medial Temporal Lobe Contributions to Episodic Future Thinking: Scene Construction or Future Projection?
Bibliographic record
Abstract
Previous research has shown that the medial temporal lobes (MTL) are more strongly engaged when individuals think about the future than about the present, leading to the suggestion that future projection drives MTL engagement. However, future thinking tasks often involve scene processing, leaving open the alternative possibility that scene-construction demands, rather than future projection, are responsible for the MTL differences observed in prior work. This study explores this alternative account. Using functional magnetic resonance imaging, we directly contrasted MTL activity in 1) high scene-construction and low scene-construction imagination conditions matched in future thinking demands and 2) future-oriented and present-oriented imagination conditions matched in scene-construction demands. Consistent with the alternative account, the MTL was more active for the high versus low scene-construction condition. By contrast, MTL differences were not observed when comparing the future versus present conditions. Moreover, the magnitude of MTL activation was associated with the extent to which participants imagined a scene but was not associated with the extent to which participants thought about the future. These findings help disambiguate which component processes of imagination specifically involve the MTL.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".