Constructing autobiographical events within a spatial or temporal context: a comparison of two targeted episodic induction techniques
Bibliographic record
Abstract
Recalling and imagining autobiographical experiences involves constructing event representations within spatiotemporal contexts. We tested whether generating autobiographical events within a primarily spatial (where the event occurred) or temporal (the sequence of actions that occurred) context affected how the associated mental representation was constructed. We leveraged the well-validated episodic specificity induction (ESI) technique, known to influence the use of episodic processes on subsequent tasks, to develop variants that selectively enhance spatial or temporal processing. We tested the effects of these inductions on the details used to describe past and future autobiographical events. We first replicated the standard ESI effect, showing that ESI enhances generating episodic details, particularly those that are perception-based, when describing autobiographical events (Experiment 1). We then directly compared the effects of the spatial and temporal inductions (Experiment 2 and 3). When describing autobiographical events, spatial induction enhanced generating episodic details, specifically perception-based details, compared to the control or temporal inductions. A greater proportion of the episodic details generated after the temporal induction were gist-based than after the spatial induction, but this proportion did not differ from a control induction. Thus, using a spatial or temporal framework for autobiographical event generation alters the associated details that are accessed.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".