Relational processing demands and the role of spatial context in the construction of episodic simulations.
Bibliographic record
Abstract
Reports on differences between remembering the past and imagining the future have led to the hypothesis that constructing future events is a more cognitively demanding process. However, factors that influence these increased demands, such as whether the event has been previously constructed and the types of details comprising the event, have remained relatively unexplored. Across two experiments, we examined how these factors influence the process of constructing event representations by having participants repeatedly construct events and measuring how construction times and a range of phenomenological ratings changed across time points. In Experiment 1, we contrasted the construction of past and future events and found that, relative to past events, the constructive demands associated with future events are particularly heightened when these events are imagined for the first time. Across repeated simulations, future events became increasingly similar to past events in terms of construction times and incorporated detail. In Experiment 2, participants imagined future events involving two memory details (person, location) and then reimagined the event either (a) exactly the same, (b) with a different person, or (c) in a different location. We predicted that if generating spatial information is particularly important for event construction, a change in location will have the greatest impact on constructive demands. Results showed that spatial context contributed to these heightened constructive demands more so than person details, consistent with theories highlighting the central role of spatial processing in episodic simulation. We discuss the findings from both studies in the light of relational processing demands and consider implications for current theoretical frameworks. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.003 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".