On the role of autobiographical knowledge in shaping belief in the future occurrence of imagined events
Bibliographic record
Abstract
Recent studies suggest that different forms of episodic simulation-mental representations of past, future, or atemporal events-recruit many of the same underlying cognitive and neural processes. This leads to the question whether there are distinctive hallmark characteristics of episodic future thinking: the subjective sense that imagined events belong to and will occur in the personal future. In this study, we aimed at shedding light on the cognitive ingredients that contribute to this sense of future occurrence by asking participants to imagine personal and experimenter-provided future events associated with high or low degrees of belief in future occurrence and then to reflect on the bases for their beliefs. Results showed that contextualising autobiographical knowledge (i.e., articulating links between items of information associated with imagined future events, goals, and personal characteristics) is a critical aspect of belief in future occurrence, and autobiographical knowledge can be flexibly used to either support or suppress belief in future occurrence. These findings indicate that episodic future thought not only depends on simulation processes (i.e., the construction of detailed mental representations for future events) but also requires that imagined events are meaningfully integrated within an autobiographical context.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".