Personal likelihood and event familiarity influence the simulation of future events.
Bibliographic record
Abstract
Episodic future thinking is the ability to project the self forward in time to preexperience a potential future event. It has been hypothesized that two components enhance simulations of future events: personal likelihood and event familiarity. Personal likelihood varies depending on the dynamics of personal goals throughout an individual's lifetime. In contrast, event familiarity varies depending on a person's accumulated schematic (also called event or semantic) knowledge about a type of event. We investigated these two components through individuals' belief in the likelihood of an event's occurrence during the next 10 years and their familiarity with a type of event. We predicted that likelihood and familiarity enhance future event simulations, making them clearer and more detailed. We used two norming studies to develop participant-specific sets of future events. In the experiment, participants simulated and described events, and they rated phenomenological aspects of their simulations. Likelihood and familiarity played individual and combined roles during future event simulation. The strongest effects were found with phenomenological ratings, with likelihood and familiarity influencing three of four measures, including interacting for other sensory details ratings. For internal details as measured using the Autobiographical Interview, likelihood influenced total details and perceptual details, and familiarity influenced total, perceptual, and time details, including their interaction for perceptual details. We conclude that event future thinking is a dynamic simulation process that uses event knowledge learned from past experience and is influenced by a person's belief of how likely an event is to occur via mental rehearsal of likely events. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".