Doing what we imagine: Completion rates and frequency attributes of imagined future events one year after prospection
Why this work is in the frame
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Bibliographic record
Abstract
Recent years have seen an explosion of studies examining behavioural and neural aspects of imagining future events. However, little is known about whether imagined future events reflect future happenings. We examined event occurrence 1 year after participants imagined highly probable future events, specific to place and time. Overall, participants did engage in most of their imagined events. Completion rates were similar to naturalistic prospective memory and implementation intention studies examining personal plan completion. Approximately 20% of events were abandoned. We found participants often imagined events that were repeated many times in the course of a year and this impacted the vividness of recollection, sense of personal importance, personal involvement in event fulfilment, and extent of positive emotionality 1 year later. Together, the results provide an important validation for prospection research and highlight novel dimensions in the temporal structure of future-thinking.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it