“These pretzels are making me thirsty” so I’ll have water tomorrow: A partial replication and extension of adults’ induced-state episodic foresight
Bibliographic record
Abstract
The ability to consider the future under the influence of an induced current state is known as induced-state episodic foresight. One study to date has examined adults' induced episodic foresight and found that adults' (like children's) preferences for the future are related to their current state such that they predicted wanting water (vs. pretzels) in the future when experiencing a current state of thirst [1]. We attempted to replicate these findings in adults. In Study 1, adults (N = 198) in a laboratory selected pretzels for tomorrow at the same rate (around 20%) in an experimental condition (thirst induced) and a control condition (thirst not induced). In a lecture, 32% of adults preferred pretzels for tomorrow without thirst induction (Study 2, N = 63). Partially replicating Kramer et al. [1], we found that a minority of adults preferred pretzels (vs. water) when experiencing a current state of thirst. However, in contrast to their findings, our results showed that when thirst was not induced, a minority of adults also preferred pretzels for tomorrow. Thus, adults' future preference was similar regardless of thirst induction. We also tested thirst as a mechanism for adults' preference for the future and found that across conditions adults' thirst predicted their choice of water (vs. pretzels) for the future. In sum, our results partially replicated Kramer et al. [1] by showing the current state, regardless of thirst induction, predicts adults' choices for the future.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".