Back to the Present: How Direction of Mental Time Travel Affects Similarity and Saving
Bibliographic record
Abstract
Abstract Many consumers say they want to save for the future yet struggle to do so. This research examines this saving behavior problem from a persuasive messaging standpoint. With the goal of helping people take better care of their future selves, we build on a stream of research that has found that the way people view their identities over time affects the saving decisions they make. Although past research on similarity judgments across time almost exclusively starts with the present self and moves forward to the future self, such judgments could theoretically start at any point in time. Here, we explore the possibility of backward mental time travel, by asking people to start in the future and return to the present. A series of studies shows that mentally traveling from the future to the present—rather than the present to the future—increases perceived similarity between selves across time by reducing the uncertainty of the destination self. Lab studies and two large-scale experiments indicate that, as an important outcome of this novel intervention, mentally traveling from the future to the present has a small but positive impact, systematically increasing savings intentions and savings behavior.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".