Mental time travel: A conceptual overview of social psychological perspectives on a fundamental human capacity
Bibliographic record
Abstract
Abstract Humans have the unique capacity to mentally travel through time, that is, to reflect on the past, anticipate the future, and construct alternate realities in their minds. The ability to mentally travel through time affects a variety of social psychological topics. Representations of events can differ considerably, depending on the event's temporal location and distance from the present. Current emotions may be influenced by thoughts of future and past times (e.g., nostalgia, hope). Judgments about future events and actions are an important aspect of everyday functioning (e.g., predictions). Indeed, hypothetical thought about counterfactual events that might never come to pass may change the perception and evaluation of present reality. Despite this varied and extensive influence of time on affect, judgment, perception, and behavior, these diverse topics have not been brought together under one common roof. In this overview article and in the special issue on Mental Time Travel, we aim to identify key themes of mental time travel research, point to communalities and differences, and help to integrate various aspects of mental time travel research. Future directions regarding open questions, need for theoretical integration, and further empirical research are discussed. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".