MétaCan
Menu
Back to cohort
Record W4396239788 · doi:10.1093/jcr/ucae029

Back to the Present: How Direction of Mental Time Travel Affects Similarity and Saving

2024· article· en· W4396239788 on OpenAlexaff
Katherine L. Christensen, Hal E. Hershfield, Sam J. Maglio

Bibliographic record

VenueJournal of Consumer Research · 2024
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSimilarity (geometry)PsychologyOutcome (game theory)Point (geometry)Intervention (counseling)Social psychologyScale (ratio)ChronesthesiaPsychological interventionCognitive psychologyComputer scienceCognitionEconomicsMicroeconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.160
GPT teacher head0.489
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Consumer ResearchSame topicBehavioral Health and InterventionsFrench-language works237,207