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Record W2776244342 · doi:10.1037/xge0000377

Hedonic nondurability revisited: A case for two types.

2017· article· en· W2776244342 on OpenAlexaff
Raegan Tennant, Christopher K. Hsee

Bibliographic record

VenueJournal of Experimental Psychology General · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsBooth University College
FundersJohn Templeton Foundation
KeywordsHappinessCategorizationPreferenceDifferential (mechanical device)PsychologyDurabilityAdaptation (eye)Differential effectsEconometricsSocial psychologyEconomicsComputer scienceMicroeconomicsArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Hedonic durability refers to the extent to which the hedonic impact of a change lasts, that is, how long the unhappiness from a loss (or happiness from a gain) will endure over time. The lesson from previous research on this topic has been that the long-term effect of most changes (e.g., larger incomes, bigger houses, shorter commutes) is negligible. The present research shows something different. Consistent with previous research, we observed a pattern of hedonic nondurability in which the impact of a change did not endure over time. However, we also observed a pattern of hedonic durability in which the impact of a change does endure over time. We demonstrate differential rates of hedonic durability for losses, both across variables (Experiment 1) and within different ranges of the same variable (Experiment 2). We also extend our research to show differential rates for gains (Experiment 3). To explain our results, we propose a distinction between preference types, arguing that comparison-independent (i.e., absolute) preference types are hedonically more durable than comparison-dependent (i.e., relative) preference types. This research offers a method for validating preference-type categorization as well as a novel paradigm for testing hedonic durability in the laboratory. Moreover, it yields theoretical insights for affective forecasting and adaptation as well as practical implications for the hedonic treadmill and the joyless economy. (PsycINFO Database Record

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.271
GPT teacher head0.558
Teacher spread0.288 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations6
Published2017
Admission routes1
Has abstractyes

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