The properties and antecedents of hedonic decline
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We review the phenomenon of hedonic decline, whereby repeated exposure to a stimulus typically reduces the hedonic response (e.g., enjoyment). We first discuss the typical trajectory of hedonic decline and the common research paradigms used to study it. We next discuss the most popular theories regarding general mechanisms widely believed to underlie hedonic decline. We then propose a taxonomy to organize these various general theories and to incorporate more recent work on top-down, self-reflective theories. This taxonomy identifies three general classes of antecedents to hedonic decline: physiological feedback, perceptual changes, and self-reflection. For each class, we review the supporting evidence for specifically identified antecedents and recent developments on how each antecedent influences hedonic decline. Our review focuses especially on more recent work in the growing area of self-reflection.
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The utility of using a mobile app may decrease with time (satiation).
“From a behavioral standpoint, such decline is consistent with abundant evidence of satiation's role in products' consumption patterns, hedonic products in particular (Galak, Kruger, & Loewenstein, 2013;Galak & Redden, 2018;Sevilla, Zhang, & Kahn, 2016).”
From On the monetization of mobile apps (2020) · cites this paper as Galak & Redden, 2018
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“From a behavioral standpoint, such decline is consistent with abundant evidence of satiation's role in products' consumption patterns, hedonic products in particular (Galak, Kruger, & Loewenstein, 2013; Galak & Redden, 2018; Sevilla, Zhang, & Kahn, 2016).”
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Ford, M. E., & Smith, P. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108869164.004
Motivation and Optimal Functioning
Ford, M. E., & Smith, P. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108869164.002
Ford, M. E., & Smith, P. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108869164.001
Your Toolbox for Motivating Self and Others
Ford, M. E., & Smith, P. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108869164.010
Ford, M. E., & Smith, P. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108869164.005
(2020) · Cambridge University Press eBooks · doi:10.1017/9781108869164.011
Guiding Principles for Motivating Self and Others
Ford, M. E., & Smith, P. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108869164.009
Ford, M. E., & Smith, P. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108869164.006
- Authors
- Galak, J., & Redden, J. P.
- Affiliations
- Carnegie Mellon University (US)
- University of Minnesota (US)
- Corresponding author
- Galak, Jeff
- Venue
- Annual Review of Psychology
- Publisher
- Annual Reviews
- Published
- 2018-01-04
- Volume
- 69
- Issue
- 1
- Pages
- 1-25
- ISSN
- 0066-4308 (ISSN-L)
- 1545-2085
- OpenAlex
- W2589932687
- Cited by
- 133
- References
- 127
- Citations by year
- 2026: 27 · 2025: 17 · 2024: 11 · 2023: 7 · 2022: 15 · 2021: 17 · 2020: 27 · 2019: 4 · 2018: 8
- FWCI
- 1.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Psychology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Aesthetic Perception and Analysis
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- repeated exposure, enjoyment, hedonic response, physiological feedback, perceptual change, self-reflection
- MeSH terms
- Humans, Perception, Self Concept, Pleasure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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