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Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective

Overview of attention for article published in Frontiers in Behavioral Neuroscience, March 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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11 X users
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1 Facebook page
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1 Google+ user

Citations

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215 Dimensions

Readers on

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423 Mendeley
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1 CiteULike
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Title
Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective
Published in
Frontiers in Behavioral Neuroscience, March 2014
DOI 10.3389/fnbeh.2014.00076
Pubmed ID
Authors

Giles W. Story, Ivo Vlaev, Ben Seymour, Ara Darzi, Raymond J. Dolan

Abstract

The tendency to make unhealthy choices is hypothesized to be related to an individual's temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn. We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure. We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a "model-based" (or goal-directed) system and a "model-free" (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues. We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 423 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Netherlands 1 <1%
Italy 1 <1%
Brazil 1 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Mexico 1 <1%
Spain 1 <1%
Japan 1 <1%
Other 0 0%
Unknown 414 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 94 22%
Student > Master 54 13%
Student > Bachelor 53 13%
Researcher 51 12%
Student > Doctoral Student 38 9%
Other 63 15%
Unknown 70 17%
Readers by discipline Count As %
Psychology 144 34%
Neuroscience 33 8%
Economics, Econometrics and Finance 27 6%
Social Sciences 25 6%
Medicine and Dentistry 24 6%
Other 69 16%
Unknown 101 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 January 2018.
All research outputs
#4,675,091
of 25,328,635 outputs
Outputs from Frontiers in Behavioral Neuroscience
#758
of 3,444 outputs
Outputs of similar age
#42,576
of 228,500 outputs
Outputs of similar age from Frontiers in Behavioral Neuroscience
#14
of 55 outputs
Altmetric has tracked 25,328,635 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,444 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 228,500 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.