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Bursts and Heavy Tails in Temporal and Sequential Dynamics of Foraging Decisions

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
Bursts and Heavy Tails in Temporal and Sequential Dynamics of Foraging Decisions
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003759
Pubmed ID
Authors

Kanghoon Jung, Hyeran Jang, Jerald D. Kralik, Jaeseung Jeong

Abstract

A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution; and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices.

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Geographical breakdown

Country Count As %
United States 2 6%
Switzerland 1 3%
Portugal 1 3%
France 1 3%
Netherlands 1 3%
Unknown 29 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 40%
Student > Ph. D. Student 8 23%
Student > Master 3 9%
Lecturer > Senior Lecturer 2 6%
Student > Doctoral Student 2 6%
Other 2 6%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 26%
Neuroscience 6 17%
Computer Science 3 9%
Psychology 3 9%
Physics and Astronomy 3 9%
Other 5 14%
Unknown 6 17%