Skip to main content
  • Poster presentation
  • Open access
  • Published:

Computational modeling of temporal and sequential dynamics of foraging decisions


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.

Figure 1
figure 1

An example of the empirical choice patterns and the mean choice percentage of consumed pellets by food location, flavor, and rank.

Figure 2
figure 2

Temporal features of the foraging behavior.

Figure 3
figure 3

Comparison of a choice sequence generated from the dual-control model with the empirical data from two representative rats.


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.


  1. Barabási A-L: The origin of bursts and heavy tails in human dynamics. Nature. 2005, 435: 207-211. 10.1038/nature03459.

    Article  PubMed  Google Scholar 

  2. Kable JW, Glimcher PW: The neurobiology of decision: consensus and controversy. Neuron. 2009, 63: 733-745. 10.1016/j.neuron.2009.09.003.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Dayan P, Balleine BW: Reward, motivation, and reinforcement learning. Neuron. 2002, 36: 285-298. 10.1016/S0896-6273(02)00963-7.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations


Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit

The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jung, K., Jang, H., Kralik, J.D. et al. Computational modeling of temporal and sequential dynamics of foraging decisions. BMC Neurosci 15 (Suppl 1), P137 (2014).

Download citation

  • Published:

  • DOI: