IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007060.html
   My bibliography  Save this article

Foraging as an evidence accumulation process

Author

Listed:
  • Jacob D Davidson
  • Ahmed El Hady

Abstract

The patch-leaving problem is a canonical foraging task, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different “strategies”, for example an incremental, decremental, or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To describe sub-optimal decisions, we introduce an energy-dependent marginal utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch-leaving decisions.Author summary: Foraging is a ubiquitous animal behavior, performed by organisms as different as worms, birds, rats, and humans. Although the behavior has been extensively studied, it is not known how the brain processes information obtained during foraging activity to make subsequent foraging decisions. We form an evidence accumulation model of foraging decisions that describes the process through which an animal gathers information and uses it to make foraging decisions. By building on studies of the neural decision mechanisms within systems neuroscience, this model connects the foraging decision process with ecological models of patch-leaving decisions, such as the marginal value theorem. The model suggests the existence of different foraging strategies, which optimize for different environmental conditions and their potential implementation by neural decision making circuits. The model also shows how state-dependence, such as satiation level, can affect evidence accumulation to lead to sub-optimal foraging decisions. Our model provides a framework for future experimental studies which seek to elucidate how neural decision making mechanisms have been shaped by evolutionary forces in an animal’s surrounding environment.

Suggested Citation

  • Jacob D Davidson & Ahmed El Hady, 2019. "Foraging as an evidence accumulation process," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-25, July.
  • Handle: RePEc:plo:pcbi00:1007060
    DOI: 10.1371/journal.pcbi.1007060
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007060
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007060&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1007060?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Joshua S. Greene & Maximillian Brown & May Dobosiewicz & Itzel G. Ishida & Evan Z. Macosko & Xinxing Zhang & Rebecca A. Butcher & Devin J. Cline & Patrick T. McGrath & Cornelia I. Bargmann, 2016. "Balancing selection shapes density-dependent foraging behaviour," Nature, Nature, vol. 539(7628), pages 254-258, November.
    2. Timothy D. Hanks & Charles D. Kopec & Bingni W. Brunton & Chunyu A. Duan & Jeffrey C. Erlich & Carlos D. Brody, 2015. "Distinct relationships of parietal and prefrontal cortices to evidence accumulation," Nature, Nature, vol. 520(7546), pages 220-223, April.
    3. repec:cup:judgdm:v:5:y:2010:i:6:p:437-449 is not listed on IDEAS
    4. Andra Thiel & Thomas S. Hoffmeister, 2004. "Knowing your habitat: linking patch-encounter rate and patch exploitation in parasitoids," Behavioral Ecology, International Society for Behavioral Ecology, vol. 15(3), pages 419-425, May.
    5. Mervyn Stone, 1960. "Models for choice-reaction time," Psychometrika, Springer;The Psychometric Society, vol. 25(3), pages 251-260, September.
    6. Simon,Herbert A., 2009. "An Empirically-Based Microeconomics," Cambridge Books, Cambridge University Press, number 9780521118361, January.
    7. Alex T. Piet & Ahmed El Hady & Carlos D. Brody, 2018. "Rats adopt the optimal timescale for evidence integration in a dynamic environment," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    8. Dominic A. Evans & A. Vanessa Stempel & Ruben Vale & Sabine Ruehle & Yaara Lefler & Tiago Branco, 2018. "A synaptic threshold mechanism for computing escape decisions," Nature, Nature, vol. 558(7711), pages 590-594, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gibbs, Richard & Landi, Pietro & Hui, Cang, 2024. "Heterogeneity in the resource landscape encourages increased cognitive and perceptive capabilities in foragers," Ecological Modelling, Elsevier, vol. 492(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diksha Gupta & Brian DePasquale & Charles D. Kopec & Carlos D. Brody, 2024. "Trial-history biases in evidence accumulation can give rise to apparent lapses in decision-making," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. J. Tyler Boyd-Meredith & Alex T. Piet & Emily Jane Dennis & Ahmed El Hady & Carlos D. Brody, 2022. "Stable choice coding in rat frontal orienting fields across model-predicted changes of mind," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Kaushik J Lakshminarasimhan & Alexandre Pouget & Gregory C DeAngelis & Dora E Angelaki & Xaq Pitkow, 2018. "Inferring decoding strategies for multiple correlated neural populations," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-40, September.
    4. Thomas Otter & Joe Johnson & Jörg Rieskamp & Greg Allenby & Jeff Brazell & Adele Diederich & J. Hutchinson & Steven MacEachern & Shiling Ruan & Jim Townsend, 2008. "Sequential sampling models of choice: Some recent advances," Marketing Letters, Springer, vol. 19(3), pages 255-267, December.
    5. Pierre O. Boucher & Tian Wang & Laura Carceroni & Gary Kane & Krishna V. Shenoy & Chandramouli Chandrasekaran, 2023. "Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex," Nature Communications, Nature, vol. 14(1), pages 1-28, December.
    6. Wan-Yu Shih & Hsiang-Yu Yu & Cheng-Chia Lee & Chien-Chen Chou & Chien Chen & Paul W. Glimcher & Shih-Wei Wu, 2023. "Electrophysiological population dynamics reveal context dependencies during decision making in human frontal cortex," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    7. Udo Boehm & Maarten Marsman & Han L. J. Maas & Gunter Maris, 2021. "An Attention-Based Diffusion Model for Psychometric Analyses," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 938-972, December.
    8. Diego Fernandez Slezak & Mariano Sigman & Guillermo A Cecchi, 2018. "An entropic barriers diffusion theory of decision-making in multiple alternative tasks," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-14, March.
    9. Yoshio Takane & Justine Sergent, 1983. "Multidimensional scaling models for reaction times and same-different judgments," Psychometrika, Springer;The Psychometric Society, vol. 48(3), pages 393-423, September.
    10. Nair, Sujith & Blomquist, Tomas, 2021. "Exploring docility: A behavioral approach to interventions in business incubation," Research Policy, Elsevier, vol. 50(7).
    11. Drew Fudenberg & Whitney Newey & Philipp Strack & Tomasz Strzalecki, 2020. "Testing the drift-diffusion model," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(52), pages 33141-33148, December.
    12. Minjie Hong & Xiaotian Zhou & Chenming Zeng & Demin Xu & Ting Xu & Shimiao Liao & Ke Wang & Chengming Zhu & Ge Shan & Xinya Huang & Xiangyang Chen & Xuezhu Feng & Shouhong Guang, 2024. "Nucleolar stress induces nucleolar stress body formation via the NOSR-1/NUMR-1 axis in Caenorhabditis elegans," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    13. Arkady Konovalov & Ian Krajbich, 2016. "Revealed Indifference: Using Response Times to Infer Preferences," Working Papers 16-01, Ohio State University, Department of Economics.
    14. Romy Frömer & Matthew R. Nassar & Benedikt V. Ehinger & Amitai Shenhav, 2024. "Common neural choice signals can emerge artefactually amid multiple distinct value signals," Nature Human Behaviour, Nature, vol. 8(11), pages 2194-2208, November.
    15. Wei Shang & Shuangyi Xie & Wenbo Feng & Zhuangzhuang Li & Jingyan Jia & Xiaoxiao Cao & Yanting Shen & Jing Li & Haibo Shi & Yiran Gu & Shi-Jun Weng & Longnian Lin & Yi-Hsuan Pan & Xiao-Bing Yuan, 2024. "A non-image-forming visual circuit mediates the innate fear of heights in male mice," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    16. Riccardo Viale, 2018. "The normative and descriptive weaknesses of behavioral economics-informed nudge: depowered paternalism and unjustified libertarianism," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 17(1), pages 53-69, November.
    17. Richard D Lange & Ankani Chattoraj & Jeffrey M Beck & Jacob L Yates & Ralf M Haefner, 2021. "A confirmation bias in perceptual decision-making due to hierarchical approximate inference," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-30, November.
    18. Tao Xie & Markus Adamek & Hohyun Cho & Matthew A. Adamo & Anthony L. Ritaccio & Jon T. Willie & Peter Brunner & Jan Kubanek, 2024. "Graded decisions in the human brain," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    19. Shinichiro Kira & Houman Safaai & Ari S. Morcos & Stefano Panzeri & Christopher D. Harvey, 2023. "A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions," Nature Communications, Nature, vol. 14(1), pages 1-28, December.
    20. Giovanni Dosi & Joseph E Stiglitz, 2021. "Introduction to the first annual special issue on Macro Economics and Development [Beyond DSGE models: toward an empirically based macroeconomics]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(2), pages 269-271.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007060. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.