Dynamic averaging and foraging decisions in horses (Equus callabus).
Abstract: The variability of most environments taxes foraging decisions by increasing the uncertainty of the information available. One solution to the problem is to use dynamic averaging, as do some granivores and carnivores. Arguably, the same strategy could be useful for grazing herbivores, even though their food renews and is more homogeneously distributed. Horses (Equus callabus) were given choices between variable patches after short or long delays. When patch information was current, horses returned to the patch that was recently best, whereas those without current information matched choices to the long-term average values of the patches. These results demonstrate that a grazing species uses dynamic averaging and indicate that, like granivores and carnivores, they can use temporal weighting to optimize foraging decisions.
Copyright 2005 APA, all rights reserved.
Publication Date: 2005-09-01 PubMed ID: 16131264DOI: 10.1037/0735-7036.119.3.352Google Scholar: Lookup
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- Journal Article
Summary
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This research study investigates whether horses utilize dynamic averaging, a method of decision-making based on fluctuating information, in their foraging behaviors. The study reveals that horses indeed apply dynamic averaging when choosing where to graze, adapting their choices based on the most recent conditions of different grazing patches.
Understanding Dynamic Averaging
- Dynamic averaging is a decision-making process that can help animals optimize their foraging behaviors in environments where resources are inconsistent.
- This method involves animals keeping a mental record of resource availability, adjusting their expectations based on recent experiences, which enables them to adapt quickly to changes.
- The study proposes that horses, like granivores (seed-eating creatures) and carnivores, utilize dynamic averaging to enhance their foraging efficiency.
Research Method and Findings
- The research examined how horses responded to different grazing patches after varying durations.
- The researchers observed that when the horse possesses up-to-date information, it will return to the grazing patch that was recently best (with most resources).
- Horses without current information, however, scattered their grazing patterns based on the long-term average conditions of a potential grazing patch.
- From these observations, it is demonstrated that horses employ dynamic averaging when making foraging decisions. They adapt their choices according to the most recent or the long-term averaged conditions of patches.
Significance and Implication of the Research
- This research provides evidence that dynamic averaging is not only utilized by granivores and carnivores but also by grazing species like horses.
- Understanding this can help to optimize animals’ conservation strategies and other applications involving animals’ consumption or resource utilization.
- Furthermore, the findings can contribute to our broader comprehension of decision-making processes in different animal species and the various factors influencing their behaviors.
Cite This Article
APA
Devenport JA, Patterson MR, Devenport LD.
(2005).
Dynamic averaging and foraging decisions in horses (Equus callabus).
J Comp Psychol, 119(3), 352-358.
https://doi.org/10.1037/0735-7036.119.3.352 Publication
Researcher Affiliations
- Department of Psychology, University of Central Oklahoma, 73034, USA. jdevenport@ucok.edu
MeSH Terms
- Animals
- Decision Making
- Feeding Behavior
- Female
- Horses / psychology
- Male
- Memory, Short-Term
- Motivation
- Orientation
- Social Environment
Citations
This article has been cited 10 times.- Brucks D, Härterich A, König von Borstel U. Horses wait for more and better rewards in a delay of gratification paradigm. Front Psychol 2022;13:954472.
- Krueger K, Esch L, Byrne R. Animal behaviour in a human world: A crowdsourcing study on horses that open door and gate mechanisms. PLoS One 2019;14(6):e0218954.
- Dunlap AS, Papaj DR, Dornhaus A. Sampling and tracking a changing environment: persistence and reward in the foraging decisions of bumblebees. Interface Focus 2017 Jun 6;7(3):20160149.
- Craig AR, Cunningham PJ, Shahan TA. Behavioral momentum and accumulation of mass in multiple schedules. J Exp Anal Behav 2015 May;103(3):437-49.
- Marshall AT, Kirkpatrick K. The effects of the previous outcome on probabilistic choice in rats. J Exp Psychol Anim Behav Process 2013 Jan;39(1):24-38.
- Gallistel CR. Extinction from a rationalist perspective. Behav Processes 2012 May;90(1):66-80.
- Dunlap AS, Stephens DW. Tracking a changing environment: optimal sampling, adaptive memory and overnight effects. Behav Processes 2012 Feb;89(2):86-94.
- Van Allsburg J, Shahan TA. Further examining how animals weigh conflicting information about reward sources over time. Anim Cogn 2025 Jul 30;28(1):74.
- Krueger K, Roll A, Beyer AJ, Föll A, Bernau M, Farmer K. Learning from eavesdropping on human-human encounters changes feeding location choice in horses (Equus Caballus). Anim Cogn 2025 Mar 17;28(1):23.
- Van Allsburg J, Shahan TA. How do animals weigh conflicting information about reward sources over time? Comparing dynamic averaging models. Anim Cogn 2024 Mar 2;27(1):11.
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