JOURNAL ARTICLE

Training numerousness to numerosity in the dog (Canis lupus familiaris).

  • Published In: Journal of the Experimental Analysis of Behavior, 2025, v. 123, n. 3. P. 486 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Cameron, Kristie E.; Muzumdar, Aryan; Briden, Kayla; Starkey, Nicola J. 3 of 3

Abstract

Dogs show numerousness, which is the ability to identify the larger of two stimuli, most often the number of treats on a plate. However, dogs seem to use mechanisms other than counting to make this discrimination. This study builds on existing research by controlling for (a) olfaction, (b) the surface area of the stimuli, and (c) delivery of a single reinforcer contingent on correct choices in the trained task. Nine dogs were trained to select a dish with 5 dots/treats in a sealed container when presented with comparison stimuli of 1, 4, and 9 dots/treats. The dogs showed numerousness in discriminating between dishes with 1 versus 5 dots, with consistent performance of more than 80% correct. Two dogs could discriminate 4 versus 5 dots, and three dogs learned the 9‐ versus 5‐dot discrimination in which there is a conflict between selecting the larger option and selecting the 5 dots to gain reinforcement in the task. Knowledge of numerical competency, particularly training dogs to select the nonreinforced choice, can offer strategies to facilitate cognitive enrichment and learning in animals or offer enhancement of the capabilities of working dogs where the concept of number might be advantageous in providing additional skills. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of the Experimental Analysis of Behavior. 2025/05, Vol. 123, Issue 3, p486
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:0022-5002
  • DOI:10.1002/jeab.70013
  • Accession Number:185349598
  • Copyright Statement:Copyright of Journal of the Experimental Analysis of Behavior is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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