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Animal Cognition

, Volume 21, Issue 3, pp 379–392 | Cite as

Proactive behavior, but not inhibitory control, predicts repeated innovation by spotted hyenas tested with a multi-access box

  • Lily Johnson-UlrichEmail author
  • Zoe Johnson-Ulrich
  • Kay Holekamp
Original Paper

Abstract

Innovation is widely linked to cognitive ability, brain size, and adaptation to novel conditions. However, successful innovation appears to be influenced by both cognitive factors, such as inhibitory control, and non-cognitive behavioral traits. We used a multi-access box (MAB) paradigm to measure repeated innovation, the number of unique innovations learned across trials, by 10 captive spotted hyenas (Crocuta crocuta). Spotted hyenas are highly innovative in captivity and also display striking variation in behavioral traits, making them good model organisms for examining the relationship between innovation and other behavioral traits. We measured persistence, motor diversity, motivation, activity, efficiency, inhibitory control, and neophobia demonstrated by hyenas while interacting with the MAB. We also independently assessed inhibitory control with a detour cylinder task. Most hyenas were able to solve the MAB at least once, but only four hyenas satisfied learning criteria for all four possible solutions. Interestingly, neither measure of inhibitory control predicted repeated innovation. Instead, repeated innovation was predicted by a proactive syndrome of behavioral traits that included high persistence, high motor diversity, high activity and low neophobia. Our results suggest that this proactive behavioral syndrome may be more important than inhibitory control for successful innovation with the MAB by members of this species.

Keywords

Innovation Inhibitory control Cognition Problem-solving Behavioral syndromes 

Notes

Acknowledgements

We thank Courtney Frenchak at the Oak Creek Zoological Conservatory, Heather Genter and the other zookeepers at the Denver Zoo, and undergraduate research assistants Mike Kowalski and Paige Barnes for reliability coding.

Funding

This work was supported by National Science Foundation Grants OISE 1556407, IOS 1755089, and DEB 1353110 to KEH, and by a National Science Foundation Graduate Research Fellowship to LJU. LJU was also supported by fellowships from the College of Natural Sciences, the Integrative Biology Department, and the program in Ecology, Evolutionary Biology, and Behavior at Michigan State University. This work was also supported in part by the BEACON Center for the Study of Evolution in Action, funded by National Science Foundation Grant OIA 0939454.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

The data collection procedure followed here was reviewed by Michigan State University Institutional Animal Care and Use Committee: AUF #04/16-050-00. All procedures were also reviewed and approved for each zoological institution’s ethical guidelines.

Data availability

The datasets generated and analyzed during the current study are available in the Knowledge Network for Biocomplexity (KNB) Repository, ID: knb.92181.2, (https://knb.ecoinformatics.org/#view/knb.92181.2).

Supplementary material

10071_2018_1174_MOESM1_ESM.docx (86 kb)
Supplementary material 1 (DOCX 86 kb)

Supplementary material 2 (MOV 61062 kb)

Supplementary material 3 (MOV 97587 kb)

Supplementary material 4 (MOV 166793 kb)

Supplementary material 5 (MOV 59428 kb)

Supplementary material 6 (MOV 9023 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Integrative BiologyMichigan State UniversityEast LansingUSA
  2. 2.Ecology, Evolutionary Biology, and Behavior ProgramMichigan State UniversityEast LansingUSA
  3. 3.BEACON Center for the Study of Evolution in ActionMichigan State UniversityEast LansingUSA
  4. 4.Department of PsychologyOakland UniversityRochesterUSA

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