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.





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References
Auersperg AMI, von Bayern AMP, Gajdon GK et al (2011) Flexibility in problem solving and tool use of kea and New Caledonian crows in a multi access box paradigm. PLoS ONE 6:e20231. https://doi.org/10.1371/journal.pone.0020231
Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48. https://doi.org/10.18637/jss.v067.i01
Benson-Amram S, Weldele ML, Holekamp KE (2013) A comparison of innovative problem–solving abilities between wild and captive spotted hyaenas, Crocuta crocuta. Anim Behav 85:349–356. https://doi.org/10.1016/j.anbehav.2012.11.003
Benson-Amram S, Dantzer B, Stricker G et al (2016) Brain size predicts problem–solving ability in mammalian carnivores. Proc Natl Acad Sci 113:2532–2537. https://doi.org/10.1073/pnas.1505913113
Borrego N, Dowling B (2016) Lions (Panthera leo) solve, learn, and remember a novel resource acquisition problem. Anim Cogn 19:1019–1025
Bousquet CAH, Petit O, Arrivé M et al (2015) Personality tests predict responses to a spatial-learning task in mallards, Anas platyrhynchos. Anim Behav 110:145–154
Bray EE, Sammel MD, Seyfarth RM et al (2017) Temperament and problem solving in a population of adolescent guide dogs. Anim Cogn 20(5):923–939. https://doi.org/10.1007/s10071-017-1112-8
Brucks D, Marshall-Pescini S, Wallis LJ et al (2017) Measures of dogs’ inhibitory control abilities do not correlate across tasks. Front Psychol 8:849
Brust V, Wuerz Y, Krüger O (2013) Behavioural flexibility and personality in zebra finches. Ethology 119:559–569
Burkart JM, Schubiger MN, van Schaik CP (2017) The evolution of general intelligence. Behav Brain Sci 40:e195. https://doi.org/10.1017/S0140525X16000959
Carere C, Locurto C (2011) Interaction between animal personality and animal cognition. Curr Zool 57:491–498
Cattell RB (1966) The scree test for the number of factors. Multivar Behav Res 1:245–276
Chittka L, Skorupski P, Raine NE (2009) Speed–accuracy tradeoffs in animal decision making. Trends Ecol Evol 24:400–407
Chow PKY, Lea SEG, Leaver LA (2016) How practice makes perfect: the role of persistence, flexibility and learning in problem–solving efficiency. Anim Behav 112:273–283. https://doi.org/10.1016/j.anbehav.2015.11.014
Diquelou MC, Griffin AS, Sol D (2015) The role of motor diversity in foraging innovations: a cross-species comparison in urban birds. Behav Ecol 27:584–591
Ducatez S, Audet JN, Lefebvre L (2015) Problem–solving and learning in Carib grackles: individuals show a consistent speed–accuracy trade-off. Anim Cogn 18:485–496
Friard O, Gamba M (2016) BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol Evol 7:1325–1330
Gosling SD, Hawk JE, Beer JS et al (1998) Personality dimensions in spotted hyenas (Crocuta crocuta). J Comp Psychol 112:107–118
Greenberg JR, Holekamp KE (2017) Human disturbance affects personality development in a wild carnivore. Anim Behav 132:303–312
Greggor AL, Thornton A, Clayton NS (2015) Neophobia is not only avoidance: improving neophobia tests by combining cognition and ecology. Curr Opin Behav Sci 6:82–89. https://doi.org/10.1016/j.cobeha.2015.10.007
Griffin AS (2016) Innovativeness as an emergent property: a new alignment of comparative and experimental research on animal innovation. Philos Trans R Soc Lond B Biol Sci 371:20150544. https://doi.org/10.1098/rstb.2015.0544
Griffin AS, Guez D (2014) Innovation and problem solving: a review of common mechanisms. Behav Process 109:121–134
Griffin AS, Guez D, Lermite F, Patience M (2013) Tracking changing environments: innovators are fast, but not flexible learners. PLoS ONE 8:e84907
Griffin AS, Guillette LM, Healy SD (2015) Cognition and personality: an analysis of an emerging field. Trends Ecol Evol 30:207–214. https://doi.org/10.1016/j.tree.2015.01.012
Guillette LM, Naguib M, Griffin AS (2017) Individual differences in cognition and personality. Behav Process 134:1. https://doi.org/10.1016/j.beproc.2016.12.001
Herrmann E, Call J, Hernàndez-Lloreda MV et al (2007) Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis. Science 317:1360–1366
Holekamp KE, Sakai S, Lundrigan B (2007) The spotted hyena (Crocuta crocuta) as a model system for study of the evolution of intelligence. J Mammal 88:545–554
Huebner F, Fichtel C (2015) Innovation and behavioral flexibility in wild redfronted lemurs (Eulemur rufifrons). Anim Cogn 18:777–787. https://doi.org/10.1007/s10071-015-0844-6
Kabadayi C, Bobrowicz K, Osvath M (2018) The detour paradigm in animal cognition. Anim Cogn 21:21–35. https://doi.org/10.1007/s10071-017-1152-0
Kaiser HF (1960) The application of electronic computers to factor analysis. Educ Psychol Meas 20:141–151
Lefebvre L, Reader SM, Sol D (2004) Brains, innovations and evolution in birds and primates. Brain Behav Evol 63:233–246. https://doi.org/10.1159/000076784
Lüdecke D (2018) Sjstats: statistical functions for regression models. R package version 0.14.0. https://cran.r-project.org/package=sjstats
MacLean EL, Hare B, Nunn CL et al (2014) The evolution of self-control. Proc Natl Acad Sci U S A 111:E2140–E2148. https://doi.org/10.1073/pnas.1323533111
Manrique HM, Völter CJ, Call J (2013) Repeated innovation in great apes. Anim Behav 85:195–202. https://doi.org/10.1016/j.anbehav.2012.10.026
Mischel W, Shoda Y, Rodriguez M (1989) Delay of gratification in children. Science 244:933–938. https://doi.org/10.1126/science.2658056
Müller CA, Riemer S, Virányi Z et al (2016) Inhibitory control, but not prolonged object-related experience appears to affect physical problem–solving performance of pet dogs. PLoS ONE 11:e0147753
Peduzzi P, Concato J, Kemper E et al (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49:1373–1379
R Core Team (2016) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna. https://www.R-project.org/
Reader SM, Morand-Ferron J, Flynn E (2016) Animal and human innovation: novel problems and novel solutions. Philos Trans R Soc B Biol Sci 371:20150182. https://doi.org/10.1098/rstb.2015.0182
Rowe C, Healy SD (2014) Measuring variation in cognition. Behav Ecol 25:1287–1292. https://doi.org/10.1093/beheco/aru090
Schuster AC, Zimmermann U, Hauer C, Foerster K (2017) A behavioural syndrome, but less evidence for a relationship with cognitive traits in a spatial orientation context. Front Zool 14:19. https://doi.org/10.1186/s12983-017-0204-2
Shaw RC (2017) Testing cognition in the wild: factors affecting performance and individual consistency in two measures of avian cognition. Behav Process 134:31–36. https://doi.org/10.1016/j.beproc.2016.06.004
Sih A, Del Giudice M (2012) Linking behavioural syndromes and cognition: a behavioural ecology perspective. Philos Trans R Soc B Biol Sci 367:2762–2772. https://doi.org/10.1098/rstb.2012.0216
Sih A, Bell AM, Johnson JC, Ziemba RE (2004) Behavioral syndromes: an integrative overview. Q Rev Biol 79:241–277
Tabachnick BG, Fidell LS (1996) Analysis of covariance. Using MultivarStat 8:321–374
Taylor AH, Hunt GR, Medina FS, Gray RD (2009) Do New Caledonian crows solve physical problems through causal reasoning? Proc R Soc London B Biol Sci 276:247–254
Thornton A, Samson J (2012) Innovative problem solving in wild meerkats. Anim Behav 83:1459–1468
Titulaer M, van Oers K, Naguib M (2012) Personality affects learning performance in difficult tasks in a sex-dependent way. Anim Behav 83:723–730
van Horik JO, Madden JR (2016) A problem with problem solving: motivational traits, but not cognition, predict success on novel operant foraging tasks. Anim Behav 114:189–198. https://doi.org/10.1016/j.anbehav.2016.02.006
van Horik JO, Langley EJG, Whiteside MA, Madden JR (2017) Differential participation in cognitive tests is driven by personality, sex, body condition and experience. Behav Process 134:22–30. https://doi.org/10.1016/j.beproc.2016.07.001
Vittinghoff E, McCulloch CE (2007) Relaxing the rule of ten events per variable in logistic and cox regression. Am J Epidemiol 165:710–718
Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer, New York
Wickham H, Francois R, Henry L, Müller K (2017) dplyr: a grammar of data manipulation. R package version 0.7.4. https://CRAN.R-project.org/package=dplyr
Yoshida KCS, Van Meter PE, Holekamp KE (2016) Variation among free-living spotted hyenas in three personality traits. Behaviour 153:1665–1722
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.
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The authors declare that they have no conflicts of interest.
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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.
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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).
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Johnson-Ulrich, L., Johnson-Ulrich, Z. & Holekamp, K. Proactive behavior, but not inhibitory control, predicts repeated innovation by spotted hyenas tested with a multi-access box. Anim Cogn 21, 379–392 (2018). https://doi.org/10.1007/s10071-018-1174-2
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DOI: https://doi.org/10.1007/s10071-018-1174-2


