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Animal Cognition

, Volume 22, Issue 6, pp 1191–1195 | Cite as

Are brain weights estimated from scaling relationships suitable for comparative studies of animal cognition?

  • Stephen H. MontgomeryEmail author
Commentary

Abstract

What is the cognitive significance of variation in brain size? This question is simply put, but hard to answer, and remains one of the most enduring questions in comparative ethology. Understanding the causative links between variation in brain size and structure, and cognition requires reliable data on both neural and behavioral traits. A recent study by Horschler et al. (Anim Cogn 22(2):187–198, 2019) demonstrated the potential of citizen science and domestic dogs to provide unprecedented behavioral datasets that can be used to tackle this question. However, data on brain weight is harder to source. To test the link between performance in various cognitive tasks and variation in brain size, the authors instead relied on data for body weight, which was transformed into ‘estimated brain weight’ using the allometric scaling relationship between brain and body size, an approach that can be found in other papers which lack sufficient neuroanatomical data. Here, I describe some probable limitations of this approach and suggest that such transformations provide no benefit to the analyses and should be avoided.

Keywords

Allometry Brain size Cognition Dogs 

Notes

Acknowledgements

I thank Jeffrey Katz for inviting this commentary. I am very grateful to Daniel Horschler for sharing data and R scripts, for thoughtful discussion on the analyses and points raised above, and for feedback on a draft manuscript. I also thank Daniel Horschler, Evan MacLean and two anonymous reviewers for comments and feedback during review. This research was supported by a NERC (Grant no. NE/N014936/1) Independent Research Fellowship.

Supplementary material

10071_2019_1300_MOESM1_ESM.xlsx (29 kb)
Supplementary material 1 (XLSX 29 kb)

References

  1. Akdemir D, Godfrey OU (2015) EMMREML: fitting mixed models with known covariance structures. R package version 3.1Google Scholar
  2. Benson-Amram S, Dantzer B, Stricker G, Swanson EM, Holekamp KE (2016) Brain size predicts problem-solving ability in mammalian carnivores. Proc Natl Acad Sci 113(9):2532–2537CrossRefGoogle Scholar
  3. Boyko AR, Quignon P, Li L, Schoenebeck JJ, Degenhardt JD, Lohmueller KE, Zhao K, Brisbin A, Parker HG, Cargill M, Auton A (2010) A simple genetic architecture underlies morphological variation in dogs. PLoS Biol 8(8):e1000451CrossRefGoogle Scholar
  4. Bronson RT (1979) Brain weight-body weight scaling in breeds of dogs and cats. Brain Behav Evol 6(3):227–236CrossRefGoogle Scholar
  5. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New YorkGoogle Scholar
  6. Careau V, Réale D, Humphries MM, Thomas DW (2010) The pace of life under artificial selection: personality, energy expenditure, and longevity are correlated in domestic dogs. Am Nat 175(6):753–758CrossRefGoogle Scholar
  7. Deaner RO, Van Schaik CP, Johnson V (2006) Do some taxa have better domain-general cognition than others? A meta-analysis of nonhuman primate studies. Evol Psychol.  https://doi.org/10.1177/147470490600400114 CrossRefGoogle Scholar
  8. Deaner RO, Isler K, Burkart J, Van Schaik C (2007) Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates. Brain Behav Evol 70(2):115–124CrossRefGoogle Scholar
  9. Herculano-Houzel S (2019) Longevity and sexual maturity vary across species with number of cortical neurons, and humans are no exception. J Comp Neurol 527(10):1689–1705CrossRefGoogle Scholar
  10. Herculano-Houzel S, Kaas JH (2011) Gorilla and orangutan brains conform to the primate cellular scaling rules: implications for human evolution. Brain Behav Evol 77(1):33–44CrossRefGoogle Scholar
  11. Horschler DJ, Hare B, Call J, Kaminski J, Miklósi Á, MacLean EL (2019) Absolute brain size predicts dog breed differences in executive function. Anim Cogn 22(2):187–198CrossRefGoogle Scholar
  12. Hsu Y, Serpell JA (2003) Development and validation of a questionnaire for measuring behavior and temperament traits in pet dogs. J Am Vet Med A 223(9):1293–1300CrossRefGoogle Scholar
  13. Jardim-Messeder D, Lambert K, Noctor S, Pestana FM, de Castro Leal ME, Bertelsen MF, Alagaili AN, Mohammad OB, Manger PR, Herculano-Houzel S (2017) Dogs have the most neurons, though not the largest brain: trade-off between body mass and number of neurons in the cerebral cortex of large carnivoran species. Front Neuroanat 11:118CrossRefGoogle Scholar
  14. Jimenez AG (2016) Physiological underpinnings in life-history trade-offs in man’s most popular selection experiment: the dog. J Comp Physiol B 186(7):813–827CrossRefGoogle Scholar
  15. Logan CJ, Avin S, Boogert N, Buskell A, Cross FR, Currie A, Jelbert S, Lukas D, Mares R, Navarrete AF, Shigeno S, Montgomery SH (2018) Beyond brain size: uncovering the neural correlates of behavioral and cognitive specialization. Comp Cogn Behav Rev 13:55–90CrossRefGoogle Scholar
  16. MacLean EL, Matthews LJ, Hare BA, Nunn CL, Anderson RC, Aureli F, Brannon EM, Call J, Drea CM, Emery NJ, Haun DB (2012) How does cognition evolve? Phylogenetic comparative psychology. Anim Cogn 15(2):223–238CrossRefGoogle Scholar
  17. MacLean EL, Hare B, Nunn CL, Addessi E, Amici F, Anderson RC, Aureli F, Baker JM, Bania AE, Barnard AM, Boogert NJ (2014) The evolution of self-control. Proc Natl Acad Sci USA 111(20):E2140–E2148CrossRefGoogle Scholar
  18. Reader SM, Hager Y, Laland KN (2011) The evolution of primate general and cultural intelligence. Phil Trans Roy Soc B 366(1567):1017–1027CrossRefGoogle Scholar
  19. Roberts T, McGreevy P, Valenzuela M (2010) Human induced rotation and reorganization of the brain of domestic dogs. PLoS One 5(7):e11946CrossRefGoogle Scholar
  20. Schmidt MJ, Amort KH, Failing K, Klingler M, Kramer M, Ondreka N (2014) Comparison of the endocranial-and brain volumes in brachycephalic dogs, mesaticephalic dogs and Cavalier King Charles spaniels in relation to their body weight. Acta Vet Scand 56(1):30CrossRefGoogle Scholar
  21. Stewart L, MacLean EL, Ivy D, Woods V, Cohen E, Rodriguez K, McIntyre M, Mukherjee S, Call J, Kaminski J, Miklósi Á (2015) Citizen science as a new tool in dog cognition research. PLoS One 10(9):e0135176CrossRefGoogle Scholar
  22. Thames RA, Robertson ID, Flegel T, Henke D, O’brien DP, Coates JR, Olby NJ (2010) Development of a morphometric magnetic resonance image parameter suitable for distinguishing between normal dogs and dogs with cerebellar atrophy. Vet Radial Ultrasound 51(3):246–253CrossRefGoogle Scholar
  23. Warton DI, Duursma RA, Falster DS, Taskinen S (2012) smatr 3–an R package for estimation and inference about allometric lines. Methods Ecol Evol 3(2):257–259CrossRefGoogle Scholar
  24. Wayne RK (1986) Developmental constraints on limb growth in domestic and some wild canids. J Zool 210(3):381–397CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ZoologyUniversity of CambridgeCambridgeUK
  2. 2.School of Biological SciencesUniversity of BristolBristolUK

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