Barbizet, J. Yawning. J. Neurol. Neurosurg. Psychiatry 21, 203–209 (1958).
Google Scholar
Baenninger, R. Some comparative aspects of yawning in Betta splendens, Homo sapiens, Panthera leo, and Papio sphinx. J. Comp. Psychol. 101, 349 (1987).
Google Scholar
de Vries, J. I. P., Visser, G. H. A. & Prechtl, H. F. R. The emergence of fetal behaviour. I. Qualitative aspects. Early Hum. Dev. 7, 301–322 (1982).
Google Scholar
Provine, R. R. Yawning as a stereotyped action pattern and releasing stimulus. Ethology 72, 109–122 (1986).
Google Scholar
Tesfaye, Y. & Lal, S. Hazard of yawning. Can. Med. Assoc. J. 142, 15 (1990).
Google Scholar
Smith, E. O. Yawning: an evolutionary perspective. Hum. Evol. 14, 191–198 (1999).
Google Scholar
Guggisberg, A. G., Mathis, J., Schnider, A. & Hess, C. W. Why do we yawn? Neurosci. Biobehav. Rev. 34, 1267–1276 (2010).
Google Scholar
Gallup, A. C. Why do we yawn? Primitive versus derived features. Neurosci. Biobehav. Rev. 35, 765–769 (2011).
Google Scholar
Provine, R. R., Tate, B. C. & Geldmacher, L. L. Yawning: no effect of 3–5% CO2, 100% O2, and exercise. Behav. Neural Biol. 48, 382–393 (1987).
Google Scholar
Gallup, A. C. & Gallup, G. G. Jr. Yawning as a brain cooling mechanism: nasal breathing and forehead cooling diminish the incidence of contagious yawning. Evol. Psychol. 5, 92–101 (2007).
Google Scholar
Gallup, A. C. & Gallup, G. G. Jr. Yawning and thermoregulation. Physiol. Behav. 95, 10–16 (2008).
Google Scholar
Gallup, A. C. & Eldakar, O. T. The thermoregulatory theory of yawning: what we know from over 5 years of research. Front. Neurosci. 6, 188 (2013).
Google Scholar
Shoup-Knox, M. L., Gallup, A. C., Gallup, G. & McNay, E. C. Yawning and stretching predict brain temperature changes in rats: support for the thermoregulatory hypothesis. Front. Evol. Neurosci. 2, 108 (2010).
Google Scholar
Gallup, G. G. & Gallup, A. C. Excessive yawning and thermoregulation: two case histories of chronic, debilitating bouts of yawning. Sleep Breath. 14, 157–159 (2010).
Google Scholar
Eguibar, J. R., Uribe, C. A., Cortes, C., Bautista, A. & Gallup, A. C. Yawning reduces facial temperature in the high-yawning subline of Sprague-Dawley rats. BMC Neurosci. 18, 3 (2017).
Google Scholar
Ramirez, V., Ryan, C. P., Eldakar, O. T. & Gallup, A. C. Manipulating neck temperature alters contagious yawning in humans. Physiol. Behav. 207, 86–89 (2019).
Google Scholar
Gallup, A. C., Miller, R. R. & Clark, A. B. Changes in ambient temperature trigger yawning but not stretching in rats. Ethology 117, 145–153 (2011).
Google Scholar
Gallup, A. C. & Eldakar, O. T. Contagious yawning and seasonal climate variation. Front. Evolut. Neurosci. 3, 3 (2011).
Massen, J. J. M., Dusch, K., Eldakar, O. T. & Gallup, A. C. A thermal window for yawning in humans: yawning as a brain cooling mechanism. Physiol. Behav. 130, 145–148 (2014).
Google Scholar
Eldakar, O. T. et al. Temperature-dependent variation in self-reported contagious yawning. Adapt. Hum. Behav. Physiol. 1, 460–466 (2015).
Google Scholar
Falk, D. Brain evolution in Homo: The “radiator” theory. Behav. Brain Sci. 13, 333–381 (1990).
Google Scholar
Kiyatkin, E. A., Brown, P. L. & Wise, R. A. Brain temperature fluctuation: a reflection of functional neural activation. Eur. J. Neurosci. 16, 164–168 (2002).
Google Scholar
Baker, M. A. Brain cooling in endotherms in heat and exercise. Annu. Rev. Physiol. 44, 85–85 (1982).
Google Scholar
Wang, H. et al. Brain temperature and its fundamental properties: a review for clinical neuroscientists. Front. Neurosci. 8, 307 (2014).
Google Scholar
Richie, J. M. Energetic aspects of nerve conduction: the relationships between heat production, electrical activity and metabolism. Prog. Biophys. Mol. Biol. 26, 147–187 (1973).
Google Scholar
Gallup, A. C., Church, A. M. & Pelegrino, A. J. Yawn duration predicts brain weight and cortical neuron number in mammals. Biol. Lett. 12, 20160545 (2016).
Google Scholar
Gallup, A. C., Crowe, B. & Yanchus, M. Yawn duration predicts brain volumes in wild cats (Felidae). Int. J. Comp. Psychol. 30, 1–5 (2017).
Google Scholar
Gallup, A. C., Moscatello, L. & Massen, J. J. M. Brain weight predicts yawn duration across domesticated dog breeds. Curr. Zool. 66, 401–405 (2020).
Kilgore, D. L., Bernstein, M. H. & Hudson, D. M. Brain temperatures in birds. J. Comp. Physiol. 110, 209–215 (1976).
Google Scholar
McKechnie, A. E. & Wolf, B. O. The physiology of heat tolerance in small endotherms. Physiology 34, 302–313 (2019).
Google Scholar
Bernstein, M. H., Sandoval, I., Curtis, M. B. & Hudson, D. M. Brain temperature in pigeons: effects of anterior respiratory bypass. J. Comp. Physiol. 129, 115–118 (1979).
Google Scholar
Porter, W. R. & Witmer, L. M. Avian cephalic vascular anatomy, sites of thermal exchange, and the rete ophthalmicum. Anat. Rec. 299, 1461–1486 (2016).
Google Scholar
Gallup, A. C., Miller, M. L. & Clark, A. B. Yawning and thermoregulation in budgerigars, Melopsittacus undulatus. Anim. Behav. 77, 109–113 (2009).
Google Scholar
Gallup, A. C., Miller, M. L. & Clark, A. B. The direction and range of ambient temperature change influences yawning in budgerigars (Melopsittacus undulatus). J. Comp. Psychol. 124, 133 (2010).
Google Scholar
Gallup, A. C. et al. Thermal imaging reveals sizable shifts in facial temperature surrounding yawning in budgerigars (Melopsittacus undulatus). Temperature 4, 429–435 (2017).
Google Scholar
Herculano-Houzel, S. & Lent, R. Isotropic fractionator: a simple, rapid method for the quantification of total cell and neuron numbers in the brain. J. Neurosci. 25, 2518–2521 (2005).
Google Scholar
Revell, L. J. Size‐correction and principal components for interspecific comparative studies. Evolution 63, 3258–3268 (2009).
Google Scholar
Prinzinger, R., Preßmar, A. & Schleucher, E. Body temperature in birds. Comp. Biochem. Phys. A 99, 499–506 (1991).
Google Scholar
Jessen, C. Temperature Regulation in Humans and Other Mammals (Springer, 2001).
O’Brien, H. D. From anomalous arteries to selective brain cooling: parallel evolution of the artiodactyl carotid rete. Anat. Rec. 303, 308–317 (2020).
Google Scholar
Tattersall, G. J., Andrade, D. V. & Abe, A. S. Heat exchange from the toucan bill reveals a controllable vascular thermal radiator. Science 325, 468–470 (2009).
Google Scholar
Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proc. Natl Acad. Sci. USA 113, 7255–7260 (2016).
Google Scholar
Iwaniuk, A. N., Dean, K. M. & Nelson, J. E. Interspecific allometry of the brain and brain regions in parrots (Psittaciformes): Comparisons with other birds and primates. Brain Behav. Evol. 65, 40–59 (2005).
Google Scholar
von Eugen, K., Ströckens, F., Backes, H., Endepols, H., & Güntürkün, O. Glucose Metabolism of the Avian Brain: an FDG-PET Study in Pigeons (Columba livia) with Estimated Arterial Input Function of Anesthetized and Awake State. Poster # 068.12/QQ22 Neuroscience Meeting Planner (Online) (Society for Neuroscience, 2018).
Herculano-Houzel, S. Scaling of brain metabolism with a fixed energy budget per neuron: implications for neuronal activity, plasticity and evolution. PLoS ONE 6, e17514 (2011).
Google Scholar
Kverková, K. et al. Sociality does not drive the evolution of large brains in eusocial African mole-rats. Sci. Rep. 8, 9203 (2018).
Google Scholar
Buffenstein, R. & Yahav, S. Is the naked mole-rat Hererocephalus glaber an endothermic yet poikilothermic mammal? J. Therm. Biol. 16, 227–232 (1991).
Google Scholar
Tucker, R. The digging behavior and skin differentiations in Heterocephalus glaber. J. Morphol. 168, 51–71 (1981).
Google Scholar
McNab, B. K. The metabolism of fossorial rodents: a study of convergence. Ecology 47, 712–733 (1966).
Google Scholar
Stephan, H. Methodische Studien über den quantitativen Vergleich architektonischer Struktureinheiten des Gehirns. Z. wiss. Zool. 164, 143–172 (1960).
Herculano-Houzel, S., Mota, B. & Lent, R. Cellular scaling rules for rodent brains. Proc. Natl Acad. Sci. USA 103, 12138–12143 (2006).
Google Scholar
Herculano-Houzel, S., Collins, C. E., Wong, P. & Kaas, J. K. Cellular scaling rules for primate brains. Proc. Natl Acad. Sci. USA 104, 3562–3567 (2007).
Google Scholar
Herculano-Houzel, S. et al. Updated neuronal scaling rules for the brains of Glires (rodents/lagomorphs). Brain Behav. Evol. 78, 302–314 (2011).
Google Scholar
Herculano-Houzel, S., Catania, K., Manger, P. R. & Kaas, J. H. Mammalian brains are made of these: a dataset of the numbers and densities of neuronal and nonneuronal cells in the brain of glires, primates, scandentia, eulipotyphlans, afrotherians and artiodactyls, and their relationship with body mass. Brain Behav. Evol. 86, 145–163 (2015).
Google Scholar
Dos Santos, S. E. et al. Cellular scaling rules for the brains of marsupials: not as “primitive” as expected. Brain Behav. Evol. 89, 48–63 (2017).
Google Scholar
Kazu, R. S., Maldonado, J., Mota, B., Manger, P. R. & Herculano-Houzel, S. Cellular scaling rules for the brain of Artiodactyla include a highly folded cortex with few neurons. Front. Neuroanat. 8, 128 (2014).
Google Scholar
Collins, C. E. et al. Cortical cell and neuron density estimates in one chimpanzee hemisphere. Proc. Natl Acad. Sci. USA 113, 740–745 (2016).
Google Scholar
Jardim-Messeder, D. et al. 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, 118 (2017).
Google Scholar
Mullen, R. J., Buck, C. R. & Smith, A. M. NeuN, a neuronal specific nuclear-protein in vertebrates. Development 116, 201–211 (1992).
Google Scholar
Mezey, S. et al. Postnatal changes in the distribution and density of neuronal nuclei and doublecortin antigens in domestic chicks (Gallus domesticus). J. Comp. Neurol. 520, 100–116 (2012).
Google Scholar
Rehkämper, G., Kart, E., Frahm, H. D. & Werner, C. W. Discontinuous variability of brain composition among domestic chicken breeds. Brain Behav. Evol. 61, 59–69 (2003).
Google Scholar
Horschler, D. J. et al. Absolute brain size predicts dog breed differences in executive function. Anim. Cogn. 22, 187–198 (2019).
Google Scholar
Rogell, B., Dowling, D. K. & Husby, A. Controlling for body size leads to inferential biases in the biological sciences. Evol. Lett. 4, 73–82 (2019).
Google Scholar
Gutierrez-Ibanez, C., Iwaniuk, A. N. & Wylie, D. R. Relative brain size is not correlated with display complexity in manakins: a reanalysis of Lindsay et al. (2015). Brain Behav. Evol. 87, 223–226 (2016).
Google Scholar
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
Google Scholar
Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Boil. Evol. 34, 1812–1819 (2017).
Google Scholar
Currie, T. E. & Meade, A. In Modern phylogenetic comparative methods and their application in evolutionary biology (ed. Garamszegi, L. Z.) 263–286 (Springer, 2014).
Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi‐trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).
Google Scholar
Gelman, A. et al. Bayesian Data Analysis (CRC Press, 2013).
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (CRC Press, 2016).
Lo, S. & Andrews, S. To transform or not to transform: using generalized linear mixed models to analyse reaction time data. Front. Psychol. 6, 1171 (2015).
Google Scholar
Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).
Google Scholar
Lemoine, N. P. Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses. Oikos 128, 912–928 (2019).
Google Scholar
Bürkner, P. C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Google Scholar
Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).
Google Scholar
McShane, B. B., Gal, D., Gelman, A., Robert, C. & Tackett, J. L. Abandon statistical significance. Am. Stat. 73, 235–245 (2019).
Google Scholar
Sawilowsky, S. New effect size rules of thumb. J. Mod. Appl. Stat. Methods 8, 467–474 (2009).
Google Scholar
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