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RESEARCH ARTICLE
How smart was T. rex? Testing claims of exceptional
cognition in dinosaurs and the application of neuron count
estimates in palaeontological research
Kai R. Caspar
1,2
| Cristi
an Gutiérrez-Ib
añez
3
| Ornella C. Bertrand
4,5
|
Thomas Carr
6
| Jennifer A. D. Colbourne
7
| Arthur Erb
8,9
|
Hady George
10
| Thomas R. Holtz Jr
11,12
| Darren Naish
13
|
Douglas R. Wylie
3
| Grant R. Hurlburt
14
1
Institute of Cell Biology, Heinrich Heine
University Düsseldorf, Düsseldorf,
Germany
2
Department of Game Management and
Wildlife Biology, Faculty of Forestry and
Wood Sciences, Czech University of Life
Sciences, Prague, Czech Republic
3
Department of Biological Sciences,
University of Alberta, Edmonton, Alberta,
Canada
4
Institut Català de Paleontologia Miquel
Crusafont, Universitat Autònoma de
Barcelona, Barcelona, Spain
5
Section of Mammals, Carnegie Museum
of Natural History, Pittsburgh,
Pennsylvania, USA
Abstract
Recent years have seen increasing scientific interest in whether neuron counts
can act as correlates of diverse biological phenomena. Lately, Herculano-
Houzel (2023) argued that fossil endocasts and comparative neurological data
from extant sauropsids allow to reconstruct telencephalic neuron counts in
Mesozoic dinosaurs and pterosaurs, which might act as proxies for behaviors
and life history traits in these animals. According to this analysis, large thero-
pods such as Tyrannosaurus rex were long-lived, exceptionally intelligent ani-
mals equipped with “macaque- or baboon-like cognition”, whereas sauropods
and most ornithischian dinosaurs would have displayed significantly smaller
brains and an ectothermic physiology. Besides challenging established views
on Mesozoic dinosaur biology, these claims raise questions on whether neuron
Institutional Abbreviations: AMNH, American Museum of Natural History, New York City, New York, USA; BMNH / NHMUK, Natural History
Museum, London, UK; BSP, Bayerische Staatssammlung für Paläontologie und historische Geologie, Munich, Germany; BYU, Brigham Young
University, Earth Science Museum, Provo, Utah, USA; CAPPA/UFSM, Centro de Apoio à Pesquisa Paleontol
ogica da Quarta Colônia / Universidade
Federal de Santa Maria, S˜
ao Jo˜
ao do Polêsine, Rio Grande do Sul, Brazil; CM, Carnegie Museum of Natural History, Pittsburgh, Pennsylvania; CMN,
Canadian Museum of Nature, Ottawa, Ontario, Canada; DINO, Dinosaur National Monument, Jensen, Utah, USA; FIP, Florida Institute of
Paleontology, Palm Beach, Florida, USA; FMNH, Field Museum of Natural History, Chicago, Illinois, USA; FPDM, Fukui Prefectural Dinosaur
Museum, Fukui, Japan; HMN / MB.R, Museum für Naturkunde, Berlin, Germany; IGM, Mongolian Institute of Geology, Ulaan Bator, Mongolia;
IRSNB / RBINS, Institut Royal des Sciences Naturelles de Belgique, Brussels, Belgium; IVPP, Institute of Vertebrate Paleontology and
Paleoanthropology, Beijing, China; KUVP, Kansas University Natural History Museum, Lawrence, Kansas, USA; MACN, Museo Argentino de
Ciencias Naturales “Bernardino Rivadavia”, Buenos Aires, Argentina; MPC-D, Institute of Paleontology and Geology, Mongolian Academy of
Sciences, Ulaan Bator, Mongolia; MUCPv-CH, Museo de la Universidad Nacional del Comahue, colecci
on del Museo Ernesto Bachmann, Villa El
Choc
on, Argentina; MOR, Museum of the Rockies, Bozeman, Montana, USA; NMC, Canadian Museum of Nature, Ottawa, Canada; NCSM, North
Carolina Museum of Natural Sciences, Raleigh, North Carolina, USA; OMNH, Sam Noble Museum at the University of Oklahoma, Norman,
Oklahoma, USA; PIN, Paleontological Institute, Russian Academy of Sciences, Moscow, Russia; PKUP, Peking University Paleontological Collections,
Beijing, China; ROM, Royal Ontario Museum, Toronto, Ontario, Canada; RTMP/TMP, Royal Tyrrell Museum of Paleontology, Drumheller, Alberta,
Canada; SGM, Ministere de l'Energie et des Mines, Rabat, Morocco; USNM, Smithsonian National Museum of Natural History, Washington, D.C.,
USA; UUVP, University of Utah, Salt Lake City, Utah, USA; YPM, Yale Peabody Museum, New Haven Connecticut, USA.
Kai R. Caspar, Cristi
an Gutiérrez-Ib
añez, and Grant R. Hurlburt contributed equally.
Received: 13 January 2024 Revised: 3 April 2024 Accepted: 7 April 2024
DOI: 10.1002/ar.25459
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited and is not used for commercial purposes.
© 2024 The Authors. The Anatomical Record published by Wiley Periodicals LLC on behalf of American Association for Anatomy.
Anat Rec. 2024;307:3685–3716. wileyonlinelibrary.com/journal/ar 3685
6
Department of Biology, Carthage College,
Kenosha, Wisconsin, USA
7
Comparative Cognition Unit, Messerli
Research Institute, University of
Veterinary Medicine Vienna, Vienna,
Austria
8
School of GeoSciences, Grant Institute,
University of Edinburgh, Edinburgh, UK
9
Center for Science, Teaching, and
Learning, Rockville Centre, New
York, USA
10
School of Earth Sciences, University of
Bristol, Bristol, UK
11
Department of Geology, University of
Maryland, College Park, Maryland, USA
12
Department of Paleobiology, National
Museum of Natural History, Washington,
District of Columbia, USA
13
School of Biological Sciences, Faculty of
Environment and Life Sciences,
University of Southampton,
Southampton, UK
14
Department of Natural History, Royal
Ontario Museum, Toronto, Ontario,
Canada
Correspondence
Kai R. Caspar, Institute of Cell Biology,
Heinrich Heine University Düsseldorf,
Düsseldorf, Germany.
Email: kai.caspar@hhu.de
Cristi
an Gutiérrez-Ib
añez, Department of
Biological Sciences, University of Alberta,
Edmonton, Canada.
Email: cgutierr@ualberta.ca
Grant R. Hurlburt, Department of Natural
History, Royal Ontario Museum, Toronto,
Ontario, Canada.
Email: ghurlburt70@yahoo.com
count estimates could benefit research on fossil animals in general. Here, we
address these findings by revisiting Herculano-Houzel's (2023) work, identify-
ing several crucial shortcomings regarding analysis and interpretation. We pre-
sent revised estimates of encephalization and telencephalic neuron counts in
dinosaurs, which we derive from phylogenetically informed modeling and an
amended dataset of endocranial measurements. For large-bodied theropods in
particular, we recover significantly lower neuron counts than previously pro-
posed. Furthermore, we review the suitability of neurological variables such as
neuron numbers and relative brain size to predict cognitive complexity, meta-
bolic rate and life history traits in dinosaurs, coming to the conclusion that
they are flawed proxies for these biological phenomena. Instead of relying on
such neurological estimates when reconstructing Mesozoic dinosaur biology,
we argue that integrative studies are needed to approach this complex subject.
KEYWORDS
brain evolution, comparative cognition, endocast, graphic double integration,
palaeoneurology
1|INTRODUCTION
The Late Cretaceous North American theropod dinosaur
Tyrannosaurus rex is a superlative predator, being among
the largest, heaviest, and most powerful (in terms of
bite force) terrestrial carnivores of all time (Gignac &
Erickson, 2017; Henderson, 2023; Sakamoto, 2022).
Recently, Herculano-Houzel (2023) proposed that anthro-
poid primate-level intelligence should be added to T. rex's
already impressive predatory resume based on high esti-
mates for the number of neurons in its forebrain. This
conclusion emerged from a paradigm whereby neurologi-
cal variables estimated from endocasts can, so it is
claimed, be used to infer metabolic parameters, behav-
iors, and longevity in fossil species. Here, we test whether
this approach and its remarkable prospects withstand
scrutiny.
The hypothesis of exceptional intelligence in dinosaurs
such as T. rex challenges the consensus of crocodile-like
cognition in these animals, a position informed by
comparative anatomical data (Hurlburt et al., 2013;
Rogers, 1998; Witmer & Ridgely, 2009). Moreover, this
claim bears ramifications that extend beyond specialized
biological disciplines due to its potential to create long-
lasting impacts on the public's perspective on dinosaurs,
evolution, and the scientific process. Given the extreme
contrast between Herculano-Houzel's (2023) proposal and
more traditional perspectives on dinosaur biology, we
revisit the claim of exceptional intelligence in these ani-
mals through an assessment of her methodology and a
3686 CASPAR ET AL.
reanalysis of the underlying data. By integrating perspec-
tives from both paleontology and neontology, we evaluate
the potential benefits and limitations of neuron count esti-
mation in research on the behavior and physiology of fos-
sil species. We begin with a brief review of dinosaur
paleoneurology and a discussion of how Herculano-Hou-
zel's (2023) approach aims to expand the field's methodo-
logical tool kit.
1.1 |Dinosaur paleoneurology and the
prospects of neuron count estimates
for the field
Paleoneurology is a subfield of paleontology dedicated to
research on the nervous systems of extinct animals.
Because soft tissues are not readily preserved in the fossil
record, paleobiologists typically rely on endocasts when
studying the brains of extinct vertebrate species (Paulina-
Carabajal et al., 2023). An endocast can be a natural (infill-
ing), artificial (mold) or virtual (digitally reconstructed) cast
of the endocranial cavity that is formed by the bones of the
braincase.
The study of extinct species' endocasts, including those
of dinosaurs, can be traced back to the 1800s (e.g.,
Cuvier, 1812; Marsh, 1879). However, the field was truly
defined by Edinger (1929) who effectively introduced the
concept of geological time to neurobiological studies.
Before her, anatomists made comparisons between endo-
casts and fresh brains, but without considering the respec-
tive stratigraphic context (Buchholtz & Seyfarth, 2001).
Jerison (1973) built on Edinger's work by studying brain
evolution in a quantitative manner and developed the
encephalization quotient (EQ) as an estimate of relative
brain size, applicable to both extant and extinct species.
Later, the advent of x-ray computed tomography at the
end of the 1990s transformed the field and provided novel
ways in which the neurosensory systems of extinct species
could be studied (e.g., Knoll et al., 1999; Witmer
et al., 2008). Despite these crucial innovations, however,
paleoneurology has so far remained largely restricted to
measuring and comparing gross morphology, limiting our
understanding of how the brains of Mesozoic dinosaurs
and other extinct animals worked.
Pterosaurs and dinosaurs (the latter including birds)
form the clade Ornithodira (Figure 1), the closest extant
relatives of which are crocodilians (Figure 1). Together,
both lineages, which separated about 250 million years
ago, comprise the clade Archosauria (e.g., Legendre
et al., 2016). Next to birds, crocodilians therefore repre-
sent a critical reference point in reconstructing the ner-
vous systems of extinct ornithodirans.
FIGURE 1 Simplified phylogeny of the Sauropsida (=total group Reptilia) with a focus on the taxon Ornithodira (the least inclusive
clade containing pterosaurs and dinosaurs, see revised definition of Nesbitt, 2011) and schematic representative color-coded brain
morphologies, excluding the pituitary (not to scale). Blue: olfactory bulb and tracts, Green: pallium (homologous to the mammalian cerebral
cortex), Orange: cerebellum, Yellow: diencephalon and optic tectum, Violet: brain stem. Olfactory structures, pallium and subpallium
comprise the telencephalon. The gray overlay indicates extinct taxa, the brain morphologies of which are approximated. Note that brain
morphology in T. rex and its relatives (Tyrannosauroidea) is conspicuously plesiomorphic when compared to other ornithodirans pictured
here (see e.g., Giffin, 1989). Silhouettes were taken from PhyloPic (listed from top to bottom): Morunasaurus (in public domain), Crocodylus
(in public domain), Rhamphorhynchus (by Scott Hartman), Olorotitan (by ДиБгд, vectorized by T. Michael Keesey), Tyrannosaurus (by Matt
Dempsey), Dromaeosaurus (by Pranav Iyer), Dromaius (by Darren Naish), Corvus (in public domain).
CASPAR ET AL.3687
Interestingly, highly disparate patterns of endocranial
tissue organization are realized in these two extant clades.
One fundamental difference relates to the portion of the
endocranial cavity which is occupied by the brain rather
than by the associated meningeal tissues (including the
dura mater and arachnoid mater) and cerebrospinal fluid
(Figure 2). In crocodilians, nervous tissue only fills a
fraction of the braincase (Hopson, 1979;Jirak&
Janacek, 2017; Watanabe et al., 2019). Longitudinal
venous sinuses course along the dorsal and ventral aspect
of the brain, obscuring its true shape in casts of the brain-
case. Furthermore, the size of the brain relative to both
the endocranial volume and total body size, decreases dur-
ing crocodilian ontogeny, even over the course of adult-
hood (Hurlburt et al., 2013; but note that absolute brain
volume increases with body size, even in adults—
Ngwenya et al., 2013). Endocast morphology indicates that
the endocranial cavity in most non-avian dinosaurs was
organized in crocodilian-like fashion and comparative
studies suggest that this configuration was indeed ances-
tral for the clade Archosauria (Fabbri & Bhullar, 2022;
Hurlburt et al., 2013; Witmer et al., 2008). For tyranno-
sauroids specifically, which are among the best-studied
dinosaurs when it comes to palaeoneurology, endocasts
representing different ontogenetic stages suggest that
brain size (relative to endocranial volume) decreased with
age (Bever et al., 2013; Brusatte et al., 2009; Witmer &
Ridgely, 2009), as is the case in modern crocodilians. Simi-
lar to crocodilians, most dinosaurian endocasts do not
faithfully capture the volume and anatomy of the brain,
particularly its posterior regions such as the cerebellum
(Watanabe et al., 2019). This contrasts with the situation
in most birds and mammals for which endocasts represent
excellent brain size proxies (e.g., Bertrand et al., 2022;
Iwaniuk & Nelson, 2002).
The avian pattern probably evolved at the root of the
theropod dinosaur clade Maniraptoriformes, which
includes ornithomimosaurs (“ostrich-mimic”dinosaurs)
and maniraptorans (the bird-like oviraptorosaurs, dro-
maeosaurids and kin, and birds themselves) (Balanoff
et al., 2013; Osm
olska, 2004; Figure 1). Maniraptoriform
brains have enlarged cerebral and cerebellar regions that
almost fully occupy the endocranial cavity, as evidenced
by brain contours faithfully captured by the endocranium
and extensive vascular imprints. There is no evidence
that the brains of other dinosaurs similarly contacted the
endocranial surface (pachycephalosaurs pose an excep-
tion to this pattern but are not covered in this article,
their endocranial anatomy is described in Evans, 2005;
Giffin, 1989, and Hopson, 1979; we discuss other sug-
gested cases of secondarily increased endocranial fills in
dinosaurs in File S1). Pterosaurs are similar to manirap-
toriforms in also possessing brains that fit tightly into the
endocranial cavity (Witmer et al., 2003).
Aside from general endocranial tissue organization,
the neuroarchitecture and circuitry of the forebrain in
birds and crocodilians differs notably from one another
(Briscoe et al., 2018; Briscoe & Ragsdale, 2018; Ulinski &
Margoliash, 1990). Comparisons with other sauropsids
demonstrate that again the crocodilian condition is more
plesiomorphic (Briscoe & Ragsdale, 2018). To which
extent non-avian dinosaurs and pterosaurs resembled the
two extant archosaur groups in these regards cannot
FIGURE 2 The endocast and endocranial tissue organization
of the American alligator (Alligator mississippiensis), illustrating the
plesiomorphic condition within the clade Archosauria. Scale
bar =2 cm in all cases. (a): Endocast of a wild A. mississippiensis
(Fla. F&G. Harvest tag 937,095), Dorsal cranial length (DCL):
342.90 mm, right lateral view. Reduced in size to match proportions
of brain in (b–c): Dura mater around the brain of A.
mississippiensis, specimen CITES FLM 12–29,409, DCL: 380 mm,
left lateral view (reversed). (c): Brain within arachnoid of FLM 12–
29,409. Brown-red material is dried blood filling the subarachnoid
space (SaS), right lateral view. Ach, arachnoid mater (covering the
cerebellum); Art, artery on external wall of dura mater over the
lateral pole of the cerebrum; Cbll, cerebellum; CCA, caudal
cerebral artery; Ch L or R, left or right cerebral hemisphere; DSS,
dorsal sagittal sinus; EthA, common ethmoid artery; I C, internal
carotid artery; InHA, interhemispheric artery; MO, medulla
oblongata; N.II, optic nerve; N.V, (cast of) trigeminal nerve; Nn R,
roots of nerves IX-XI; OC, occipital condyle; OcS, occipital sinus;
OlBu & Tr, olfactory bulb & tract; OtC F, fossa of otic capsule;
Pineal Loc, pineal gland location; Pit, pituitary gland; SaSMe,
mesencephalic subarachnoid space; SaSR, rostral SaS; SaSVe,
ventral SaS; SN.I, first spinal nerve. The rostral end of the cerebrum
is below the arrow for SaSR in (b). Both specimens are housed in
the private collection of G. R. Hurlburt.
3688 CASPAR ET AL.
be reliably reconstructed, since they lack osteological
correlates.
The inferred brain anatomy of various dinosaur
groups has been discussed elsewhere (Paulina-Carabajal
et al., 2023) and reviewing it here is beyond the scope of
this article. We aim instead to focus on what endocast-
based methods potentially reveal about the behavior
and cognition of extinct species. While considering the
aforementioned limitations, endocasts from fossil
ornithodirans allow us to reasonably estimate basic neu-
roanatomical measures such as EQ, as well as to deduce
specific sensory specializations (e.g., Witmer et al., 2003;
Witmer & Ridgely, 2009;Zelenitskyetal.,2011). None-
theless, it is generally assumed that the predictive power
of these data in elucidating the cognitive capacities of
fossil species is low (Paulina-Carabajal et al., 2023).
Researchers have long sought to identify robust mor-
phological correlates of cognition but have found tradi-
tional proxies such as EQ and absolute brain size to be
limited and problematic regarding their conceptual jus-
tifications (Van Schaik et al., 2021). Current debates
focus on whether refined neuroanatomical measures
such as “cognitive brain size”(Van Schaik et al., 2021)
and brain region-specific neuron counts (Herculano-
Houzel, 2011; Kabadayi et al., 2016; Logan et al., 2018;
Sol et al., 2022) might be able to overcome these issues.
The quantification of the latter, however, seemed out of
reach for vertebrate paleontology.
With this in mind, the approach proposed by
Herculano-Houzel (2023) is of great potential signifi-
cance: it entails that endocasts of extinct taxa can be used
to model neuron counts if neurological data from related
extant species can be taken into account. If valid, this
technique would potentially allow researchers to eluci-
date aspects of brain physiology that cannot be inferred
from endocast morphology alone. Herculano-Houzel and
Kaas (2011) and Herculano-Houzel et al. (2011) pio-
neered this approach for fossil hominins and extinct giant
rodents, but Herculano-Houzel (2023) was first in apply-
ing this methodology to fossil sauropsid groups separated
from their extant relatives by hundreds of millions of
years of evolution, namely, pterosaurs and Mesozoic
dinosaurs.
Indeed, Herculano-Houzel (2011,2017,2023) has
argued emphatically that neuron counts represent reli-
able estimates for cognitive abilities in extant vertebrates,
markedly outperforming other measures such as relative
or absolute brain size. If we accept this premise, accurate
modeling of neuron counts in dinosaurs based on endo-
cast volumes and comparative neurological data might
appear as a promising new method to elucidate the
behavior and cognitive capacities of various extinct
animals.
1.2 |The methodology and rationale
of Herculano-Houzel (2023)
Herculano-Houzel (2023) reconstructed relative brain size
and neuron counts for 29 dinosaur and pterosaur species
based on comparative data from extant non-avian and avian
sauropsids (“reptiles”and birds respectively; Kverkov
a
et al., 2022; Olkowicz et al., 2016). Although we want to
avoid lengthy discussions about taxonomy, it is worth noting
that some of these are no longer considered valid taxonomic
entities (see below; an updated nomenclature for relevant
dinosaurspeciesisincludedinTable1). For instance, Rham-
phorhynchus muensteri and R. gemmingi have long been syn-
onymized (Bennett, 1995). Surprisingly, Herculano-Houzel
(2023) inferred an ectothermic metabolism for one, and
endothermy for the other based on assumptions about their
relative brain size Herculano-Houzel (2023).
Neuron count estimates for fossil taxa only concerned
the forebrain or telencephalon, a major brain region
which is critically involved in cognitive and motor func-
tions as well as the processing of sensory information. It
Definitions of notable dinosaur clades: Ornithischia—a large group of primarily herbivorous dinosaurs, exclud-
ing the long-necked sauropodomorph dinosaurs, defined as the most inclusive clade including Triceratops but
not Diplodocus nor Tyrannosaurus. Most popular representatives of this group include horned or otherwise
heavily armored forms such as Triceratops,Stegosaurus and Ankylosaurus as well as the hadrosaurs, colloqui-
ally known as duck-billed dinosaurs. Sauropodomorpha—the long-necked and often particularly large-bodied
herbivorous dinosaurs, defined as the most inclusive clade including Diplodocus but not Triceratops nor Tyran-
nosaurus; Theropoda—the bipedal, mostly carnivorous dinosaurs, the most inclusive clade including Tyranno-
saurus but not Diplodocus or Triceratops. The birds are part of this clade (see Baron et al., 2017 for definitions
of Ornithischia, Sauropodomorpha and Theropoda); Tyrannosauroidea, the most inclusive clade of theropods
containing Tyrannosaurus but not more bird-like taxa such as Velociraptor and Ornithomimus (Sereno
et al., 2009); Maniraptoriformes, the least inclusive clade containing Velociraptor and Ornithomimus but not
earlier-diverging theropods like Tyrannosaurus (Holtz, 1996).
CASPAR ET AL.3689
TABLE 1 Estimates of brain (MBr, g; derived from brain endocast volume, BrEV) and body mass (MBd, kg) in Mesozoic dinosaurs. Literature sources for endocranial volumes (EV) and
the methods they employed to determine it are listed.
Species Group
Body mass
(MBd) (kg)
Lower 25%
PPE (kg)
Upper 25%
PPE (kg)
FC
(mm)
HC
(mm)
MBd
specimen
MBd source for FC
&HC
Brain mass
(MBr) (g)
MBr/
BrEV
BrEV
(mL/
cm
3
)
EV
specimen EV method Original EV source
Bambiraptor
feinbergi[1]
Non-avian
maniraptoriform
8.0632 5.9966 10.1298 47 NA FIP 002 Cast of right femur 14 1 14 KUVP 129737 Water
displacement
Burnham (2004)
Archaeopteryx
lithographica[2]
Non-avian
maniraptoriform
0.344 0.256 0.433 14.93 NA BMNH 37001 Gatesy, 1991 1.52 1 1.52 BMNH 37001 CT Dominguez Alonso et al.
(2004)
Citipati osmolskae Non-avian
maniraptoriform
123.993 92.214 155.772 127 NA IGM 100/978 Benson et al. (2017),
#321
22.05 1 22.05 IGM 100/978 CT Balanoff et al. (2013)
Khaan mckennai Non-avian
maniraptoriform
21.920 16.302 27.538 67.62 NA IGM 100/1127 Benson et al. (2017),
#452
8.8 1 8.8 IGM 100/973 CT Balanoff et al. (2013)
Ornithomimus
edmontonicus
Non-avian
maniraptoriform
83.524 62.117 104.931 110 NA ROM 851 Benson et al. (2017),
#522
49.89 1 49.89 NMC 12228 GDI (this study) Photos of endocast by GRH
Shuvuuia deserti Non-avian
maniraptoriform
3.0497 2.2681 3.8313 33 NA IGM 100/1304 Benson et al. (2017),
#574
1.52 1 1.52 IGM 100/977 CT Balanoff et al. (2013),
Balanoff et al. (2024)
Stenonychosaurus
inequalis
Non-avian
maniraptoriform
47.376 35.234 59.518 89.5 NA MOR 748
(MTC)
Benson et al. (2017),
#621
38.65 1 38.65 RTMP
86.36.457 &
79.8.1
CT Morhardt (2016)
Acrocanthosaurus
atokensis
Non-maniraptoriform
theropod
3.454.954 2.569.449 4.340.46 426 NA NCSM 14345 Benson et al. (2017),
#242
51.55 0.42 122.74 OMNH 10146 CT Franzosa and Rowe (2005)
Acrocanthosaurus
atokensis
Non-maniraptoriform
theropod
3.454.954 2.569.449 4.340.46 426 NA NCSM 14345 Benson et al. (2017),
#242
38.05 0.31 122.74 OMNH 10146 CT Franzosa and Rowe (2005)
Allosaurus fragilis Non-maniraptoriform
theropod
2.541.814 1.890.347 3.193.28 381 NA AMNH 680 Benson et al. (2017),
#253
41.37 0.42 98.5 UUVP 294 GDI (this study) Photos of cast by GRH
Allosaurus fragilis Non-maniraptoriform
theropod
2.541.814 1.890.347 3.193.28 381 NA AMNH 680 Benson et al. (2017),
#253
30.54 0.31 98.5 UUVP 294 GDI (this study) Photos of cast by GRH
Carcharodontosaurus
saharicus
Non-maniraptoriform
theropod
3.269.147 2.431.265 4.107.03 417.52 NA BSP 1922 X46 Benson et al. (2017),
#307
69.44 0.31 224 SGM-Din 1 CT Larsson et al. (2000)
Carcharodontosaurus
saharicus[3]
Non-maniraptoriform
theropod
3.269.147 2.431.265 4.107.03 417.52 NA BSP 1922 X46 Benson et al. (2017),
#307
94.08 0.42 224 SGM-Din 1 CT Larsson et al. (2000)
Carnotaurus sastrei Non-maniraptoriform
theropod
1.641.829 1.221.028 2.062.63 325 NA MACN CH
894
Benson et al. (2017),
#308
45.49 0.42 108.3 MACN CH-
894
CT Cerroni and Paulina-
Carabajal (2019)
Carnotaurus sastrei Non-maniraptoriform
theropod
1.641.829 1.221.028 2.062.63 325 NA MACN CH
894
Benson et al. (2017),
#308
33.57 0.31 108.3 MACN CH-
894
CT Cerroni and Paulina-
Carabajal (2019)
Giganotosaurus
carolinii
Non-maniraptoriform
theropod
6.136.771 4.563.916 7.709.625 525 NA MUCPv-Ch1 Benson et al. (2017),
#399
94.5 0.42 225 MUCPv-CH 1 CT Paulina-Carabajal and
Canale (2010)
Giganotosaurus
carolinii
Non-maniraptoriform
theropod
6.136.771 4.563.916 7.709.625 525 NA MUCPv-Ch1 Benson et al. (2017),
#399
69.75 0.31 225 MUCPv-CH 1 CT Paulina-Carabajal and
Canale (2010)
Majungasaurus
crenatissimus
Non-maniraptoriform
theropod
1.614.201 1.200.481 2027.92 323 NA FMNH PR
2278
Benson et al. (2017),
#482
37.51 0.42 89.32 FMNH PR
2100
CT Sampson and Witmer (2007)
3690 CASPAR ET AL.
TABLE 1 (Continued)
Species Group
Body mass
(MBd) (kg)
Lower 25%
PPE (kg)
Upper 25%
PPE (kg)
FC
(mm)
HC
(mm)
MBd
specimen
MBd source for FC
&HC
Brain mass
(MBr) (g)
MBr/
BrEV
BrEV
(mL/
cm
3
)
EV
specimen EV method Original EV source
Majungasaurus
crenatissimus
Non-maniraptoriform
theropod
1.614.201 1.200.481 2027.92 323 NA FMNH PR
2278
Benson et al. (2017),
#482
27.69 0.31 89.32 FMNH PR
2100
CT Sampson and Witmer (2007)
Sinraptor dongi Non-maniraptoriform
theropod
1.122.287 834.645 1.409.929 283 NA TMP 93.115.1 Campione &
Evans, 2020
29.45 0.31 95 IVPP 10600 CT Paulina-Carabajal and Currie
(2012)
Sinraptor dongi[4] Non-maniraptoriform
theropod
1.122.287 834.645 1.409.929 283 NA TMP 93.115.1 Campione &
Evans, 2020
39.9 0.42 95 IVPP 10600 CT Paulina-Carabajal and Currie
(2012)
Tarbosaurus bataar Non-maniraptoriform
theropod
2.345.113 1744.06 2.946.165 370 NA MPC-D 552/1 Benson et al. (2017),
#610
66.86 0.42 159.2 PIN, no. 553–
3/1
Estimated from
latex half-cast
Saveliev and Alifanov (2007)
Tarbosaurus bataar Non-maniraptoriform
theropod
2.345.113 1744.06 2.946.165 370 NA MPC-D 552/1 Benson et al. (2017),
#610
49.35 0.31 159.2 PIN, no. 553–
3/1
Estimated from
latex half-cast
Saveliev and Alifanov (2007)
Tyrannosaurus rex Non-maniraptoriform
theropod
8.070.46 6.002.001 10.138.919 580 NA FMNH PR
2081
Persons et al., 2020 128.4 0.31 414.19 FMNH PR
2081
CT Hurlburt et al. (2013)
Tyrannosaurus rex Non-maniraptoriform
theropod
6.430.357 4.782.256 8.078.457 534 NA CM 9380 Persons et al., 2020 160.34 0.42 381.76 AMNH 5029 CT Hurlburt et al. (2013)
Tyrannosaurus rex Non-maniraptoriform
theropod
6.430.357 4.782.256 8.078.457 534 NA CM 9380 Persons et al., 2020 118.35 0.31 381.76 AMNH 5029 CT Hurlburt et al. (2013)
Tyrannosaurus rex Non-maniraptoriform
theropod
5.515.247 4.101.69 6.928.805 505 NA BHI 3033 Persons et al., 2020 178.77 0.57 313.64 AMNH FR
5117
CT Morhardt (2016)
Tyrannosaurus rex Non-maniraptoriform
theropod
5.515.247 4.101.69 6.928.805 505 NA BHI 3033 Persons et al., 2020 131.73 0.42 313.64 AMNH FR
5117
CT Hurlburt et al. (2013)
Tyrannosaurus rex Non-maniraptoriform
theropod
5.515.247 4.101.69 6.928.805 505 NA BHI 3033 Persons et al., 2020 97.23 0.31 313.64 AMNH 5117 CT Hurlburt et al. (2013)
Tyrannosaurus rex Non-maniraptoriform
theropod
8.070.46 6.002.001 10.138.919 580 NA FMNH PR
2081
Persons et al., 2020 173.96 0.42 414.19 FMNH PR
2081
CT Hurlburt et al. (2013)
Amargasaurus cazaui Non-theropod
dinosaur
10.194.61 7.581.73 12.807.49 505 388 MACN-N 15 Benson et al. (2017),
#20
35.28 0.42 84 MACN-N 15 CT Paulina Carabajal et al.
(2014)
Amargasaurus cazaui Non-theropod
dinosaur
10.194.61 7.581.73 12.807.49 505 388 MACN-N 15 Benson et al. (2017),
#20
26.04 0.31 84 MACN-N 15 CT Paulina Carabajal et al.
(2014)
Apatosaurus sp. Non-theropod
dinosaur
41.268.719 30.691.546 51.845.891 845 640 CM 3018 Benson et al. (2017),
#33
43.04 0.42 102.48 BYU 17096 CT Balanoff et al. (2010)
Apatosaurus sp. Non-theropod
dinosaur
41.268.719 30.691.546 51.845.891 845 640 CM 3018 Benson et al. (2017),
#33
31.77 0.31 102.48 BYU 17096 CT Balanoff et al. (2010)
Buriolestes schultzi Non-theropod
dinosaur
6.424 4.777 8.070 43.27 NA CAPPA/UFSM
0035
Müller et al. (2021) 1.021 0.42 2.43 CAPPA/
UFSM
0035
CT Müller et al. (2021)
Buriolestes schultzi Non-theropod
dinosaur
6.424 4.777 8.070 43.27 NA CAPPA/UFSM
0035
Müller et al. (2021) 0.753 0.31 2.43 CAPPA/
UFSM
0035
CT Müller et al. (2021)
Diplodocus sp. Non-theropod
dinosaur
14.813.081 11.016.488 18.609.673 563 460 USNM 10865 Benson et al. (2017),
#86
42 0.42 100 CM 11161 CT L. M. Witmer, pers. comm.
(2023)
(Continues)
CASPAR ET AL.3691
TABLE 1 (Continued)
Species Group
Body mass
(MBd) (kg)
Lower 25%
PPE (kg)
Upper 25%
PPE (kg)
FC
(mm)
HC
(mm)
MBd
specimen
MBd source for FC
&HC
Brain mass
(MBr) (g)
MBr/
BrEV
BrEV
(mL/
cm
3
)
EV
specimen EV method Original EV source
Diplodocus sp. Non-theropod
dinosaur
14.813.081 11.016.488 18.609.673 563 460 USNM 10865 Benson et al. (2017),
#86
31 0.31 100 CM 11161 CT L. M. Witmer, pers. comm.
(2023)
Edmontosaurus
annectens
Non-theropod
dinosaur
6.610.079 4.915.916 8.304.243 512.3 250.5 AMNH 5730 Benson et al. (2017),
#757
126 0.42 300 YPM 618 GDI (Jerison, 1973)
Lull & Wright (1942)
Edmontosaurus
annectens
Non-theropod
dinosaur
6.610.079 4.915.916 8.304.243 512.3 250.5 AMNH 5730 Benson et al. (2017),
#757
93 0.31 300 YPM 618 GDI (Jerison, 1973)
Lull & Wright (1942)
Euoplocephalus tutus Non-theropod
dinosaur
2.329.632 1.732.548 2.926.717 278 244 AMNH 5404 Benson et al. (2017),
#766
34.73 0.42 82.7 AMNH 5337 GDI (this study) Hopson (1979)
Euoplocephalus tutus Non-theropod
dinosaur
2.329.632 1.732.548 2.926.717 278 244 AMNH 5404 Benson et al. (2017),
#766
25.64 0.31 82.7 AMNH 5337 GDI (this study) Hopson (1979)
Giraffatitan brancai Non-theropod
dinosaur
34.003.143 25.288.137 42.718.148 730 654 HMN SII Benson et al. (2017),
#107
130.2 0.42 310 MB.R.2223.1 Plasticine cast Janensch (1935-1936)
Giraffatitan brancai Non-theropod
dinosaur
34.003.143 25.288.137 42.718.148 730 654 HMN SII Benson et al. (2017),
#107
96.1 0.31 310 MB.R.2223.1 Plasticine cast Janensch (1935-1936)
Hypacrosaurus
altispinus
Non-theropod
dinosaur
3.689.151 2.743.622 4.634.681 395 222 CMN 8501 Benson et al. (2017),
#800
85.53 0.31 275.9 ROM 702 CT Evans et al. (2009)
Hypacrosaurus
altispinus
Non-theropod
dinosaur
3.689.151 2.743.622 4.634.681 395 222 CMN 8501 Benson et al. (2017),
#800
115.88 0.42 275.9 ROM 702 CT Evans et al. (2009)
Iguanodon
bernissartensis
Non-theropod
dinosaur
8.268.265 6.149.108 10.387.421 490 337.5 RBINS R51 Benson et al. (2017),
#805
149.94 0.42 357 RBINS R51 CT Lauters et al. (2012)
Iguanodon
bernissartensis
Non-theropod
dinosaur
8.268.265 6.149.108 10.387.421 490 337.5 RBINS R51 Benson et al. (2017),
#805
110.67 0.31 357 RBINS R51 CT Lauters et al. (2012)
Kentrosaurus
aethiopicus
Non-theropod
dinosaur
1.596.86 1.187.585 2.006.136 245 210 HMN
composite
specimen
Benson et al. (2017),
#813
22.092 0.42 52.6 HMN Ki 124 GDI (this study) Galton (1988)
Kentrosaurus
aethiopicus
Non-theropod
dinosaur
1.596.86 1.187.585 2.006.136 245 210 HMN
composite
specimen
Benson et al. (2017),
#813
16.306 0.31 52.6 HMN Ki 124 GDI (this study) Galton (1988)
Protoceratops
andrewsi
Non-theropod
dinosaur
82.695 61.5 103.889 93 62 AMNH 6424 Benson et al. (2017),
#892
12.6 0.42 30 AMNH 6466 GDI Brown & Schlaikjer (1940)
Protoceratops
andrewsi
Non-theropod
dinosaur
82.695 61.5 103.889 93 62 AMNH 6424 Benson et al. (2017),
#892
9.3 0.31 30 AMNH 6466 GDI Brown & Schlaikjer (1940)
Psittacosaurus
lujiatunensis
Non-theropod
dinosaur
28.61 21.278 35.944 74.5 NA AMNH 6541 Benson et al. (2017),
#898
6.006 0.42 14.3 PKUP V1060 CT Zhou et al. (2007)
Psittacosaurus
lujiatunensis
Non-theropod
dinosaur
28.61 21.278 35.944 74.5 NA AMNH 6541 Benson et al. (2017),
#898
4.433 0.31 14.3 PKUP V1060 CT Zhou et al. (2007)
3692 CASPAR ET AL.
TABLE 1 (Continued)
Species Group
Body mass
(MBd) (kg)
Lower 25%
PPE (kg)
Upper 25%
PPE (kg)
FC
(mm)
HC
(mm)
MBd
specimen
MBd source for FC
&HC
Brain mass
(MBr) (g)
MBr/
BrEV
BrEV
(mL/
cm
3
)
EV
specimen EV method Original EV source
Stegosaurus
ungulatus
Non-theropod
dinosaur
6.953.916 5.171.627 8.736.205 425 352 YPM 1853 Benson et al. (2017),
#927
26.964 0.42 64.2 CM 106 GDI (this study) Galton (2001)
Stegosaurus
ungulatus
Non-theropod
dinosaur
6.953.916 5.171.627 8.736.205 425 352 YPM 1853 Benson et al. (2017),
#927
19.902 0.31 64.2 CM 106 GDI (this study) Galton (2001)
Thescelosaurus
neglectus
Non-theropod
dinosaur
338.505 251.746 425.263 183 NA AMNH 5891 Benson et al. (2017),
#946
11.614 0.42 27.653 NCSM 15728 CT Button and Zanno (2023)
Thescelosaurus
neglectus
Non-theropod
dinosaur
338.505 251.746 425.263 183 NA AMNH 5891 Benson et al. (2017),
#946
8.572 0.31 27.653 NCSM 15728 CT Button and Zanno (2023)
Triceratops sp. Non-theropod
dinosaur
13.274.61 9.872.328 16.676.893 493 490 AMNH 5033 Benson et al. (2017),
#950
96.075 0.42 228.75 MOR 1194 CT Morhardt (2016), L. M.
Witmer, pers. comm.
(2024)
Triceratops sp. Non-theropod
dinosaur
13.274.61 9.872.328 16.676.893 493 490 AMNH 5033 Benson et al. (2017),
#950
70.913 0.31 228.75 MOR 1194 CT Morhardt (2016), L. M.
Witmer, pers. comm.
(2024)
Note: For maniraptoriform theropods, we assumed that brain endocast volume equals brain volume. For other dinosaurs, we assumed a brain:endocast ratio of 31%–42%. Body mass was calculated based on stylopodial circumference (femoral
circumference (FC) for bipedal and femoral as well as humeral (HC) circumference for quadrupedal species). [1] Bambiraptor feinbergi. MBd from femur circumference, measured on a cast of the right femur of an adult specimen now in the collection
of the Vertebrate Paleontology Division, ROM (FIP 002). Original elements of this specimen are now in the collection of the AMNH. [2] Archaeopteryx lithographica. MBd from FC (14.93 mm) estimated from femur length (60.5 mm) of BMNH 37001
(Gatesy, 1991). FC was calculated using the equation from Benson et al. (2017): log
10
(Femur circumference estimate from femur length) =1.132 log
10
(Femur length) 0.8429. [3] Carcharodontosaurus saharicus. MBd from estimated femur
circumference (FC =417.52 mm) from Benson et al. (2017) for BSP 1922 X46. The specimen has been destroyed and is no longer accessible. [4] Sinraptor dongi. MBd based on FC (283 mm) from Campione and Evans (2020), measurement taken from
TMP 93.115.1 (cast of IVPP 10600). Benson et al. (2017) consider IVPP 10600 a subadult based on reported incomplete fusion of cervical vertebrae; However, Paulina-Carabajal and Currie (2012) noted that the degree of cranial suture fusion indicates
that the specimen is an adult or at least a large subadult. We consider it an adult here. [5] Tyrannosaurus rex specimens. MBd based on FC values listed by Persons IV et al. (2020). For FMNH PR 2081 (“Sue”), but not the other individuals considered
here, both FC and EV are available. We associated the EV (313.636 mL) of AMNH 5117 with the MBd (5515 kg) of BHI 3033 (“Stan”), as both specimens have been considered proxies for each other (G. M. Erickson, pers comm. to G.R. Hurlburt,
2005). The EV (381.8 mL) of AMNH 5029 is here linked to the MBd (6430 kg) of CM 9380 (holotype specimen) because it fell between MBd's associated with EVs of FMNH PR 3081 and AMNH 5117. Apart from our brain:endocast ratios, we also
apply the 57% ratio proposed by Morhardt (2016) to this species.
CASPAR ET AL.3693
encompasses the pallium (which is homologous to the
cerebral cortex in humans and other mammals) and sub-
pallium as well as the olfactory bulbs and tracts
(Figure 1). To understand the rationale behind Hercu-
lano-Houzel's (2023) approach of reconstructing neuron
counts in fossil species, two important matters must be
pointed out: first, among jawed vertebrates, body size and
brain size are highly correlated, exhibiting a constant
allometric relationship overall (Tsuboi et al., 2018). It
should be noted however, that scaling relationships can
vary to some extent between major taxa as well as
between early- and late-diverging members of a clade
(Bertrand et al., 2022; Ksepka et al., 2020). Second, neuro-
nal densities (the number of neurons in a given volume
of nervous tissue) can differ profoundly between different
vertebrate taxa. Based on current evidence, the highest
neuron densities among land vertebrates are found in the
bird clade Telluraves, consisting of birds of prey, rollers,
parrots, songbirds and kin, while the lowest occur among
crocodilians and turtles (Kverkov
a et al., 2022). For
instance, the goldcrest (Regulus regulus), short-tailed
shrew (Blarina sp.) and painted turtle (Chrysemys picta)
have brains of equal mass (ca. 0.37 g), but there is
remarkable disparity in their whole brain neuron num-
bers, which range from 14.3 M in the turtle over 58.8 M
in the shrew to 164 M in the passerine bird (Olkowicz
et al., 2016; Kverkov
a et al., 2022). This example illus-
trates that brain size alone is not a reliable predictor of
neuron counts across distantly related clades (compare
Herculano-Houzel et al., 2014; Olkowicz et al., 2016),
which makes their inference in fossil groups inherently
difficult.
To decide which neuronal density patterns apply to spe-
cific groups of dinosaurs and pterosaurs, Herculano-Houzel
(2023) relied on brain x body mass regressions. The brain
and body mass datasets used were taken from various liter-
ature sources and, as we attempt to show here, both are
problematic. In the resulting regression plot, she identified
theropods clustering distinctly from most other included
fossil species. On average, they appeared to exhibit larger
brains for a given body size than the remaining dinosaur or
pterosaur taxa. When comparing the regression lines for
extinct groups with those of living birds on the one hand
and non-avian sauropsids on the other, Herculano-Houzel
(2023) noted that the theropod regression fit with the avian
one, while the remaining ornithodiran groups aligned more
with the non-avian sauropsid regression line.
Based on these analyses, two critical assumptions were
made: first, since only theropod brain–body data aligned
with those of endothermic extant sauropsids, namely,
birds, the other groups (aside from specific pterosaurs and
ornithischians that cluster with theropods) should be con-
sidered ectothermic. Second, telencephalic neuron densities
in theropod brain tissue should have been comparable to
those found in certain extant bird taxa (i.e., to those found
in a polyphyletic assemblage denoted as “pre-K-Pg birds”
that includes Palaeognathae, Galloanserae and Columbi-
formes and which is considered to form a neurological
grade - Kverkov
a et al., 2022) with similar relative brain
sizes, whereas those of the other groups should have
resembled densities encountered in ectothermic crocodil-
ians, squamates and turtles. No further justification for
these suggestions was provided.
Applying the avian scaling regime, Herculano-Houzel
(2023) estimated remarkably high telencephalic neuron
counts in large-bodied theropods such as Acrocanthosaurus
atokensis (2.1 billion) and T. rex (3.3 billion) which would
exceed those of any extant bird and be comparable to large-
bodied Old World monkeys such as baboons (Papio anubis
-Olkowiczetal.,2016). Based on this apparent similarity to
anthropoid primates, she further speculated that these giant
theropods would have crafted and used tools and exhibited
cultural behaviors (Herculano-Houzel, 2023).
We regard the methodology of Herculano-Houzel
(2023) as problematic and disagree with her physiological
and behavioral interpretations. Before we attempt to repli-
cate her findings with a more refined analytical approach,
we want to enumerate the most important flaws of the
article and how they affect the inferences made.
1.3 |Issues with Herculano-Houzel's
method and rationale
A key problem for paleoneurology lies in the fact that an
endocast does not necessarily reflect the morphology of
an animal's brain. As discussed in previous sections, the
endocasts of most non-avian dinosaurs differ markedly
in size and shape from the actual brain, as is the case
in crocodilians (Figure 1). Unfortunately, not all studies
from which Herculano-Houzel (2023) derived her raw
data considered this issue (see below). In addition, the
percentage of endocranial space filled by the brain, as
well as its proportions may be further influenced by
ontogeny (Bever et al., 2013; Hu et al., 2021; Hurlburt
et al., 2013; Jirak & Janacek, 2017; Ngwenya
et al., 2013). The latter point is relevant because
Herculano-Houzel (2023) included several specimens
which corresponded to juveniles rather than adults, and
thus might have introduced biases to the dataset. Inter-
estingly, at least in crocodilians, neuronal densities in
the brain are also affected by ontogenetic stage
(Ngwenya et al., 2016). To arrive at the estimated telen-
cephalic neuron count of >3 billion for T. rex,
Herculano-Houzel (2023) assumed a brain mass of 343 g.
However, this presupposes that endocast volume equaled
brain volume in this species. While it has indeed been
claimed that the brain filled the entire endocranial
3694 CASPAR ET AL.
cavity in theropods such as T. rex (Balanoff et al., 2013),
this hypothesis is, as previously discussed, contradicted
by multiple lines of evidence. More conservative infer-
ences suggest a brain mass of approximately 200 g
(Hurlburt, 1996; Hurlburt et al., 2013; Morhardt, 2016)
or possibly even lower (this study; Table 1) for T. rex.
Herculano-Houzel (2023) acknowledged these lower esti-
mates but chose to rely on the inflated values for large
theropod brain masses in accompanying figures and in
the Discussion section of her article.
Moreover, while the literature-derived brain mass
estimates used for the analyses did in some cases include
the olfactory tracts and bulbs (Balanoff et al., 2013;
Franzosa & Rowe, 2005), these structures were not con-
sidered in others (Hurlburt, 1996; Hurlburt et al., 2013).
This incongruence creates critical biases, affecting both
the inference of telencephalic neuron counts and relative
brain size estimates. The latter are additionally skewed
by the fact that body masses used by Herculano-Houzel
(2023) were not determined via a uniform methodology
but compiled from sources applying various approaches.
There are several ways to estimate body mass in extinct
animals and they can differ greatly regarding their out-
comes and precision (Campione & Evans, 2020). When
compared to body mass estimates derived from stylopo-
dial circumference, a well-established and robust method
(Campione & Evans, 2020), some striking differences
become apparent (Table 1; Herculano-Houzel, 2023).
Another flaw of Herculano-Houzel's (2023)approachis
the neglect of brain morphology to inform its analyses. To
estimate telencephalic neuron numbers in fossil species,
the mass of the telencephalon needs to be approximated
first. For theropods, Herculano-Houzel (2023:6) extrapo-
lated this variable from extant bird data while stating that
“within a clade, brain mass has strongly predictive power
to arrive at estimates of numbers of telencephalic neurons
in a brain of known mass, once the neuronal scaling rules
that presumably apply are known.”However, this state-
ment can only hold true if the general proportions of the
telencephalon compared to the remaining brain are roughly
constant in the group of concern, which is a precondition
that Herculano-Houzel (2023) did not test for in the fossil
sample. Indeed, avian brains only poorly reflect the brain
morphologies found in the majority of Mesozoic dinosaurs
(reviewed by Paulina-Carabajal et al., 2023)andtheirgen-
eral proportions are only comparable to those found among
maniraptoriform theropods (Balanoff et al., 2013; Figure 1).
An important difference concerns the pallium, which cru-
cially contributes to higher cognitive functions, and greatly
increased in size within the maniraptoriform radiation
(Balanoff et al., 2013). The same is true for the cerebellum,
a part of the brain which is not encompassed by the telen-
cephalon but is also involved in various aspects of cognition
in amniotes (Spence et al., 2009). Thus, the telencephalic
mass and proportions of non-maniraptoriform theropods,
such as T. rex, cannot be adequately modeled based on
extant birds. Similar limitations need to be considered
when reconstructing traits of, for instance, the pterosaur or
sauropodomorph telencephalon based on extant non-avian
sauropsids and they also apply to our own empirical
approach.
Herculano-Houzel (2023) hypothesized that the
inferred incongruence in relative brain size between the-
ropods and other dinosaurs reflects differences in thermo-
biology, which would justify applying avian neuronal
scaling schemes to the former and non-avian sauropsid
scaling to the latter. Sauropodomorphs as well as selected
ornithischians and pterosaurs are instead suggested to be
ectothermic due to their relatively smaller brains. Both of
these assumptions are problematic: First, multiple lines of
evidence suggest that ornithodiran endothermy evolved
long before theropods emerged and was likely already pre-
sent in the Early Triassic common ancestor of dinosaurs
and pterosaurs (e.g., Benton, 2021;Griggetal.,2022). We
will revisit this evidence and how it challenges the afore-
mentioned hypothesis in more detail in the Discussion
section of this paper. Herculano-Houzel (2023) only refer-
enced a single article on dinosaur thermobiology, that of
Wiemann et al. (2022), to defend her standpoint on the
matter. The study in question applies a promising but novel
technique to infer endothermy based on lipoxidation end
products in fossil bone that still has to prove itself. While it
indeed suggests lowered metabolic rates in some ornithis-
chians, it also infers an endothermic metabolism for ptero-
saurs and sauropodomorphs (Wiemann et al., 2022). Thus,
its findings do not align with Herculano-Houzel's (2023)
assumptions.
Second, comparisons between groups of extant verte-
brates, especially birds and mammals, strongly suggest
that there is no uniform relationship between neuron
density and relative brain size or elevated metabolic rates
(Kverkov
a et al., 2022; see also Estienne et al., 2024). We
will elaborate on this aspect in the Discussion section but
would like to state at this point already that it is not
straight-forward to assume avian neuron densities in
Mesozoic theropods simply because they exhibited endo-
thermy or a potential increase in relative brain size. On
the other hand, the extensive evidence for endothermy in
other dinosaurs and pterosaurs does not entail that these
groups could not have had neuron densities similar to
those found in extant ectothermic sauropsids.
Other issues relate to the statistical methods
employed by Herculano-Houzel (2023). Despite dealing
with a large multi-species dataset, the analyses did not
take phylogeny into account, which can produce mathe-
matical artifacts. Phylogenetic relationships among taxa
CASPAR ET AL.3695
need to be statistically addressed because shared ancestry
can result in non-independence of species-specific data
points (Revell et al., 2008). Such non-independence is
known as the phylogenetic signal, and it has been promi-
nently recovered for relative brain size in extant saurop-
sids (Font et al., 2019). Hence, phylogenetically-informed
modeling is necessary for adequately analyzing such
datasets (Font et al., 2019).
In light of these substantial shortcomings, we attempt
to replicate the findings of Herculano-Houzel (2023) with
phylogenetically informed models of telencephalic neu-
ron counts in fossil dinosaurs that acknowledge the
issues lined out above. Different from her, we do not
include pterosaurs into our analysis due to difficulties
with estimating their body mass (especially for taxa with
incomplete postcrania such as Scaphognathus) and
because of the unclear taxonomic and ontogenetic status
of some of the few available endocasts.
1.4 |Empirical part: Modeling
neurological variables in dinosaurs
1.4.1 | Endocast sample composition, with
notes on endocranial volumes provided by
Hurlburt (1996)
We estimated the mass of the brain (MBr, g; excluding the
olfactory tracts and bulbs) as well as its size relative to
body mass (MBd, g) in 31 Mesozoic dinosaur taxa for
which data on endocranial volume (EV, mL) have been
published (Table 1;File S1). Note that this study does not
aim to provide a comprehensive dataset of dinosaur brain
sizes. Given the questions we want to address, we focus on
large-bodied theropods and taxa covered in previous com-
parative analyses. We included one endocast per species,
except for T. rex, for which three adult endocasts (AMNH
5029, AMNH 5117, FMNH PR 2081) were considered. We
only included species for which we could calculate body
mass based on stylopodial circumference (see below) to
reliably infer encephalization. Due to this, our analysis
does not cover all dinosaur species for which complete
endocasts are available, nor all species that Herculano-
Houzel (2023) included in her analyses (namely, Conchor-
aptor gracilis,Tsaagan mangas,Zanabazar junior, and the
unnamed troodontid IGM 100/1126). Juvenile specimens
considered by that study (Alioramus altai IGM 100/1844,
Gorgosaurus libratus ROM 1247, and Tyrannosaurus rex
CMN 7541 =“Nanotyrannus lancensis”) were omitted in
this analysis to eliminate the confounding variable of
ontogeny.
The only juvenile we include is Bambiraptor feinbergi
KUVP 129737, which is one of the few maniraptoriform
theropods that we can take into account. For this species,
an adult femur (FIP 002) is available, allowing us to esti-
mate body mass in fully grown individuals. Our method
of body mass inference suggests that KUVP 129737 had
attained about 45% of adult body mass when it died. Data
from similar-sized extant rheas (Rhea americana),
palaeognath birds which are close neuroanatomical ana-
logs to highly derived theropods such as Bambiraptor
(Balanoff et al., 2013), suggest that adult brain mass is
already approached at that point of somatic development
(Picasso, 2012; Picasso et al., 2011). We therefore com-
bine the juvenile endocranial measurement of Bambirap-
tor with adult body mass estimates.
Just as Herculano-Houzel (2023) did, we derive a sig-
nificant portion of our EV values from Hurlburt (1996).
However, many EV figures communicated in this refer-
ence must be considered outdated or otherwise flawed
and were carefully bypassed here. We give detailed rea-
sons for discarding or modifying data from Hurlburt
(1996) and the references provided therein (Hopson, 1979;
Jerison, 1973)inFile S1 Part A. Given that EVs from this
problematic dataset are still widely used (e.g., Button &
Zanno, 2023;Mülleretal.,2021), we consider their revi-
sion an important aspect of this study. In cases where EVs
appeared doubtful but appropriate illustrations or photo-
graphs of specimens were available, we recalculated EV
using manual graphic double integration (GDI; see below
for methodology). This was done for four species (Allosau-
rus fragilis, Euoplocephalus tutus,Kentrosaurus aethiopicus
and Ornithomimus edmontonicus;seeFile S1 for details on
specimens).
1.5 |Brain mass estimates
We estimated the brain mass (MBr, g) of fossil dinosaurs
from endocast volume (EV, mL). Because the specific
gravity (density) of living amniote brain tissue approxi-
mates one (1.036 g/mL—Iwaniuk & Nelson, 2002), we
used brain volume and mass interchangeably (compare
Herculano-Houzel, 2023; Hurlburt et al., 2013). For man-
iraptoriform species, because their endocasts preserve
brain contours similar to those of avians, we assumed a
brain:endocast ratio of 100%. This is consistent with
empirical data on the relationship between MBr and EV
in modern birds, which suggest contributions of menin-
geal tissue to endocast volume to be largely negligible
(Iwaniuk & Nelson, 2002; but note that there is some var-
iation in endocranial fill among extant birds, see e.g.,
Knoll et al., 2024). For other dinosaurs, we assumed MBr:
EV ratios of 31% and 42%. Many previous studies have
assumed a 50% ratio in these groups (reviewed in
Morhardt, 2016), while some even assumed 100%
3696 CASPAR ET AL.
(Balanoff et al., 2013) or advocated for intermediate
values (e.g., Evans et al., 2009; Knoll & Schwarz-
Wings, 2009; Knoll et al., 2021). The widely adopted 50%
ratio was originally proposed by Jerison (1973) and based
on measurements from a likely subadult green iguana
(Iguana iguana) and a mere visual estimate of endocra-
nial filling in the tuatara (Sphenodon punctatus; the
endocranial fill in this species is now known to be 30% in
adults—Roese-Miron et al., 2024). We abandon the prob-
lematic 50% estimate and replace it here by the two afore-
mentioned ratios that are based on the morphology of
extant crocodilians, the closest extant analogs to most
non-avian dinosaurs in regards to endocranial tissue
organization, body size and braincase ossification.
Excluding one anomalous value, the lowest MBr:EV ratio
among the three longest American alligators (Alligator
mississippiensis) studied by Hurlburt et al. (2013) was
found to be 31% (n
total
=12, note that this figure excludes
the olfactory bulb and tract portions of the endocranial
cavity). This is consistent with observations on the largest
Nile crocodile (Crocodylus niloticus; a 16-year-old female)
studied by Jirak and Janacek (2017) when excluding the
olfactory tracts and bulb portion of the endocast.
The 42% ratio is derived solely from American alligators.
An endocranial fill of 42% was found in an adult female
with a total length of 2.87 m, which roughly approxi-
mates both (a) the maximum length for a female Ameri-
can alligator and (b) the midpoint length within the size
spectrum of sexually mature alligators of this species
(Hurlburt et al., 2013; Hurlburt & Waldorf, 2002;
Woodward et al., 1991).
The majority of EV data for Mesozoic dinosaurs were
taken and modified from the literature (detailed out in
Table 1). In many cases, original sources communicated
measurements that correspond to total EV. This is the
volume of the entire endocast, often including the region
of the olfactory tract and bulbs as well as portions of the
cranial spinal cord, among other structures. For our anal-
ysis, we exclusively relied on the so-called “brain”endo-
cast volume instead (BrEV; Figure 3), which was
popularized by Jerison (1973) and has been commonly
used since then (e.g., Hurlburt, 1996; Hurlburt
et al., 2013; Larsson et al., 2000; Paulina-Carabajal &
Canale, 2010). It excludes the spinal cord portion of the
endocast caudal to cranial nerve XII, the volume of nerve
trunks from infillings of respective foramina and blood
vessel casts, the labyrinth of the inner ear, the infundibu-
lum, the pituitary fossa, and especially the volume of the
olfactory bulbs and tracts (Figure 4). The latter are often
only poorly preserved in fossil endocasts, so that relying
on specimens with intact casts of olfactory structures
would have reduced our sample size.
In some dinosaurs, there is an obvious constriction
and/or a change in surface morphology at the junction of
the cerebrum and olfactory tract (e.g., Euoplocephalus
tutus AMNH 5337 —Hopson, 1979;Stegosaurus ungula-
tus CM 106 —Galton, 2001;Diplodocus longus CM 11161
—Witmer et al., 2008). If present, this was used as a
landmark to delineate these brain regions from one
another. In less obvious cases, the junction between the
cerebrum and olfactory tract portion was assumed to be
where the ventral curve of the rostral cerebrum flattens
out to approach a horizontal orientation. When selecting
the boundary, we erred toward a more rostral location, to
assign as much of the endocast as part of the BrEV as
seemed reasonable. In American alligators, the rostral
termination of the cerebrum within the rostral subarach-
noid space is clearly visible (Figure 2c; SaSR) and consis-
tent with the change in curvature referred to above.
We used manual GDI to extract BrEV from total EV
(relevant details for each specimen are provided in File
S1 Part B). The method involves drawing an outline
around two scaled orthogonal two-dimensional views of
an endocast, and adding equally spaced lines perpendicu-
lar to the endocast midline (Figure 3). The mean length
(cm) of these lines in each view (i.e., dorsal, lateral) pro-
vides diameters D1 and D2. The volume (mL) of the
desired region is calculated using these two diameters
FIGURE 3 Exemplary graphic double integration (GDI) of the
endocast of Tyrannosaurus rex AMNH 5029. Equally spaced lines
are drawn across the right lateral and dorsal views respectively.
Mean lengths of the lines drawn across the “brain”portion (BrEV)
were 4.8 and 6.6 cm for dorsal and lateral views respectively.
BrEV =π0.25 4.8 6.6 16.2 =404 mL (the volume of the
entire endocast was 536 mL). “Bulb”: Olfactory tract and bulbs;
“Cord”: spinal cord; V: Trigeminal nerve with its three branches
(V1, V2,& V3); X: vagus nerve; XII: hypoglossal nerve. (Adapted
from Fig. 2.7 in Jerison,1973, p. 51).
CASPAR ET AL.3697
and the length (L, cm) in the formula for the volume
(mL or cm
3
) of a cylinder where (all lengths in cm):
Volume mL½¼πðÞ0:25ðÞD1ðÞD2ðÞLðÞ
GDI has been demonstrated to produce reason-
able estimates of endocast volumes (Figure 3). For
instance, Jerison (1973)usedGDItocalculatea
total endocast volume of 536 mL for a T. rex speci-
men (AMNH 5029), which was 101.13% of the
530 mL volume determined for it by means of water
displacement (Osborn, 1912;Figure3). For the same
specimen, Jerison (1973) calculated a 404 mL vol-
ume for the “brain region”of the endocast (extend-
ing from cranial nerve XII to the rostral cerebral
limit), which was 106.04% of the 381 mL obtained
by CT volumetry (Hurlburt et al., 2013).
1.6 |Body mass estimates
We calculated the body mass (MBd, g) of the selected
dinosaur taxa (and its mean absolute percent predic-
tion error—PPE or %PE —Campione & Evans, 2020)
in a standardized manner based on the minimum
femoral circumference (as well as humeral circumfer-
ence in case of quadrupedal taxa) by aid of the QE()
and cQE() functions from the MASSTIMATE package
(Campione, 2020)inR(RCoreTeam,2023). Data on
relevant long bone dimensions were primarily
obtained from Benson et al. (2017). Corresponding
specimens as well as additional sources and informa-
tion on stylopodium circumference measurements are
listed in Table 1. To the best of our knowledge, all
data correspond to adult specimens.
1.7 |Phylogenetic modeling of
neurological variables
We used data from extant sauropsids to place brain size var-
iables for Mesozoic dinosaurs into their phylogenetic con-
text. To examine variations in the relative size of the brain
in fossil taxa and to calculate potential neuronal scaling
regimes in extinct dinosaurs, we relied on log-transformed
published data on brain mass, telencephalon mass and tel-
encephalic neuronal numbers in extant groups (see below).
Allometric equations were calculated with least squares lin-
ear regressions using phylogenetic generalized least squares
(PGLS) to account for phylogenetic relatedness (Garland
Jr & Ives, 2000). PGLS allows the covariance matrix to be
modified to accommodate the degree to which trait evolu-
tion deviates from Brownian motion, through a measure of
phylogenetic correlation, Pagel's λ(Pagel, 1999). PGLS and
maximum likelihood estimates of λwere performed using
the ape (Paradis & Schliep, 2019)andnlme (Pinheiro
et al., 2017) packages in R.
To compare differences in relative brain size across
groups, phylogenetically corrected ANCOVA with Tukey
post hoc comparisons were performed using a modified
version of the multcomp package (Hothorn et al., 2015;
modification allowed outputs of the nlme package (gls
objects) to be processed). Because of the uncertainty in
estimating both brain mass and body mass in Mesozoic
dinosaurs, we opted to test for inter-group differences in
two datasets: One with the greatest possible relative brain
size, that is, the lowest body mass estimate (lower PPE) for
each species and the brain mass estimated from the high-
est assumed endocranial fill (42%), and one with
the lowest relative brain size, that is, the highest body
mass estimates (upper PPE) and the lowest assumed endo-
cranial fill (31%). Since we assumed that the brain filled
100% of the endocranial cavity in maniraptoriform thero-
pods, inferred relative brain mass for these species was
only affected by differences in the applied body mass esti-
mates. As mentioned above, an important assumption of
Herculano-Houzel (2023) is that theropods in general had
relative brain sizes similar to extant birds. However, there
is notable discontinuity in relative brain size and brain
morphology between maniraptoriforms and more basal
non-maniraptoriform theropods (Figure 1, Balanoff
et al., 2013). Because of this, we divided our sample of
Mesozoic theropods into these two groups (Tables 1
and 2). For T. rex, mean values for the three available adult
brain mass and corresponding body mass estimates were
used. We grouped Sauropodomorpha and Ornithischia
together as non-theropod dinosaurs and compared relative
brain size in this group with that in the two theropod sam-
ples. PGLS models of brain mass versus body mass with
clade as a covariate were used to test if relative brain size
in these groups differs significantly between them and
from extant birds and/or non-avian sauropsids. Relative
brain size data for 63 extant non-avian sauropsids (includ-
ing lepidosaurs, crocodilians, and turtles) and 84 bird spe-
cies (not including members of the large-brained clade
Telluraves) were derived from Hurlburt (1996), Chentanez
et al. (1983) and Roese-Miron et al. (2024) and are listed in
File S2. Importantly, these sources provide brain mass esti-
mates excluding the olfactory tracts and bulbs and thus fit
our brain size estimates for Mesozoic dinosaurs. To test for
differences in relative brain size we built a phylogenetic
tree for all 175 fossil and extant species. To construct the
phylogeny of bird species, we extracted 1000 fully resolved
trees from birdtree.org (Jetz et al., 2012)usingtheHackett
et al. (2008) backbone, and built a maximum clade credi-
bility (MCC) tree using phangorn (Schliep et al., 2019). For
3698 CASPAR ET AL.
TABLE 2 Estimates of telencephalic neuron counts (N; excluding the olfactory system) in Mesozoic dinosaurs.
Species Group
MBr
range (g)
N(non‐
avian_min)
N(non‐
avian_max)
N
(avian_min)
N
(avian_max)
MBr
(g), HH
N(non‐
avian), HH
N
(avian), HH
Archaeopteryx lithographica Non‐avian maniraptoriform 1.52 16.2 M 16.2 M 56.5 M 56.5 M 1.47–
1.76
15.8 M–17.7
M
54.2 M–62.1
M
Bambiraptor feinbergi Non‐avian maniraptoriform 14.00 69.2 M 69.2 M 277.7 M 277.7 M 14 62.9 M 295.8 M
Citipati osmolskae Non‐avian maniraptoriform 22.05 93.2 M 93.2 M 384.6 M 384.6 M 22.62 84.4 M 424.5 M
Khaan mckennai Non‐avian maniraptoriform 8.80 51.1 M 51.1 M 199.1 M 199.1 M 8.83 47.4 M 209.1 M
Ornithomimus
edmontonicus
Non‐avian maniraptoriform 49.89 159.1 M 159.1 M 690.7 M 690.7 M 87.85 193.6 M 1179 M
Shuvuuia deserti Non‐avian maniraptoriform 1.52 16.1 M 16.1 M 56.5 M 56.5 M 0.83 11.2 M 35.2 M
Stenonychosaurus inequalis Non‐avian maniraptoriform 38.65 134.1 M 134.1 M 575.2 M 575.2 M 41 121.4 M 664.4 M
Acrocanthosaurus atokensis Non‐maniraptoriform theropod 38.05–51.55 133.2 M 162.5 M 568.8 M 707.2 M 191 311.3 M 2116 M
Allosaurus fragilis Non‐maniraptoriform theropod 30.54–41.37 115.3 M 140.7 M 485.8 M 603.9 M 168 287.8 M 1921 M
Carcharodontosaurus
saharicus
Non‐maniraptoriform theropod 69.44–94.08 197.5 M 241 M 875.6 M 1088 M
Carnotaurus sastrei Non‐maniraptoriform theropod 33.57–45.49 122.7 M 149.7 M 519.9 M 646.5 M
Giganotosaurus carolinii Non‐maniraptoriform theropod 69.75–94.50 198.1 M 241.7 M 878.4 M 1092 M
Majungasaurus
crenatissimus
Non‐maniraptoriform theropod 27.69–37.51 108.2 M 131.9 M 452.9 M 563.1 M
Sinraptor dongi Non‐maniraptoriform theropod 29.45–39.90 112.6 M 137.4 M 473.3 M 588.5 M
Tarbosaurus bataar Non‐maniraptoriform theropod 49.35–66.86 157.9 M 192.7 M 685.4 M 852 1 M
Tyrannosaurus rex AMNH
5029
Non‐maniraptoriform theropod 118.35–160.34 280.1 M 341.7 M 1283 M 1595 M 343 445.5 M 3289 M
Tyrannosaurus rex AMNH
FR 5117 (Morhardt, 2016)
Non‐maniraptoriform theropod 178.37 365 M 365 M 1722.1 M 1722.1 M
Tyrannosaurus rex AMNH
FR 5117
Non‐maniraptoriform theropod 97.23–131.73 246.2 M 300.4 M 1114 M 1385 M
Tyrannosaurus rex FMNH
PR 2081
Non‐maniraptoriform theropod 128.40–173.96 295.4 M 360.5 M 1360 M 1691 M 202 322.2 M 2207 M
Amargasaurus cazaui Non‐theropod dinosaur 26.04–35.28 103.9 M 126.8 M 433.4 M 538.8 M
Apatosaurus sp. Non‐theropod dinosaur 31.77–43.04 118.4 M 144.4 M 499.8 M 621.4 M
Buriolestes schultzi Non‐theropod dinosaur 0.75–1.02 10.2 M 12.4 M 34.1 M 42.5 M
(Continues)
CASPAR ET AL.3699
TABLE 2 (Continued)
Species Group
MBr
range (g)
N(non‐
avian_min)
N(non‐
avian_max)
N
(avian_min)
N
(avian_max)
MBr
(g), HH
N(non‐
avian), HH
N
(avian), HH
Diplodocus sp. Non‐theropod dinosaur 31.00–42.00 116.4 M 142.1 M 491.1 M 610.6 M 57 148.5 M 851.4 M
Edmontosaurus annectens Non‐theropod dinosaur 93.00–126.00 239.2 M 291.9 M 1079 M 1342 M 150 268.5 M 1764 M
Euoplocephalus tutus Non‐theropod dinosaur 25.64–34.73 102.9 M 125.5 M 428.6 M 532.8 41 121.4 M 664.4 M
Giraffatitan brancai Non‐theropod dinosaur 96.10–130.20 244.4 M 298.2 M 1105 M 1374 M 186 306.3 M 2075 M
Hypacrosaurus altispinus Non‐theropod dinosaur 85.53–115.88 226.4 M 276.2 M 1016 M 1264 M
Iguanodon bernissartensis Non‐theropod dinosaur 110.67–149.94 268.1 M 327.1 M 1223 M 1520 M 125 240.2 M 1538 M
Kentrosaurus aethiopicus Non‐theropod dinosaur 16.31–22.09 76.5 M 93.3 M 309.8 M 385.2 M 24 87.5 M 443.9 M
Protoceratops andrewsi Non‐theropod dinosaur 9.30–12.60 52.9 M 64.5 M 207.1 M 257.5 M 28 96.1 M 498.5 M
Psittacosaurus lujiatunensis Non‐theropod dinosaur 4.43–6.01 32.5M 39.6 M 121.8 M 151.4 M
Stegosaurus ungulatus Non‐theropod dinosaur 19.90–26.96 87.1 M 106.3 M 357.4 M 444.3 M 22.5 84.1 M 422.8 M
Thescelosaurus neglectus Non‐theropod dinosaur 8.57–11.61 49.9 M 60.9 M 195.4 M 242.9 M
Triceratops sp. Non‐theropod dinosaur 70.91–96.08 199.5 M 243.4 M 888.9 M 1105.1 M 72.2 171.7 M 1017M
Note: Our inferences are compared with those presented by Herculano‐Houzel (2023; HH) if respected species were included in both studies (see text for the rationale of our sample composition). Per species,
minimum and maximum estimates based on both avian and non‐avian sauropsid regressions and the inferred range of plausible brain size are provided.
3700 CASPAR ET AL.
lepidosaurs, we followed Kverkov
aetal.(2022) by using a
species level time-calibrated phylogeny (Tonini
et al., 2016) and built a MCC tree the same way as we did
for birds. For phylogenetic information on turtles and
crocodilians, we relied on the Timetree of Life (Kumar
et al., 2017). We then stitched the trees together manually,
using the divergence times from the Timetree of Life. For
Mesozoic dinosaurs (31 species) we used an updated ver-
sion of the composite phylogeny of Benson et al. (2014,
2018). Phylogenies for fossil dinosaurs, extant non-avian
sauropsids, and birds were stitched together manually
using Mesquite (Maddison & Maddison, 2023). We opted
to set all branch lengths to 1. This was done because clade-
specific trees were obtained from various sources applying
different phylogenetic methods, which, together with
issues related to the precise dating of some of the fossils
covered, makes it difficult to have well calibrated branch
lengths. Importantly, simulation studies have found that
independent contrasts and PGLS are robust to errors in
both phylogenetic topology and branch lengths, so that we
do not expect uniform branch lengths to compromise our
analyses (Diaz-Uriarte & Garland, 1998; Martins &
Housworth, 2002; Stone, 2011; Symonds, 2002).
Tree building procedures were the same for telence-
phalic neuron count regressions, but trees used here
included branch lengths. We calculate regression lines
between brain mass and telencephalic number of neurons
for extant non-telluravian birds and non-avian sauropsids.
Analogous to Herculano-Houzel (2023), these regressions
were then used to estimate telencephalic neuron counts in
dinosaurs, applying either an avian or a reptilian scaling
regime. Since our estimates are based on brain portion
endocasts that exclude the olfactory system, our telence-
phalic neuron counts correspond to the pallium and sub-
pallium of fossil species. Data on whole brain and
telencephalic neuron counts as well as on total telence-
phalic and brain mass (including olfactory tract and bulbs,
since neuron count data excluding these structures are
FIGURE 4 Relative brain size and forebrain neuronal numbers in Mesozoic dinosaurs and other amniotes. (a) The log-transformed mass
of the brain is plotted as a function of the mass of the body for extant and fossil sauropsids. In the case of fossil species, the mean body and/or
brain size is shown along with standard deviations. The orange dotted line represents the regression line for avian species (excluding the large-
brained clade Telluraves) obtained from PGLS while the pink one represents the same for extant non-avian sauropsids (“reptiles”in the
colloquial sense). (b) A detail of the plot shown in (a) to illustrate the range of relative brain sizes in Tyrannosaurus rex and other Mesozoic
dinosaurs that we consider plausible. Besides our own brain size estimates, the plot contains those from Morhardt (2016) (specimen AMNH FR
5117, endocranial fill =57%) and Balanoff et al. (2013) (specimen AMNH 5029, endocranial fill =100%, assumed MBd =5840 kg) (c) Plot
showing log-transformed brain mass for different groups of extant amniotes plotted against body mass. (d) Plot showing log-transformed
numbers of telencephalic neurons as a function of the mass of the brain, illustrating neuronal density. Note that non-avian sauropsids and non-
primate mammals differ only moderately from one another here, although mammals have markedly larger brains relative to body size, as
shown in (c). See methods for data sources. Silhouettes were taken from PhyloPic (listed clockwise from top left): Anas (in public domain)
Morunasaurus (in public domain), Dromaeosaurus (by Pranav Iyer), Stegosaurus (by Matt Dempsey), Allosaurus (by Tasman Dixon),
Tyrannosaurus (by Matt Dempsey), Corvus (in public domain), Hylobates (by Kai R. Caspar), Antidorcas (by Sarah Werning).
CASPAR ET AL.3701
currently unavailable for many of the taxa considered
here) for birds (n=112) were derived from Kverkov
a
et al. (2022) and Sol et al. (2022), for non-avian sauropsids
(n=108) from Kverkov
aetal.(
2022)andformammals
(n=39) from Herculano-Houzel et al. (2015). The dataset
is included in File S3.
Togetamorepreciseestimateofthepossiblenumber
of telencephalic neurons in T. rex, we also modeled scaling
regimes for telencephalon mass versus telencephalic neu-
ron numbers in extant sauropsids (non-avian sauropsids
and non-telluravian birds), using the same references listed
above. We then calculated telencephalic neuron numbers
in T. rex using the obtained scaling regimes and applying
the telencephalic volumes estimated with a comparative 3D
landmark approach by Morhardt (2016)(referredtoas
“cerebral hemispheres”therein, excluding olfactory bulbs
and tracts) for specimen AMNH FR 5117. Based on the esti-
mates of Morhardt (2016), we also comparatively assessed
the mass of the telencephalon and cerebellum in T. rex.
We want to note that our neuron count estimates
might be biased by the fact that we predict neuron num-
bers in the pallium and subpallium (telencephalon
excluding the olfactory system) based on total telence-
phalic neuron counts (including the olfactory system)
here. This is an issue that in parts also applies to
Herculano-Houzel (2023) and which we cannot circum-
vent due to limitations of the available raw data.
2|RESULTS
2.1 |Relative brain size
We did not recover notably large relative brain sizes in
large-bodied theropods like T. rex. Instead, our analyses
suggest that these animals had relative brain dimensions
comparable to extant non-avian sauropsids such as liz-
ards and crocodilians, as did Mesozoic dinosaurs outside
of the clade Theropoda (Table 3). Relative brain sizes
similar to those of extant birds seem to only have
emerged among the maniraptoriform theropods: PGLS
models showed a significant difference in relative brain
size (intercept) between non-maniraptoriform theropods,
such as T. rex, and the more bird-like Maniraptoriformes,
which tended to have larger brains than other dinosaurs
(PGLS, max: F
4,172
=9.49, p≤0.0001, λ=0.707; min:
F
4,172
=14.03, p≤0.0001, λ=0.707; Figure 4a,b). Post
hoc analysis shows that both the maximum and
minimum relative brain size estimates for non-
maniraptoriform theropods like T. rex are not signifi-
cantly different from what would be expected from extant
non-avian sauropsids (Table 3). However, both minimum
and maximum relative brain size estimates for these car-
nivorous dinosaurs are significantly smaller than what
would be expected for extant birds (Table 3; Figure 4).
On the other hand, we found that maniraptoriforms
show no significant differences in relative brain size com-
pared to extant birds (Table 3) regardless of whether max-
imum or minimum relative brain size was assumed
(Table 3; Figure 4; note that some maniraptoriforms such
as Shuvuuia deserti cluster with non-avian sauropsids
rather than with birds, though). In contrast to manirap-
toriforms, other theropods did not exhibit significantly
larger brains than the non-theropod dinosaurs of the
clades Sauropodomorpha and Ornithischia (Table 3),
data for which we pool here. Relative brain sizes in these
dinosaurs were not recovered to differ notably from those
of non-avian sauropsids. However, if minimum figures
are assumed, their relative brain sizes would have been
notably small, approaching a significant difference to
TABLE 3 p-values derived from Tukey post hoc comparisons for a phylogenetic ANCOVA testing for differences in relative brain size
between groups of Mesozoic dinosaurs and extant sauropsids.
Aves
Non-avian
Maniraptoriformes
Non-maniraptoriform
Theropoda
Non-theropod
Dinosauria
Minimum relative brain size
Non-avian Maniraptoriformes 0.57
Non-maniraptoriform Theropoda <0.00001 <0.00001
Non-theropod Dinosauria <0.00001 <0.00001 0.103
Non-avian Sauropsida <0.00001 0.033 0.96 0.051
Maximum relative brain size
Non-avian Maniraptoriformes 0.61
Non-maniraptoriform Theropoda 0.000106 0.0015
Non-theropod Dinosauria <0.00001 <0.00001 0.103
Non-avian Sauropsida <0.00001 0.0033 0.91 0.79
Note: Significant p-values (α=0.05) are shown in bold.
3702 CASPAR ET AL.
extant non-avian sauropsids (Table 3; however, note the
great disparity of relative brain sizes among non-thero-
pod dinosaurs illustrated in Fig. 4).
2.2 |Numbers of neurons
We re-calculated estimates for the number of forebrain neu-
rons in Mesozoic dinosaurs based on PGLS-derived regres-
sions of brain size vs. number of telencephalic neurons in
extant non-avian sauropsids and birds. Our neuron count
estimates are listed in Table 2and are compared to those of
Herculano-Houzel (2023), whereas regression parameters
are provided in Table 4. While many of the estimates do
not differ notably from one another, the differences for
some taxa, especially large theropods, are striking. For
T. rex,Herculano-Houzel(
2023) provided an estimate of
300–450 M forebrain neurons if modeled based on extant
non-avian sauropsids, and 2-3 B based on an avian regres-
sion. In contrast, we estimated a range of 245–360 M neu-
rons with a reptilian regression (=one derived from non-
avian sauropsid data) and 1-2 B with an avian one
(Table 2; Figure 5). Using the forebrain volumes estimated
for T. rex by Morhardt (2016), we predict 133–166 M telen-
cephalic neurons in this species if applying a reptilian scal-
ing and 0.989 to 1.25B based on an avian scaling (Figure 5).
FIGURE 5 Predicted numbers of neurons in the telencephalon (excluding olfactory tracts and bulbs) of Tyrannosaurus rex.Pointsrepresent
the estimated number of neurons in three adult specimens of T. rex using different inference methods. Estimates from this study, using a
regression that takes phylogenetic relationships into account (PGLS, see methods, filled circle, triangle, square and cross), are plotted in cyan. The
estimates from Herculano-Houzel (2023) are based on a non-phylogenetic regression and are shown in red (crossed square, asterisk). Different
underlying ratios of brain volume:endocranial volume are annotated. On the left, predicted numbers of forebrain neurons (derived from either the
extant avian or non-avian sauropsid scaling regime) based on the estimated volume of the brain portion of the endocast are shown. On the right,
analogous to that, the predicted count of telencephalic neurons based on forebrain volumetric estimates by Morhardt (2016) is plotted.
TABLE 4 Regression parameters for different models describing the scaling of neurological traits in extant non-avian sauropsids
(“reptiles”) and non-telluravian birds.
Model Slope (SE) Intercept (SE) λ
Avian GLS Log (tel. neuron N)log (brain mass) 0.821 (0.043) 17.5 (0.063) 0
Avian PGLS Log (tel. neuron N)log (brain mass) 0.717 (0.05) 17.55 (0.12) 0.50
Reptilian GLS Log (tel. neuron N)log (brain mass) 0.615 (0.03) 16.347 (0.06) 0
Reptilian PGLS Log (tel. neuron N)log (brain mass) 0.655 (0.03) 16.324 (0.17) 0.82
Avian PGLS Log (brain mass) log (body mass) 0.584 (0.02) 2.584 (0.25) 0.96
Reptilian PGLS Log (brain mass) log (body mass) 0.56 (0.03) 4.077 (0.26) 0.70
Note: Pagel's λ(ranging between 0 and 1) was used to quantify the phylogenetic signal. See methods for details. SE, standard error; tel. neuron N, telencephalic
neuron count.
CASPAR ET AL.3703
3|DISCUSSION
3.1 |Discussion of empirical results
We want to emphasize two aspects of our empirical find-
ings that contrast with those of Herculano-Houzel (2023).
First, we did not find relative brain size to notably differ
between non-maniraptoriform theropods such as T. rex
and extant non-avian sauropsids like crocodilians and liz-
ards. We also recovered no significant difference in rela-
tive brain size between these theropods and other
dinosaurs outside the clade Theropoda; rather, our data
support a grade shift in this trait between maniraptori-
form and non-maniraptoriform theropods, which at least
in parts relates to an increase in endocranial fill. As we
have argued beforehand, we see no support for the brains
of non-maniraptoriform theropods, sauropodomorphs,
and most ornithischians to have filled the endocranial
cavity in a bird-like fashion. However, we are aware that
such a condition, or one that is at least intermediate
between modern birds and crocodilians has been proposed
for all of these groups at one point (e.g., Balanoff
et al., 2013; Knoll et al., 2024; Knoll et al., 2021; Knoll &
Schwarz-Wings, 2009; Morhardt, 2016;seeFile S1 Part C
for further comments on that topic). Obviously, future
research might significantly change our understanding of
endocranial tissue organization in Mesozoic dinosaurs
and thus challenge the assumptions that we make here.
For the time being, however, we consider our crocodilian-
based inferences more plausible and parsimonious than
the alternative suggestions proposed so far.
Our approach suggests that relative brain size in all
dinosaurs, except for the majority of maniraptoriform the-
ropods, does not differ significantly from values present in
extant non-avian reptiles. These results agree with previ-
ous conclusions (e.g., Hurlburt et al., 2013;
Morhardt, 2016). Nonetheless, we want to stress that it
remains unclear how meaningful the transfer of brain size
scaling rules established for the given extant bird (32 g–
120 kg) and non-avian sauropsid (1 g–71 kg) datasets to
large-bodied dinosaurs actually is. Brain–body size ratios
in extant cetaceans drop dramatically in taxa that evolved
multi-ton body masses (Tartarelli & Bisconti, 2006), sug-
gesting that such allometric trajectories need to be
accounted for. However, the restricted body mass spec-
trum of extant birds and reptiles as well as the limited
availability of large-bodied crocodilians and turtles for
neurological research hinders the compilation of such
datasets for sauropsids. Furthermore, the scarcity of com-
plete and adult dinosaur endocasts from taxa that also pre-
serve stylopodial elements to derive body mass estimates
from, limits our understanding of differences in brain size
scaling between taxa. Different clades of mammals and
birds have been shown to have distinct allometric relation-
ships for relative brain size (Ksepka et al., 2020;Smaers
et al., 2021). The same might have been the case in non-
avian dinosaurs, biasing comparisons between the group-
ings we selected here. In addition to that, there might also
be temporal effects on relative brain size. Such a phenom-
enon appears to be rampant in mammalian evolution dur-
ing the Cenozoic (Bertrand et al., 2022). To our
knowledge, this pattern has not been properly described
yet in other vertebrate groups but should be considered in
future studies on brain evolution in long-lived clades such
as dinosaurs.
Second, our empirical findings do not support
Herculano-Houzel's (2023) claim of exceptionally high
telencephalic neuron counts in dinosaurs, particularly in
T. rex and other large theropods. Instead, T. rex likely did
not exhibit more than approximately 1.5 B (or at a maxi-
mum 2 B) telencephalic neurons, even when an avian
neuronal density is assumed. If we assume reptilian neu-
ronal densities, it might even have exhibited neuron
numbers an order of magnitude lower than the 3.3 B sug-
gested by Herculano-Houzel (2023). Apart from the diffi-
culty of estimating brain mass from a dinosaurian
endocast, there is one additional caveat to our neuron
count estimates that needs to be acknowledged and that
also applies to Herculano-Houzel's (2023) study: Telence-
phalic neuron numbers can only be reliably derived from
total brain mass when the proportions of the studied
brains are comparable. Since brain morphology in many
dinosaurian lineages differs significantly from both
extant birds and non-avian sauropsids (Figure 1, Paulina-
Carabajal et al., 2023), biases are thus ingrained into such
estimates. Volumetric modeling of brain regions from
endocasts, on which we relied here for T. rex exclusively,
could potentially ameliorate this problem to some degree
(Morhardt, 2016) but it is challenging and not yet widely
used. For T. rex, such inferences yield lower telencephalic
neuron numbers than would be hypothesized based on
our total brain volume estimates, if reptilian scaling rules
are applied (Figure 5). They overlap if an avian neuron
count scaling is assumed (Figure 5).
We want to emphasize that there is little reason to
assume that the brains of non-maniraptoriform theropods
such as T. rex had a telencephalic neuronal density similar
to that of extant birds. In living sauropsids, relative brain
size is positively correlated with neural density (Kverkov
a
et al., 2022). We show that this measure likely did not dif-
fer significantly between large-bodied theropods and
extant non-avian sauropsids. Consequently, relative brain
size cannot be used as an argument to defend elevated
neuron densities in these animals. The presence of endo-
thermy in dinosaurs (see below) does also not entail avian
neuronal density (contra Herculano-Houzel, 2023):
3704 CASPAR ET AL.
Similar to birds, mammals have evolved endothermy and
exhibit large relative brain sizes (Figure 4c; Tsuboi
et al., 2018). Furthermore, they display a uniquely derived
multilayered cerebral neuroarchitecture (Briscoe &
Ragsdale, 2018). Yet their average forebrain neuronal den-
sity is only moderately elevated compared to extant non-
avian sauropsids (at least if anthropoid primates are not
considered) and there is a broad overlap in neuronal den-
sity between the groups (Figure 4d;Kverkov
aetal.,2022),
indicating remarkable conservatism in this trait. With
respect to birds, however, the typical mammalian telence-
phalic neuron density is remarkably low (Figure 4d).
Interestingly, brain cell densities (suggestive of high neu-
ron counts but including endothelial and glia cells) on par
with or even higher than those of telluravian birds have
recently been identified among ectothermic teleost fish,
with comparatively small relative brain sizes (Estienne
et al., 2024). All of this suggests that metabolic rate and
neuronal density are not tightly coupled and that endo-
thermy cannot be used as a proxy for the latter. Finally,
the shape of the endocast and volumetric estimates of its
forebrain and cerebellar portions (compare Figure 6)sug-
gest that the brains of T. rex and other large non-
maniraptoriform theropods were not dissimilar to those of
extant crocodilians (Hurlburt et al., 2013;Morhardt,2016;
Rogers, 1998), which reflect the plesiomorphic archosau-
rian condition (Fabbri & Bhullar, 2022). Given this mor-
phological conservatism and the rather static neuron
densities of non-avian amniote groups, it appears appro-
priate to assume reptilian neuronal densities for these car-
nivorous dinosaurs.
It is tempting to speculate that the increased neuronal
density that sets extant birds apart from other sauropsids
and mammals coevolved with the marked changes in
brain morphology and size that occurred in maniraptori-
form theropods. If we indeed assume that an avian-like
brain organization and high neuronal density emerged
early within this clade's history, it seems plausible that
the Mesozoic dinosaurs with the highest neuron counts,
perhaps above the extant avian range, are represented by
the largest-bodied taxa within this group (for which no
complete endocasts are currently available). These
include bizarre animals such as the immense ornithomi-
mosaur Deinocheirus mirificus (7 t), the scythe-clawed
Therizinosaurus cheloniformis (5 t) and the giant ovirap-
torosaur Gigantoraptor erlianensis (2 t). Alternatively,
FIGURE 6 Estimated relative size of the telencephalon (excluding olfactory bulb and tracts) and cerebellum in T. rex. (a): The log-
transformed mass of the telencephalon in extant non-telluravian birds and non-avian sauropsids is plotted as a function of the mass of the
rest of the brain (total brain—telencephalon—cerebellum volume). Green dots show the maximum, mid and minimum estimates for the
mass of the T. rex telencephalon as modeled from digital endocasts of AMNH FR 5117 by Morhardt (2016). The orange dotted line represents
the regression line for non-telluravian bird species obtained from PGLS while the pink one represents the same for non-avian sauropsids.
(b): Analogous plot to (a), but for the cerebellum. Note that telencephalic mass in extant species includes the olfactory bulb and tracts.
CASPAR ET AL.3705
the emergence of volancy in small maniraptoriforms sim-
ilar to Archaeopteryx might have driven the evolution of
elevated neuron densities, since active flight likely
imposes constraints on skull and brain size (Olkowicz
et al., 2016; Shatkovska & Ghazali, 2021). However, the
lack of reliable morphological markers to infer neuron
density renders all these notions speculative. Such a
vagueness is inherent to predictions about the biology of
extinct taxa without close living relatives and obviously
needs to be acknowledged. The main argument for
assuming avian neuronal densities in any group of Meso-
zoic dinosaurs is that the emergence of this trait within
the avian stem-lineage cannot be reliably dated and thus
might have significantly preceded the origins of crown
birds. Hence, both a non-avian sauropsid and an avian
neuron density (as well as intermediate conditions) could
principally be justified for dinosaurs, although we advo-
cate to assume the former if taxa outside the Maniraptori-
formes are concerned. Importantly, however, even if we
had robust evidence for high neuron counts in Mesozoic
dinosaurs, this would by no means automatically suggest
exceptional cognitive capacities.
3.2 |General discussion—implications
for neuron count and brain size estimates
for vertebrate paleontology
3.2.1 | Are neuron counts good predictors of
cognitive performance?
To infer cognitive abilities in extinct animals from brain
neuron count estimates, we first need to be assured that
this measure can give us meaningful insight into behav-
iors of extant ones. However, while there is some evi-
dence for effects of pallial neuron counts on species-level
cognitive performance in primates (Deaner et al., 2007—
but see below) and birds (impulse inhibition—Hercu-
lano-Houzel, 2017; but see Kabadayi et al., 2017 for con-
flicting evidence; foraging-related innovativeness—Sol
et al., 2022; but consider limitations on how innovative-
ness is measured—Logan et al., 2018), the available data
do not provide reliable support for the hypothesis that
more neurons per se enhance cognition (Barron &
Mourmourakis, 2023). As an example, Güntürkün et al.
(2017) reviewed the performance of domestic pigeons
(Columba livia), corvids and anthropoid primates in a
number of cognitive tasks with the aim to determine
whether a “cognitive hierarchy”between the three
groups exists. They note that pallial neuron counts in cor-
vids are about 2–6 times lower than in large-bodied mon-
keys and apes but 6–17 times higher than in pigeons.
Thus, one would predict major increases in cognitive
capacities from pigeons to corvids to anthropoids. Yet,
corvids typically perform on par with
anthropoid primates (see also Kabadayi et al., 2016; Pika
et al., 2020), and pigeons do so as well in some cognitive
dimensions, such as numerical competence and short-
term memory (Güntürkün et al., 2017). In addition, stan-
dardized testing of various primate species suggests that
small-brained lemurs with comparatively low neuronal
densities (Kverkov
a et al., 2022), monkeys and great apes
rival each other in a number of cognitive dimensions
(Fichtel et al., 2020; Schmitt et al., 2012). In fact, findings
that report the influence of absolute brain size (and thus
neuron numbers) on cognitive performance in primates
(Deaner et al., 2007) have repeatedly failed to replicate
(Fichtel et al., 2020). As a final example, we want to point
out large-bodied dolphin species, which have remarkably
high neocortical neuron counts (Globicephala melas —37
B, Orcinus orca —43 B; Ridgway et al., 2019). Although
neuron numbers in these animals vastly exceed those of
humans (15–20 B), there is no evidence that cetacean
cognition is on par or even superior to that of our species
(e.g., Güntürkün, 2014; Manger, 2013). Hence, even
immense differences in telencephalic neuron numbers do
not necessarily create cognitive divides and their value in
predicting cognitive performance is remarkably limited.
The case becomes even more untenable when we con-
sider specific examples of complex behaviors, such as
habitual tool use. Remarkably, Herculano-Houzel (2023)
suggested that this might be within the realm of possibil-
ity for large theropods such as T. rex, as it is for primates
and telluravian birds today. However, tool use even
within these groups is rare, especially if the more rigor-
ous definition of “tooling”(requiring the deliberate man-
agement of a mechanical interface, see Fragaszy &
Mangalam, 2018) is employed: this occurs in only 9 avian
and 20 primate genera (Colbourne et al., 2021). While it
is true that telencephalon size in birds has an association
with tool use (Lefebvre et al., 2002), this correlation does
not hold any predictive power in the sense that all birds
with a certain-sized telencephalon exhibit this behavior.
Even within corvids, which telencephalic neuron counts
and sophisticated cognitive abilities overlap with those of
anthropoid primates (Olkowicz et al., 2016; Ströckens
et al., 2022), New Caledonian crows (Corvus monedu-
loides), and Hawaiian alal
a crows (C. hawaiiensis) are the
only species known to employ and manufacture tools in
the wild. Notably, both species inhabit remote islands,
and they share unusually straight beaks and greater bin-
ocular overlap than other crows, which are thought to be
specific morphological adaptations to enable tool use
(Rutz et al., 2016; Troscianko et al., 2012). A similar situ-
ation can be observed in parrots. These birds probably
display the highest avian telencephalic neuron counts
3706 CASPAR ET AL.
(Kverkov
a et al., 2022; Olkowicz et al., 2016; Ströckens
et al., 2022), and a greatly enlarged medial spiriform
nucleus, which acts as an interface between the pallium
and the cerebellum, enabling enhanced motor cognition
(Gutiérrez-Ib
añez et al., 2018). However, the Tanimbar
corella (Cacatua goffiniana) is the only parrot known to
be a sophisticated tool user in the wild (O'Hara
et al., 2021); tellingly, the Tanimbar corella also inhabits
an isolated Indonesian archipelago. Cases like these indi-
cate that while there might be a chance that a gross neu-
ron count threshold must be met for such sophisticated
vertebrate tool use to emerge (a notion we would reject
since ants evolved remarkable tool use skills with brains
that are small and few in neurons even for the standard
of hymenopteran insects—Godfrey et al., 2021), it is
highly unlikely to happen without sufficient ecological
pressure, and the differences between tool using and
non-tool using species are likely too subtle to detect via
measurement of neuronal quantities.
Considering these findings, it is unsurprising that
taxa converging in neuronal counts often differ mark-
edly in cognition and behavior. Herculano-Houzel
(2023) ranked her neuronal count estimates for large
theropods against those of anthropoid primates, but she
might as well have done so for giraffes (1.7 B neurons),
which exceed tool-proficient capuchins (1.1 B) and cor-
vids (0.4–1.2 B) in telencephalic neuron numbers, rival-
ing macaques (0.8–1.7 B) (Olkowicz et al., 2016). We
know little about giraffes’cognitive abilities (Caicoya
et al., 2019), but it would be appropriate to be skeptical
of any claim that they might exhibit “macaque-like”
cognition based simply on that measure. Too many
other biological traits divide these taxa, perhaps most
strikingly body size. While we agree with many contem-
porary authors that relative brain size per se is a flawed
measure of cognitive complexity (e.g., Van Schaik
et al., 2021), it must not be ignored. This is especially
true if comparisons between primates and Mesozoic
dinosaurs are drawn, since the species concerned may
differ in body mass by several orders of magnitude. Con-
trary to the assumptions of Herculano-Houzel (2023),
the size of the telencephalon and number of its neurons
must be related to the dimensions of the body, because
it processes sensory, visceral, and motoric information,
which scale with body size (Chittka & Niven, 2009; Van
Schaik et al., 2021). This fact is clearly reflected by the
pronounced intra- as well as interspecific body size-
dependent scaling of brain size in vertebrates (Bertrand
et al., 2022; Ksepka et al., 2020; Tsuboi et al., 2018; Van
Schaik et al., 2021), which can hardly be explained oth-
erwise. Relative brain size and body size are thus not
negligible variables in comparative cognition and need
to be considered in paleoneurology.
The confounding factor of body size on neurological
measures might be mitigated by calculating clade-specific
portions of telencephalic mass dedicated to somatic func-
tions (the regulation of visceral, sensory and motor pro-
cesses unrelated to cognition) based on intraspecific
variation (Triki et al., 2021; Van Schaik et al., 2021)orby
focusing on neuron counts in brain regions that are evi-
dently not involved in somatic processing (Herculano-
Houzel, 2017; Logan et al., 2018). In fact, a number of
studies, particularly in birds, were able to associate intra-
specific differences in certain cognitive dimensions to
localized neurological variation, making this approach a
promising one (discussed by Logan et al., 2018). At the
same time however, the great intra- and interspecific het-
erogeneity in brain tissue architecture and neurochemistry
enormously complicates any interspecific extrapolations
(Barron & Mourmourakis, 2023; Logan et al., 2018). Thus,
researchers cannot translate these findings to extinct spe-
cies with any tolerable degree of certainty. This issue is of
special relevance when comparing sauropsids with mam-
mals. The mammalian forebrain exhibits a layered cortex
but the pallium of extant sauropsids (and thus likely
Mesozoic dinosaurs) is largely nuclear in organization. As
the forebrain increases in size and neuron counts, a corti-
cal organization can reduce axon length (and therefore
processing time and energetic demands) by bringing adja-
cent areas closer together through folding, something that
is impossible in a nuclear organization (see Reiner, 2023
for an extensive review).
Neuron counts corresponding to major brain regions,
whether empirically determined or estimated, dramati-
cally simplify neuronal tissue complexity, as do measures
such as absolute brain size or EQ. Based on current evi-
dence, they also represent flawed cognitive proxies that
need to be viewed in the broader context of an animal's
ecology, neuroanatomy, connectomics, and neurochemis-
try (Barron & Mourmourakis, 2023; Eyal et al., 2016;
Fields & Stevens-Graham, 2002; Logan et al., 2018;
Reiner, 2023). All in all, we want to discourage attempts
to predict cognitive performance in extinct species based
on endocast-derived neuron count estimates.
3.2.2 | Inferring metabolic rate
Herculano-Houzel (2023) suggested that relative brain
size should be established as a new thermobiological
indicator in vertebrate paleontology: Relatively large
brains, as she inferred for theropods, should be viewed as
indicators of endothermy, while smaller ones, as were
attributed to pterosaurs, sauropodomorphs and many
ornithischians, would indicate ectothermy. Although
overall brain size in vertebrates is indeed correlated with
CASPAR ET AL.3707
metabolic rate (e.g., Yu et al., 2014—but also note the
extreme variability within ecto- and endothermic
groups), Herculano-Houzel's (2023) approach simplifies
the matter and ignores a vast body of already available
evidence on dinosaur thermobiology. First, as we have
extensively discussed here, relative brain size in large the-
ropods was probably markedly smaller than suggested by
Herculano-Houzel (2023) and more similar to the condi-
tion in extant crocodilians and lizards than to that found
among birds. Second, it is important to point out that
there is a spectrum of metabolic rates in vertebrates
(Legendre & Davesne, 2020) rather than a dichotomy, as
suggested by Herculano-Houzel (2023).
Where exactly certain ornithodiran taxa align within
this spectrum continues to be debated, but there is con-
sensus that dinosaurs and pterosaurs, despite their in
parts rather small brains, had metabolic rates well above
the range of extant ectothermic sauropsids (see references
below). Rather than emerging with theropods, contempo-
rary evidence suggests that endothermy evolved in the
ornithodiran stem-lineage or even earlier (Benton, 2021;
Grigg et al., 2022; Legendre et al., 2016) and hence was
inherited by pterosaurs and dinosaurs. The extensive data
supporting the presence of endothermy across Ornitho-
dira has recently been reviewed by Grigg et al. (2022) and
includes the presence of hair-like, sometimes branched,
integumentary structures (Benton et al., 2019; Campione
et al., 2020), the efficiency of the ornithodiran respiratory
system (Aureliano et al., 2022; Butler et al., 2009; Wang
et al., 2023; Wedel, 2006), bone histology and high skele-
tal growth rates (Curry Rogers et al., 2024; de Ricqlès
et al., 2000; Legendre et al., 2016; Padian et al., 2004;
Prondvai et al., 2012; Redelstroff et al., 2013), paleoenvir-
onmental data (Druckenmiller et al., 2021), models of
locomotor costs (Pontzer et al., 2009) and geochemically-
derived thermometric findings (Barrick et al., 1996;
Dawson et al., 2020; Wiemann et al., 2022). Nevertheless,
osteohistological evidence suggests that both theropod
and non-theropod ornithodiran taxa varied in their
growth and associated metabolic rates (D'Emic
et al., 2023; Erickson et al., 2009; Jenkins Jr et al., 2001;
Redelstroff et al., 2013) and a secondary reduction of met-
abolic rate in some ornithischian groups appears plausi-
ble (Padian et al., 2004; Redelstroff & Sander, 2009;
Wiemann et al., 2022), albeit still compatible with an
endothermic physiology (Grigg et al., 2022).
Overall, we want to emphasize the need for a nuanced
perspective on this trait. The assumption that relative
brain size alone (even if estimated correctly) can outper-
form all the aforementioned thermophysiological predic-
tors to infer endothermy appears at best improbable. Its
utility to gauge metabolic rate across ornithodiran groups
therefore remains highly doubtful and must be viewed in
the framework of other, more robust lines of evidence.
3.2.3 | Inferring life history traits
Finally, Herculano-Houzel (2023) suggested that neuron
count estimates can be used to model life history traits in
Mesozoic ornithodiran taxa. This notion is based on pre-
vious empirical work that showed an association between
pallial neuron counts and selected ontogenetic variables
in extant mammals and birds (Herculano-Houzel, 2019).
Applied to T. rex, the respective equations predict that
females reached sexual maturity at an age of 4–5 years
and that the longevity of the species was 42–49 years
(Herculano-Houzel, 2023). These calculations rest on the
assumption that T. rex had 2.2–3.3 billion pallial neurons.
As we have shown, this premise appears exceedingly
unlikely. Furthermore, the aforementioned life history
predictions are contradicted by the fossil evidence: Sexual
maturity in extinct nonavian dinosaurs can be estimated
histologically by the presence of medullary bone, a tissue
that forms as a calcium reservoir for egg shell production
and which is also seen in female birds (Schweitzer
et al., 2005; Woodward et al., 2020). The earliest estimate
of sexual maturity in T. rex, as estimated by the presence
of medullary bone, is 15 years (Carr, 2020; Woodward
et al., 2020). If the life history of T. rex was similar to
extant American alligators where sexual maturity occurs
in animals that attain half of adult size (which would be
in line with the available fossil data—Carr, 2020), then
the earliest onset of sexual maturity in T. rex happened in
its 12th year of life. Based on these lines of evidence,
Herculano-Houzel's (2023) method greatly underesti-
mates the onset of sexual maturity by 8–11 years. Based
on the number of lines of arrested growth in its long
bones, which are thought to indicate annual cessations of
growth, the chronologically oldest T. rex sampled so far
lived up to 33 years (Cullen et al., 2020). Although it is
not unreasonable to assume that T. rex lived longer than
three decades, there is yet no histological evidence to sup-
port that hypothesis. Given that Herculano-Houzel's
(2023) longevity estimate is based on problematic pre-
mises, it should not be considered a plausible alternative.
In fact, if applied to species other than T. rex,the
limitations of the aforementioned method become even
more visible. For instance, if the life history of the sau-
ropod Apatosaurus, a gigantic dinosaur with an adult
weight exceeding 30 tonnes, is modeled based on our
own neuron count estimates derived from an avian
regression and an assumed endocranial fill of 42%, the
equations suggest a longevity of only 24.5 years and an
onset of sexual maturity at 2.2 years (note that assuming
a non-avian sauropsid regression or smaller brain size
wouldresultinanevenmorefast-pacedlifehistorypre-
diction). These figures are obviously unfeasible. We are
aware that Herculano-Houzel (2023) assumes that sau-
ropods such as Apatosaurus were ectothermic animals,
3708 CASPAR ET AL.
which would mean that the given equations could not
be applied to them. However, since this notion defies
essentially all available evidence on the biology of sau-
ropods (see above), we choose to ignore it here. To con-
clude, the relationships between life history and
neurology that were calculated from a selection of
extant mammals and birds by Herculano-Houzel (2019)
cannot be used to reliably infer ontogenetic parameters
across non-avian dinosaurs (and potentially other fossil
groups). We strongly discourage relying on them in
palaeontological practice.
3.3 |Beyond endocasts: What are the
limits of inference on dinosaur cognition?
If neuron count estimates and other endocast-derived
variables do not allow reliable predictions about the cog-
nitive abilities of non-avian dinosaurs to be made, what
other methods are available? First of all, trace fossils can
provide direct evidence on how dinosaurs exploited their
environment and interacted with both hetero- and con-
specifics (e.g., Brown et al., 2021; Carpenter et al., 2005;
Lockley et al., 2016; Varricchio et al., 2007). While such
fossils can provide precise and diverse insights into dino-
saur behavior, obvious limitations render perspectives
gained from them extremely patchy, nonetheless.
One further way of inferring cognitive traits in dino-
saurs is by comparatively studying relevant behavioral
phenomena in living crocodilians and birds, the groups
that form their extant phylogenetic bracket. While such
approaches are starting to gain pace (Zeiträg et al., 2023),
we are not aware that ethological research could so far
identify shared physical or social cognitive skills in croco-
dilians and birds that have not also been found in turtles
and squamates (in case such comparative data is indeed
available —Font et al., 2023; Zeiträg et al., 2022). Thus,
the behavioral resolution of such approaches appears lim-
ited thus far. Cognitive traits identified exclusively in birds
or crocodiles cannot simply be extrapolated to Mesozoic
dinosaurs with any degree of certainty since they might
represent crown group apomorphies. Although it might be
appealing to hypothesize that cognitive patterns found
among modern palaeognaths are representative for their
maniraptoriform forerunners (Jensen et al., 2023;Zeiträg
et al., 2023), this idea is (in most cases) not testable and
should hence not be disseminated uncritically.
Inferences on dinosaur cognition are hindered by the
fact that both extant crocodilians and birds are highly
derived in their own ways: Convergently to mammals,
birds have not only evolved an enlarged forebrain and cere-
bellum but also extensive connections between these two
brain regions (Gutiérrez-Ib
añez et al., 2018) as well as
descending projections from the pallium to the brainstem
and/or spinal cord (Medina & Reiner, 2000; Ulinski &
Margoliash, 1990). These circuits are likely essential for
enabling various avian behaviors but are not present in
extant non avian sauropsids (Gutiérrez-Ib
añez et al., 2023;
Ulinski & Margoliash, 1990). It remains obscure when they
evolved. Crown-group birds also possess an apomorphic
dorsal projection of the telencephalon, the eminentia sagit-
talis or wulst, which appears to be absent even from endo-
casts of derived non-avian maniraptoriforms such as
Archaeopteryx and Stenonychosaurus and is prominently
involved in visual cognition (Iwaniuk & Wylie, 2020;
Walsh & Milner, 2011). Crocodilians on the other hand
conserve a plesiomorphic brain morphology and cerebral
tissue organization (Briscoe et al., 2018; Briscoe &
Ragsdale, 2018). They are unusual in being secondary ecto-
therms (e.g., Botha et al., 2023; Legendre et al., 2016;
Seymour et al., 2004) and it is unclear how this might have
affected their neurology and cognition. Thus, the extant
archosaurian groups leave us in a rather suboptimal posi-
tion to infer cognitive traits in non-avian dinosaurs.
Obviously, even the absence of a given cognitive trait
in both crocodilians and basal extant birds like palaeog-
naths does not refute its existence in Mesozoic dinosaurs,
considering the diversity and long evolutionary history of
this group. In fact, a species' ecology is typically more
indicative of certain behaviors and associated cognitive
phenomena than its phylogenetic affinities. For instance,
habitual tool use is an adaptation typically found in
omnivorous extractive foragers (Parker, 2015;Parker&
Gibson, 1977) and is only rarely reported in predators
(Shumaker et al., 2011). This is reflected by the fact that
the most common types of tooling actions that have
evolved comprise reaching, probing or pounding, usually
in order to access food (Colbourne et al., 2021). It has long
been observed that tool use emerges when a species is
found in an uncharacteristic niche, for which it lacks the
appropriate morphological adaptations, and thus compen-
sates by using tools to generate functionally equivalent
behaviors (Alcock, 1972; Parker & Gibson, 1977). This is
likely why a notable number of birds that use tools are
found on islands, yet the ability appears absent in their
close mainland relatives (Rutz et al., 2016). Simply put, in
order for tool use to evolve, there needs to be a reason for
it to evolve, and there are very few ecological contexts
where tool use is a superior adaptation to its morphologi-
cal equivalent (Hansell & Ruxton, 2008). Unfortunately,
this type of extremely specific contextual information is
nearly absent in long extinct species. From its iconic tooth
and jaw morphology, one can confidently predict that a
hypercarnivorous species like T. rex would have no need
for tools, but the problem remains that few assumptions
about extinct animal cognition are falsifiable.
CASPAR ET AL.3709
In sum, reconstructing cognition in dinosaurs and
other fossil taxa without close living analogs is a chal-
lenging endeavor that requires integrative approaches if
we are to provide compelling inferences (de Sousa
et al., 2023). Bare neuronal count estimates might be con-
sidered a rather minor contribution to this effort and
need to be aligned with data from comparative anatomy
and neurology, ecology, trace fossils, and comparative
behavioral studies on extant animals to offer a plausible
picture of cognition in extinct lineages. While communi-
cating such findings, researchers should acknowledge the
limitations of the presented inferences to allow their
audience to delineate between reasoned conclusions and
speculation. In a field such as dinosaur research —avidly
followed by popular media and the public eye —a
nuanced view appears especially warranted.
4|CONCLUSIONS
The dinosaurian neuron count and relative brain size
estimates presented by Herculano-Houzel (2023) are
inaccurate due to methodological shortcomings, in par-
ticular for T. rex. Accordingly, the biological inferences
drawn from them are implausible. As we show here,
there is no compelling evidence that relative brain size in
large-bodied theropods differed significantly from that of
extant non-avian sauropsids, and their telencephalic neu-
ron counts were likely not exceptional, especially for ani-
mals of their size. Furthermore, we highlight issues
associated with neuron count estimates in vertebrate
paleontology and argue against their use in reconstruct-
ing behavioral and life history variables, especially in ani-
mals such as non-avian dinosaurs, for which disparate
neuron densities might be hypothesized based on differ-
ent phylogenetic and morphological arguments.
For obvious reasons, many inferences we might make
about Mesozoic dinosaur behavior will remain limited.
Nevertheless, we can justify certain predictions —to a
degree —within integrative empirical frameworks to
which neuron count estimates might well be added in the
future. Before such steps can be taken, however, a substan-
tially improved understanding of the relationship between
neuron counts and other biological variables, especially
cognitive performance, in extant animals is required.
AUTHOR CONTRIBUTIONS
Kai R. Caspar: Conceptualization; investigation;
writing –original draft; methodology; visualization;
writing –review and editing; project administration; data
curation. Cristi
an Gutiérrez-Ib
añez: Conceptualization;
methodology; data curation; investigation; formal analysis;
visualization; writing –original draft; writing –review and
editing. Ornella C. Bertrand: Conceptualization;
writing –original draft; writing –review and editing;
methodology. Thomas Carr: Conceptualization;
writing –original draft; writing –review and editing;
methodology. Jennifer A. D. Colbourne: Conceptualiza-
tion; writing –original draft; writing –review and editing;
methodology. Arthur Erb: Conceptualization; writing –
original draft; writing –review and editing; methodol-
ogy. Hady George: Conceptualization; writing –origi-
nal draft; writing –review and editing; methodology.
Thomas R. Holtz: Conceptualization; writing –review
and editing; writing –original draft; methodology. Dar-
ren Naish: Conceptualization; writing –original draft;
writing –review and editing; methodology. Douglas
R. Wylie: Conceptualization; methodology; writing –
original draft; writing –review and editing. Grant R.
Hurlburt: Conceptualization; investigation; writing –
original draft; writing –review and editing; visualiza-
tion; methodology; formal analysis; resources; data
curation.
ACKNOWLEDGMENTS
We want to thank David Burnham, Gregory M. Erickson,
Ariana Paulina-Carabajal, and Lawrence M. Witmer for
sharing valuable information on fossil specimens and Nico-
las E. Campione for recommendations on body mass calcu-
lations. Andrew N. Iwaniuk is acknowledged for helpful
discussions on the methodology and structure of the study
and Jonathan Stone for allowing GRH to perform alligator
dissections in his lab. Finally, we thank Stig Walsh and two
anonymous reviewers for their thoughtful and constructive
commentsonearlierdraftsofthismanuscript.OpenAccess
funding enabled and organized by Projekt DEAL.
FUNDING INFORMATION
The author received no funding for this study.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
The authors confirm that the data supporting the find-
ings of this study are available within the article and its
supplementary materials.
ORCID
Kai R. Caspar https://orcid.org/0000-0002-2112-1050
Cristi
an Gutiérrez-Ib
añez https://orcid.org/0000-0002-
0468-8223
Ornella C. Bertrand https://orcid.org/0000-0003-3461-
3908
Jennifer A. D. Colbourne https://orcid.org/0000-0003-
4340-9087
3710 CASPAR ET AL.
Hady George https://orcid.org/0009-0009-6700-195X
Thomas R. Holtz Jr https://orcid.org/0000-0002-2906-
4900
Darren Naish https://orcid.org/0000-0002-0952-4471
Grant R. Hurlburt https://orcid.org/0000-0002-5445-
672X
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SUPPORTING INFORMATION
Additional supporting information can be found online
in the Supporting Information section at the end of this
article.
How to cite this article: Caspar, K. R.,
Gutiérrez-Ib
añez, C., Bertrand, O. C., Carr, T.,
Colbourne, J. A. D., Erb, A., George, H., Holtz,
T. R. Jr, Naish, D., Wylie, D. R., & Hurlburt, G. R.
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3716 CASPAR ET AL.
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