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Acquisition of Paleolithic toolmaking abilities involves structural remodeling to inferior frontoparietal regions

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Human ancestors first modified stones into tools 2.6 million years ago, initiating a cascading increase in technological complexity that continues today. A parallel trend of brain expansion during the Paleolithic has motivated over 100 years of theorizing linking stone toolmaking and human brain evolution, but empirical support remains limited. Our study provides the first direct experimental evidence identifying likely neuroanatomical targets of natural selection acting on toolmaking ability. Subjects received MRI and DTI scans before, during, and after a 2-year Paleolithic toolmaking training program. White matter fractional anisotropy (FA) showed changes in branches of the superior longitudinal fasciculus leading into left supramarginal gyrus, bilateral ventral precentral gyri, and right inferior frontal gyrus pars triangularis. FA increased from Scan 1-2, a period of intense training, and decreased from Scan 2-3, a period of reduced training. Voxel-based morphometry found a similar trend toward gray matter expansion in the left supramarginal gyrus from Scan 1-2 and a reversal of this effect from Scan 2-3. FA changes correlated with training hours and with motor performance, and probabilistic tractography confirmed that white matter changes projected to gray matter changes and to regions that activate during Paleolithic toolmaking. These results show that acquisition of Paleolithic toolmaking skills elicits structural remodeling of recently evolved brain regions supporting human tool use, providing a mechanistic link between stone toolmaking and human brain evolution. These regions participate not only in toolmaking, but also in other complex functions including action planning and language, in keeping with the hypothesized co-evolution of these functions.
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ORIGINAL ARTICLE
Acquisition of Paleolithic toolmaking abilities involves structural
remodeling to inferior frontoparietal regions
E. E. Hecht D. A. Gutman N. Khreisheh
S. V. Taylor J. Kilner A. A. Faisal
B. A. Bradley T. Chaminade D. Stout
Received: 27 September 2013 / Accepted: 25 April 2014
Springer-Verlag Berlin Heidelberg 2014
Abstract Human ancestors first modified stones into
tools 2.6 million years ago, initiating a cascading increase
in technological complexity that continues today. A par-
allel trend of brain expansion during the Paleolithic has
motivated over 100 years of theorizing linking stone tool-
making and human brain evolution, but empirical support
remains limited. Our study provides the first direct exper-
imental evidence identifying likely neuroanatomical targets
of natural selection acting on toolmaking ability. Subjects
received MRI and DTI scans before, during, and after a
2-year Paleolithic toolmaking training program. White
matter fractional anisotropy (FA) showed changes in
branches of the superior longitudinal fasciculus leading
into left supramarginal gyrus, bilateral ventral precentral
gyri, and right inferior frontal gyrus pars triangularis. FA
increased from Scan 1–2, a period of intense training, and
decreased from Scan 2–3, a period of reduced training.
Voxel-based morphometry found a similar trend toward
E. E. Hecht D. Stout
Department of Anthropology, Emory University, 1557 Dickey
Drive, Room 114, Atlanta, GA 30322, USA
e-mail: dwstout@emory.edu
E. E. Hecht (&)
Department of Psychology, Center for Behavioral Neuroscience,
Georgia State University, Atlanta, GA, USA
e-mail: ehecht@gsu.edu
D. A. Gutman
Department of Biomedical Informatics, School of Medicine,
Emory University, Atlanta, USA
e-mail: dagutman@emory.edu
D. A. Gutman
Department of Psychiatry and Behavioral Science, School
of Medicine, Emory University, Atlanta, USA
D. A. Gutman
Center for Comprehensive Informatics, Emory University,
Psychology Building, Suite 563, 36 Eagle Row, Atlanta,
GA 30322, USA
N. Khreisheh B. A. Bradley
Department of Archaeology, University of Exeter, Laver
Building, North Park Road, Exeter EX4 4QE, UK
e-mail: N.N.Khreisheh@ex.ac.uk
B. A. Bradley
e-mail: B.A.Bradley@exeter.ac.uk
S. V. Taylor A. A. Faisal
Department of Bioengineering, Imperial College London, Royal
School of Mines Building, Prince Consort Rd,
SW7 2AZ London, UK
e-mail: s.taylor10@imperial.ac.uk
A. A. Faisal
e-mail: a.faisal@imperial.ac.uk
J. Kilner
The Sobell Department of Movement Neuroscience and Motor
Disorders, Institute of Neurology, University College London,
Queen Square, London WC1N 3BG, UK
e-mail: j.kilner@ucl.ac.uk
A. A. Faisal
Department of Bioengineering and Computing, Imperial College
London, Royal School of Mines Building, Prince Consort Rd,
SW7 2AZ London, UK
A. A. Faisal
MRC Clinical Sciences Center, London, UK
T. Chaminade
Institut de Neurosciences de la Timone UMR 7289, Aix
Marseille Universite
´, CNRS, 13385 Marseille, France
e-mail: tchamina@gmail.com
123
Brain Struct Funct
DOI 10.1007/s00429-014-0789-6
gray matter expansion in the left supramarginal gyrus from
Scan 1–2 and a reversal of this effect from Scan 2–3. FA
changes correlated with training hours and with motor
performance, and probabilistic tractography confirmed that
white matter changes projected to gray matter changes and
to regions that activate during Paleolithic toolmaking.
These results show that acquisition of Paleolithic tool-
making skills elicits structural remodeling of recently
evolved brain regions supporting human tool use, provid-
ing a mechanistic link between stone toolmaking and
human brain evolution. These regions participate not only
in toolmaking, but also in other complex functions
including action planning and language, in keeping with
the hypothesized co-evolution of these functions.
Keywords Diffusion tensor imaging Plasticity Tool
use Brain evolution Language Superior longitudinal
fasciculus
first labour, after it and then with it speechthese
are the two most essential stimuli under the influence
of which the brain of the ape gradually changed into
that of man.
Friedrich Engels, The Part Played by Labour in the
Transition from Ape to Man, 1876.
Introduction
Many species make and use tools, but humans are distin-
guished by the extent and complexity of their technological
behavior. It is likely that the neural prerequisites for this
uniquely human facility evolved during the Paleolithic
(2,600,000–10,000 years ago), a period which witnessed
the origin (Semaw et al. 2003) and evolution (Stout 2011)
of stone toolmaking. During the same time period, the
hominin brain underwent a roughly threefold increase from
ape to human proportions (Holloway et al. 2004a,b). Over
100 years of theorizing has linked Paleolithic toolmaking
to human brain evolution (Oakley 1949; Holloway 1967;
Engels 2003 [1876]), but empirical evidence bearing on the
exact nature of this interaction has remained scant. In order
to identify likely neuroanatomical targets of selection act-
ing on toolmaking ability, we replicated the skill learning
challenges faced by our early toolmaking ancestors and
measured structural effects on the brain. This experiment
builds on previous comparative work to test the hypothesis
that stone toolmaking can elicit plastic structural responses
in evolutionarily relevant brain structures. Such phenotypic
plasticity enables learning to facilitate evolutionary adap-
tation via what is commonly termed the ‘‘Baldwin effect’
(Weber and Depew 2003; Bateson 2004), potentially
providing a mechanistic link between Paleolithic tool-
making and human brain evolution.
Our experiment builds on two lines of research into
human brain evolution: comparative studies with nonhu-
man primates and experimental ‘‘neuroarchaeological’’
studies with modern humans replicating Paleolithic tech-
nology. These methods have complementary strengths and
weaknesses, and are best used in conjunction. Comparative
studies provide evidence of differences in brain structure
and function between extant species that can be used to
identify evolutionary changes specific to the human line-
age. Comparative studies cannot, however, reveal the more
specific timing and behavioral context of adaptations that
occurred since the last common ancestor of species under
consideration [e.g., chimpanzees and human LCA, ca. 7–8
million years ago (Langergraber et al. 2012)]. The arche-
ological record provides an underutilized resource to fill
this gap. Archeological evidence allows the detailed
reconstruction of ancient human behaviors, which may
then be studied using experimental neuroscience methods.
A limitation of this approach is that it must use evidence
from modern human subjects to support inferences about
brain responses in pre-modern hominins. It is thus impor-
tant that neuroarchaeological hypotheses and interpreta-
tions be constrained by comparative evidence. Here we
focus on shared brain systems known to support tool use in
humans and nonhuman primates, and which also show
additional human specializations for tool use. The sub-
stantial homology of these systems across nonhuman pri-
mates and modern humans makes it unlikely that ancestral
hominins displayed a uniquely divergent overall functional
organization. Moreover, experimental results linking par-
ticular, archeologically attested toolmaking behaviors to
modern human specializations within this network support
inferences regarding the likely timing and behavioral
context for the emergence of these specializations.
Comparative studies with nonhuman primates point
toward human specializations in frontoparietal tool-use
networks. For example, humans activate a region of ante-
rior supramarginal gyrus during the observation of tool use
actions, while macaque monkeys fail to activate the
homologous region (Peeters et al. 2009). This region
responds to the rigid kinematics of tools as opposed to the
biological motion of hands (Peeters et al. 2013) and,
together with enhanced parietal contributions to 3-dimen-
sional form-from-motion perception (Vanduffel et al.
2002), has been proposed to support uniquely human tool
use capacities (Peeters et al. 2009; Orban and Rizzolatti
2012). While macaques do not naturally use tools, exper-
imentally tool-trained macaques show increases in gray
matter in superior temporal sulcus, secondary somatosen-
sory cortex, and intraparietal sulcus (Quallo et al. 2009).
Compared to humans, macaques also have relatively more
Brain Struct Funct
123
prefrontal activation during object perception (Denys et al.
2004), and relatively more frontal and less parietal acti-
vation during the perception of grasping actions (Nelissen
et al. 2005). A comparison between human and chimpan-
zee brain activation during the observation of object-
directed grasping found relatively greater human activation
in ventral premotor cortex, inferior parietal cortex, and
inferotemporal cortex, with human activation being gen-
erally more posterior than chimpanzee activation (Hecht
et al. 2013b). Anatomically, lateral frontal, parietal and
temporal association cortices are among the most volu-
metrically expanded portions of the human brain (Hill et al.
2010; Avants et al. 2006). This parallels comparative
studies of frontoparietal white matter connectivity, which
have reported relatively greater human connectivity with
parietal and temporal regions (Rilling et al. 2008; Hecht
et al. 2013a) in tracts that may be involved in human tool
use (Ramayya et al. 2010). We have argued (Hecht et al.
2013b) that this indicates a greater contribution for ‘‘bot-
tom-up’’ representation of observed action in the human
brain, with more neural resources devoted to processing the
details of objects and movements. Because action under-
standing is fundamental to tool use, social learning, and
cumulative culture, we suggest that the evolution of these
functions may have involved modifications of frontopari-
etal interactions.
Neuroarchaeological methods have previously been
applied in functional studies of Paleolithic toolmaking
using FDG-PET (Stout and Chaminade 2007; Stout et al.
2008), fMRI (Stout et al. 2011), and transcranial Doppler
ultrasound (Uomini and Meyer 2013). Echoing the com-
parative evidence of frontoparietal elaboration in human
evolution, experimental results have identified a distributed,
bilateral frontoparietal network supporting stone toolmak-
ing in modern humans. Some of these activation loci are
plotted in Fig. 8. Early toolmaking (‘‘Oldowan’’, ca. 2.6
million years ago) activates ventral premotor and parietal
regions identified by comparative studies as likely foci of
human perceptual-motor specializations for tool use (see
above). More advanced toolmaking (‘‘Acheulean handaxe’’,
ca. 500,000 years ago) generates increased activation of
inferior prefrontal cortex (Stout et al. 2008,2011), another
major locus of structural and functional change in human
brain evolution (Falk 1983; Holloway et al. 2004a,b;
Schenker et al. 2008; Teffer and Semendeferi 2012). This
supports an evolutionary scenario in which perceptual-
motor adaptations enabled the initial stages of human
technological evolution, whereas later developments were
dependent on enhanced cognitive control (Faisal et al. 2010;
Stout 2010). Furthermore, observed overlap in functional
neuroanatomy (Stout and Chaminade 2012) and the time-
course of hemodynamic responses (Uomini and Meyer
2013) between stone toolmaking and language has renewed
support to longstanding hypotheses of an evolutionary
connection between these two distinctive human capacities
(Holloway 1967). In keeping with broader ‘‘gestural origin’
hypotheses of language evolution (e.g., Arbib 2005, Cor-
ballis 2002), it has been proposed that adaptations for
toolmaking were evolutionarily co-opted [‘‘exapted’
(Gould and Vrba 1982)] at various points in human evolu-
tion to support proto-linguistic communicative behaviors
(Pulvermu
¨ller and Fadiga 2010; Greenfield 1991; Stout and
Chaminade 2012).
Together, the comparative (Peeters et al. 2009,2013;
Vanduffel et al. 2002; Orban and Rizzolatti 2012; Denys
et al. 2004; Nelissen et al. 2005; Hecht et al. 2013a,b;
Rilling et al. 2008; Ramayya et al. 2010) and neuroar-
chaeological (Stout and Chaminade 2007; Stout et al. 2008,
2011; Uomini and Meyer 2013; Faisal et al. 2010) studies
reviewed here suggest that selection pressure for toolmak-
ing ability may have influenced the evolution of human
inferior frontoparietal regions. However, task-related
functional activations cannot show that stone toolmaking
actually generates pressure for structural changes of the
kind (e.g., frontoparietal expansion and increased connec-
tivity) indicated by the comparative evidence. To test this,
we replicated the learning challenges faced by our early
toolmaking ancestors and measured structural effects on the
brain. Six subjects underwent an intensive, 2-year training
program in a variety of archeologically attested Paleolithic
toolmaking methods (Fig. 1). Structural MRI and DTI scans
were collected at the start (Time 1), during (Time 2), and at
the end (Time 3) of training. Individual training time and
sensorimotor performance data for these time points were
also collected, in order to directly test the correlation of
observed structural changes with stone toolmaking training
(cf. Thomas and Baker 2013). Stone toolmaking is a com-
plex, real-world craft and a simple, objective method for
quantifying variation in overall toolmaking ‘‘skill’’ does not
exist. We chose to measure one key aspect of toolmaking
skill, the accuracy of percussion strikes, using a controlled
experimental task. Previous research has shown that control
over percussive accuracy is critical to the planning and
control of toolmaking outcomes and varies with toolmaking
experience (Nonaka et al. 2010).
We expected that, if stone toolmaking does indeed place
acute demands on the anatomical structure of frontoparietal
cortex, this should be reflected in measurable changes in
gray and white matter in this region and that the magnitude
of these changes should correlate with training time and
behavioral measures. These are conservative predictions
because it is likely that the evolved frontoparietal cortices
of our modern human subjects are already structurally
well-adapted to the skilled use of tools, and thus less likely
to show additional plastic response to stone toolmaking
practice. If toolmaking nevertheless generates a measurable
Brain Struct Funct
123
response in modern human subjects, this provides a strong
indication that comparable toolmaking by ancestral homi-
nins would have stressed homologous substrates to an
equal or greater extent.
Materials and methods
Subjects
Subjects were recruited from the undergraduate and post-
graduate programs in Archaeology at Exeter University.
Subjects were ages 18–25 at the time the first scan was col-
lected, five males and one female. All were right-handed by
self-report and had no neurological or psychiatric illness. All
subjects consented to participate and the study was approved
by the Ethics Committee at Exeter University.
Toolmaking training
Paleolithic stone toolmaking involves striking a stone
‘core’’ with a ‘‘percussor’’ of bone, antler, or stone to
detach carefully controlled chips and incrementally achieve
various design goals. This demanding task requires precise
sensorimotor coordination and strategic action sequence
planning. Training occurred on site at Exeter University
and during intensive field trips to the USA, France, and
Denmark. Subjects were trained by Bruce Bradley, Ph.D.,
Director of the Experimental Archaeology Masters Pro-
gramme at Exeter University, and an expert stone tool-
maker with decades of experience teaching Paleolithic
technologies. Training included instruction, coaching, and
demonstration as well as independent practice, which was
recorded by subjects in a log book. The Paleolithic tool-
making methods learned by subjects included: (1) basic
flake production, comparable to the earliest known
(Oldowan) tools of Homo habilis 2.6–1.5 million years ago
(mya); (2) ‘‘Handaxe’’ making, comparable to the Acheu-
lean tools of Homo erectus and Homo heidelbergensis
1.7–0.25 mya; and (3) ‘‘prepared core’’ flake production,
comparable to the Levallois tools of Neanderthals and early
Homo sapiens \0.25 mya. Training was naturalistic and
self-paced, resulting in substantial variation across subjects
in the duration, intensity, and content of practice.
Paleolithic toolmaking occurred over a vast time period
and across many millions of square miles, and naturally
Fig. 1 Intensive 2-year training program in Paleolithic stone tool-
making. aTraining included direct instruction and individual practice,
both on site and during intensive field trips (04/2011, 09/2011,
04/2012). bStone tools produced by a representative subject at T1,
T2, and T3. The last three tools were created using porcelain which
was coated with red paint in order to facilitate recognition of the
original surface (Khreisheh et al. 2013). cAn actual Paleolithic stone
tool (Acheulean handaxe, *500,000 years old)
Brain Struct Funct
123
encompasses a great deal of variation that could not be
included in the methods learned by our subjects. The
methods we did select are considered broadly representa-
tive of Early and Middle Paleolithic technology, whereas
details of the production techniques employed closely
match those documented in specific archeological collec-
tions (e.g., Stout et al. 2014, Bradley and Sampson 1986).
This gives us confidence that our training protocol was
both generally representative and specifically accurate in
re-creating learning challenges actually faced by Paleo-
lithic toolmakers.
Motor performance
Motor testing took place at the Brain and Behaviour Lab,
Department of Bioengineering and Department of Com-
puting, Imperial College London, on the same days as MR
image acquisition. Due to scheduling constraints perfor-
mance data were collected for only five (four male) of the
six subjects. All subjects provided written informed con-
sent, and experiments were carried out in accordance with
institutional guidelines. A local ethics committee approved
the experimental protocols.
Subjects held in their right hand an object (250 g, hand-
sized cylinder) with an embedded Liberty motion tracking
node sampled at 240 Hz, calibrated to sub-millimeter
precision and tracked by a Polhemus Liberty electromag-
netic tracking system [POLHEMUS, Colchester (VT)]. The
measurement markers of this system recorded horizontal,
vertical and depth position of the marker in a calibrated
reference coordinate system (see Faisal et al. 2010). The
coordinate system is aligned veridical with an augmented
reality projection system linked to a computer controlling
the experiment and processing the position information in
real time (akin to Faisal and Wolpert 2009).
Subjects were instructed to strike from three different
strike positions towards a fixed target point. The start
points were located on a line 7.5, 24, and 40 cm away from
the target point. The spatial position of the start point and
target were located on an imaginary line, and mimicked the
motion of percussion strikes during natural knapping. Start
positions were randomly selected before each trial. Sub-
jects had to move the marker to the start position before
being able to initiate the strike. Subjects could initiate a
strike at any time of their choosing, but once movement
was initiated subjects had a time constraint of 200 ms to hit
the target, or had to repeat the trial (classified as miss).
Invalid trails (e.g., movement not executed in time win-
dow) were discarded. Subjects first performed 500 trials
with a circular target of 2 cm, then 500 trials with a circular
target of 1 cm, for a total average of 920 ±31.7 (range
845–991) valid trials per session. We calculated accuracy
(proportion of valid trials passing through target) per
session and tested for correlation between changes in
accuracy and changes in fractional anisotropy (FA, see
Sect. ‘Tract-based spatial statistics’) using Pearson’s
rcorrelation coefficient.
Image acquisition and preprocessing
Imaging took place at the Wellcome Department of
Imaging Neuroscience in London. All subjects consented
and the research was approved by the National Hospital for
Neurology and Neurosurgery and Institute of Neurology
Joint Research Ethics Committee. 61-direction DTI images
were acquired on a Siemens Trio 3.0 T scanner with a
voxel size of 1.7 mm
3
. Seven volumes were acquired with
no diffusion weighting (B0s); these images provide a
‘baseline’’ from which diffusion magnitude and direc-
tionality can be measured. The FSL software package
(Smith et al. 2004; Woolrich et al. 2009; Jenkinson et al.
2012) was used for image processing and analysis. The first
B0 image from each scan was discarded to allow for
equipment warm-up and the remaining 6 were averaged.
FSL’s brain extraction tool (BET) (Smith 2002) was
applied to the averaged B0s, which were then aligned to
template space using a nonlinear registration algorithm
(FNIRT) (Andersson et al. 2007) with an initial affine
registration (FLIRT) (Jenkinson and Smith 2001; Jenkin-
son et al. 2002). FSL’s eddy correction algorithm (EDDY)
was used to correct distortion caused by eddy currents.
DTIFIT, part of FSL’s FDT package (Behrens et al. 2003)
was used to fit the raw diffusion data to a tensor model at
each voxel to obtain diffusion scalars, including fractional
anisotropy. Dates for each subject’s scans at Time 1, 2, and
3 are listed in Table 1.
Registration to template space
Since the goal of the present study was to compare scans
within subjects across time, we used an approach similar to
a previous longitudinal DTI study (Engvig et al. 2012)in
order to ensure accurate within-subject alignment across
time points despite potential differences in the subject’s
Table 1 Time points for each subject’s MRI and DTI scans, given in
days from the beginning of the study
Subject Days from Scan
1 to Scan 2
Days from Scan
1 to Scan 3
Subject 1 308 644
Subject 2 307 644
Subject 3 306 644
Subject 4 306 644
Subject 5 266 603
Subject 6 265 604
Brain Struct Funct
123
position in the scanner or in equipment calibration. First,
for each time point, each subject’s fractional anisotropy
(FA) image, which is a scalar measure of diffusion mag-
nitude at each voxel, was linearly registered to the FMRIB
1mm
3
FA template, which is a standard T1-weighted MRI
template formed by averaging the scans of 152 subjects.
Next, FSL’s midtrans algorithm was used to find the
transformation matrix from a geometrically intermediate
space of each subject’s 3 FA images to the FA template
(their ‘‘midspace’). This matrix was inverted and applied
to the FA template to transform the template into each
subject’s FA midspace. Each subject’s 3 native-space FA
images were then linearly registered to their own midspace
template. These images were fed into the FSL software
package’s standard TBSS pipeline (see below), which
includes nonlinear registration to a common template
space. For probabilistic tractography, each subject’s mid-
space FA template was nonlinearly aligned to the MNI-
space FMRIB 1 mm
3
FA template. This warp was inverted
and combined with the linear midspace-to-native-space
transformation matrix in order to produce the MNI space-
to-native-space transformation matrix for each subject,
which was used to apply standard-space regions of interest
to native diffusion space (Engvig et al. 2012). The inverse
transformation (a linear native space-to-midspace trans-
form combined with a nonlinear midspace-to-MNI-space
warp) was used to register all tractography results into a
common template space for viewing.
Tract-based spatial statistics
DTI is a structural neuroimaging method that is sensitive to
the diffusion of water. Because water diffusion is more
directionally constrained and homogenous inside than
outside an axon bundle, DTI measurements can be used to
trace white matter pathways and to identify changes in
those pathways over time (for a primer, see Mori and
Zhang, 2006). FSL’s Tract-Based Spatial Statistics (TBSS)
package (Smith et al. 2006) was used to identify regions of
white matter with changes in white matter integrity over
time. This algorithm has been successfully used by multi-
ple previous studies to identify changes in white matter and
links between white matter variation and behavioral vari-
ation (e.g., Scholz et al. 2009; Schlegel et al. 2012; Lee
et al. 2010; Taubert et al. 2010). TBSS performs voxelwise
statistical analysis of fractional anisotropy (FA) images.
FA reflects constraints on the directionality of water dif-
fusion and is thought to be related to the degree of axon
myelination, axon diameter, and/or tract density (Zatorre
et al. 2012). Each subject’s FA images for each time point
were first nonlinearly registered to the FMRIB 1 mm
3
FA
template, which is an MNI-space average of FA images
from 58 subjects. All subjects’ template-aligned FA images
were then averaged to create a study-specific mean FA
image at all time points. This mean FA image was thinned
to create a mean FA skeleton representing the centers of all
white matter tracts common to the group. Aligned FA data
from each subject at each time point was then projected
onto this skeleton for voxelwise cross-time point statistical
comparisons.
Previous functional studies of stone toolmaking found
task-related activations in inferior frontoparietal regions
(Stout and Chaminade 2007; Stout et al. 2008,2011), so
statistical comparison was constrained to the portion of the
white matter skeleton that fell within the white matter
beneath lateral frontoparietal cortex. This ROI was based
on the superior longitudinal fasciculus (SLF) ROI from the
JHU White Matter Atlas included in FSL, which was
generated using probabilistic tractography in 28 subjects
(Wakana et al. 2007; Hua et al. 2008). While the atlas’s
probabilistic SLF ROI terminates around the vicinity of
premotor cortex, previous functional imaging studies have
indicated that more anterior regions of inferior frontal
gyrus within the atlas may be involved in toolmaking
(Stout et al. 2008,2011; Stout and Chaminade 2012).
Monkey tract-tracing studies have shown that these ante-
rior ventrolateral prefrontal regions do indeed receive
white matter connections from the SLF (Schmahmann et al.
2007; Petrides and Pandya 2009), and diffusion imaging
studies have successfully tracked these connections in both
monkeys and humans (Schmahmann et al. 2007; Hecht
et al. 2013a; Thiebaut de Schotten et al. 2012). Therefore,
we thresholded the atlas’s SLF ROI at 25 % and then
manually extended it toward the anterior aspect of the
inferior frontal gyrus. The threshold of 25 % was chosen as
a conservative compromise between including voxels
belonging to any subject in the probabilistic atlas (which
would reflect a wide range of inter-subject variability) and
including only voxels common to all subjects in the
probabilistic atlas (which would create a very small ROI).
This ROI was then used to mask the mean FA skeleton. As
a control tract, we included the forceps major ROI from the
JHU White Matter Atlas (Wakana et al. 2007; Hua et al.
2008), also thresholded at 25 %. Like the SLF, the forceps
major is a cortico-cortical tract. It carries callosal connec-
tions between the left and right primary visual cortices and
other early visual areas. To our knowledge there is no
reason to suspect that this tract would be altered by tool-
making training. A single analysis was carried out on one
mask that included both the SLF and forceps major in order
to use a single test to establish whether toolmaking training
had an effect on the SLF but not the forceps major (Nie-
uwenhuis et al. 2011).
Statistical comparisons were carried out using random-
ize, FSL’s tool for permutation-based testing with a general
linear model (GLM) (Nichols and Holmes 2002; see
Brain Struct Funct
123
Friston et al. 1994 for a discussion of the integration of
general linear models with statistical parametric maps in
neuroimaging). GLMs were constructed to compare FA
changes along the 3 time points, from Time 1 to Time 2,
Time 2 to Time 3, and Time 1 to Time 3 (i.e., not con-
sidering Time 2), both across the whole brain and in the
SLF ROI. The GLMs incorporated the time point of each
scan for each subject in days, with Time 1 being day 0.
Threshold-free cluster enhancement (TFCE) (Smith and
Nichols 2009) was used to identify statistically significant
clusters of voxels. Results were corrected for multiple
comparisons and thresholded at p\.05.
In clusters that showed significant FA change over time,
we also examined axial and radial diffusivity. Fractional
anisotropy is a measure of diffusivity in the primary dif-
fusion direction (axial diffusivity) relative to diffusion in
the other two perpendicular directions (radial diffusivity:
the mean of these two latter measurements); changes in FA
can therefore be due to underlying changes in axial and/or
radial diffusivity. Preliminary research suggests that these
may correspond to different biological mechanisms of
change, although additional research in this area is war-
ranted (Wheeler-Kingshott and Cercignani 2009).
Increased axial diffusivity may reflect increases in axon
density, axon caliber, microtubule packing, and/or micro-
tubule organization (Kumar et al. 2010,2012; Song et al.
2002,2003; Choe et al. 2012), whereas decreased radial
diffusivity may reflect increased axon myelination (Keller
and Just 2009; Zhang et al. 2009; Bennett et al. 2010).
Radial diffusion images were created by averaging the
images representing the magnitude of diffusion in the
second and third directions; axial diffusion images were
generated automatically by FSL in the standard DTI pre-
processing pipeline. Both radial and axial images were then
subjected to the same registration and processing steps as
the FA images in order to enable measurement in equiva-
lent voxels within and across subjects.
Voxel-based morphometry
Voxel-based morphometry is an approach to identifying
structural differences in gray matter between subjects or
within subjects across time which involves registering all
scans to a common space and then measuring the spatial
difference between each scan and the group average (for a
primer, see Ashburner and Friston 2000). FSL’s VBM tool
(Douaud et al. 2007) was used to compare gray matter
volume across time points. This algorithm performs vo-
xelwise statistical analysis of T1-weighted MRI images.
Scans were segmented into CSF, white matter, and gray
matter, with the latter voxels being retained for further
analyses. A study-specific, bilaterally symmetric gray
matter template was produced by nonlinearly registering
each scan to the MNI T1 template, flipping each scan along
the midline, and then averaging all scans. Each subject’s
native gray matter data was then nonlinearly registered to
this template and modulated to correct for local expansion
or contraction resulting from nonlinear registration, and
then smoothed using a Gaussian kernel with a sigma of
3 mm (which is equivalent to FWHM approximately
7.08 mm). VBM compares individual subjects’ template-
space images to the template-space average, and intensity
differences between the two indicate differences in gray
matter volume. Analysis was constrained to gray matter
regions connected by SLF (BA 45, 44, 6, 40, 39, and 37—
the regions of experimental interest) and regions of early
visual cortex (BA 17 and 18), since these regions are linked
by the forceps major, the tract used as a control ROI in the
TBSS analysis. As in the TBSS analysis, both the experi-
mental and control regions were combined into one mask
and subjected to a single statistical test in order to establish
whether toolmaking training effected the gray matter areas
connected by the SLF but not the gray matter areas con-
nected by the forceps major (Nieuwenhuis et al. 2011).
Voxelwise statistical comparisons of gray matter expan-
sion/contraction were carried out using the same GLMs as
the TBSS analyses, and threshold-free cluster enhancement
(TFCE) (Smith and Nichols 2009) was used to identify
statistically significant clusters of voxels. Clusters were
thresholded at p\.01 but were not corrected for multiple
comparisons.
Tractography from regions of white matter
with significant change in fractional anisotropy
In order to obtain information about the anatomical con-
nectivity of clusters showing significant changes in frac-
tional anisotropy in the TBSS analysis, we used these
clusters as seeds for further tractography analyses. Trac-
tography was performed on each seed individually. Tracts
were thresholded, normalized, binarized, and summed to
produce composite images following the same approach as
in the control tractography analyses. These tracts were
produced to give anatomical elaboration to the TBSS
results, and were not statistically compared across time
points since they were the result of a previous longitudinal
statistical analysis.
Tractography was performed in native diffusion space
using FSL’s BEDPOSTX and probtrackx tools (Behrens
et al. 2003). BEDPOSTX uses Markov chain Monte Carlo
sampling of the raw diffusion information to build a
Bayesian distribution diffusion information at each voxel.
Probtrackx samples from these distributions in order to
generate probabilistic streamlines; in the resultant trac-
tography output images, the intensity at each voxel corre-
sponds to the number of streamlines that reached that voxel
Brain Struct Funct
123
(so greater intensity corresponds to a greater probability of
connectivity within the model). Both BEDPOSTX and
probtrackx model diffusion information not only along the
principal direction of diffusion, but also along the other
two orthogonal axes. Importantly, sampling of the Bayes-
ian distributions for these additional diffusion directions
allows tractography through regions with low fractional
anisotropy, i.e., in regions of crossing fibers within white
matter, and through the gray matter/white matter boundary
(Behrens et al. 2007).
Results
Changes in white matter fractional anisotropy
FA changes occurred in white matter leading into left su-
pramarginal gyrus (SMG), bilateral ventral precentral
gyrus (vPrCG), and right inferior frontal gyrus pars tri-
angularis (IFGpt). FA increased in these locations from
Time 1 to Time 2 and decreased in almost identical loca-
tions from Time 2 to Time 3, although note that the vPrCG
increase occurred in the right hemisphere and the vPrCG
decrease occurred in almost exactly the same location in
the left hemisphere (Fig. 2). No changes were observed in
the forceps major. Coordinates, size, baseline FA values,
and t values for FA changes for each cluster are reported in
Table 2. The connectivity of each of these clusters is
shown in Fig. 8(described below).
In the clusters that showed significant FA change, we
also measured axial and radial diffusivity in order to
determine whether change in one or both of these measures
was driving change in FA. From Time 1 to Time 2, axial
diffusivity showed a significant increase (paired samples
ttest; t(17) =5.299, p\.001, 2-tailed), while radial dif-
fusivity showed no significant change [paired samples
ttest; t(17) =-.954, p=.353, 2-tailed]. From Time 2 to
Time 3, axial diffusivity showed a significant decrease
[paired samples ttest; t(17) =-2.565, p=.020, 2-tailed],
while radial diffusivity again showed no significant change
[paired samples ttest; t(17) =-.596, p=.559, 2-tailed],
suggesting that FA changes may have been due to changes
in axon density, axon caliber, microtubule packing, and/or
microtubule organization, rather than changes in myelina-
tion (Keller and Just 2009; Zhang et al. 2009; Bennett et al.
2010; Kumar et al. 2010,2012; Song et al. 2002,2003;
Choe et al. 2012).
Correlation with training time
White and gray matter changes mirrored the intensive
training (mean 120 h) from Time 1 to Time 2 and the
relative decrease (mean 47 h) from Time 2 to Time 3 (see
Fig. 1). Total FA change (DFA) was correlated with
training hours since the previous scan (r=.580, p=.024,
1-tailed) (Fig. 3). DFA values within each cluster individ-
ually were not significantly correlated with training time,
likely reflecting individual variability in the allocation of
structural responses (Sect. ‘Variability in FA change
across subjects’).
Correlation with motor performance
Across all time points, accuracy ranged from .74 to .96
(mean =.86, SD =.05). Accuracy at Time 3 did not differ
significantly from Time 1 (paired ttest, t=1.717,
p=.161), reflecting substantial variance in individual
differences (range ?.10 to -.04). Total FA change (DFA)
was correlated with changes in accuracy (Daccuracy
r=.616, p=.029, 1-tailed) (Fig. 4). Performance dif-
fered (t=3.226, df =8, p=.012) between the two sub-
ject groups identified from PCA factor scores, with Group
1 showing a mean Daccuracy of ?4 % across time points
and Group 2 a mean of -1 %. Thus, Group 1 subjects
improvement their striking accuracy over the training
period (mean =?8 %, paired t=5.869, df =2,
p=.028), whereas Group 2 subjects did not (mean =
-1 %, paired t=-.467, df =1, p=.722). From Time 1
to Time 2, Daccuracy correlated with PCA Component 2
factor scores (r=.878, p=.025, 1-tailed), whereas from
Time 2 to Time 3 it correlated with Component 1
(r=.973, p=.003, 1-tailed). Analysis at the level of
individual clusters is consistent with this: from Time 1 to
Time 2 Daccuracy is positively correlated with right vPrCG
(r=.822, p=.044, 1-tailed) and negatively correlated
with right IFGpt (r=-.989, p=.001, 1-tailed), whereas
from Time 2 to Time 3, Daccuracy is positively correlated
with right IFGpt (r=.837, p=.039, 1-tailed).
Variability in FA change across subjects
Closer examination of each subject’s pattern of FA changes
across clusters reveals substantial individual variability
(Fig. 5). A principal components analysis identified 2
components accounting for 83 % of this variance (Fig. 6).
Component 1 (61 % of variance) captures the covariance
of all clusters apart from the right vPrCG, while component
2 (22 % of variance) primarily captures variation in right
vPrCG, including a negative correlation with right IFGpt.
Inspection of individual factor scores for component two
suggests the presence of two alternative responses to
training. Group 1 (subjects 1,3, 5 and 6) is characterized by
positive loadings from Time 1 to Time 2 and Group 2
(subjects 2 and 4) by negative loadings (Fig. 7). For all
subjects, this pattern reverses from Time 2 to Time 3,
reflecting the symmetric decreases in FA noted above
Brain Struct Funct
123
(Fig. 2). Training time was not correlated with PCA factor
scores.
Changes in gray matter volume
The VBM analysis in regions surrounding the SLF found
gray matter expansion from Time 1 to Time 2 in the su-
pramarginal gyrus, and a reversal of this effect from Time
2 to Time 3 (Fig. 8c), mirroring the pattern of change in the
TBSS analysis. This effect was significant at the p\.01
level without correction for multiple comparisons, and
should therefore be considered a trend. At this threshold
there were no voxels showing gray matter change over time
in visual cortex.
Probabilistic tractography from regions of white matter
change
Probabilistic tractography confirmed that the voxels
showing white matter change projected to the voxels
showing gray matter change. Cortical regions identified by
tractography and morphometry are known to be function-
ally responsive to stone toolmaking (Stout and Chaminade
2007; Stout et al. 2008,2011) (Fig. 8).
Fig. 2 Structural changes associated with the acquisition of Paleo-
lithic stone toolmaking skills. Changes in SLF white matter (p\.05,
corrected). Yellow–red increases from T1 to T2; cyan–blue decreases
from T2 to T3. Insets show mean FA at each time point. Note the
close overlap in the spatial location of increases/decreases in IFGpt
and SMG; increases/decreases were observed in very similar locations
of vPrCG in each hemisphere. While FA within the R vPrCG cluster
continued to increase from T2 to T3, note that this was not significant
(i.e., the voxelwise analysis that identified clusters showing signif-
icant change from T2 to T3 did not find such an increase at this
location). R/L vPrCG right/left ventral precentral gyrus, R aIFG right
anterior inferior frontal gyrus, L pSMG left posterior supramarginal
gyrus
Brain Struct Funct
123
Discussion
Skill acquisition and structural remodeling
These results establish that the acquisition of Stone Age
toolmaking skills causes structural remodeling to fronto-
parietal circuits. This is in agreement with previous studies
that have found structural changes with skill acquisition
[e.g., (Maguire et al. 2000; Draganski et al. 2004; Floyer-
Lea and Matthews 2004; Lappe et al. 2008; Scholz et al.
2009; Lee et al. 2010; Taubert et al. 2010,2011; Sisti et al.
2012; Steele et al. 2012)]. The robustness of the anatomical
changes we observed is underscored by the fact that they
reached statistical significance in a relatively small sample
size. This is likely related to the intensity and duration of
our training program. Previous studies have reported sig-
nificant results from larger samples using considerably
shorter, more limited, and less evolutionarily relevant
training regimes. For example, in a group of studies on
balancing (Taubert et al. 2010,2011,2012), 14 subjects
received six 45-min training sessions over the course of
6 weeks. In another study (Steele et al. 2012), 13 subjects
were trained on a motor sequence task over a period of only
5 days. Significant effects have also been obtained using
relatively small samples (\12) (Floel et al. 2009; Takeuchi
et al. 2010; Antonenko et al. 2012; Gebauer et al. 2012;
Schlegel et al. 2012; Wang et al. 2012; Tseng et al. 2013).
In our study training was both intensive and long-term,
extending over a period of 2 years. This enabled us to study
the complex, real-world skill of stone toolmaking, which
can take years to master (Stout 2002). The current study
thus applied a well-established experimental approach to
address for the first time questions about human brain
evolution. We did so by studying an evolutionarily relevant
skill that can be archeologically tied to particular times and
places in prehistory.
The cellular mechanisms responsible for experience-
dependent gray and white matter change are under active
Table 2 Baseline FA, tscores
for FA change, volume, and
coordinates for clusters showing
significant change in FA over
time
Coordinates are given in 1 mm
MNI template space
Baseline FA at time 1 Max t score in
cluster for
change in FA
Volume
(mm
3
)
Coordinates of peak: voxels (mm)
xyz
Clusters from Time 1–Time 2 analysis
L pSMG .141127 17.5 73 132 (-42) 83 (-43) 103 (31)
R aIFG .184215 18.5 35 56 (34) 164 (38) 77 (5)
R vPrCG .142852 13.1 25 44 (46) 127 (1) 102 (30)
Clusters from Time 2–Time 3 analysis
L pSMG .142939 14.7 93 132 (-42) 83 (-43) 103 (31)
R aIFG .139591 13.3 44 57 (33) 162 (36) 78 (6)
L vPrCG .158409 15.8 147 126 (-36) 128 (2) 101 (29)
Fig. 3 Correlation between change in FA and practice hours. Gray
lines denote 95 % confidence interval. Pearson’s r=.580, p=.024,
1-tailed
Fig. 4 Correlation between change in FA and change in accuracy on
striking task. Gray lines denote 95 % confidence interval. Pearson’s
r=.616, p=.029, 1-tailed
Brain Struct Funct
123
investigation, but are not yet well understood (Zatorre et al.
2012). Recent reviews (e.g., Thomas and Baker 2013)
highlight the need to ensure that structural change over
time is due to training and not some other temporally
varying process such as exam schedules or equipment
change. In the current study, structural changes correlated
with both practice hours and motor performance, con-
firming their causal association with stone toolmaking
training. Training time between scans varied across sub-
jects and predicted total DFA. This included increases in
total FA associated with intense training from Time 1 to
Time 2 as well as symmetrical decreases in FA associated
with lower levels of training from Time 2 to Time 3. A
trend toward gray matter expansion and contraction
observed in the supramarginal gyrus followed the same
pattern. These sensitive anatomical responses likely reflect
the acute nature of behavioral demands on brain structure,
a finding which is supported by a growing body of evi-
dence: white matter changes can occur in as little as 2 h of
behavioral training in humans (Sagi et al. 2012), and
changes in fractional anisotropy of white matter can be
detected in as little as 1 h in mice (Ding et al. 2013). The
fact that white and gray matter changes rapidly reversed
with reduction of toolmaking activity may also reflect the
metabolic costliness of maintaining such changes. This
acute response confirms the structurally demanding nature
of Paleolithic toolmaking behavior, even in our modern
human subjects, and is consistent with toolmaking’s
hypothesized role in generating selective pressure on
frontoparietal cortex (see below).
Total DFA also correlated with changes in striking
accuracy across subjects. Striking accuracy is essential in
stone toolmaking to control fracture and achieve desired
effects. Experimental studies of modern stone craftsmen
have shown that control over the elementary striking ges-
ture is a more reliable indicator of expertise than years of
experience or amount of technical knowledge (Bril et al.
2005). Stone toolmakers must adapt motor performance to
variable conditions (e.g., composition, size, and shape of
both percussor and core), and experimental manipulations
Fig. 5 Individual variation in pattern of FA change across clusters
Fig. 6 Factor loadings of each cluster in each of two components
identified by principal components analysis. Note that the left
hemisphere clusters have very similar factor loadings (dashed circle)
Fig. 7 Individual subjects’ factor scores for Component 2. Note that
subjects are arranged on the x-axis to emphasize the distinction
between Subjects 2 and 4 versus the others
Brain Struct Funct
123
have documented the flexible adaptation of skilled tool-
makers (Bril et al. 2005,2010). We examined the transfer
of motor skill from toolmaking training to a controlled
virtual reality striking task and found that individual
changes in accuracy (rate of hitting the target) correlated
with FA change. In a naturalistic study, Nonaka et al.
(2010) had subjects strike stone cores to remove flakes and
found that the mean distance between predicted and actual
points of impact for novice toolmakers was 7.4 mm, with a
SD of approximately 4.6 mm (see Fig. 6 in Nonaka et al.
Fig. 8 Regions of white matter change project to regions of gray
matter change, and to regions of gray matter activated in previous
Paleolithic toolmaking studies. Clusters of white matter change
(Fig. 2) were used to seed tractography analyses. In ac, hot scale
indicates increases from T1 to T2; cool scale indicates decreases from
T2 to T3. dShows the same tracts, all made partially transparent and
overlaid. aTracts produced after seeding with the L and R vPrCG
TBSS clusters. These projections connect the body of the SLF to the
cortex of the ventral precentral gyrus. bTracts produced after seeding
with the R aIFG clusters. These projections connect the pars
triangularis of the right IFG with the right dorsomedial frontal pole.
cTracts produced after seeding with the L pSMG clusters. These
projections connect the posterior inferior parietal cortex with lateral
temporal cortex and project into a region of gray matter which
showed a trend toward VBM increases from T1 to T2 and decreases
from T2 to T3 (p\.01, uncorrected). dAll tractography results
rendered on the MNI template brain. Dots indicate activations related
to Paleolithic toolmaking from previous studies that fall within or
immediately adjacent to tractography results from the present study.
Stout and Chaminade (2007);
à
Stout et al. (2008);
§
Stout et al. (2011)
Brain Struct Funct
123
2010). Assuming a normal distribution, this implies that
approximately 84 % of strikes would have been within
12 mm of the target (mean ?1 SD), which is comparable to
the mean hit rate in our experiment (86 %) using targets
with an average diameter of 15 mm. Our most successful
subject achieved a hit rate of 96 % by Time 3, which is
roughly that expected for the ‘‘intermediate’’ subjects of
Nonaka et al. (mean error =4.3 mm, SD =5.7, mean ?2
SD =15.7), but falls below the expected rate for ‘‘experts’
(mean error =.6 mm, SD =1.1, mean ?13SD =14.9)
which should approximate 100 %. To the extent that
increases in FA are correlated with increases in accuracy,
this suggests that the training-related anatomical adapta-
tions we observed may be relatively modest compared to
those that would have been experienced by expert Pleis-
tocene toolmakers (compare Fig. 1b vs. c, see also Stout
et al. 2014).
In contrast to total DFA, training time did not correlate
with DFA in individual clusters. This reflects individual
variation in the specific response to training. Despite the
limitations of our small sample size for investigating
individual variation, we did observe apparent patterning
that might be further investigated in future research. TBSS
analysis identified six clusters of significant FA change at
the group level, but also substantial individual variability in
the relative allocation of structural changes across these
clusters. A Principal Components Analysis revealed two
main dimensions of this variation, including a pattern of
coordinated change across all clusters apart from right
vPrCG (Component 1), and an inverse relationship
between right vPrCG and right IFGpt (Component 2).
Changes in striking accuracy were predicted by Compo-
nent 2 from Time 1 to Time 2 and by Component 1 from
Time 2 to Time 3. In other words, initial increases in
striking accuracy were associated with increased right
vPrCG FA at the expense of right IFGpt whereas sub-
sequent increases in accuracy were associated with reten-
tion of FA increases (i.e., less negative DFA) outside right
vPrCG, especially in right IFGpt. Changes in accuracy did
not correlate with training time, in keeping with the dis-
junction between stone working skill and experience
reported by Bril et al. (2005) and again reflecting a variable
response to training across our subjects.
Interestingly, subjects fell into two groups with respect
to Component 2 scores. Group 1 showed positive scores
from Time 1 to Time 2 followed by negative scores from
Time 2 to Time 3 whereas Group 2 showed the opposite
pattern. Group 1 subjects improvement their striking
accuracy over the training period whereas Group 2 subjects
did not, suggesting that the former pursued a more effec-
tive learning strategy associated with a different pattern of
structural brain changes. However, our data do not allow us
to test the possibility that Group 2 subjects prioritized other
aspects of skill learning (e.g., conceptual knowledge) that
we were unable to measure, rather than simply displaying
less effective learning overall.
This variable pattern is consistent with the known
involvement of right inferior frontal cortex in action pro-
gramming, and with the context-sensitive nature of inter-
actions between IFG, the ventral premotor cortex or
vPrCG, and primary motor cortex. Whereas ventral pre-
motor cortex directly modulates primary motor activity,
right IFGpt is thought to play a role in the more abstract
cognitive control of action [e.g., inhibition under condi-
tions of response uncertainty (Levy and Wagner 2011)]
through its extensive projections to ventral premotor cortex
(Dum and Strick 2005). Using a paired-pulse transcranial
magnetic stimulation paradigm, Buch et al. (2010) found
that ventral premotor cortex variably facilitated or inhibited
primary motor corticospinal activity, depending on task
context (reach vs. switch targets), and that individual var-
iation in the magnitude of these effects correlated with FA
in right precentral and inferior frontal gyrus clusters closely
approximating (\9.2 mm Euclidean distance) those iden-
tified here. Our results suggest that anatomical changes in
right vPrCG and IFGpt may play somewhat different roles
in the acquisition of complex skills, perhaps corresponding
to early vs. late learning processes and/or different learning
objectives (e.g., motor skill vs. conceptual understanding).
Toolmaking, neural plasticity, and brain evolution
Previous functional imaging research (Stout and Chamin-
ade 2007; Stout et al. 2008,2011) has established that
Paleolithic toolmaking by modern human subjects recruits
portions of frontoparietal cortex which have been identified
by comparative studies (Peeters et al. 2009,2013; Orban
and Rizzolatti, 2012; Hecht et al. 2013a,b; Rilling et al.
2008; Ramayya et al. 2010) as likely sites of human-spe-
cific adaptations for tool use. Current results demonstrate
that active practice of Paleolithic toolmaking skills elicits
structural remodeling in these same regions (Figs. 2,8),
providing further confirmation for hypothesized links
between stone toolmaking and frontoparietal brain struc-
ture and function. The observation of anatomical effects in
our subjects indicates that, even in large-brained, modern
humans, stone toolmaking is sufficiently demanding to
induce the metabolically costly re-allocation of structural
resources in ways that parallel inferred evolutionary
changes (increased frontoparietal gray matter volume and
connectivity). Habitual toolmaking by smaller-brained
Paleolithic ancestors would have stressed these anatomical
substrates to a similar or even greater degree, generating
positive selection pressure on any genetic variants that
enhanced the reliability and efficiency of the plastic phe-
notypic response.
Brain Struct Funct
123
This potential role of phenotypic plasticity in facilitating
evolutionary adaptation has been appreciated since the
nineteenth century (Baldwin 1896; Osborn 1896) and is
now widely known as the ‘‘Baldwin effect’’ (Weber and
Depew 2003; Bateson 2004). Briefly, phenotypic plasticity
allows organisms to adapt to new environmental conditions
and/or generate novel adaptive behaviors. In the context of
these new conditions or behaviors, any variation in the
ease, efficiency, or reliability of the expression of the
plastic trait will be acted on by selection. This would
potentially lead to alterations in the genes regulating the
trait and to the inheritance of a predisposition to develop
the modified condition more or less independently of the
original environmental/behavioral stimulus. We suggest
that such a process may have occurred over the 2.5 million
years during which stone toolmaking was a key adaptive
skill for our ancestors.
The earliest, Oldowan, stone tools consist of sharp stone
flakes struck from river cobbles (Semaw et al. 2003). This
early technology predates fossil evidence of substantial
hominin brain expansion, although it has been suggested
that size-independent adaptations (i.e., ‘‘re-organization’’)
of posterior parietal and inferior frontal cortex may already
have been present (Holloway et al. 2004a,b). Functional
investigations of Oldowan toolmaking (Stout and Cham-
inade 2007; Stout et al. 2008) observed activations in
vPrCG and in SMG which are now closely paralleled by
evidence of training-related structural white and gray
matter changes reported here. Fossil, functional and
structural evidence are thus consistent with the hypothesis
(Stout and Chaminade 2007) that early hominin toolmak-
ing was supported by evolutionary elaborations of a
primitive ventral frontoparietal circuit for object manipu-
lation that is shared with other primates (Maravita and Iriki
2004; Rizzolatti et al. 1998). Indeed, a VBM study of tool-
use (rake) learning in macaques found evidence of parietal
gray matter expansion encompassing potions of anterior
intraparietal sulcus (Quallo et al. 2009) that may be com-
parable to the human gray matter expansion reported here.
It has previously been suggested (Iriki and Taoka 2012)
that such plasticity, already present in monkeys, may have
contributed to the evolution of human inferior parietal
areas through the Baldwin effect. Our results extend this
argument to include frontoparietal white matter connec-
tions, and link it to specific, archeologically observable,
behaviors known to have occurred during human evolution.
In addition to changes in white matter underlying ventral
PrCG and SMG, we observed a significant effect under
right IFGpt. Previous functional studies have shown
increased activation of this region by Acheulean, but not
Oldowan, toolmaking (Stout et al. 2008,2011). Early
Acheulean toolmaking first appeared around 1.75 million
years ago (Lepre et al. 2011; Beyene et al. 2013), roughly
.25 million years after the first widely accepted fossil
evidence of brain expansion and inferior frontal re-orga-
nization (Holloway et al. 2004a,b). The Acheulean is
widely considered to represent a major advance in tool-
making sophistication over the preceding Oldowan, pro-
viding the first clear evidence of artifacts (‘‘handaxes’’,
‘picks’’) made to a predetermined design. Acheulean tools
also show evidence of increasing refinement through time
(Beyene et al. 2013) and by 500,000 years ago (Fig. 1c)
their production involved an elaborate sequence of hierar-
chically embedded goals and sub-goals (Stout 2011; Stout
et al. 2014). This later Acheulean time period
(780–400,000 years ago) coincides with the fastest rate of
hominin encephalization in the past 2 million years (Ruff
et al. 1997), and includes the form of ‘‘Late Acheulean’
toolmaking in which our research subjects were trained
(Fig. 1b). In keeping with evidence that the basic manip-
ulative complexity of Oldowan vs. Late Acheulean tool-
making does not differ (Faisal et al. 2010), we have
previously interpreted (Stout et al. 2008,2011) increased
right inferior frontal gyrus pars triangularis (IFGpt) acti-
vation as reflecting the cognitive control [e.g., inhibition,
task switching (Aron et al. 2004; Koechlin and Jubault
2006; Levy and Wagner 2011)] demands of the more
complex action sequences involved in this technology.
Current evidence of training-related FA changes in white
matter under right IFGpt corroborates functional evidence
of this region’s involvement in stone toolmaking, and
provides the first direct evidence that Paleolithic tool-
making can elicit measurable structural responses in pre-
frontal cortex.
Toolmaking and language
The frontoparietal regions in which we observed structural
change participate not only in tool use, but also in other
complex cognitive tasks including action planning and
language. Loci of overlap between praxis and communi-
cation have been identified in left ventral premotor (Riz-
zolatti and Arbib 1998) and parietal (Frey 2008) cortex,
and both are now specifically linked to Paleolithic tool-
making by functional (Stout and Chaminade 2007; Stout
et al. 2008,2011) and structural results. This supports the
hypothesized co-evolution of tool use and other complex
functions (Rizzolatti and Arbib 1998; Corballis 2002;
Pulvermu
¨ller and Fadiga 2010; Stout and Chaminade
2012). Our results also corroborate functional evidence of
right IFGpt involvement in toolmaking (Stout et al. 2008,
2011). Right IFGpt is not a core language or tool use
region, but supports domain-general cognitive control
(Vigneau et al. 2011) relevant to a range of complex
communicative [e.g., discourse comprehension (Menenti
et al. 2009)] and instrumental [e.g., task switching (Aron
Brain Struct Funct
123
et al. 2004; Koechlin and Jubault 2006; Levy and Wagner
2011)] behaviors.
Structural remodeling to these regions in response to
Paleolithic toolmaking is consistent with longstanding
models of the mutually reinforcing interaction between
technological, social, communicative, and neural com-
plexity in human evolution (Holloway 1967; Engels 2003
[1876]). More specifically, we propose that human fron-
toparietal circuits underwent adaptations for Paleolithic
toolmaking that were behaviorally co-opted [‘‘exapted’
(Gould and Vrba 1982)] to support proto-linguistic com-
munication and then subsequently altered by secondary
adaptations specific to language.
Acknowledgments This research was funded by a grant from the
Leverhulme Trust, ‘‘Learning to Be Human: Skill Acquisition and the
Development of the Human Brain’’, F/00 144/BP. SVT and AAF
acknowledge support by the ‘Biotechnology and Biological Sciences
Research Council’’ and funding by the Human Frontiers in Science
Program (HFSP RPG00022/2012). JK received funding from the
Wellcome Trust. We would like to extend our gratitude to the vol-
unteer subjects whose dedication, good humor and reliability made
this project possible. Thanks are also due to Chris Frith for advice and
support on this project, to Antony Whitlock for assistance with
toolmaking training, and to Anderson Winkler for helpful input on
GLM designation in FSL.
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... Neurobehavioral mechanisms are subject to evolutionary selection pressures and survive only if they are relevant in natural tasks. Thus, studying operation in natural contexts allows us to evaluate mechanisms the nervous system has been designed for 16,17 . Example in sensory neuroscience the use of natural sensory stimuli has led to a revolution of our mechanistic understanding of perception 18,19 . ...
... throwing a frisbee while the subject's trunk is strapped to the chair to prevent trunk movement 23 . Another line of inquiry used free-behaviour in real-world tasks such as tool-making or juggling 17,[24][25][26][27] . In these studies, we and others focused on anatomical and functional MRI measurable changes following learning. ...
... In these studies, we and others focused on anatomical and functional MRI measurable changes following learning. In a 3-year long complex tool-making apprenticeship experiment 17 we were able to quantify changes in motor control precision and improvements of task outcomes, but given the 100 s of hours of training involved and complexity of the task itself, we were not able to record trial-by-trial learning effects. Therefore, the findings and insights of learning www.nature.com/scientificreports/ ...
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The neurobehavioral mechanisms of human motor-control and learning evolved in free behaving, real-life settings, yet this is studied mostly in reductionistic lab-based experiments. Here we take a step towards a more real-world motor neuroscience using wearables for naturalistic full-body motion-tracking and the sports of pool billiards to frame a real-world skill learning experiment. First, we asked if well-known features of motor learning in lab-based experiments generalize to a real-world task. We found similarities in many features such as multiple learning rates, and the relationship between task-related variability and motor learning. Our data-driven approach reveals the structure and complexity of movement, variability, and motor learning, enabling an in-depth understanding of the structure of motor learning in three ways: First, while expecting most of the movement learning is done by the cue-wielding arm, we find that motor learning affects the whole body, changing motor-control from head to toe. Second, during learning, all subjects decreased their movement variability and their variability in the outcome. Subjects who were initially more variable were also more variable after learning. Lastly, when screening the link across subjects between initial variability in individual joints and learning, we found that only the initial variability in the right forearm supination shows a significant correlation to the subjects’ learning rates. This is in-line with the relationship between learning and variability: while learning leads to an overall reduction in movement variability, only initial variability in specific task-relevant dimensions can facilitate faster learning.
... Cognitive archaeology examines "cultural remains" of the daily lives of our ancestors to hypothesize the cognitive processes involved in their making and use. Neuro-archaeology researchers teach modern humans to learn skills of the kind inferred by archaeologists, see what parts and connections of the brain are "exercised" by learning the ancient skill, and hypothesize that their enlargement may have been a step in brain evolution (e.g., Hecht et al., 2015). ...
... Presumably, it becomes more specific as knapping proceeds. (ii) Stout (personal communications) argues that improved visuomotor skill in flake removal was also involved and this required biological changes in neural pathways (Hecht et al., 2015). Together, these make the perceptual-motor organization of Acheulean industry qualitatively different from Oldowan flaking. ...
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Citation Info: Arbib, M. A. (2021). The aboutness of language and the evolution of the construction-ready brain (updated draft of January 2021). The Oxford Handbook of Symbolic Evolution. A. Lock, C. Sinha and N. Gonthier, Eds. Oxford: Oxford University Press. (In press.) Abstract: The chapter presents the hypothesis that early Homo sapiens were language-ready in the sense that they had brains that could have supported language had it already been developed, but were not yet language-using. Informed by data from comparative neuroprimatology, the approach sees protolanguage emerging from complex recognition and imitation of manual skills via biocultural evolution, while cultural evolution alone supported the emergence of language from protolanguage. This approach supports the view that the Homo sapiens language-ready brain had the more general property of being construction-ready, and that this made possible the emergence of drawing and painting through later cultural evolution.
... This sample includes 5 sequences for which upper limb movements and manual joint angles were recorded as part of a previous study 27 , and 6 for which the tools and waste produced were analyzed and compared with actual Paleolithic artifacts from the Middle Pleistocene site of Boxgrove, UK 28 . Building on this and other previous research [29][30][31][32] , we focused our current study on archaeologically documented tool-making methods from the early and late Lower Paleolithic, a period that witnessed a nearly 3-fold increase in hominin brain size. This allows us to empirically address the over 100 years of theorizing linking increasingly complex tool-making with brain evolution and language origins 6,[33][34][35] . ...
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Human behaviors from tool-making to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. Testing this idea will require objective, generalizable methods for measuring the structural complexity of real-world behavior. Here we present a data-driven approach for extracting action grammars from basic ethograms, exemplified with respect to the evolutionarily-relevant behavior of stone tool-making. We analyzed sequences from the experimental replication of ~2.5 Mya Oldowan vs. ~0.5 Mya Acheulean tools, finding that, while using the same “alphabet” of elementary actions, Acheulean sequences are quantifiably more complex and Oldowan grammars are a subset of Acheulean grammars. We illustrate the utility of our complexity measures by re-analyzing data from an fMRI study of stone tool-making to identify brain responses to structural complexity. Beyond specific implications regarding the co-evolution of language and technology, this exercise illustrates the general applicability of our method to investigate naturalistic human behavior and cognition.
... By provisioning captive Japanese macaques -who also use a range of tools in the wild (Leca et al., 2008) -with rakes with which to obtain food rewards, they were able to demonstrate how using such tools actually modifies neural connections within the lifetime of a single individual. Similar neural plasticity has been demonstrated for humans by Mithen & Parsons (2008), whose experiment focused on musical ability, and by Stout, Hecht, and colleagues focusing on flint tool production (Hecht et al., 2015;Stout & Chaminade, 2007). These studies, working from evolutionary questions, align with numerous advances in the cognitive and medical sciences which have demonstrated how different forms of neural plasticity are fundamental to acquisition, mastery, and specialization in complex motorial, cognitive, and social activities (e.g., Blakemore & Frith, 2005;Kolb & Whishaw, 1998;Magee & Grienberger, 2020). ...
... In contrast to lab-based tasks, real-world neuroscience approaches study neurobehavioral processes in natural behavioural settings [17][18][19][20]. We recently presented a naturalistic realworld motor learning paradigm, using wearables for full-body motion tracking and EEG for mobile brain imaging, while making people perform actual real-world tasks, such as playing the competitive sport of pool-table billiards [21,22]. ...
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Motor-learning literature focuses on simple laboratory-tasks due to their controlled manner and the ease to apply manipulations to induce learning and adaptation. Recently, we introduced a billiards paradigm and demonstrated the feasibility of real-world-neuroscience using wearables for naturalistic full-body motion-tracking and mobile-brain-imaging. Here we developed an embodied virtual-reality (VR) environment to our real-world billiards paradigm, which allows to control the visual feedback for this complex real-world task, while maintaining sense of embodiment. The setup was validated by comparing real-world ball trajectories with the trajectories of the virtual balls, calculated by the physics engine. We then ran our short-term motor learning protocol in the embodied VR. Subjects played billiard shots when they held the physical cue and hit a physical ball on the table while seeing it all in VR. We found comparable short-term motor learning trends in the embodied VR to those we previously reported in the physical real-world task. Embodied VR can be used for learning real-world tasks in a highly controlled environment which enables applying visual manipulations, common in laboratory-tasks and rehabilitation, to a real-world full-body task. Embodied VR enables to manipulate feedback and apply perturbations to isolate and assess interactions between specific motor-learning components, thus enabling addressing the current questions of motor-learning in real-world tasks. Such a setup can potentially be used for rehabilitation, where VR is gaining popularity but the transfer to the real-world is currently limited, presumably, due to the lack of embodiment.
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A vast amount of literature suggests a co-evolutionary relationship between Palaeolithic stone toolmaking, and cognition and specifically language. However, empirical data remain limited to indirect findings of neurophysiological studies. Furthermore, most Oldowan studies have used chert and have not investigated retouch, even though quartz and lava were predominant raw materials during periods of chert unavailability, and even though chert was disproportionately more frequently used for retouch compared to other raw materials during periods of chert availability, at least in the Olduvai Gorge. The study recruited 13 young adults with no prior experience in knapping. Subjects were taught by an experienced knapper to produce quartz choppers and chert sidescrapers in either a verbal or gestural condition. Two raters rated on a 5-point scale the subjects’ performances on specific steps of the two stone toolmaking tasks. In a post-experimental interview, subjects stated which aspects of the tasks they preferred or disfavored. Subjects also performed on a neuropsychological battery encompassing visuospatial, executive functioning, and linguistic tasks. Given the small sample size, the results should be regarded as exploratory and preliminary. Our results are further limited to the early acquisition phase and may not reflect processes in modern experienced knappers. Descriptive data suggested better performance across all stone toolmaking variables in the verbal compared to gestural condition, but only flake quality on the sidescraper task was significantly different between groups. Analyses of the stone toolmaking variables suggested subjects perceived quartz and chert flaking very differently. Correlational and other analyses suggested that quartz chopper manufacture was not associated with cognitive performance. Conversely, chert flaking and retouch were strongly associated with visuospatial working memory, showing that subjects with a higher memory span produced better chert flakes and retouch. Retouch only was moderately associated with executive functioning measures, showing subjects who made fewer errors on the tasks were better on retouch. Specific aspects of chert flaking were also associated with verbal fluency performance, showing, among others, moderate and strong positive associations with the productivity and rate of production of syntactically transitive verbs on action fluency. Evolutionary implications can be drawn from our research only if we controversially assume similar results would have been obtained had we tested early hominins and not modern humans. Following this axiom, our results suggest that Oldowan hominins relied on modern-like visuospatial working memory during chert flaking and retouch, and, to a lesser degree, modern-like executive functioning during chert retouch. This is contrary to previous Oldowan studies suggesting no involvement of executive functioning during Oldowan-like flaking. Results from the linguistic tasks controversially suggest that some of the prerequisites for aspects of action language and syntactic transitivity (verb-object phrases) in modern humans were to some degree present in Oldowan hominin populations. Because Olduvai Gorge hominins readily incorporated chert for stone toolmaking in periods of chert availability, our results suggest that these cognitive capacities were phylogenetically not related to chert knapping. Finally, we propose that the quality of performance on Oldowan flaking and retouch may not reflect the full level of cognitive capacities of Oldowan populations. We provide the first direct evidence for an association between Palaeolithic stone toolmaking and cognitive performance in modern humans, while previous studies have inferred cognitive processes from neuroimaging data. We also provide the first direct evidence for an association between Palaeolithic stone toolmaking, and action language and simple syntactic transitivity in modern humans.
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