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Cognitive Demands of Lower Paleolithic
Dietrich Stout
*, Erin Hecht
, Nada Khreisheh
, Bruce Bradley
, Thierry Chaminade
1Department of Anthropology, Emory University, Atlanta, Georgia, United States of America, 2Department
of Psychology, Georgia State University, Atlanta, Georgia, United States of America, 3Department of
Archaeology, University of Exeter, Exeter, United Kingdom, 4Institut de Neurosciences de la Timone, Aix
Marseille Université, Marseille, France
Stone tools provide some of the most abundant, continuous, and high resolution evidence
of behavioral change over human evolution, but their implications for cognitive evolution
have remained unclear. We investigated the neurophysiological demands of stone toolmak-
ing by training modern subjects in known Paleolithic methods (Oldowan,Acheulean) and
collecting structural and functional brain imaging data as they made technical judgments
(outcome prediction, strategic appropriateness) about planned actions on partially complet-
ed tools. Results show that this task affected neural activity and functional connectivity in
dorsal prefrontal cortex, that effect magnitude correlated with the frequency of correct stra-
tegic judgments, and that the frequency of correct strategic judgments was predictive of
success in Acheulean, but not Oldowan, toolmaking. This corroborates hypothesized cogni-
tive control demands of Acheulean toolmaking, specifically including information monitoring
and manipulation functions attributed to the "central executive" of working memory. More
broadly, it develops empirical methods for assessing the differential cognitive demands of
Paleolithic technologies, and expands the scope of evolutionary hypotheses that can be
tested using the available archaeological record.
Enhancement of prefrontal executive control is seen as critical to the emergence of modern
human cognition [14], but evidence regarding the actual neurophysiological demands of
archaeologically-visible behaviors remains scant. Although long tradition [5,6] links toolmak-
ing to human brain evolution, many recent analyses have concluded that stone tools provide
relatively little evidence of pre-modern cognition. For example, it has been argued that Paleo-
lithic technological change is poorly correlated with brain size change [7], that increasing tech-
nological sophistication is likely epiphenomenal to underlying changes in social cognition [8],
and that technological variation is better explained in terms of economic and environmental
factors [9]. Others have concluded that stone tools provide evidence of spatial [10] and proce-
dural learning abilities but not of executive functions [1] or that Paleolithic toolmaking was
PLOS ONE | DOI:10.1371/journal.pone.0121804 April 15, 2015 1/18
Citation: Stout D, Hecht E, Khreisheh N, Bradley B,
Chaminade T (2015) Cognitive Demands of Lower
Paleolithic Toolmaking. PLoS ONE 10(4): e0121804.
Academic Editor: Nuno Bicho, Universidade do
Received: November 13, 2014
Accepted: February 12, 2015
Published: April 15, 2015
Copyright: © 2015 Stout et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
Data Availability Statement: Due to ethical
restrictions imposed by the IRB and the small number
of subjects in the study, the authors cannot deposit
the data publicly. However, data will be made
available upon personal request to Thierry
Chaminade (
Funding: 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 to
BB and DS. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
supported by a specialized domain lacking the cognitive fluiditycharacteristic of modern hu-
mans [11]. Still other researchers see evidence of complex cognition in Paleolithic toolmaking,
including executive functions associated with prefrontal cortex [4,12,13]. Largely missing
from this debate is empirical evidence of the cognitive demands of particular stone toolmaking
behaviors, leading one recent review to conclude that "links among brain size, cognitive com-
plexity, and technological skill [...] are more articles of faith than a hypothesis based on solid
middle-range research." [9: 51] To remedy this, we have adopted an experimental neuroscience
approach, training modern subjects in Lower Paleolithic stone toolmaking methods and col-
lecting structural and functional brain imaging data as they performed controlled
experimental tasks.
Experimental replication of prehistoric behavior is a core research method in archaeology
[14,15] that has been widely used to investigate the techniques [16], skills [17], biomechanics
[18], and fracture mechanics [19] involved in the production of flaked stone tools. In order to
support inferences about the past, experimental archaeologists aim to identify necessary rela-
tions between behavioral variation and material traces of the kind that can be observed in the
archaeological record. The application of neuroscience methods to experimental archaeology
allows more detailed characterization of this behavioral variation, including physiological [20
22] and structural [23] responses in the brain, and thus expands the range of inferences that
can be drawn from archaeological evidence. Here we seek to identify brain systems supporting
particular aspects of stone toolmaking competence, and to relate variation in the functional re-
sponse of these systems to variation in the experimental artifacts produced.
Our previous research examined brain responses to naturalistic stone toolmaking behavior
execution [20,21] and observation [22], identifying a bilateral frontoparietal network support-
ing stone toolmaking and documenting increased response to more recent stone technology.
These findings support an evolutionary scenario in which perceptual-motor adaptations en-
abled the initial stages of human technological evolution whereas later developments were de-
pendent on enhanced cognitive control [24], and particularly the inhibitory and task-set
shifting functions of the right inferior frontal gyrus. Research to date has not, however, indicat-
ed the involvement of dorsolateral prefrontal cortex regions thought to support executive func-
tions such as relational and temporal abstraction [25], or information selection, monitoring
and updating [26], that are attributed to the "central executive" of working memory [1].
Prior experiments prioritized ecological validity and studied naturalistic tasks to reveal gen-
eralized demands over relatively extended timescales (20s ~ 40m), but were not designed to dis-
sect task sub-components or detect infrequent but potentially important brain responses (e.g.
those associated with a small number of critical strategic choices). To better focus on these
questions here, we adapted a behavioral paradigm developed by Bril and colleagues [17] for use
as an fMRI experiment. Subjects were shown predictions of toolmaking action outcomes and
asked to make judgments about them. By varying questions we manipulated cognitive task de-
mands across identical stimuli, distinguishing between judgments on the physical accuracy of
predicted outcomes vs. their strategic appropriateness in achieving toolmaking goals. This ma-
nipulation approximates a conventional archaeological distinction between savoir-faire (know-
how) and connaissance (knowledge about) in stone toolmaking [27], which is itself loosely con-
vergent with contrasts of procedural vs. declarative memory and perceptual-motor vs. cogni-
tive skill [28] developed in other disciplines. We anticipated that the strategy task especially
would rely on the selection, monitoring and updating of abstract technological concepts, and
thus elicit greater prefrontal response, whereas the prediction accuracy task would rely on in-
ternal simulation and thus elicit greater perceptual-motor response.
Brain responses to the observation of skilled actions are modulated by experience [22,29],
and accounts of expert cognition based on the formation of task-specific knowledge structures
Cognitive Demands of Lower Paleolithic Toolmaking
PLOS ONE | DOI:10.1371/journal.pone.0121804 April 15, 2015 2/18
Competing Interests: The authors have declared
that no competing interests exist.
("chunking" [30]) suggest greater working memory demands during learning vs. expert perfor-
mance. To address this, we employed a longitudinal design, training subjects for two years in a
variety of archaeologically attested Paleolithic toolmaking methods and conducting fMRI ex-
periments at the start (T1), mid-point (T2), and end (T3) of training. This demanding training
program limited sample size but enabled investigation of the acquisition of a real-world, evolu-
tionarily-relevant skill in a manner not previously achieved in either archaeology or neurosci-
ence. We evaluated behavioral and brain responses to stimuli representing simple flake
production (cf. Oldowan,Mode 1,Mode C[31], hereafter "Oldowan") and refined biface
shaping (cf. Later Acheulean handaxe,Mode 2,Mode E2[31], hereafter "Acheulean").
We predicted an interaction between Task (Prediction vs. Strategy), Technology (Oldowan vs.
Acheulean), and Time (T 1, 2, 3) such that prefrontal response would be greater for the Strate-
gy task, especially with respect to the more complex Acheulean technology and at earlier stages
of skill acquisition. Our training program also allowed us to study tools produced by our re-
search subjects outside the scanner [32]. We expected that individual performance on our MRI
tasks would be predictive of actual success with stone toolmaking.
Subjects and training
Subjects were recruited from undergraduate and postgraduate programs in Archaeology at Ex-
eter University. Subjects were ages 1825 at the time the first scan was collected, 5 male and 1
female. All were right-handed by self-report and subsequent observation, had no neurological
or psychiatric illness, and provided written informed consent before the study and the study
was approved by the Ethics Committee at Exeter University. Imaging took place at the Well-
come Department of Imaging Neuroscience in London. All subjects provided additional writ-
ten informed consent for imaging data collection and the research was approved by the
National Hospital for Neurology and Neurosurgery and Institute of Neurology Joint Research
Ethics Committee (Reference #: 1825/003).
Stone toolmaking involves striking a stone corewith a percussorof bone, antler, or
stone to detach controlled flakes and incrementally achieve design goals. Training was con-
ducted by BB and NK, as detailed in [32], and included instruction, coaching, and demonstra-
tion as well as independent practice, which was recorded by subjects in a log book. Pedagogical
techniques were not restricted in any way and the explicit aim of instruction was to elicit maxi-
mum skill development by drawing on the extensive tool-making and training experience of
the instructors. Toolmaking methods introduced to the subjects included: 1) basic flake pro-
duction, comparable to the earliest known (Oldowan) tools of Homo habilis 2.61.5 million
years ago (mya); 2) Handaxemaking, comparable to the Acheulean tools of Homo erectus
and Homo heidelbergensis 1.70.25 mya; and 3) prepared coreflake production, comparable
to the Levallois tools of Neanderthals and early Homo sapiens <0.25 mya. Training was natu-
ralistic and self-paced, leading to intersubject variation in the duration and content of practice.
Learning was assessed through comparison of artifacts produced (Fig. 1) during formal evalua-
tions before and after training in each technology [32]. For Oldowan evaluations, subjects were
asked to detach five flakes from a flint core. For Acheulean and Levallois evaluations, subjects
were asked to produce a tool (handaxe or preferential Levallois flake) from a standardized por-
celain core [33].
Paleolithic toolmaking occurred over a vast time period and many millions of square miles,
and encompasses substantial variation that could not be included in our training program. The
methods we did select are considered broadly representative of Lower and Middle Paleolithic
technology, and details of the production techniques employed closely match those
Cognitive Demands of Lower Paleolithic Toolmaking
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Fig 1. Handaxes produced for the first (left) and last (right) evaluations, ranked by T3 fMRI task
performance (circled numbers).
Cognitive Demands of Lower Paleolithic Toolmaking
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documented in specific archaeological collections [34]. We thus consider our training protocol
to be both generally representative and specifically accurate in re-creating learning challenges
actually faced by Paleolithic toolmakers.
Experimental design
Stimuli were 1.5-second videos of rotating stone cores (Oldowan, Acheulean or Levallois)
marked with coloured cues indicating the next strike of a notional toolmaker: a red dot indicat-
ed the intended point of impact, and a white area showed the flake predicted to result from per-
cussion at this point [17]. Levallois stimuli were omitted from subsequent analyses, because
instructor evaluations indicated that subjects failed to develop basic proficiency in this technol-
ogy [32]. Before each scan (T1, 2, 3) subjects received a standard briefing on the technologies
and experimental tasks (Prediction: "if the core were struck in the place indicated, is what you
see a correct prediction of the flake that would result?"; Strategy: "is the indicated place to hit
the core a correct one given the objective of the technology?"). In the scanner, stimuli were pre-
sented in blocks of 4. For each block subjects were given a 2 s text prompt indicating which
Technology and Task they would be responding to, followed 0.5 s later by a series of 4 stimulus
presentations (0.25 s black screen, 5 s video, 0.25 s black screen, 2.5 s response screen). Re-
sponse screens indicated which button (left/right) to use for yes and no, in a
randomized fashion.
MRI data acquisition
Each scanning time included seven acquisitions: a fieldmap (double echo FLASH), four func-
tional runs (EPI, FOV 192×192 mm
, inplane voxel size 3×3 mm
, 48.0 3-mm tick descending
axial slices without gap, TR 3264.0 ms, 136 repetitions) covering the whole brain, a T1 anatomy
(MPRAGE) and a Diffusion Tensor Imaging scan [23].
fMRI data analysis
SPM8 and associated toolboxes were used for the analysis of MRI data [35]. Realignment and
unwarping procedures were applied to fMRI time series to correct for both the static distortions
of the magnetic distortions with the voxel displacement map obtained from the fieldmap and
the movement-induced distortions of the time series [36]. The high-resolution anatomical im-
ages were coregistered with the mean EPI image, before being segmented using VBM8 toolbox.
For each subject, the three anatomical images were realigned and a mean image created. The
DARTEL toolbox was used for diffeomorphic registration of the six mean anatomical images.
Realignment parameters, DARTEL transformations from original to template image and nor-
malization parameters of the DARTEL template were combined for the normalization of func-
tional time series [37] with a 8-mm FWHM Gaussian kernel smoothing and voxel resampling
to 1.5 mm
. A mean anatomical volume image was created by averaging the individual anato-
mies transformed and normalized according to DARTEL parameters.
For each Subject and Time analysis a separate analysis was run (first-level analysis), in
which the six experimental conditions (2 Tasks Prediction&Strategyby 3 Technologies
Oldowanby Acheuleanby Levallois) were modeled as 32-second boxcar functions. Con-
dition regressors were convolved with the canonical hemodynamic response function with a
high pass filter (128 s). Contrast images between conditions and rest for each of the four re-
cording sessions per subject and time were used in second-level repeated-measure analysis of
variance using GLMFlex toolbox, with Time (T1, 2, 3), Task and Technology as factors of inter-
est and Sessions and Subjects as random factors.
Cognitive Demands of Lower Paleolithic Toolmaking
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The conn toolbox [38] was used to investigate the functional connectivity of the left superior
frontal gyrus cluster. Sources of confounding variance (estimated motion parameters; BOLD
signals in grey matter, white matter and cerebrospinal fluid resulting from the VBM segmenta-
tion; main effects of the tasks) were removed from the smoothed time series through linear re-
gression. Data were high-pass filtered (cut-off 128 s) to eliminate low frequency drifts. Mean
time courses were extracted in the region of interest and correlated with activity in all voxels
creating whole-brain maps of regression coefficients for each Subject, Time, Task and Technol-
ogy. Regression coefficient images were used in a repeated-measure analysis of variance using
GLMFlex toolbox, with Time, Task and Technology as factors of interest and Subjects as a
random factor.
Probabilistic tractography
A binarized mask of the left superior frontal gyrus cluster was used as a seed for probabilistic
tractography using FSL [39], a software library of analysis tools for neuroimaging data. Each
subjects B0 image was registered to their T1-weighted structural image using a 6-degree of
freedom, rigid-body registration computed by FSLs FLIRT algorithm. T1 images were first reg-
istered to the MNI 1mm template using a 12-degree of freedom, affine registration computed
by FLIRT, which was then used to constrain a nonlinear warp computed by FSLs FNIRT algo-
rithm. The T1-to-MNI and B0-to-T1 registrations were then inverted and concatenated to
warp MNI-space functional activations into individual subjectsdiffusion space. These diffu-
sion-space activation masks were used to seed probabilistic tractography analyses using prob-
trackx, a tool in FSLs FDT software package. Tracts were thresholded at 0.1% of the waytotal,
binarized, warped into MNI template space, and summed. We measured, in native diffusion
space, the number of above-threshold voxels from each tract that reached each of the gray mat-
ter regions included in the AAL atlas [40].
Results and Discussion
The ratio of correct over expressed responses was calculated for each Subject, Time, Task,
Technology and Session. Within subjects mixed effect analysis of variance, using Session as a
random variable, revealed a significant effect of Time (F(2,257) = 12.3, p<. 001) and an inter-
action between Time and Task (F(2,257) = 3.3, p<. 04) on the proportion of correct responses
(Fig. 2). The three way interaction Time by Task by Technology did not reach significance (F-
(2,257) = 2.5, p= .085). Other effects and interactions were also non-significant (p>0.05).
Post-hoc pairwise comparisons showed that the proportion of correct responses for the Strate-
gy Task was significantly lower than for physical Prediction. All pairwise comparisons between
Times were significant for the Strategy Task (Fig. 2a), but none for the Prediction Task. Thus,
the judgments of strategic appropriateness were more difficult at the outset, but improved with
training whereas judgments on the physical accuracy of predictions did not.
To assess the real-world relevance of our experimental tasks, we compared task perfor-
mance with subjectsactual tool production. For Oldowan flaking, we measured total area
(Length x Breadth) of flakes produced. For Acheulean handaxe-making, we measured the
width/thickness ratio ("refinement") of finished artifacts, a conventional index of skill for in bi-
facial tool production [34]. There was a clear increase in Oldowan flake area from the first to
last evaluation (mean = 6253 mm
vs. 19008 mm
, each subject increased flake area by at least
1.9x). Handaxe refinement showed no such trend (mean = 2.23 vs. 2.25), although more quali-
tative progress by individuals seems apparent (Fig. 1). Success at Oldowan flake production
was not significantly correlated with performance rank on Oldowan stimuli during associated
Cognitive Demands of Lower Paleolithic Toolmaking
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fMRI scans (Spearmans rho = 0.436, df = 11, p = 0.09, one-tailed), especially when Strategy
(Spearmans rho = 0.360, df = 11, p = 0.138, one-tailed) and Prediction (Spearmans rho =
-0.146, df = 11, p = 0.334, one tailed) tasks are considered separately. In contrast, subjects who
produced relatively thinner handaxes scored better on Acheulean stimuli in the scanner (Spear-
mans rho = 0.750, df = 9, p = 0.010, one-tailed) including both Strategy (Spearmans
rho = 0.619, df = 9, p = 0.038, one-tailed) and Prediction (Spearmans rho = 0.583, df = 9,
p = 0.050, one tailed) tasks. Thus, our training group gained practical competence in Oldowan
flaking irrespective of ability to correctly judge flake predictions or strategy. Conversely, there
was no measurable group-level increase in handaxe making skill, but individual success was
linked with the ability to make technological judgments as measured in our paradigm. Detailed
data on all artifacts produced during training are presented in [32].
fMRI response and relation to behavior
Factorial analyses identified significant main effects of Time and Technology as well a three-way
interaction between Time, Task and Technology (p<0.001 uncorrected, extent >75 mm
Fig 2. Location of the significant 3-way interaction in left SFG (top) and the relation of fMRI signal change to task performance (bottom). Arrows a
dindicate significant pairwise differences across time and tasks. eis a regression line (r = 0.294).
Cognitive Demands of Lower Paleolithic Toolmaking
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predicted by our hypothesis. Effects of Time and Technology were observed in occipital, parietal
and premotor cortex and are consistent with previous work on perceptual learning generally
and stone toolmaking specifically.
fMRI main effect of Technology. The main effect of technology was associated with clus-
ters in the occipital and parietal cortex. However, parietal effects were small, and failed to reach
significance in post-hoc comparisons. Occipital effects were localized in the left (x,y,z = -27,
-91, 25; z-score = 3.76; extent = 316 mm
) and right (x,y,z = 36, -90, 18; z-score = 3.57; ex-
tent = 29 mm
) middle occipital gyrus (MOG, BA 19). This portion of dorsal middle occipital
gyrus comprises early visual association cortex, and it is likely these activations reflect low-level
differences in the visual properties (e.g. size and shape) of Oldowan vs. Acheulean stimuli.
Right MOG activity was significantly correlated with individual performance on tasks involv-
ing Acheulean stimuli (n = 136, Pearson's r = 0.322, p <0.001), possibly indicating that atten-
tional modulation played some role in variation of response across subjects. Other Region by
Technology correlations with performance were not significant.
fMRI main effect of Time. Fig. 3 depicts brain regions showing a main effect of the Time
of scanning. Two patterns are evident. First, activity in the left ventral MOG (BA 19) showed a
significant increase across each time-step. Attention modulates activity in visual cortex [41],
and it is likely that these increases reflect response enhancement arising from training-related
changes in visual attention to stimulus features. This would be consistent with numerous stud-
ies indicating changes in neural activity associated with perceptual learning [42]. A previous
FDG-PET study of stone toolmaking skill acquisition [20] found similar training-related activi-
ty increases in MOG and reached similar conclusions. Consistent with this interpretation, left
MOG response was weakly, but significantly, correlated with behavioral performance (n = 272,
Fig 3. fMRI main effect of Time in (from left to right) left middle occipital gyrus, right posterior
intrapareital sulcus, and right precentral gyrus. Brackets indicate significant post-hoc comparisons.
Cognitive Demands of Lower Paleolithic Toolmaking
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Pearson's r = 0.140, p = 0.021). This overall correlation was specifically driven by a relationship
with the Strategy task (n = 136, Pearsons r = 0.287, p = 0.001), whereas there was no correla-
tion with Prediction (n = 136, Pearsons r = -0.034, p = 0.693) considered separately. Thus it
appears similar perceptual strategies were deployed across tasks, but were only effective for
Second, activity at the fundus of the posterior part of the right posterior intraparietal sulcus
(attributable to functional area IPS0) and in the right precentral gyrus (PrCG) showed initial
increases from T1 to T2, followed by reduction from T2 to T3. This parallels structural changes
observed in the same subjects using DTI [23], which showed T1 to T2 increases followed by
symmetrical T2 to T3 decreases in white matter fractional anisotropy within branches of the
superior longitudinal fasciculus leading into precentral gyrus and posterior parietal cortex.
These anatomical changes correlated with subjectshours of practice prior to each scan (train-
ing was most intense before T2) and appear to reflect transient responses to the perceptual-
motor demands of stone toolmaking practice. It is likely that the observed effect of Time on
frontoparietal activity here reflects a corresponding functional response.
Frontoparietal activity, including PrCG, supramarginal gyrus (SMG) and intraparietal sul-
cus (IPS), has been a consistent result in FDG-PET [20,21] and fMRI [22] studies of stone tool-
making. We have previously attributed such activations to demands for grasp control (PrCG),
visuospatial processing (posterior IPS), and sensorimotor transformation (SMG) in the coordi-
nated control of action. In our current paradigm, which involved judgments about visually pre-
sented tools without actual prehension, PrCG response was modulated by experience but did
not correlate with performance. This is consistent with the well-documented participation of
premotor cortex in the perception of graspable objects [43], and its modulation by experience
with object function [44]. Activity in right IPS0, a retinotopic visual area modulated by spatial
attention [45] and preferentially responsive to tools [46], did correlate with behavioral perfor-
mance (n = 272, Pearson's r = 0.143, p = 0.018). As with left MOG, this relationship was driven
by correlation with the Strategy task (n = 136, Pearsons r = 0.246, p = 0.004), and not Predic-
tion (n = 136, Pearsons r = 0.006, p = 0.947). It is unclear why visual areas in right IPS0 and
left MOG display different patterns of response to training, although similar variability across
studies of perceptual learning is thought to reflect the existence of multiple learning stages [47]
with different effects at different locations along visual pathways [48]. It is also notable that
IPS0 and PrCG activations are both in the right hemisphere. This is a consistent feature of
frontoparietal activations associated with stone toolmaking [21,24], and stands in contrast to
the left lateralization of everyday tool-use [49].
fMRI Interaction of Time, Task and Technology. Consistent with our research hypothe-
sis, we observed a three-way interaction in the left superior frontal gyrus (lSFG: x, y, z = -8, 38,
49; z-score = 3.36; extent 22 voxels), a prefrontal region implicated in cognitive control functions
[50] including working memory [51](Fig. 2, top). Post-hoc comparisons show that response to
Oldowan Strategy (OS) was initially high and decreased through time (Fig. 2b), whereas re-
sponse to Acheulean Strategy (AS) was initially lower and remained constant. At the same time,
response to Acheulean Prediction (AP) decreased through time (Fig. 2c), but Oldowan Predic-
tion (OP) did not. As a result, OS was greater than OP at T1 (Fig. 2c) whereas AS was greater
than AP at T3 (Fig. 2d). This complex pattern likely reflects experimentation with different cog-
nitive strategies over learning, as is typical of early/intermediate stage skill acquisition [22]. In
fact, the relationship of lSFG activity to actual task success suggests (Fig. 2) that much of this ex-
perimentation was ineffective and/or misguided. SFG activity was uncorrelated with Prediction
success at any time point, consistent with the expectation that this task should require perceptu-
al-motor simulation rather than cognitive control. Conversely, lSFG activity was positively cor-
related with Strategy success (Fig. 2e), consistent with the expectation that this task demands the
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cognitive manipulation of information, but only at T3 (n = 48, Pearson's r = 0.294, p = 0.042)
when (some) relevant concepts had been learned and performance was at its highest.
Probabilistic tractography and functional connectivity
Intrinsic functional connectivity analyses have identified large-scale functional networks in the
human brain [52], including a control network associated with planning and cognitive control,
a dorsal attention network associated with external attention, a default network associated with
internal attention (e.g. memory and prospection), and a somatomotor network involved in
motor control. It has been proposed that lSFG is a key region supporting interaction between
default and control networks during goal-directed cognition [50,53], which would be consis-
tent with its involvement in our tasks. We thus predicted that the lSFG cluster identified in our
fMRI analysis would: 1) be anatomically connected with default and control networks, and 2)
show shifts in functional connectivity with these networks during task performance.
To investigate connectional anatomy, we used our lSFG cluster as a seed for probabilistic
tractography. The top three cortical targets identified for our cluster were elements of the con-
trol (anterior cingulate cortex, middle frontal gyrus) and default (inferior frontal gyrus) net-
works, supporting the hypothesis that this portion of lSFG enables coupling of control and
default networks. Li et al. [54] parcellated SFG into three sub-regions: anteromedial (SFGam;
connected with anterior and mid-cingulate cortices assigned to control and the default net-
works), dorsolateral (SFGdl; connected with middle and inferior frontal gyri linked to the con-
trol and default networks), and posterior (SFGp; connected with the precentral gyrus and
frontal operculum of the somatomotor network). We compared the connectional fingerprint
of our cluster with reported values for these three sub-divisions and found it to be intermediate
between SFGam and SFGdl (Fig. 4a), consistent with its intermediate location on the sub-
region probability map.
To investigate functional connectivity, we conducted a factorial analysis of maps of regres-
sion coefficients with lSFG activity during stimulation blocks. We observed significant
(p<0.001 uncorrected, extent >75 mm
) effects of Task in frontal cortex and interactions be-
tween Time and Task in frontoparietal cortex (Fig. 4b) as well as interactions between Time
and Technology in the middle temporal gyrus and cerebellum, and a complex three-way inter-
action between Time, Task, and Technology in right SFG. The observed effect of Task and its
interaction with Time are perhaps the most relevant results for the current investigation, since
these factors were significant sources of behavioral variation and influenced its correlation with
lSFG activity (see above).
fcMRI Main Effect of Task. Task effects on functional connectivity were seen in regions
attributed [52] to default (medial frontopolar cortex) and control networks (posterior and an-
terior middle frontal gyrus). Increased functional connectivity during Prediction vs. Strategy
tasks (Fig. 4b, hot color scale) was seen in the left posterior middle frontal gyrus (dorsal anteri-
or premotor cortex, BA 8: x, y, z = -40, 16, 33; z-score = 5.56; extent 41 voxels) whereas in-
creased connectivity for Strategy vs. Prediction (Fig. 4b, cold color scale) was observed in left
anterior middle frontal gyrus (mid-DLPFC, BA 9/46: x, y, z = -49, 40, 21; z-score = 4.09; extent
25 voxels) and medial frontopolar cortex (mFPC, BA 10: x, y, z = 8, 60, 4; z-score = 3.92; extent
25 voxels). Insofar as frontal cortex function is organized along a posterior-to-anterior gradient
of increasing cognitive abstraction [25], this pattern is consistent with the expectation of great-
er demands for abstract information processing in Strategy vs. Prediction tasks.
fcMRI Interaction of Task and Time. Interactions between Task and Time (Fig. 4b, tan-
gerine color) were observed in left inferior frontal gyrus (IFG, BA 45: x, y, z = -48, 30, 26;
z-score = 3.23; extent 24 voxels), posterior supramarginal gyrus (BA 40: x, y, z = -40, -50, 44;
Cognitive Demands of Lower Paleolithic Toolmaking
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z-score = 2.96; extent 22 voxels), and the posterior portion of dorsal anterior cingulate cortex
(dACC, BA 24: x, y, z = -6, 18, 43; z-score = 3.11; extent 25 voxels), brain regions involved in
executive planning [55] and monitoring [56] functions, including tool-use planning [49],
working memory, and language [57] tasks. Post-hoc comparisons (Fig. 4c) show that, for Pre-
diction, functional connectivity with each of these regions was initially high but decreased with
Time whereas for Strategy it was initially low but held steady or increased. As a result, T3 con-
nectivity is greater for Strategy vs. Prediction in each region. The decreases in functional con-
nectivity across time for Prediction are not paralleled by significant change in behavior
suggesting a shift between equally viable cognitive strategies. Conversely, neutral to positive
changes in functional connectivity for the Strategy task were accompanied by incremental in-
creases in behavioral performance, suggesting a more uniform role across time.
fcMRI Interaction of Time and Technology. Fig. 5 depicts brain regions for which func-
tional connectivity was affected by the interaction of time and technology, specifically the right
middle temporal gyrus (MTG) and lateral cerebellar cortex. The MTG is important in the re-
presentation of conceptual knowledge, including the association between objects (especially
Fig 4. Anatomical and functional connectivity of the left SFG cluster. (a) Radar plot of top targets for SFG sub-regions usingdata reported by Li et al.
[40] (blue: lSFGam, red: lSFGdl, green: lSFGp) and our own analysis of the lSFG cluster reported here (purple). ACC: anterior cingulate cortex, Cau:
caudate, MFG: middle frontal gyrus, IFG_Tri: inferior frontal gyrus pars triangularis, IFG_Oper: inferior frontal gyrus pars opercularis, PreCG: precnetral
gyrus, Th: thalamus, PCC: posterior cingulate cortex, MCC: middle cingulate cortex. (b) Surface renders of significant experimental effects on functional
connectivity (hot scale: increased for Prediction vs. Strategy, cold scale: increased for Strategy vs. Prediction, tangerine: Task x Time interaction). (c)
Regression coefficients for the Task x Time interaction with significant post-hoc comparisons indicated by brackets.
Cognitive Demands of Lower Paleolithic Toolmaking
PLOS ONE | DOI:10.1371/journal.pone.0121804 April 15, 2015 11 / 18
tools [58]) and related actions [59]. Increased of functional connectivity between lSFG and
MTG over time is seen for Acheulean stimuli only and irrespective of task. We speculate that
this could represent increasing reference to semantic knowledge about handaxe making that
was acquired over training. We also note that the MTG cluster reported here approximates one
node in the cortical default network [60] and that potions of lateral cerebellum may also be
linked to this network [61]. Again speculatively, this might suggest an increased role for intro-
spective access to semantic knowledge following training.
fcMRI Interaction of Time, Task and Technology. Fig. 6 shows the portion of right supe-
rior frontal gyrus (rSFG) where a significant three way (Time, Task, Technology) interaction
effect on functional connectivity with lSFG was observed. This cluster is likely located in the
right homolog of the functional cortical area containing the lSFG seed. Substantial functional
connectivity via callosal connections between the regions is thus expected, and is consistent
with the bilateral/bimanual coordination required for successful stone toolmaking [62]. How-
ever, the complex patterning of this connectivity across conditions is unexpected. One possible
explanation for this unexpected complexity could be that the functional importance of inter-
hemispheric coordination varied over the course of learning as subjects experimented with dif-
ferent behavioral strategies. For example, recent lesion work has linked rSFG to the self-
focused reappraisal of negative emotions, perhaps reflecting a more general cognitive role in in-
hibition [63]. We have previously argued that inhibition, particularly by the right hemisphere,
is an important element in both the execution and simulation of stone tool-making strategies
[13,21]. This might potentially relate to increased functional connectivity with rSFG under
Fig 5. fcMRI interaction of Time and Technology in middle temporal gyrus and cerebellum. Brackets
indicate significant post-hoc comparisons.
Cognitive Demands of Lower Paleolithic Toolmaking
PLOS ONE | DOI:10.1371/journal.pone.0121804 April 15, 2015 12 / 18
some conditions (e.g. for Oldowan strategy vs. prediction following acquisition of flake produc-
tion skill).
General Discussion
We have shown that making technical judgments about Lower Paleolithic toolmaking affects
neural activity and functional connectivity in dorsal prefrontal cortex, that effect magnitude
correlates with the frequency of correct strategic judgments, and that the ability to make such
judgments is predictive of success in Acheulean, but not Oldowan, toolmaking. This corrobo-
rates hypothesized cognitive control demands of Acheulean toolmaking, specifically including
information monitoring and manipulation functions attributed to the "central executive" of
working memory.
Stone toolmaking is a demanding technical skill that can take years to master. With an aver-
age of 167 hours practice over 22 months, our subjects gained competence in flake production
but showed less improvement in handaxe-making. This provides a reference point for estimat-
ing the learning investments of Paleolithic toolmakers [17,34]. Ability to judge strategic appro-
priateness increased steadily with training, whereas concrete fracture prediction did not,
corroborating evidence that technological concepts are more easily acquired than the perceptu-
al-motor skills needed for controlled and predictable flake detachment [17]. Interestingly,
training effects in visual cortex (IPS0, MOG) were predictive of success on the Strategy task,
suggesting that the education of attention is also an important aspect of such conceptual
Fig 6. fcMRI interaction of Time, Technology, and Task in right Superior Frontal Gyrus. Brackets indicate significant post-hoc comparisons.
Cognitive Demands of Lower Paleolithic Toolmaking
PLOS ONE | DOI:10.1371/journal.pone.0121804 April 15, 2015 13 / 18
understanding in stone toolmaking. The demands of perceptual-motor skill acquisition should
be taken into account when evaluating the cognitive implications of prehistoric technologies,
and particularly the self-regulatory capacities and social scaffolding that may have been neces-
sary for sustained, deliberate practice [4,17].
We did not find a strong relationship between the predictive and strategic abilities measured
by our experimental tasks and actual success at Oldowan flaking. This reinforces the point that
flake production is a simple technology [64] with limited contingency between successive ac-
tions [65,66], so that unexpected outcomes and sub-optimal choices are easily accommodated
if basic requirements for forceful, accurate percussion are met [21]. In contrast, success making
technological judgments was consistently predictive of success at handaxe-making. This re-
flects the fact that bifacial thinning is a more difficult technique requiring the reliable produc-
tion of particular flake features and contingent action sequences [66], and is consistent with
previous imaging studies showing increased prefrontal responses to Acheulean toolmaking
[21,22]. We conclude that explicit prediction and evaluation of toolmaking action outcomes
may be unnecessary for effective Oldowan flaking but is a normal part of Acheulean handaxe-
making skill.
The three way interaction in lSFG confirms our prediction that the cognitive control de-
mands of toolmaking are modulated by a combination of task, training, and technology. Execu-
tive function, including the central executiveof working memory, is most classically
associated with mid-DLPFC but a broader network of regions is clearly relevant [26,52,67].
Lesion evidence indicates that lSFG is an important part of this network, specifically contribut-
ing to information monitoring and manipulation [51]. We did not anticipate the complexity of
the interaction of Time, Task and Technology in lSFG, the details of which may reflect the ex-
ploration and even misunderstanding [28] typical of early/intermediate stage learning. Consis-
tent with this interpretation, the relationship of lSFG activity with actual task success rates did
meet expectations, being uncorrelated for Prediction but positively correlated for Strategy. This
lSFG contribution to strategic evaluation likely arises from the regions position as a key node
for interaction between default and control networks during internally focused, goal-directed
cognition [50,54], and particularly the planning/simulation of future actions [55] (cf. mental
time travel[68]). Indeed, we found that the lSFG cluster identified in our study had its most
salient anatomical connections of with elements of the control and default networks, and mod-
ulated its functional connectivity with these networks in response to our experimental tasks
and training.
Independent of training, the Strategy task elicited greater functional connectivity with left
mid-DLPFC (control network) and medial FPC (default network). Both regions are involved
in planning and decision making, with mid-DLPFC classically contributing to monitoring
task-relevant information in working memory and FPC to metacognitive management of ab-
stract relations and competing goals [25]. Medial FPC specifically is involved in the manipula-
tion of information held in memory [69], including the prospective memory of planned
actions and intentions [70]. In contrast, the Prediction task produced greater functional con-
nectivity with anterior premotor cortex, a region associated with lower-level cognitive control
functions such as domain-specific working memory maintenance and action selection based
on contextual cues [25]. This dichotomy confirms the task-sensitive coupling of default and
control networks via lSFG in our experiment, and supports the hypothesized involvement of
abstract information monitoring and manipulation during strategic judgments about Paleolith-
ic toolmaking action plans. Such integration was also evident in the interaction between Task
and Time, which involved a subset of default and control regions recently shown to experience
coupling during future planning by process simulation[55]. The pattern of this interaction
Cognitive Demands of Lower Paleolithic Toolmaking
PLOS ONE | DOI:10.1371/journal.pone.0121804 April 15, 2015 14 / 18
suggests that training led subjects to rely less on such prospective simulation when anticipating
physical outcomes, but that it continued to be relevant for strategic evaluation.
Sixty years ago, it was uncontroversial to assert that Even the crudest Paleolithic artifacts indi-
cate considerable forethought...Using a hammerstone to make a hand-axe, and striking a
stone flake to use in shaping a wooden spear, are activities which epitomize the mental charac-
teristics of man[6: 15]. Although progress in archaeological and comparative research has fos-
tered healthy skepticism regarding such naive appraisals [64], results presented here lend
support to the intuitions of an earlier generation and offer hope for further insights into
human cognitive evolution. It has been proposed that modern human cognition emerged
through changes in prefrontal executive function [1,3] but that, unfortunately, most behaviors
preserved in the archaeological record do not document these changes. Stone tools in particular
are seen as products of mundane, over-learnt routines that would not have required flexible
cognitive control [1]. This contrasts with the introspection of some toolmakers, who assert that
toolmaking based on raw material which is never standard, and with gestures of percussion
that are never perfectly delivered[27: 117] cannot be reduced to formulaic routines and neces-
sarily involves flexible prospection and planning. We hypothesized that such demands, if pres-
ent, would have been most pronounced during learning, with effortful cognitive control
processes acting as a scaffoldduring unskilled performance [71]. This was confirmed by our
results, which show that novice toolmakers rely on the executive functions of lSFG, and partic-
ularly its connectivity with functional networks involved in prospective simulation, to make
correct strategic judgments. Furthermore, we found that the ability to make such judgments
was predictive of success in handaxe-making but not simple flake production. This is consistent
with previous findings of greater prefrontal responses to naturalistic Achuelean vs. Oldowan
toolmaking, and indicates that the increased cognitive control demands of Acheulean toolmak-
ing specifically include dorsal PFC information monitoring and manipulation. Apart from
these specific conclusions, our results more broadly show that it is possible to measure the dif-
ferential cognitive control demands of even the simplest Lower Paleolithic technologies. This
information will not resolve the directionality of causation between technological, cognitive
and neuroantomical changes over human evolution, which must be addressed in other ways
[7,23]. What it does is allow for objective comparison of the cognitive control demands of
archaeologically observable behaviors, thus expanding the scope of hypotheses regarding the
context and timing of evolutionary developments [10] that can be tested using the millions of
stone artifacts which dominate the "Stone Age" archaeological record.
Thanks are due to Chris Frith and James Kilner for advice and support on this project, to An-
tony Whitlock for assistance with toolmaking training, and to an anonymous reviewer for
helpful comments.
Author Contributions
Conceived and designed the experiments: DS TC. Performed the experiments: DS BB NK. An-
alyzed the data: TC EH DS. Wrote the paper: DS TC EH. Trained subjects: NK BB. Analyzed
experimental artifacts: NK.
Cognitive Demands of Lower Paleolithic Toolmaking
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... During the production of both types of tools, there were acti vated the posterior occipital areas, superior and inferior parietal lobules, as well as the lateral and ventral precentral gyri (Brodmann's field 6). Pro duction of Acheulean tools required much stronger activation of the inferior parietal, dorsal and ven tral premotor brain areas in both hemispheres, as well as the right homolog of the Broca's area [120,122]. Another large scale study conducted at the Emory University (Atlanta) showed that after two years of training to make Acheulean tools, partici pants of the experiment exhibited a change in the volume of the superior longitudinal fasciculus, which correlated with the training time and suc cessfulness of tool making [122]. As shown above, the superior longitudinal fasciculus is part of the dorsal pathway, which underwent maximum changes during speech evolution. ...
... Pro duction of Acheulean tools required much stronger activation of the inferior parietal, dorsal and ven tral premotor brain areas in both hemispheres, as well as the right homolog of the Broca's area [120,122]. Another large scale study conducted at the Emory University (Atlanta) showed that after two years of training to make Acheulean tools, partici pants of the experiment exhibited a change in the volume of the superior longitudinal fasciculus, which correlated with the training time and suc cessfulness of tool making [122]. As shown above, the superior longitudinal fasciculus is part of the dorsal pathway, which underwent maximum changes during speech evolution. ...
... While Oldowan tools of the first Homo represented rough flakes, minimally improved, obtained by striking one stone with another [100], Acheulean tools, as exemplified by hand axes-more or less symmetri cal tear drop shaped tools well suitable for meat cutting, required a multitude of goal directed movements for attaining a certain predetermined shape [120]. In the above experiments, making Acheulean tools led to a stronger activation of the prefrontal cortex, which may reflect higher demands for cognitive control and working mem ory [122]. Probably, the same functions were needed to analyze complex messages in the vocal or gestural form. ...
The communicative system is most developed in social animals and represents a determinative factor in human evolution. Here we address the possible evolutionary ways of the emergence and formation of speech functions. Specifically, we discuss the possibility of the formation of voluntary voice control at the early stages of Homo evolution on the anatomo-physiological basis of the gesture control system. The possible morphofunctional basis for the evolutionary emergence of speech is considered. The structure and connectivity of different regions of the cerebral cortex (temporal, inferior, parietal) are compared in humans and monkeys. Anatomy of the auditory cortex is well known to be relatively alike in different primate species, likewise are the pathways that link the auditory cortex with the associative areas of the anterior temporal cortex and the ventrolateral prefrontal cortex (human homologs of these structures form the ventral speech stream). At the same time, the pathways that link the auditory cortex with the ventral premotor and inferior motor cortex and make up in humans the dorsal speech stream are poorly developed in monkeys. The underdevelopment of this speech stream accounts for communication in monkeys based on gestural speech, because the morphological basis for voluntarily regulated vocalization and articulation has not yet formed during their evolution. The emergence of a sophisticated communication system based on oral speech may have been possible due to a gradual accumulation of evolutionary changes in speech-related and some other brain structures and, most importantly, a complication of connectivity between them. The evolution of speech is discussed in terms of its close association with tool use and labor activity.
... They have also trained novices to knap stone, imaging their brains at set intervals over months of learning. 25 The theoretical basis for interpreting the neuroimagery is that of cognitive neuroscience, which links experimentally based cognitive models such as cognitive control to the patterns of neural activation. ...
... They also imaged expert knappers performing similar tasks. 25 Patterns of activation differed between Oldowan and Acheulean tasks. Areas of the pars triangularis of the right prefrontal cortex were active in handaxe manufacture but not in the simple knapping of flakes. ...
... Once again, Stout and colleagues have provided evidence of the role of working memory through experimental archeology and neuroimaging. 24,25 In their fMRI investigation of stone knapping, they have been able to contrast neural activation supporting simple Oldowan-like knapping with activation supporting the knapping of handaxes typical of the late Acheulean. One clear difference was activation of the right frontal pars triangularis in knapping the handaxe. ...
How did the human mind evolve? How and when did we come to think in the ways we do? The last thirty years have seen an explosion in research related to the brain and cognition. This research has encompassed a range of biological and social sciences, from epigenetics and cognitive neuroscience to social and developmental psychology. Following naturally on this efflorescence has been a heightened interest in the evolution of the brain and cognition. Evolutionary scholars, including paleoanthropologists, have deployed the standard array of evolutionary methods. Ethological and experimental evidence has added significantly to our understanding of nonhuman brains and cognition, especially those of nonhuman primates. Studies of fossil brains through endocasts and sophisticated imaging techniques have revealed evolutionary changes in gross neural anatomy. Psychologists have also gotten into the game through application of reverse engineering to experimentally based descriptions of cognitive functions. For hominin evolution, there is another rich source of evidence of cognition, the archeological record. Using the methods of Paleolithic archeology and the theories and models of cognitive science, evolutionary cognitive archeology documents developments in the hominin mind that would otherwise be inaccessible.
... For example, knappers have long used copper pressure flakers or percussion "boppers" as a substitute for antler pressure flakers or billets (Whittaker, 1994(Whittaker, , 2004. Experimenters have used both glass and porcelain as substitutes for conchoidally fractured stone (Dibble and Pelcin, 1995;Dibble and Rezek, 2009;Dogandžićet al., 2020;Hecht et al., 2015;Iovita et al., 2014Iovita et al., , 2016Khreisheh et al., 2013;McPherron et al., 2020;Rezek et al., 2011;Speer, 2018;Stout et al., 2015), and foam, plasticine, and even potatoes have been used as substitutes for stone specifically or reductive materials more generally (Clarkson, 2017;Schillinger et al., 2014aSchillinger et al., , 2014bSchillinger et al., , 2015Schillinger et al., , 2016Schillinger et al., , 2017. Some recent experiments have featured conchoidally fracturing stone specimens that were ground into shape with modern lapidary equipment, rather than knapped (e.g. ...
... Expertise, experience, and intuition are excellent tools for developing hypotheses, but cannot serve as the test or proof of those hypotheses. 2. A popular modern material substitute for conchoidally-fracturing rock is porcelain Khreisheh et al., 2013;Speer, 2018;Stout et al., 2015;Tsirk, 2014), which is knapped or broken in specific ways for various experimental archaeological investigations. While we fully acknowledge the advantage and insights that porcelain can provide to archaeological experiments, we are surprised thatto our knowledgethere has not been a comparison of amount of force necessary to initiate a crack in porcelain versus stone. ...
Experimental archaeology continues to mature methodologically and theoretically. Around the world, practitioners are increasingly using modern materials that would have been unavailable to prehistoric people in archaeological experiments. The use of a modern material substitute can offer several benefits to experimental method, design, control, replicability, feasibility, and cost, but it should be directly compared to its “traditional” analogue to understand similarities and differences. Here, aluminum is introduced as a substitute for chert in prehistoric ballistics research because, critically, aluminum is safe, inexpensive, easy to process, and it and chert possess densities that differ by less than 4%. The aluminum casting process for replicating stone artifacts is presented, and it is shown that the aluminum castings are essentially identical in form, flake-scar patterning, and mass to their stone counterparts. We then present a proof-of-concept ballistics experiment that demonstrates no difference between aluminum and stone points in terms of target penetration.
... The earliest Oldowan tools were simply made by chipping off a stone core through direct percussion ). However, the ability to make a Lower Paleolithic hand axe depends on complex cognitive control by the prefrontal cortex (Stout et al. 2015). ...
... Consequently, some form of human language would have existed not only for devising Acheulean tools but also for promoting their universal use. Coincident with language-processing regions, strategic thinking for attaining the final product or for predicting the resulting flake relies on the prefrontal cortex and the posterior parietal lobe (Stout et al. 2015). Specifically, simple sounds of communication, such as the words yes and no, words used for planning and following step-by-step instructions, predicting where flakes will fall, and words that define egocentric direction, including right and left, are needed between tool-makers. ...
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Although the definition of intelligence is debatable, it can be allocated to only one anatomical location: the brain. Arguments regarding general measures of animal intelligence and discussions of its evolution up to the Neanderthals arise only because hominids have evolved to have larger brains; i.e., they have become more “intelligent”. Hominids clearly evolved in the past, but whether evolution is still ongoing is debated. Ironically, because hominids have created technologies and innovations to aid their survival, their evolution has included adaptation to the environment generated by their inventions. Similar to the recent evolution of ADHD traits or gluten tolerance, the hominid brain has undergone major changes over the past seven million years due to man-made habitats and technologies. Tool-making creates an environment conducive to increased social interactions, as it facilitates increased provisioning and protection, while increased opportunities for interactions and observations lead to advances in toolmaking. These changes have been offset by the concurrent evolution of language and tool-making. Biologically, hominid brains have increased in size in areas where toolmaking and language-processing coincide. This increase in brain size allowed advanced provisioning and tools, including the use of fire, and the technological advances during the Palaeolithic that stood on the shoulders of the previous evolutionary innovations of bipedalism and versatile hands enhanced the momentum of brain evolution. The beginnings of the reciprocal cause and effect between brain evolution and tool-making cannot be identified. The applicability of the hunting and fire hypotheses to the evolution of human intelligence is further discussed.
... This shift is regarded as a major break from the preceding Oldowan. The emergence of the Acheulian is argued to represent higher cognitive abilities from those of Oldowan-making hominins and is suggested to mark a fundamental change in hominin lifeways (de la Torre, 2016; Stout, 2018;Stout et al., 2015). ...
Current models of early hominin biological and cultural evolution are shaped almost entirely by the data accumulated from the East African Rift System (EARS) over the last decades. In contrast, little is known about the archaeological record from the high-elevation regions on either side of the Rift. Melka Wakena is a newly discovered site-complex on the Southeastern Ethiopian Highlands (SEH) (>2300 m above mean sea level) just east of the central sector of the Main Ethiopian Rift (MER), where eight archaeological and two paleontological localities were discovered to date. Nine archaeological horizons from three localities were tested so far, all dated to the second half of the early Pleistocene (~1.6 to >0.7 Ma). All the lithic assemblages belong to the Acheulian technocomplex. Here we report on geochronological, stratigraphic, paleontological and lithic technological aspects of the tested localities and contextualize them in the broader framework of hominin cultural evolution in eastern Africa. Findings from Melka Wakena, assessed against the backdrop of the few other highland sites (Melka Kunture and Gadeb), support a scenario of expansion rather than dispersal from the Rift to the highlands. When considered in the context of the Rift-highlands interface, results of the first-phase research at Melka Wakena help to parse existing general models into archaeologically testable hypotheses and demonstrate the site's potential to contribute to research of early prehistory and to understanding the dynamics of early Pleis-tocene hominin populations in eastern Africa.
... The auditory capacities seem to have been fine-tuned to a basically modern constitution by H. heidelbergensis (de Boer, 2012;Martínez et al., 2004;Martínez et al., 2008;Martínez et al., 2013;Stoessel et al., 2016) and the Neanderthal hyoid bone is documented to be physiologically and biomechanically essentially modern-like, implicating the possibility of a roughly human speech capacity (Arensburg et al., 1989;Arensburg & Tillier, 1991;D'Anastasio et al., 2013). These physiological insights are complemented by stone tool analyses as well as experimental data evaluating the demands on cognition in tool production and assessing the role of communication in learning, concluding that even pre-Neanderthal technologies likely necessitated active teaching and involved extensive planning (Lombao et al., 2017;Lycett, von Cramon-Taubadel, & Eren, 2016;Morgan et al., 2015;Stout, Hecht, Khreisheh, Bradley, & Chaminade, 2015; but see Cataldo, Migliano, & Vinicius, 2018, on how speech alone was also a poor model). Adding to this picture, the modern variant of the FOXP2 gene, which is, among a multitude of other functions (Nudel & Newbury, 2013), linked to a normative development of language, has been shown to be present in both Neanderthals and Denisovans and therefore seems to date back to at least their last common ancestor with the lineage toward anatomically modern humans (Krause et al., 2007;Paixáo-Côrtez, Vicardi, Salzano, Hunemeier, & Bortolini, 2012;Reich et al., 2010). ...
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The inference of Neanderthal cognition, including their cultural and linguistic capabilities, has persisted as a fiercely debated research topic for decades. This lack of consensus is substantially based on inherent uncertainties in reconstructing prehistory out of indirect evidence as well as other methodological limitations. Further factors include systemic difficulties within interdisciplinary discourse, data artifacts, historic research biases, and the sheer scope of the relevant research. Given the degrees of freedom in interpretation ensuing from these complications, any attempt to find approximate answers to the yet unsettled pertinent discourse may not rest on single studies, but instead a careful and comprehensive interdisciplinary synthesis of findings. Triangulating Neanderthals' cognition by considering the plethora of data, diverse perspectives and aforementioned complexities present within the literature constitutes the currently most reliable pathway to tentative conclusions. While some uncertainties remain, such an approach paints the picture of an extensive shared humanity between anatomically modern humans and Neanderthals. This article is categorized under: • Cognitive Biology > Evolutionary Roots of Cognition • Linguistics > Evolution of Language Abstract Inferences on Neanderthal cognition, culture, and language are inherently unreliable when drawn on a case‐by‐case basis. Though some uncertainties remain, a broad interdisciplinary synthesis creates a compelling case in favor of high‐functioning Neanderthals.
... Stout (2011), Chaminade (2007, 2012), Stout et al. (2008Stout et al. ( , 2011Stout et al. ( , 2015 and Hecht et al. (2013) made the case for a relationship between brain lateralization and tool making. They argued that the activation of areas for lithic tool production is left brain focused and overlaps with language areas, singling out the importance of Acheulean tool production, related to its overall symmetry, platform preparation and the bifaciality of artifacts. ...
Labial striations on the anterior teeth have been documented in numerous European pre-Neandertal and Neandertal fossils and serve as evidence for handedness. OH-65, dated at 1.8 mya, shows a concentration of oblique striations on, especially, the left I¹ and right I¹, I² and C¹, which signal that it was right-handed. From these patterns we contend that OH-65 was habitually using the right hand, over the left, in manipulating objects during some kind of oral processing. In living humans right-handedness is generally correlated with brain lateralization, although the strength of the association is questioned by some. We propose that as more specimens are found, right-handedness, as seen in living Homo, will most probably be typical of these early hominins.
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Traditional and new disciplines converge in suggesting that the parietal lobe underwent a considerable expansion during human evolution. Through the study of endocasts and shape analysis, paleoneurology has shown an increased globularity of the braincase and bulging of the parietal region in modern humans, as compared to other human species, including Neandertals. Cortical complexity increased in both the superior and inferior parietal lobules. Emerging fields bridging archaeology and neuroscience supply further evidence of the involvement of the parietal cortex in human-specific behaviors related to visuospatial capacity, technological integration, self-awareness, numerosity, mathematical reasoning and language. Here, we complement these inferences on the parietal lobe evolution, with results from more classical neuroscience disciplines, such as behavioral neurophysiology, functional neuroimaging, and brain lesions; and apply these to define the neural substrates and the role of the parietal lobes in the emergence of functions at the core of material culture, such as tool-making, tool use and constructional abilities.
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The Acheulean handaxe is one of the longest-known and longest-surviving artifacts of the Palaeolithic and, despite its experimentally tested functionality, is often regarded as puzzling. It is unnecessary to invoke a unique-for-mammals genetic mechanism to explain the handaxe phenomenon. Instead, we propose that two nongenetic processes are sufficient. The first is a set of ergonomic design principles linked to the production of sturdy, hand-held cutting tools in the context of a knapped-stone technology that lacked hafting. The second is an esthetic preference for regular forms with gradual curves and pleasing proportions. Neither process is a cultural meme but, operating together in a cultural context, they can account for all of the supposedly puzzling time-space patterns presented by handaxes.
Archeologists have long assumed that earlier hominins were obligatory stone tool users. This assumption is deeply embedded in traditional ways of describing the lithic record. This paper argues that lithic evidence dating before 1.7 Ma reflects occasional stone tool use, much like that practiced by nonhuman primates except that it involved flaked-stone cutting tools. Evidence younger than 0.3 Ma is more congruent with obligatory stone tool use, like that among recent humans. The onset of habitual stone tool use at about 1.7 Ma appears correlated with increased hominin logistical mobility (carrying things). The onset of obligatory stone tool use after 0.3 Ma may be linked to the evolution of spoken language. Viewing the lithic evidence dating between 0.3-1.7 Ma as habitual stone tool use explains previously inexplicable aspects of the Early-Middle Pleistocene lithic record.
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Human infants and primates use similar strategies to organize utterances and motor actions. These strategies, called "grammars of action," are initially similar followed by an ontogenetic divergence in children that leads to a separation of complex linguistic and action grammars. Thus, more complex grammars arose after the emergence of the hominin lineage. Stone tools are by-products of action grammars that track the evolutionary history of hominin cognition, and this study develops a model of the essential motor actions of stoneworking interpretable in action grammar terms. The model shows that controlled flaking is achieved through integral sets of geometrical identifications and motor actions collectively referred to as the "flake unit." The internal structure of the flake unit was elaborated early in technological evolution and later trends involved combining flake units in more complex ways. Application of the model to the archaeological record suggests that the most complex action grammars arose after 270 kya, although significant epistemological issues in stone artifact studies prevent a more nuanced interpretation.
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One of the biggest problems faced by archaeologists engaged in flaked stone experiments is that of control. Controls are universally accepted as a way of ensuring reliability of experimental results used to interpret the archaeological record. Here, the use of a new and tightly controllable material in flaked stone experiments is proposed: porcelain. Unlike traditional approaches using stone, porcelain—while demonstrating comparable properties and fracture mechanics—can be more tightly controlled. As such, we suggest that its use ensures greater reliability in results derived from studies of tool manufacture and use and more potential for repeatability of experimental studies. A critique of previous approaches is followed by an assessment of porcelain as a suitable material for experimentation, in which two case studies that piloted the material are discussed. The first demonstrates its use in experiments regarding tool manufacture and skill. The second demonstrates its success in experiments dealing with observations of use-wear, such as projectile point impact fractures. The varied nature of these two studies aptly demonstrates the benefits of using porcelain as a controllable, moldable material.
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Stone tools provide some of the most abundant, continuous, and high resolution evidence of behavioral change over human evolution, but their implications for cognitive evolution have remained unclear. We investigated the neurophysiological demands of stone toolmaking by training modern subjects in known Paleolithic methods ("Oldowan", "Acheulean") and collecting structural and functional brain imaging data as they made technical judgments (outcome prediction, strategic appropriateness) about planned actions on partially completed tools. Results show that this task affected neural activity and functional connectivity in dorsal prefrontal cortex, that effect magnitude correlated with the frequency of correct strategic judgments, and that the frequency of correct strategic judgments was predictive of success in Acheulean, but not Oldowan, toolmaking. This corroborates hypothesized cognitive control demands of Acheulean toolmaking, specifically including information monitoring and manipulation functions attributed to the "central executive" of working memory. More broadly, it develops empirical methods for assessing the differential cognitive demands of Paleolithic technologies, and expands the scope of evolutionary hypotheses that can be tested using the available archaeological record.
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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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The ability to reappraise the emotional impact of events is related to long-term mental health. Self-focused reappraisal (REAPPself), i.e., reducing the personal relevance of the negative events, has been previously associated with neural activity in regions near right medial prefrontal cortex, but rarely investigated among brain-damaged individuals. Thus, we aimed to examine the REAPPself ability of brain-damaged patients and healthy controls considering structural atrophies and grey matter intensities, respectively. Twenty patients with well-defined cortex lesions due to an acquired circumscribed tumor or cyst and 23 healthy controls performed a REAPPself task, in which they had to either observe negative stimuli or decrease emotional responding by REAPPself. Next, they rated the impact of negative arousal and valence. REAPPself ability scores were calculated by subtracting the negative picture ratings after applying REAPPself from the ratings of the observing condition. The scores of the patients were included in a voxel-based lesion-symptom mapping (VLSM) analysis to identify deficit related areas (ROI). Then, a ROI group-wise comparison was performed. Additionally, a whole-brain voxel-based-morphometry (VBM) analysis was run, in which healthy participant’s REAPPself ability scores were correlated with grey matter intensities. Results showed that 1) regions in the right superior frontal gyrus (SFG), comprising the right dorsolateral prefrontal cortex (BA9) and the right dorsal anterior cingulate cortex (BA32), were associated with patient’s impaired down-regulation of arousal, 2) a lesion in the depicted ROI occasioned significant REAPPself impairments, 3) REAPPself ability of controls was linked with increased grey matter intensities in the ROI regions. Our findings show for the first time that the neural integrity and the structural volume of right SFG regions (BA9/32) might be indispensable for REAPPself. Implications for neurofeedback research are discussed.
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically,Statistical Parametric Mappingprovides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. * An essential reference and companion for users of the SPM software * Provides a complete description of the concepts and procedures entailed by the analysis of brain images * Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data * Stands as a compendium of all the advances in neuroimaging data analysis over the past decade * Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes * Structured treatment of data analysis issues that links different modalities and models * Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible.