Cave acoustics in prehistory: Exploring the association of Palaeolithic visual motifs and acoustic response

Abstract

During the 1980 s, acoustic studies of Upper Palaeolithic imagery in French caves—using the technology then available—suggested a relationship between acoustic response and the location of visual motifs. This paper presents an investigation, using modern acoustic measurement techniques, into such relationships within the caves of La Garma, Las Chimeneas, La Pasiega, El Castillo, and Tito Bustillo in Northern Spain. It addresses methodological issues concerning acoustic measurement at enclosed archaeological sites and outlines a general framework for extraction of acoustic features that may be used to support archaeological hypotheses. The analysis explores possible associations between the position of visual motifs (which may be up to 40 000 yrs old) and localized acoustic responses. Results suggest that motifs, in general, and lines and dots, in particular, are statistically more likely to be found in places where reverberation is moderate and where the low frequency acoustic response has evidence of resonant behavior. The work presented suggests that an association of the location of Palaeolithic motifs with acoustic features is a statistically weak but tenable hypothesis, and that an appreciation of sound could have influenced behavior among Palaeolithic societies of this region.

Full text

Cave acoustics in prehistory: Exploring the association of Palaeolithic visual motifs
and acoustic response
Bruno Fazenda, Chris Scarre, Rupert Till, Raquel Jiménez Pasalodos, Manuel Rojo Guerra, Cristina Tejedor,
Roberto Ontañón Peredo, Aaron Watson, Simon Wyatt, Carlos García Benito, Helen Drinkall, and Frederick
Foulds
Citation: The Journal of the Acoustical Society of America 142, 1332 (2017); doi: 10.1121/1.4998721
View online: http://dx.doi.org/10.1121/1.4998721
View Table of Contents: http://asa.scitation.org/toc/jas/142/3
Published by the Acoustical Society of America
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Cave acoustics in prehistory: Exploring the association
of Palaeolithic visual motifs and acoustic response
Bruno Fazenda
a)
Acoustics Research Centre, School of Computing, Science and Engineering, University of Salford,
The Crescent, Salford M5 4WT, United Kingdom
Chris Scarre
Department of Archaeology, Durham University, South Road, Durham DH1 3LE, United Kingdom
Rupert Till
Department of Music and Drama, University of Huddersfield, Queensgate, Huddersfield HD1 3DH,
United Kingdom
Raquel Jim
enez Pasalodos
Secci
on Departamental de Historia y Ciencias de la M
usica, Facultad de Filosof
ıa y Letras,
University of Valladolid, Plaza del Campus s/n, 47011, Valladolid, Spain
Manuel Rojo Guerra and Cristina Tejedor
Departamento Prehistoria, Arqueolog
ıa, Antropolog
ıa Social y Ciencias y T
ecnicas Historiogr
aficas,
Facultad de Filosof
ıa y Letras, University of Valladolid, Plaza del Campus s/n, 47011, Valladolid, Spain
Roberto Onta~
n
on Peredo
Cuevas Prehist
oricas de Cantabria, Consejer
ıa de Educaci
on, Cultura y Deporte, Carretera de las Cuevas s/n,
39670 Puente Viesgo, Spain
Aaron Watson
Department of Archaeology, Durham University, South Road, Durham DH1 3LE, United Kingdom
Simon Wyatt
Bristol, United Kingdom
Carlos Garc
ıa Benito
Departamento de Ciencias de la Antig€
uedad, University of Zaragoza, Pedro Cerbuna, 12, 50009 Zaragoza,
Spain
Helen Drinkall and Frederick Foulds
Department of Archaeology, Durham University, South Road, Durham DH1 3LE, United Kingdom
(Received 1 December 2016; revised 4 July 2017; accepted 26 July 2017; published online 11
September 2017)
During the 1980 s, acoustic studies of Upper Palaeolithic imagery in French caves—using the
technology then available—suggested a relationship between acoustic response and the location of
visual motifs. This paper presents an investigation, using modern acoustic measurement techniques,
into such relationships within the caves of La Garma, Las Chimeneas, La Pasiega, El Castillo, and
Tito Bustillo in Northern Spain. It addresses methodological issues concerning acoustic measurement
at enclosed archaeological sites and outlines a general framework for extraction of acoustic features
that may be used to support archaeological hypotheses. The analysis explores possible associations
between the position of visual motifs (which may be up to 40 000 yrs old) and localized acoustic
responses. Results suggest that motifs, in general, and lines and dots, in particular, are statistically
more likely to be found in places where reverberation is moderate and where the low frequency acous-
tic response has evidence of resonant behavior. The work presented suggests that an association of the
location of Palaeolithic motifs with acoustic features is a statistically weak but tenable hypothesis, and
that an appreciation of sound could have influenced behavior among Palaeolithic societies of this
region. V
C2017 Author(s). All article content, except where otherwise noted, is licensed under a
Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
[http://dx.doi.org/10.1121/1.4998721]
[FM] Pages: 1332–1349
I. INTRODUCTION
Around 40 000 yrs ago, important cultural and artistic
innovations appear among the early human societies of
Western Europe. These include cave paintings (parietal art),
a)
Electronic mail: B.M.Fazenda@salford.ac.uk
1332 J. Acoust. Soc. Am. 142 (3), September 2017 V
CAuthor(s) 2017.0001-4966/2017/142(3)/1332/18

the production of bone aerophones, and portable items of
mobiliary art, including both human and animal figures and
occasional theriomorphs (Clottes et al., 1995;Conard et al.,
2009;Morley, 2013). Considerable evidence exists for the
significance of organized sound in prehistory (Megaw, 1968;
Scarre and Lawson, 2006;Till, 2009;Fazenda, 2013;Wyatt,
2009;Morley, 2013) and previous researchers have sug-
gested links between painted caves and sound or music mak-
ing (Reznikoff and Dauvois, 1988;Waller, 1993b).
The use of musical instruments by these early European
societies indicates an appreciation of sonic aesthetics and
acoustic ecology in what would have been an exclusively
oral and aural culture, long before the adoption of writing
systems. Our aim is to explore whether this appreciation of
sound extended to the acoustic response of spaces, and how
significant this was among Palaeolithic societies. This paper
seeks evidence for a relationship between early visual motifs
(Palaeolithic paintings and engravings on cave walls), partic-
ularly their positioning, and an appreciation of acoustic
effects that originated from interactions of sound with physi-
cal features of the surrounding environment at those posi-
tions, termed in this paper the acoustic response. It provides
a full description of methods, results, and conclusions.
I
egor Reznikoff and Michel Dauvois, both together and
individually, have explored how Palaeolithic human-made
motifs in caves might be related to acoustic response
(Reznikoff and Dauvois, 1988;Dauvois, 1996,1999, 2005;
Reznikoff, 1995,2002, 2006,2011). Their research “shows a
relationship between these paintings or signs, and the sounds
that might have been produced adjacent to them”
(Reznikoff, 2002: p. 3), at a series of French caves, including
Le Portel, Niaux, Isturitz, and Arcy-sur-Cure.
Our research builds upon and develops this earlier work
of Dauvois and Reznikoff and applies a systematic scientific
approach to establish whether there is an association
between the location of motifs in caves and the acoustic
response at those locations. A set of five caves, each contain-
ing numerous motifs, are investigated in terms of the nature
and location of the motifs and the acoustic response at those
positions measured by state-of-the-art techniques and equip-
ment. For comparative statistical analysis, a number of con-
trol positions where motifs are absent (or exceedingly rare)
were also included in the analysis.
In the discussion of our results, we have used terms such
as likely,explanatory, and association, strictly in a statistical
rather than an interpretative sense. Also, the term motif is
employed here for a number of reasons: “art” is a problem-
atic and potentially anachronistic term carrying numerous
post-prehistoric implications; “painting” is inaccurate as it
does not extend to sculptures or engravings. Furthermore,
the motifs are highly variable, from simple dots or lines, to
subtle exaggerations of natural rock shapes, to the well-
known but much less numerous illustrations of animals.
“Motif” is a term that covers all examples.
This paper presents relevant research context in existing
publications (Sec. II), the archaeological setting of the caves
studied (Sec. III), details of acoustic measurement and the
acoustic responses obtained (Sec. IV), statistical analysis
(Sec. V), and a discussion and interpretation of the results
(Sec. VI), before concluding remarks.
II. RESEARCH CONTEXT
In his study of the French caves, Reznikoff explored a
number of research questions. Are there “more paintings or
signs in locations with the best resonance or sound quality”
(Reznikoff, 2002: p. 39)? “To what extent would it be possi-
ble to establish on this factual and experimental evidence the
use these people made of sound and voice in relation with
the paintings or other signs in caves? (…) Is there a link
between the location of a painting or a sign and the sound
value of this location in the cave?” (Reznikoff, 2002: p. 40).
Reznikoff explored the “resonance of sounds” in terms of
their intensity and duration, and also considered the number
of echoes present. Intensity in this case referred to ampli-
tude, or volume. Duration expressed how a sound is sus-
tained, and is perhaps best thought of as reverberation time
(RT), although echoes complicate such a definition. A sound
level meter was used to measure intensity, and a wristwatch,
or counting off seconds aloud, was used to calculate duration.
Excitation of these acoustic effects was effected through
vocalizations or the generation of noise signals.
Developed in the 1980 s, the methodology employed by
Reznikoff in these studies presents a number of difficulties.
The Palaeolithic populations that inhabited and decorated
the caves were Anatomically Modern Humans, with vocali-
zation capacities similar to our own. Repeated vocalizations
by a human performer will never be sufficiently standardised
to provide a repeatable test source, however, since even
slight differences between successive vocalizations might
excite different acoustic responses. In addition, the experi-
menter is prone to introduce bias when using his or her own
vocalizations to identify particular points with interesting
acoustics. Furthermore, the voice only covers a limited fre-
quency range that varies widely between individuals, from
low basses to high sopranos. The use of counting or a watch
to measure reverberation is, by contemporary standards, also
inadequate. An individual’s assessment of when reverbera-
tion has ceased, perhaps expressed to the nearest second, is,
by its very nature, subjective, and the measured RT becomes
dependent on: the loudness of each individual vocal sound,
the background noise, and the hearing acuity of the listener.
Dauvois (1999,2005) used continuous noise signals in
the range 25 to 300 Hz (Dauvois, 1996: p. 24) to carry out
similar tests. The approach is more repeatable, but his meth-
odology lacks a detailed description in the available publica-
tions. Details of source and receiver positions, sound source
type, or capture methods are not provided. The limited fre-
quency range of the source signal suggests that Dauvois was
interested in the low frequency response of the space, and
the use of steady-state noise as an excitation signal means
that measures of reverberation or echo were not directly pos-
sible. Nonetheless, based on his experimentation, Dauvois
(1996) reports that, “it is the particular natural morphology
of the cave that provides the resonance”. The choice of
source placement, “also took account of the sonority, a com-
bination of sound, site and figure, but this is not systematic.
J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al. 1333

Elsewhere there is a significant co-incidence between signs
and resonance (…) there is a Paleolithic definition of an
acoustic space” (Dauvois, 1996: p. 25).
Although Dauvois suggests that the relationship
between motif and sound is occasional rather than system-
atic, he postulates a strong relationship, but only provides
circumstantial evidence to support his claims. He shows that
acoustic results vary in positions where paintings are present,
but there is no way to establish whether the two are related;
whether, for example, acoustics might vary in a similar fash-
ion in positions where there are no motifs. Neither results
nor methodology were published in detail.
Reznikoff suggests that, “the Palaeolithic people pro-
gressed in the cave by using the voice and resonance’s
response as a sonar” (Reznikoff, 2002: p. 42). He defines res-
onance as “strong” where the average intensity of sound
increases by more than 10 dB, or where resonance lasts for
more than 3 s. “Most pictures are located in, or in immediate
vicinity to, resonant places (…) Most ideal resonance places
are locations for pictures (there is a picture in the nearest
suitable place). Among the ideal resonant places, the best are
always decorated, or at least marked.” His search was for the
“relationship between the location of the drawings and posi-
tions where resonance was present” (Reznikoff, 2002: p. 43).
According to Reznikoff (2002: p. 49), “the location for a
rock painting was chosen to a large extent because of its
sound value”.
In a later publication, Reznikoff (Reznikoff, 2006:
p. 79) recognizes the importance of statistical analysis in
demonstrating these relationships, stating that,
“a meaningful connection between man-made signs and
the resonance of a cave (or of an open space in connection
with rock-art), can, in my view, be established only on a sta-
tistical basis. Only such a systematic study is reliable: if
among signs and pictures some are found to correspond to
resonant locations, then we can assert this relationship as
shown, if the positive connections are statistically signifi-
cant. Otherwise doubt remains: perhaps the connection
appears just by coincidence. For a statistical study to be
effective, it must be based first for (i.e., on) a given cave (or
space) and then, by collecting several such studies, one
might begin a general comparative study.”
Reznikoff estimates the correlation of “pictures found in
well resonating locations,” at 80% in Le Portel and Arcy-
sur-Cure and 90% at Niaux (Reznikoff, 2006: p. 79). He
acknowledges that in Niaux almost all the paintings are in
the Salon Noir, where the whole chamber has very rich
acoustics (i.e., long RT). Thus all the paintings in the Salon
Noir are associated with similar acoustics. These percentages
are clearly approximations and are not intended as a scien-
tific statistical analysis. Reznikoff makes clear the need for a
more detailed statistical study.
In the same publication Reznikoff suggests that, “red
dots or marks are related closely to the resonance of the part
of the cave where they are located” (Reznikoff, 2006: p. 79).
The reference here is to amplitude, rather than (for example)
to reverberation. Reznikoff also asserts that, “as a general
rule, niches or recesses that are painted (with red dots, some
marks or pictures) resonate strongly” (Reznikoff, 2006:p.
80). Indeed elsewhere he discusses red dots as being the
most closely associated with sound.
In a separate series of studies, Waller (1993a,2006)
explores the relationships between rock art more generally
(in open spaces as well as in caves) and sound. He suggests,
“an acoustical motivation for the content and context of at
least some rock art” (Waller, 1993b: p. 91). In Palaeolithic
caves, Waller proposes that, for example, images of hooved
animals may be placed in positions where echoes are pre-
sent, to reflect the sounds made by the animal represented.
He also argues that rock art is generally linked to sound,
quoting numerous examples of rock art sites with unusual
acoustics, as well as ethnographic and historical traditions
indicating mythical or ritual relationships between rock art
and sound, reverberation and echo. The methods used to test
these relationships, employing cassette tape and simple
impulse sounds such as the voice as a source, are again
rather simplistic by today’s standards and, while suggestive,
do not provide any level of certainty.
Following on from research by Dauvois, Reznikoff, and
Waller, the study presented here defines a methodology that
looks for association between cave art and acoustic response
within five caves in the Asturian and Cantabrian regions of
Northern Spain. Both regions share the same sequence and
approximate chronology of successive Upper Palaeolithic
phases, from Aurignacian [42 000–35 000 Before Present
(BP)], through Gravettian (35 000–25 000 BP) and Solutrean
(25 000–20 000 BP) to Magdalenian (20 000–15 000 BP)
(Zilh~
ao, 2014: p. 1736). The caves involved are part of the
Cave of Altamira and Paleolithic Cave Art of Northern
Spain World Heritage Site (UNESCO 2: Onta~
n
on et al.,
2008). The study focuses on four Cantabrian caves: La
Garma, El Castillo, La Pasiega, and Las Chimeneas; and one
Asturian cave, Tito Bustillo.
We explore a number of research questions. Can a sta-
tistical association be scientifically established between
Palaeolithic visual motifs in caves and acoustics? What is
the nature of the relationship between the two, if any? Are
specific types of motifs (such as red dots) correlated with
acoustic response? More generally, what can an acoustic
study tell us about the archaeology of these caves, and the
way they may have been perceived and experienced by
prehistoric populations? In order to answer these questions,
specific archaeological information was needed, notably an
understanding of the typology and chronology of motif
creation.
III. ARCHAEOLOGICAL DETAILS OF THE CAVES
A. Cave morphology and setting
The material culture found in the caves included in this
study corresponds to the same cultural horizons as that in the
French caves studied by Dauvois and Reznikoff. At the same
time it must be recognized that the internal morphology and
structure of the caves has undergone processes of modifica-
tion (both human and natural) that inevitably affect their
acoustics. Some areas of these caves may hence exhibit
acoustic responses that have changed since prehistory. The
five caves were selected to provide a range of alteration from
1334 J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al.

slight (La Garma) to significant (Tito Bustillo, El Castillo).
The largest, most dramatic caves (Tito Bustillo and El
Castillo), are the most changed, following 20th century alter-
ations to make them accessible to the visiting public.
The morphology of these caves is intricate, composed of
galleries that branch off into other galleries or smaller side
chambers, through narrow passages. As a result, the architec-
tural effects of each gallery or section are typically acousti-
cally decoupled from those adjacent to it. Plans of the caves
can be found in the project archive (https://tinyurl.com/
n5pmm8m).
The most significant naturally occurring change to the
architecture of the caves came about through the closing or
sealing of their original entrances by rock-falls or by sedi-
ment accumulation. All of the locations chosen for acoustic
measurements included in the analysis are a sufficient dis-
tance away from the original or modern entrances for that to
have little effect. Some of the measurements were taken in
places where the morphology of the cave is altered (for
example through modern lowering or levelling of cave floors
or the provision of a modern staircase) although most were
taken in spaces where the archaeologists believe the original
morphology is preserved, particularly in difficult-to-access
side chambers. Although exceptions to this were observed in
very few side chambers, none of these would have recorded
a different acoustic response had the original entrance been
open at the time of our measurements. Where possible, the
positions of the microphone and sound source were selected
to avoid direct influence from modern modifications to the
cave morphology.
B. Chronology
The chronology of Upper Palaeolithic parietal art has
long been a subject of debate. Early attempts at establishing
a chronology were based on the assumption of a unilinear
stylistic progression (Breuil, 1952;Leroi-Gourhan, 1965).
From the 1990 s, however, the application of scientific dating
techniques, particularly accelerator mass spectrometry radio-
carbon and uranium series dating (e.g., Clottes et al., 1995;
Garc
ıa-Diez et al., 2013;Pike et al., 2012;Valladas et al.,
2001;Valladas et al., 2005) have challenged these earlier
schemes. While the validity of the dates and the methods
that underpin them have met with varying degrees of criti-
cism, it is undeniable that we can no longer treat the chrono-
logical arrangement of Upper Palaeolithic art as a simple
progression from rudimentary to complex forms.
Despite these advances an overarching chronology for
parietal art has yet to be realized. Although scientific techni-
ques provide a somewhat clearer picture, only a limited
amount of Upper Palaeolithic cave art has been reliably dated.
Given the sparse radiometric dating of the motifs within the
caves included in our study, we have taken a heuristic
approach to the interpretation of their chronology, categorizing
them into three phases: early (Aurignacian/Gravettian c.
42 000–25 000 BP), middle (Solutrean 25 000–20 000 BP), and
late (Magdalenian 20 000–15 000 BP). This incorporates stylis-
tic considerations alongside recorded absolute dates (where
available).
The earliest motifs appear to be dots, discs, and lines
(Pike et al., 2012), followed by hand stencils, usually in red
(Pettitt et al., 2014). These we attribute to our “early” phase.
Animals, mainly in outline, and geometrics such as tecti-
forms constitute our “‘middle’” phase, whereas the elaborate
and sometimes polychrome figures of the Magdalenian
period, well represented at caves such as Altamira, are coded
as “late.” This chronology is supported by studies seeking to
reconcile stylistic and radiometric dating (e.g., Alcolea
Gonz
alez and de Balb
ın Behrmann, 2007).
Chronology is important when addressing cave acous-
tics for several reasons. First, given the cumulative and
potentially shifting distribution of motifs within these caves,
it is probable (and in some cases it is documented) that the
earliest motifs in a given cave were located in specific pla-
ces, or limited to one section or gallery. Later motifs may
not only have filled out this pattern but may also have
extended to new areas. Hence any attempt to relate cave
acoustics to the distribution of motifs that did not control for
chronology would risk conflating a series of potentially dis-
tinct patterns. There may have been a close association
between the location of motifs and acoustic signals in some
phases, but not necessarily in all phases of cave art.
Second, the likelihood that behaviors associated with
the motifs changed over time make chronology especially
important. Cave acoustics may have been significant for cer-
tain kinds of behaviors in certain periods, but not necessarily
in the same way throughout the entirety of the long period
(over 30 000 yrs) during which motifs were being painted or
engraved in these caves. The contention that behaviors
will have changed through time makes controlling for chro-
nology, albeit inexactly, essential in a statistical assessment
of the relationship between acoustics and the placement of
motifs.
The coding of motifs in the individual segments of these
caves that were targeted in this study is summarized in Table
I. It should be noted that, as a control, measurements were
taken in a number of sections without (or with minimal)
recorded Palaeolithic motifs [La Garma section 7; La
Pasiega Gallery A (outer); and Tito Bustillo side chambers
TB1 and TB2].
IV. ACOUSTIC MEASUREMENT AND RESPONSE
A. Acoustic measurement
In order to explore potential associations between visual
motifs and acoustics, information on both had to be collected
systematically and collected in a manner that allowed for
statistical analysis. Relevant literature on the caves was
explored in order to contextualize the research archaeologi-
cally (Arias et al., 2001;de Balb
ın Behrmann, 1989;
Berenguer Alonso, 1985;Breuil et al., 1913;Cabrera
Valde`s, 1984;Gonz
alez Echegaray, 1974;Gonz
alez Sainz
et al., 2003). Professor Roberto Onta~
n
on of the University of
Cantabria and director of the Cantabria Prehistory and
Archaeology Museum, who had archaeological oversight of
many of the caves, and Professor Manuel Rojo Guerra of the
University of Valladolid, both took part in the field work
advising on archaeological matters.
J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al. 1335

Our methodology was to capture the impulse response by
acoustic measurements at a number of specific positions in a
cave, and to record information about the archaeological con-
text at each position. A range of data was recorded at each
measurement point, including the specific position and type of
source (loudspeaker) and receiver (microphones) within the
cave, and their distance from motifs (where the latter were
present); the presence or absence of a motif or motifs; the
type of motif(s) (painting, engraving, rock sculpture, dot,
disk, line, sign, horse, bison, bovid, reindeer, ibex, bear, bird,
whale, fish, cetacean, anthropomorph, hand stencil); how
many of each type were present; colors (for painted motifs);
distance to the cave’s original entrance; chronological infor-
mation (phase); reference number of the audio file created;
and reference codes for photographs taken at each position.
The data were recorded in standardised field notes and plans,
and all information was later collated in a spreadsheet.
Every acoustic measurement can hence be traced to spe-
cific source and receiver positions within the caves. Acoustic
measurements were taken according to guidelines in ISO
3382 (2009) although a number of adaptations had to be
implemented to accommodate the added difficulty of mea-
suring within a cave environment. Source positions were
chosen toward the centre of each cave section (chamber or
gallery) that was being measured, always maintaining a suf-
ficient distance from microphones to avoid source near-field
effects. For each section, data for at least one source position
and three microphone positions were collected. ISO 3382
(2009) recommends two source positions, and this was fol-
lowed where possible and relevant. Some of the spaces mea-
sured were small (c. 25 m
3
) rendering more than three
measurement positions redundant. In addition, the uneven
ground surface made it difficult to position source and
microphone stands firmly in more than a few positions. In
other cases positions were restricted because equipment could
not be placed on fragile archaeological material. These and
similar factors place constraints on acoustic measurements in
archaeological sites such as these caves and differentiate them
from the typical architectural acoustics measurements repre-
sented by ISO standards. These standards typically have a dif-
ferent purpose to the forensic examinations of the type
required within this project; for example, the multiple source
and receiver positions recommended in ISO 3382 (2009) are
intended to obtain an average of the acoustic response to rep-
resent the diffuse field reverberation, whereas we were inter-
ested additionally in the variety of response.
Where motifs were present, measurement positions
were selected by placing a microphone in front of them at a
distance of 1 m from the motif. In some cases this was
impossible to achieve, but in general the principle was
followed. Control measurements, where no motifs were pre-
sent, followed the same procedure, the microphone being
positioned about 1 m from selected surfaces with no motifs.
To collect impulse responses, the sine sweep measure-
ment method was used (M€
uller and Massarani, 2001). A loga-
rithmic sine sweep, in digital format, sampled at 48 kHz, 16
bits, was generated within the range 20 Hz to 20 kHz with
duration of 15s. These settings, rather than higher sample-
rates or bit-depths, were considered appropriate as they pro-
vide signal-to-noise ratios (SNRs) above 60dB, which is suf-
ficient for extraction of acoustic metrics, such as T30, from
the impulse response. The restrictions on SNR in these situa-
tions are defined by the electroacoustic transducers and the
environmental conditions rather than the recording equip-
ment. The main measurement system employed a laptop and
professional soundcard (Focusrite Saffire Pro 26 i/o, Focusrite
Audio Engineering Ltd., UK). The sound source was a battery
powered Bang & Olufsen Beolit 12 (Bang and Olufsen,
Denmark) amplified loudspeaker, and the signal was fed to
the speaker from the soundcard via a cable. The microphone
signal was acquired via the soundcard and EASERA
(www.easera.afmg.eu) measurement software was used to run
the measurement and obtain the impulse response.
The Bang & Olufsen Beolit 12 speaker was chosen for a
number of reasons. It has a reasonably flat frequency
response, an acceptably wide polar pattern, and sufficient
acoustic power; its small size and battery autonomy enables
measurements without a power supply for several hours. The
frequency and directivity response of the speaker measured
in a fully anechoic room can be accessed via the online pro-
ject repository in https://tinyurl.com/k7pxt95. Further speci-
fications provided by the manufacturer can be found in
https://tinyurl.com/n2ckb8j.
The performance of our measurement system was com-
pared against a RT measurement taken in the large reverber-
ation room at the University of Salford (7.4 m long 
6.6 m wide 4.5 m high) which has been designed with
hard surfaces and non-parallel walls to give long empty
room RTs with uniform decays. The room has the shape of a
truncated wedge and has 11 plywood panels, each panel
1.22 m 2.44 m, hung in the room to improve diffusion of
TABLE I. Chronology of cave sections. Sections of the five caves have
been assigned to three phases based on the style and inferred age of the
motifs that are present: “Early” ¼Aurignacian and Gravettian
c.42 000–25 000 BP; “Middle” ¼Solutrean c. 25 000–20 000 BP; “Late” ¼
Magdalenian c. 20 000–15 000 BP. For the locations of the cave sections see
plans in https://tinyurl.com/n5pmm8m.
Cave Section Early Middle Late
El Castillo Panel de las Manos
El Castillo Sala del Bisonte
Las Chimeneas Main chamber
Las Chimeneas Deer chamber
La Garma Section 1
La Garma Section 6
La Garma Section 7
La Garma Section 9
La Pasiega Gallery A (outer)
La Pasiega Gallery A
Tito Bustillo El Conjunto de la Ballena
Tito Bustillo El Carmar

ın de las Vulvas
Tito Bustillo Galer

ıa Larga
Tito Bustillo Galer

ıa de los Caballos
Tito Bustillo El Conjunto de los
Signos Grabados
Tito Bustillo Side chamber TB1
Tito Bustillo Side chamber TB2
Tito Bustillo Galer

ıa de los
Antropomorfos
1336 J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al.

the sound field. The measurements in this facility follow
Clause 6.2.1.1 in BS EN ISO 354 (2003) with an excitation
signal comprised of wideband random noise played into
the room via a loudspeaker system mounted in a cabinet
facing a corner. The sound is monitored at six positions
with Br€
uel & Kjær type 4166 [Br€
uel & Kjær Sound &
Vibration Measurement A/S (HQ), Denmark] random inci-
dence condenser microphones. Our measurement system
was then benchmarked using the same source and micro-
phone positions, replacing the original source with the
Bang & Olufsen Beolit 12 amplified loudspeaker and using
the logarithmic sine sweep signal defined above to excite
the room. Each of the microphone signals were then decon-
volved in a post-processing stage as described in M€
uller
and Massarani (2001) to obtain the impulse responses from
which the benchmark values of T30 were determined. To
extract T30, we follow the procedure originally proposed
by Schroeder (1965), which is based on the backward inte-
gration of the energy contained in the impulse response.
This results in a curve that represents the decay of energy
from the arrival of direct sound through to the last reflec-
tions from the surrounding boundaries. From this curve,
the T30 values are extrapolated by means of linear regres-
sion between the 5and35 dB values, obtained at each
octave band after appropriate filtering. Table II shows T30
obtained when testing our system in the reverberant cham-
ber. When compared to the reference measurements of the
chamber, minimum and maximum errors of 0.01 and 0.2 s,
respectively, were observed.
The measurement system and, in particular the excita-
tion source, differs from the typical omnidirectional source
prescribed in ISO 3382 (2009) for standard measurements,
or systems employing studio reference loudspeakers, often
with a matched sub-woofer to enhance the bass response
such as in the work of Murphy (2006). These systems are,
however, often large and heavy, which makes them impracti-
cal in a cave environment. A more portable configuration
was thus devised to obtain responses in the most difficult to
access spaces or where main power could not be delivered.
This comprised the same Bang & Olufsen Beolit 12 sound
source being driven with a pre-generated sine sweep, identi-
cal to that used in the main measurement system. The signal,
sampled at the same sample rate and bit depth, was played
on a handheld portable player connected directly to the
sound source. The signal from the microphone was recorded
directly onto a professional standard portable digital recorder
(Sound Devices 744 T, Sound Devices, LLC, Reedsburg,
WI) at a 48 kHz sample rate and 16 bit depth. The recorded
sine sweeps were converted to room impulse responses as
described in M€
uller and Massarani (2001). In both
configurations of the measurement system, the same micro-
phones (omnidirectional DPA 4006 microphones with B&K
diaphragms) were used.
B. Acoustic responses
It is likely that both speech and music were part of the
cultures that used the caves, given that speech evolved ear-
lier (Fitch, 2010) and examples of musical instruments in the
human cultures under study here have been reported in
archaeological studies (Conard et al., 2009;Buisson, 1990;
Garc
ıa Benito et al., 2016;Ib
a~
nez et al., 2015). Therefore it
is appropriate to analyze the responses using a mixture of
metrics that have been shown to relate well to a subjective
response in room acoustics for music and speech. Although
these metrics have been derived for and are widely used in
performance spaces, they have also been commonly
employed in the characterization of a multitude of human
environments from churches (Magrini and Ricciardi, 2002)
to soundscapes (Rycht
arikov
a and Vermeir, 2013), including
spaces both big and small (Stephenson, 2012;Vanderkooy,
2007). They represent common metrics that describe acoustic
response in enclosed spaces and are thus useful for general
interpretation of the data collected. Their interpretation is intui-
tive allowing an objective quantification of the responses mea-
sured using well established and perceptually relevant metrics
which may be understood by all and, as we will demonstrate
in Sec. V, useful in establishing and interpreting one of the
principal dimensions of variance in the data collected.
From the measured impulse responses, 23 acoustic met-
rics were extracted, following well known methods reported
in ISO 3382 (2009),Barron (2009),Kuttruff (2009),
Steeneken and Houtgast (1980),Stephenson (2012), and
Dietsch and Kraak (1986). These metrics comprise:
•T30 and early decay time (EDT) each extracted across six
octave bands between 125 and 4000 Hz. The extraction of
T30 values is as described above in Sec. IV A. The extrac-
tion of EDT follows the same method of Schroeder’s back-
wards integration of the impulse response as that for T30 but
the linear regression is obtained between the 0 and 10 dB
points on the decay curve. Average values for T30 and EDT
are obtained from the values at 500 Hz and 1 kHz octave
bands as defined in ISO 3382 (2009). T30 and EDT are com-
mon acoustic metrics used to describe the acoustic response
of spaces. While T30 pertains to the decay of acoustic
energy homogeneously within a space and is related to the
physical properties of the space (volume and surface area),
EDT is perceptually more relevant to the sensation of
reverberance and sensitive to the effects of early reflections
(ISO 3382, 2009;Barron, 2009;Kuttruff, 2009).
•D50 and C80 each determined as a mean of the values
obtained at 500, 1000, and 2000 Hz octave bands (Barron,
2009). D50 and C80 are temporal metrics of balance between
early and late arriving energy, calculated for a 50 or 80 ms
early time of arrival limit, depending on whether speech or
music are the subject of analysis. C50 is directly correlated to
D50 and has therefore not been used in this study.
•Speech Transmission Index is a metric describing the
quality of the speech signal in terms of the loss of speech
TABLE II. T30, in seconds, measured in a reverberant chamber for Bang
and Olufsen Beolit 12 and the facility’s RT measurement sound source.
Frequency 100 Hz 250 Hz 500 Hz 1000 Hz 2000 Hz 4000 Hz Avg error
REF 4.50 4.20 4.65 4.27 3.58 2.18
Beolit 12 4.51 4.00 4.60 4.20 3.40 2.21
Error 0.01 0.20 0.05 0.07 0.18 0.03 0.09
J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al. 1337

modulation caused by reverberation (Steeneken and
Houtgast, 1980).
•LFRT60diffs, LFRT60thr, LFdevflat, and LFdevsmooth
are four figures of merit derived to quantify the quality of
low frequency response of small rooms. Each of these fig-
ures of merit calculates a score between zero and one,
where one corresponds to a response free of the particular
low frequency artefacts it has been designed to identify.
The frequency band within 32 and 250 Hz has been ana-
lyzed in third octave bands. LFRT60diffs determines
absolute differences in T30 values between adjacent third
octave bands, revealing a modal sound field when those
differences are large; LFRT60thr reports on the degree to
which the measured response in each third octave band is
above the perceptual modal thresholds identified in
Fazenda et al. (2015); LFdevflat calculates the deviation
from the measured magnitude spectra to a flat magnitude
spectra, and LFdevsmooth does the same to a smoothed
version (third order polynomial fitting) of the measured
response [see Stephenson (2012) and citations therein for
more detail on these figures of merit].
•Echo criteria has been used for the detection of audible
echoes in both speech and music signals (Dietsch and
Kraak, 1986).
A general analysis of the acoustic response within the
caves is now presented, including the measured T30 aver-
aged for each section in each cave (Figs. 1–5).
In terms of T30, the acoustic response generally follows a
common tendency in architectural acoustics, showing higher
levels of reverberation at low frequencies and a decrease
toward the higher frequencies. The values for reverberation are
typically under 2 s, except in the large central gallery of Tito
Bustillo, and in a large section near the entrance of La Pasiega
and, even here, only at low frequencies.
It might appear surprising that we did not find a high RT
(>3 s) in these caves. Indeed, we encountered a range of
acoustic conditions, from small, very dry spaces, with rever-
beration (T30) below 0.4 s, to large spaces with T30 above
2.5 s at 500 Hz and below.
The rock faces within these caves were varied and their
particular geology and morphology, i.e., the shape and sur-
face conditions, do not, in general, support very long RTs.
Although sections of La Pasiega featured smooth rock faces,
many other areas of the cave walls were characterized by
much rougher surfaces, as for example throughout La
Garma. Soft or porous rock can be worn into irregular
shapes, and granular geology forms rough textures. The rea-
son why very long RTs are not found in some caves might
be due to the fact [as suggested by Cox (2014)] that the
many passage-ways to adjacent cave sections, together with
the diffusion produced by irregular or rough surfaces, force
large amounts of wave-surface interaction, which has the
effect of reducing the energy quickly.
La Garma section 7, where motifs are very rare, has a
longer reverberation than the other three sections measured
in this cave, where many more motifs are present. Section 6,
where large numbers of dots and some hand stencils are pre-
sent, appears to have a long reverberation at very low fre-
quencies. Interestingly, in this section, the measured
responses also suggest the existence of low frequency reso-
nances reported by the low frequency metrics. Namely, the
scores for LFdevflat are an order of magnitude smaller than
at other positions in the cave, suggesting these positions
might be associated with modal behavior (i.e., a specific fre-
quency or frequencies which exhibit a long temporal decay
and a marked amplitude level). This is also the case for the
other low frequency figures of merit although the effect is
not as marked. In the large cave of El Castillo, two areas
were measured: a large open area (EC1, the “vertical bison”
section) and a smaller contained space with a lower ceiling
(EC2, the “hands panel”). Both sections appear to sustain a
similar response, although the “vertical bison” section
FIG. 1. (Color online) T30 for La Garma. Means and 95% confidence inter-
vals are presented for measurements in four different sections within the
cave.
FIG. 2. (Color online) T30 for El Castillo. Means and 95% confidence inter-
vals are presented for measurements in two different sections within the
cave.
FIG. 3. (Color online) T30 for Tito Bustillo. Means and 95% confidence
intervals are presented for measurements in ten different sections within the
cave.
1338 J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al.

understandably sustains longer RTs given it is larger and has
a higher ceiling. The hands panel is directly adjacent to a
large section with a high ceiling, and acoustic coupling
between the two may account for the similarities in response.
In Tito Bustillo, a number of small side chambers, of similar
size and volume, were measured. These small chambers
have similar RTs. The Chamber of the Anthropomorphs
(TB8 in Fig. 3), extremely difficult to access and connected
to the main gallery via a sequence of narrow passages at var-
ious heights, is larger than the other side chambers that were
measured and sustains a longer RT. Longer RTs are also
observed in the main central gallery of this cave, off which
the side chambers open.
La Pasiega differed from the other caves in consisting of
a network of long narrow passages. It has long RTs at low
frequencies as a result of its tunnel-like shape (Kang, 2002).
This can be clearly seen in the steep increase of RT values
toward the lower frequencies. The corridor where most
motifs are found (LP1 in Fig. 4) has lower values of T30
than the area near the modern entrance, where motifs are
absent (LP2). All measured sections at Las Chimeneas seem
to have a similar response, with no clear differences between
sections, apart from the 1000 Hz values.
In general, the trends observed for RT (T30) across the
caves are matched by other acoustic metrics derived from
the impulse responses, such as EDT.
Figure 6shows median and interquartiles for average
T30 values obtained within each of the sections for each
cave. The ISO 3382 (2009) standard defines single figure
values for T30 and EDT, utilising the average of values
obtained in the 500 and 1000 Hz octave bands. Average T30
values are contained between 0.2 s and around 1.2 s with two
sections exhibiting T30 larger than 1.5 s. One of these meas-
urements was taken in the very large central gallery of the
Tito Bustillo cave. The other was in La Pasiega where two
long corridors crossed. Both T30 and EDT relate to the time
it takes for the energy in the space to decay by 60 dB. T30
accounts for this decay after the first 5 dB drop and is there-
fore not overly dependent on very early reflections and, con-
sequently, to local conditions at each measurement position.
On the other hand, EDT corresponds to the time taken for
the energy to decay by 10 dB immediately after the arrival of
the direct sound, making it more sensitive to early reflections
and thus to local conditions (Barron, 2009). The values
obtained for EDT in each section are similar to those for T30
albeit with a slight decrease, as would be expected since the
early energy often decays more rapidly than late reverbera-
tion. These results are shown in Figs. 6and 7.
The deeper parts of the caves, away from the entrance,
were probably used for ritual purposes rather than occupa-
tion (which was mostly near cave entrances: Arias, 2009),
FIG. 4. (Color online) T30 for La Pasiega. Means and 95% confidence inter-
vals are presented for measurements in two different sections within the cave.
FIG. 5. (Color online) T30 for Las Chimeneas. Means and 95% confidence
intervals are presented for measurements in three different sections within
the cave.
FIG. 6. (Color online) T30 boxplots showing median, interquartile range,
maximum, and minimum values. Circles represent outliers. Data are shown
for each section within the cave. Sections are grouped per cave with differ-
ent shades.
FIG. 7. (Color online) EDT boxplots showing median, interquartile range,
maximum, and minimum values. Circles represent outliers. Data are shown
for each section within the cave. Sections are grouped per cave with differ-
ent shades.
J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al. 1339

and thus metrics widely used in acoustic description of con-
temporary ceremonial spaces such as concert halls and
churches are used here to provide a well-grounded compari-
son between the conditions found in caves and those found
in the modern built environment. These metrics, calculated
from the measured impulse responses, relate to the way the
reflected energy is distributed over time and define aspects
of speech intelligibility (STI), clarity for musical sources in
concert halls (C80), and the distinctness of sound or defini-
tion (D50) (Kuttruff, 2009). These are typical acoustic met-
rics, often used to describe the performance of spaces where
acoustic performances involving either spoken word or
musical activity are to take place. The average values for
C80 and D50 have been obtained from the measured values
at 500 Hz, 1 kHz, and 2 kHz as per Barron (2009). The values
for STI have been obtained according to Steeneken and
Houtgast (1980).
The extracted metrics for each cave section are presented
as medians and interquartile ranges in Figs. 8,9, and 10.
STI across the measurement positions lies within 0.5
and 0.9, which is a range where STI is considered “good” or
better. C80 values range between 1 and 20 dB. The pre-
ferred range for this metric in auditorium acoustics is above
2dB (Barron, 2009). D50 ranges from around 0.3 to
around 0.9. The preferred range for this metric is above 0.5.
Overall, the values found in the caves indicate conditions
with good clarity and, mainly, intelligible speech. If these
were modern auditoria they might be described for example
as offering favourable conditions for musical activity. Hence
most measurements within the caves indicate spaces without
the typical acoustic problems, such as echoes or over-long
reverberation, which are known to mask certain aspects of
sound in communication (speech in particular) and to inter-
fere with music making (Barron, 2009;Kuttruff, 2009).
V. STATISTICAL ANALYSES
To investigate associations between the position of
motifs and the acoustic response at these positions, statistical
models were fitted to the acoustic data in order to compare
that with data on the presence of motifs and their type.
Models of this kind generally require a significant number of
samples in order to ensure sufficient statistical power for a
valid test. Initial analyses focused on responses obtained in
each cave but did not reveal statistically significant data
owing to low sample count and, in the cases of El Castillo,
La Garma, and La Pasiega, to the lack of sufficient samples
in control positions, i.e., at places where no motifs are found.
Indeed, at Las Chimeneas there were no positions without
motifs except at the collapsed original entrance. Our interest,
however, lies in the association between the behavior of
those who created the motifs and the acoustic response they
would have experienced when near to the motifs. The dataset
has therefore been collated to allow a meta-analysis across
all five caves. This results in a significant count of data sam-
ples (N¼177) and the statistical analyses thus exhibit higher
power. Such integration of data also makes sense archaeo-
logically, as the caves are situated within a restricted geo-
graphic region, and the motifs that they contain belong to a
shared series of cultural traditions.
FIG. 8. (Color online) STI boxplots showing median, interquartile range,
maximum, and minimum values. Circles represent outliers. Data are shown
for each section within the cave. Sections are grouped per cave with differ-
ent shades.
FIG. 9. (Color online) C80 boxplots showing median, interquartile range,
maximum, and minimum values. Circles represent outliers. Data are shown
for each section within the cave. Sections are grouped per cave with differ-
ent shades.
FIG. 10. (Color online) D50 boxplots showing median, interquartile range,
maximum, and minimum values. Circles represent outliers. Data are shown
for each section within the cave. Sections are grouped per cave with differ-
ent shades.
1340 J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al.

A. Building an explanatory statistical model
The purpose of the statistical analyses that follow is to
build an explanatory model and test whether the acoustic var-
iables in this model have a statistically significant relation-
ship to the human behaviors under study. These behaviors are
selected based upon the following research questions:
(1) Is there an association between motifs of the earliest
phase and acoustic response? This first investigation
focuses on dots and lines, followed by an analysis of
hand stencils, which are also early in date.
(2) Is there an association between acoustic response and
motifs across all three periods under study: early, mid-
dle, and late? This considers whether the chronological
categorization of motifs can be explained by the acoustic
response.
(3) Can the color of motifs be explained by acoustic
response?
(4) Is there an association between acoustic response and
the position of any type of motif, regardless of its type,
color, or era? This analysis is divided into two parts.
First, it explores situations where the acoustic response
is individually associated with a motif within a 1 m
radius. Second, it (re)codes acoustic measurements taken
within an entire section of the cave, according to the
presence or absence of motifs within that section. As we
will see, this difference in coding has an effect on the
explanatory power of the statistical model.
The final statistical model explores whether factors
other than acoustic response (such as proximity to the origi-
nal cave entrance) might aid in explaining the positioning of
motifs. This puts in perspective the relative importance of
variables other than those reporting acoustic response met-
rics in explaining the position of motifs.
For the analyses listed above, the dependent variable is
either dichotomous (presence or absence of motifs), categor-
ical (e.g., animal, hands, or dots for type of motifs) or ordinal
(early, middle, and late era). For variables of these kinds,
binary logistic regressions, multinomial logistic regressions,
and ordinal logistic regressions, respectively, are suitable
models, and it is these that are the object of the analyses that
follow. Given the sparse number of samples for each condi-
tion, normal distributions of data cannot be assumed and the
more typical and powerful parametric analyses cannot be
applied.
Where statistically significant models can be found, they
define the probability that the dependent variable is a func-
tion of the explanatory (i.e., independent) variables. In lay
person’s terms, this tests whether there is a statistical associa-
tion between acoustic parameters and motif-related parame-
ters, and also quantifies the statistical probability of that
relationship. The data collected have been tested for compli-
ance with the underlying assumptions required by these sta-
tistical models, and those assumptions have been met in all
cases presented. Particular tests for this are indicated where
appropriate. The data for the study are available and may be
downloaded from https://tinyurl.com/n5pmm8m, citing this
paper as the source.
As mentioned previously, details of every acoustic
response sampled were recorded on a spreadsheet. At each
position a range of data was collated, including presence,
shape, color, position, and date of motif. Every measurement
contains coding of archaeological data, and hence in the sim-
plest categorization, the binary presence/absence of a motif
near the position of the acoustic measurement is known. For
this categorization, the existence of a motif within 1 m of the
measurement microphone means that that particular mea-
surement position is coded as motif present.
Data cases (177) have been collected in the five caves
studied. A binary coding has been applied for the following
variables:
Presence/absence of motif (N¼177; Yes ¼98, No ¼79).
Presence/absence of dots-lines (N¼177; Yes ¼64,
No ¼113).
Presence/absence of hand stencils (N¼177; Yes ¼16,
No ¼161).
For all cases where motifs are present, the relevant
archaeological data within the sample were coded. The cate-
gorical variables in these cases are (sample counts in each
category):
Chronology: early, middle, late (26,30,38).
Type: dots-lines, animals, hand stencils, symbols (64,27,5,2).
Color: black, red, violet (27,52,8).
B. Reducing the number of variables
Twenty-three different acoustic metrics were extracted
from each of the impulse responses, as discussed in more
detail in Sec. IV above. Most of these are correlated, mean-
ing there is redundancy in the set (i.e., some of these 23 met-
rics provide very similar information). Furthermore,
performing the following statistical analysis on each of the
23 variables individually would ignore relationships and
interaction effects between the variables. In order to reduce
the data, a Principal Component Analysis (PCA) has been
performed. PCA is a dimensionality reduction technique
which here allows a more useful interpretation of the acous-
tic data, grouping the granular information into principal
components or dimensions, which more directly explain the
variance found in the dataset with regards to acoustic
response. The dimensions provided by the PCA can be seen
as synthetic variables that contain within them the contribu-
tions of each of the original acoustic metrics extracted from
the measurements. These dimensions will, however, be one
step removed from those original acoustic metrics (such as
T30, EDT, and STI) making the interpretation of results
somewhat more complex.
A number of assumptions are made for the PCA. It is
assumed that all variables submitted to the PCA are continu-
ous and that a linear relationship exists between most varia-
bles. This has been tested using a correlation matrix, and
most variables are correlated at 0.9 or above, while the low-
est correlation value found is 0.08. The Kaiser-Meyer-Olkin
measure of sampling adequacy was 0.909, suggesting that a
PCA is adequate for this dataset. Using Bartlett’s test of
J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al. 1341

sphericity, the null hypothesis that the correlation matrix of
the data is equivalent to an identity matrix was rejected
(v
2
¼11 842, df ¼253, p<0.000) indicating good suitability
for data reduction. Outliers have been checked by comparing
the mean with the 5% trimmed mean (Sarkar et al., 2011).
For all variables the difference between the two means was
below or much below 5% of the original mean, except for
LFdevflat and LFdevsmooth where the difference was 12%
and 8% of the original mean, respectively. No variables
were therefore removed.
The initial unrotated PCA reveals three dimensions
explaining 87.5% of the total variance in the data. A null
hypothesis test for the correlation between dimensions was
shown to be highly significant (all p<0.000) suggesting no
significant correlation between the three extracted dimen-
sions. A further PCA was thus limited to three dimensions,
and rotated using the Varimax method. Here dimension 1
explains 72% of the variance whilst dimensions 2 and 3
explain 11% and 4.5%, respectively. The results of the
rotated PCA will now be discussed.
Figures 11 and 12 show the three principal components,
or dimensions, extracted from the acoustic data. The loading
of each acoustic metric on each dimension can be obtained
from the projection of its vector onto the corresponding
dimension axis. For example, in Fig. 11, variables related to
reverberation (T30, EDT) load strongly in the positive direc-
tion of dimension 1, while clarity, definition, and STI (C80,
D50, and STI) load strongly in the negative direction. The
resultant PCA indicates this loading as a correlation coeffi-
cient (q) between each of the metrics and each of the
extracted dimensions. In detail, the highest significant corre-
lations are found for metrics based on T30 (q0.98,
p<0.01) and EDT (q0.98, p<0.01) in the positive direc-
tion, and STI (q0.97, p<0.01), D50 (q0.96,
p<0.01), and C80 (q0.94, p<0.01) in the negative
direction of dimension 1. Dimension 1 thus appears to
describe aspects of energy decay, with large positive values
corresponding to very reverberant responses whereas large
negative values correspond to responses with very low
reverberation.
Dimension 2 has significant correlations with metrics
reporting the low frequency response of the measurements—
Lfdevsmooth (q0.72, p<0.01), LFRT60diffs (q0.68,
p<0.01), and LFdevflat (q0.67, p<0.01). This dimen-
sion thus appears to describe the merit of low frequency
response of the spaces, where high values along this dimen-
sion correspond to spaces with “acceptable” low frequency
response, whereas low values correspond to spaces that devi-
ate from “optimal” low frequency response (as defined for
modern sound reproduction spaces) and might therefore
exhibit audible modal behavior or, as they are commonly
known, resonances.
For dimension 3, significant negative correlations are
found for the two metrics used to detect echoes—EKSpeech
(q0.68, p<0.01) and EKMusic (q0.65, p<0.01).
It thus appears this dimension is associated with evidence or
otherwise of audible echoes. Larger values along this dimen-
sion indicate the presence of echoes in the acoustic response.
Importantly, further analysis of the tabulated raw data
obtained for each measurement shows that none of the val-
ues obtained for the echo metrics were found above the echo
audibility threshold, demonstrating that audible echoes have
not been found in this dataset. This is corroborated by the
low value of variance explained (4.5%) by this third dimen-
sion. It is nonetheless interesting to observe that metrics for
echo detection form a dimension that is distinct (orthogonal)
from the first two principal dimensions.
The dimensions identified will be the basis for further
analysis, and it is useful therefore to summarize their inter-
pretations. Those are shown in Table III.
Figure 13 shows the position of each data sample
(acoustic measurement) along dimensions 1 and 2 and its
categorisation according to whether a motif is present at the
measurement point or not. The 95% confidence ellipses are
also plotted for each category and provide an indication of
significant differences between these. The presence or
absence of motif is coded in a different shade (color online).
The non-overlapping ellipses suggest there are statistically
significant differences between the two categories along
each of the dimensions. It can be further observed that data
points associated with motifs appear to be concentrated
toward the central values, particularly along dimension 1,
FIG. 11. Dimensions 1 and 2 resulting from the PCA of the 23 acoustic met-
rics. Metrics of energy decay (e.g., T30, EDT) and intelligibility (e.g., STI)
load onto opposite ends of dimension 1, which explains 72% of the variance
in the data. Metrics of merit of low frequency response (LFdevflat,
LFRT60diffs, LFdevsmooth) load onto dimension 2, which explains 11% of
variance in the data.
−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5
−1.0 −0.5 0.0 0.5 1.0
Dim 1 (72.34%)
Dim 3 (4.50%)
EDT
T30
EDT125
T30125 EDT250
T30250
EDT500
T30500
EDT1000
T301000
EDT2000
T302000
EDT4000
T304000
C80
D50
STI
EKSpeech
EKMusic
LFRT60diffs
LFRT60thr LFdevflat
LFdevsmooth
FIG. 12. Dimensions 1 and 3 resulting from the PCA of the 23 acoustic met-
rics. Echo criteria metrics (EKSpeech, EKMusic) load more strongly onto
dimension 3, which explains 4.5% of variance in the data.
1342 J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al.

while data points where no motif is present seem to occur
over a larger range of this dimension. In other words, the
density of points associated with motifs is larger where
energy decay is moderate, neither too high nor too low.
Motifs appear less likely to be present in those positions that
are either very reverberant or very dry. This suggests a qua-
dratic distribution for this dimension. Given this observation,
a transformation of the dimension 1 variable into its square
was also included in the statistical analysis below. This thus
defines a fourth variable in the model, which explores the
likelihood of extreme or central values along dimension 1.
C. Dots and lines
Dots and lines are currently believed to be the earliest
motifs in these caves. The following statistical model
explores whether their location is associated with the acous-
tic response. To investigate this, the data have been coded on
a presence/absence basis [dots-lines ¼64; none (control) ¼
113]. Note here that any positions coded as having motifs
that are not dots or lines (such as animal images) have been
grouped with the control positions, since these motifs were
probably added at a later date. The statistical model chosen
to analyze the data is the logistic regression, which is repre-
sented as
log pi
1pi

¼b0þb1xi1þb2xi2þb3xi3þb4xi4;(1)
where p
i
is the probability of finding a dot or line, x
i1
,x
i2
,
and x
i3
are independent variables associated with the three
dimensions identified in the original data using PCA, and x
i4
is a square transformation of dimension 1, representing an
independent variable in the model which accounts for its
apparent quadratic distribution. The dependent variable in
this analysis is presence/absence of dots or lines at each mea-
surement point, so the model calculates the probability of
finding dots or lines at a specific location given the values of
acoustic metrics at that location.
A logistic regression was performed to ascertain the
effect of each of the independent variables on the likelihood
that a dot and/or line will be found at a particular position.
The logistic regression model was statistically significant,
v
2
(4) ¼25.126, p<0.0005. The model explained 18.1%
(Nagelkerke R
2
) of the variance in the presence/absence of a
dot/line and correctly classified 71.2% of cases. It was found
that the probability of finding a dot or line decreases with
increasing values of dimension 1 (b
1
¼0.41, e
b1
¼0.664,
p<0.05). The e
b1
¼0.664 indicates the odds that a dot and/
or line will be found if the measured value for dimension 1
increases by one unit (after controlling for the other factors in
the model). The interpretation of odds here, reporting effect
size, is consistent with the typical interpretation of logistic
regression results. For example, given that logistic regression
outputs the natural logarithm of the odds, the exponential of
the coefficients represents the result of the odds ratio. Odds
are easier to interpret if they are presented as values above 1,
indicating the likelihood of an event. Decimal odds (below 1)
can be inverted (i.e., 1/e
b#
) as long as their interpretation is
adapted accordingly. Applying this principle to the result
above (1/e
b1
¼1/0.664 ¼1.5), we can infer that dots or lines
are 1.5 times more likely to be found if the measured value in
dimension 1 decreases by one unit. In other words, as rever-
beration decreases and clarity/definition/STI increases, it
becomes more probable that dot or line motifs will be found.
Measurement positions near dots/lines have a T30 in the
range of 0.6 s to about 1.7 s, whereas T30 at control positions
may be as low as 0.3 s and as high as 2.53 s.
The statistical model further shows that an increase in
dimension 2 makes it statistically less likely that a dot or line
will be found (b
2
¼0.773, e
b2
¼0.462, p<0.05). Following
the principle introduced in the previous paragraph, as the value
of dimension 2 decreases by one unit for a given acoustic mea-
surement position, it is twice as likely (1/e
b2
¼1/0.462 ¼2.2)
that dots and/or lines will be found there. This result suggests
TABLE III. Variance explained for each dimension extracted through a PCA of the 23 acoustic metrics used in the study. An interpretation is provided on the
basis of the acoustic metrics which more strongly load onto each dimension.
Dimension Variance explained Interpretation
1 72% A measure of energy decay. Large positive values along this dimension are represented by spaces
with larger values of reverberation (T30, EDT). Large negative values are represented by spaces
with high clarity (C80), definition (D50), and STI.
2 11% A measure of low frequency response merit. Large positive values along this dimension correspond
to spaces approaching optimal low frequency behavior as defined for modern sound reproduction in
rooms. As the value of this dimension decreases, the associated spaces deviate significantly from
optimal low frequency response and may therefore exhibit audible modal behavior.
3 4.5% A measure of presence or absence of echoes. Less negative values suggest the presence of echoes.
FIG. 13. (Color online) Individual samples (measurements) along dimen-
sions 1 and 2. Ninety-five percent confidence ellipses are also plotted for
both motif and no-motif data sets. The non-overlapping ellipses show signif-
icant differences between the two categories (motif, none) along each of the
dimensions.
J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al. 1343

that dots or lines are more probable in places with resonant
artefacts, since dimension 2 reports on the existence of reso-
nances for low values.
The variables associated with dimension 3 (x
i3
) and the
square of dimension 1(x
i4
) were not found to be significant
in this model (p>0.05). There is thus no evidence that the
positions of dots or lines are associated with the presence of
audible echoes.
In summary, these results show some evidence that it is
statistically more probable to find dots and lines in places
where reverberation is not high and where the response is
more modal thus sustaining potentially audible resonances.
D. Hand stencils
Hand stencils belong to the early period of cave motifs,
and from the evidence of U-series dating of calcite formations,
may be as old as dots and lines (Pike et al., 2012). To investi-
gate whether there is an association between the positions of
hand stencils and acoustic response, the data have been recoded
to examine the presence/absence of motifs included in this cat-
egory. Measured positions without hand motifs were catego-
rized as “no motif” (N¼177; Hands ¼16, None ¼161).
A logistic regression was performed to ascertain the
impact of each of the acoustic dimensions on the likelihood
that a hand stencil will be found at a particular position. The
logistic regression model was statistically significant,
[v
2
(4) ¼16.371, p<0.0005]. It explained 19.4% (Nagelkerke
R
2
) of the variance in the presence/absence of a hand stencil.
Although the model correctly classified 91% of cases, this
arises because it predicts that all instances have no motifs
and fails to predict any of the instances where a motif is pre-
sent. Since the latter (locations with hand stencils) are much
more rare in this dataset, the model appears to have a high
correlation with the data but this is merely a mathematical
artefact (see Table IV). Grouping the results for hand stencils
with those of dots and lines together was explored, but pro-
vided no additional explanatory power, i.e., the model was
identical to the one obtained for dots-lines save that its
explained variance decreases slightly. We cannot therefore
infer that the positioning of hand stencils has a statistically
significant association with acoustic metrics.
E. Chronology, type, and color of motifs
The motifs in these caves have been divided chronologi-
cally into three periods: early, middle, and late, as described
in Sec. III B. In analyzing the association between the chro-
nological period of motifs and the acoustic response, the
dependent variable is polytomous and has three levels. An
ordinal logistic regression in which date is the dependent
variable, with three levels, has therefore been performed.
The independent variables were the same four acoustic vari-
ables as before (dimensions 1, 2, and 3 and dimension 1
squared). In this case, the result of the model fit v
2
test is not
significant (p>0.05) and therefore an ordinal regression
model of association between age of motif and acoustic
response was not substantiated.
The association between type of motif (Animal ¼27,
Dot/Line ¼64, Hand ¼5, Symbol ¼2) and acoustic response
was modelled using a multinomial logistic regression. The
model fit again was not significant [v
2
(9) ¼10.8, p>0.05]
and hence none of the factors in the model were found to be
significant. An association between the type of motif and
acoustic response was not found.
A multinomial logistic regression analysis was run to
check for an association between color of motif (black ¼27,
red ¼52, violet ¼8) and the acoustic response measured at
that position. Again, the model fit was not significant
[v
2
(6) ¼10.9, p>0.05]. An association was thus not found
between the color of motif and acoustic response.
F. Presence or absence of motifs in general—position
dependent
In addition to exploring relationships between specific
categories of motif and acoustic response, a final analysis
was undertaken to investigate whether there is statistical evi-
dence that the location of a motif (regardless of date or type)
might be associated with particular acoustic responses. We
have seen that dots and/or lines are more likely to be found
in locations with low reverberation and resonant artefacts.
Here we carry out a similar analysis but consider the pres-
ence/absence of any motif as our dependent variable. The
independent variables are the same as in Eq. (1).
A logistic regression produced a statistically significant
model, v
2
(4) ¼34.001, p<0.0005. The model explained
23.4% (Nagelkerke R
2
) of the variance in the presence/
absence of a motif and correctly classified 68.4% of cases.
Variables in this model can be seen in Table V.
It was found that the probability of finding a motif
decreases with increasing values of dimension 2 (b
2
¼0.54,
e
b2
¼0.582, p<0.05). This result is similar to that noted ear-
lier for dots and lines. In this case motifs are 1.7 times more
likely to be found in places exhibiting a more modal
TABLE IV. Classification table for logistic model predicting the presence
of hand motifs according to acoustic response. It can be seen that the high
percentage of identification comes from the model predicting all instances
as belonging to no presence of the hand stencils. As places with no hand
stencils are disproportionally more represented within our dataset, the pre-
dictive power of the model is misleading and, as such, cannot be relied
upon.
Hand stencils Observed Predicted (%)
0 161 100
1160
Total 177 91
TABLE V. Logistic regression model for data where motif presence is
coded at individual positions. B is beta coefficient. S.E. is standard error, df
is degrees of freedom, Sig. is significance, and Exp(B) is the odds ratio.
Variables in the Equation B S.E. Wald df Sig. Exp(B)
Dimension 1 0.357 0.190 3.537 1 0.060 0.700
Dimension 2 0.540 0.182 8.812 1 0.003 0.582
Dimension 3 0.008 0.170 0.002 1 0.965 0.992
Dimension 1 squared 0.766 0.212 13.117 1 0.000 0.465
Constant 0.884 0.239 13.648 1 0.000 2.421
1344 J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al.

response. The odds have decreased because now we are look-
ing at any type of motif, rather than only dots or lines. This
small drop in effect size may, perhaps, suggest that the addi-
tion of any type of motif to the dots-lines category weakens
the statistical association which exists mainly for dots-lines
but is less strong for other, later motifs.
It was further observed that an increase in x
i4
, the square
of dimension 1, makes the presence of a motif less likely
(b
4
¼0.766, e
b4
¼0.465, p<0.05). That is an interesting
result which suggests that motifs are twice (1/0.465 ¼2.15)
more likely to be found if the value of this dimension
decreases by one unit, after controlling for other factors in
the model. It means motifs are more common in places of
moderate reverberation—neither very high or very low
(because x
i4
has large values at either extreme of dimension
1). This indicates that motifs are mainly found in positions
where a balance between reverberation and clarity is present
(avoiding high levels of reverberation, but where metric
scores pertaining to intelligibility, clarity, and definition also
are not high).
G. Presence or absence of motifs in general—cave
section dependent
So far, the presence/absence of a motif has been coded
on whether that motif is found within a radius of 1 m from
the measurement microphone. This is more restrictive than
Reznikoff’s coding which used a 2 m radius (Reznikoff,
2002: p. 43). Use of a 1 m radius presumes that any notable
acoustic effects that might influence the location of a given
motif would be perceived only in that precise position. This
might not always be the case. Although low frequency reso-
nance is often tightly localized, reverberation is associated
with diffuse fields, meaning its effects are spread equally
across a large space. Thus it might be argued that in some
cases, the presence or absence of motifs should be assessed
in relation to all measurement positions within the same sec-
tion of acoustic space. Such analysis might reveal whether
the acoustic response of sections of caves where motifs exist
differs significantly from that in other sections where no
motifs are found.
The 177 original measurement points were thus recoded
to Motif ¼136 and No Motif ¼41, where the coding for
presence of motif was defined on the basis that the measure-
ment was taken in a section of the cave which had at least
one motif present. In such cases, measurement points might
be several meters distant from motifs but within the same
physically enclosed space, in other words, the same section
of the cave. In this stage of the analysis, many measurement
positions, even those not immediately adjacent to a motif,
will be grouped as “motif present.”
Figure 14 shows the distribution of the data along acous-
tic dimensions 1 and 2 ordered according to this latter
definition.
A logistic regression model was calculated using the
same independent variables as in Eq. (1), but with the data
points recoded in terms of their membership to a particular
cave section rather than specific proximity to a motif. A sta-
tistically significant model was found, v
2
(4) ¼26.888,
p<0.0005. The model explained 21.3% (Nagelkerke R
2
)of
the variance in the presence/absence of a motif and correctly
classified 80.2% of cases. The explanatory power of the
model has decreased slightly from that presented in Sec.
VF. The model variables can be seen in Table VI.
Interestingly, the only significant variable in this model
is x
i4
, the square of dimension 1, and this is further supported
by the increased overlap of ellipses observed in Fig. 14.It
was found that the probability of finding a motif decreases
with increasing values of x
i4
(b
4
¼0.585, e
b4
¼0.557,
p<0.05). A motif is 1.8 (1/0.557 ¼1.8) times more likely to
be found for every unit decrease of dimension 1 squared.
This result is similar to that already observed in Sec. VF,
i.e., that motifs are more likely to be present in places with
moderate values for reverberation.
It should be noted that the variable associated with
dimension 2, x
i2
, is no longer significant in this model, sug-
gesting that under these new assumptions the presence/
absence of motifs is no longer statistically associated with
low frequency resonances. Acoustic theory indicates that
modal effects in rooms are localized within a physically
enclosed space (Kuttruff, 2009), but grouping all the meas-
urements in a given section together has effectively averaged
out those effects. In contrast, the metrics associated with x
i4
,
which by theoretical definition assume a more homogeneous
distribution across spaces, retain their significant explanatory
power.
TABLE VI. Logistic regression model for data where motif presence is
coded as a function of cave section. B is beta coefficient. S.E. is standard
error, df is degrees of freedom, Sig. is significance, and Exp(B) is the odds
ratio.
Variables in the Equation B S.E. Wald df Sig. Exp(B)
Dimension 1 0.363 0.191 3.611 1 0.057 0.696
Dimension 2 0.296 0.194 2.320 1 0.128 0.744
Dimension 3 0.136 0.200 0.464 1 0.496 0.873
Dimension 1 squared 0.585 0.186 9.898 1 0.002 0.557
Constant 1.889 0.282 45.023 1 0.000 6.612
FIG. 14. (Color online) Individual samples (measurements) along dimen-
sions 1 and 2, with data grouped by sections within each cave. Ninety-five
percent confidence ellipses are also plotted for both motif and no-motif data
sets. In contrast with data coded individually, there is a substantial overlap
between 95% confidence ellipses suggesting that significant differences
between the two categories (motif, none), particularly along dimension 2,
are no longer present.
J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al. 1345

H. Other variables in the model—proximity to the
original entrance
So far, the model that best explains the position of
motifs from the acoustic metrics has a 23.4% explanatory
power, identifying low frequency resonances and reverbera-
tion or lack thereof as significant variables(Sec. VF). 23.4%
is a somewhat low level of explanatory power, and it is
important to consider other systematic factors that may have
an association with the placement of a motif in a given loca-
tion. Such a factor, which has been included in the field
measurements, is distance from the original cave entrance.
We recorded data on the distance between the original
entrance of each cave and each of the motifs, and included
this data as an added variable in our “best fit” model estab-
lished in Sec. VF. The new logistic regression model hence
contains one additional variable, x
i5
, representing distance of
motif from cave entrance:
log pi
1pi

¼b0þb1xi1þb2xi2þb3xi3þb4xi4þb5xi5:
(2)
The resulting model was statistically significant [v
2
(5)
¼45.065, p<0.0005]. The model explained 30.1%
(Nagelkerke R
2
) of the variance in the presence/absence of
motifs and correctly classified 72.3% of cases. Table VII
shows the variables in the model. Significant variables are
x
i1
(b
1
¼0.635, e
b1
¼0.530, p<0.05), the dimension
describing energy decay and intelligibility/clarity/definition;
x
i2
(b
2
¼0.505, e
b2
¼0.604, p<0.05), the dimension
describing low frequency response; x
i4
(b
4
¼0.768, e
b4
¼0.464, p<0.05), the square of dimension 1; and x
i5
(b
5
¼0.006, e
b5
¼0.994, p<0.05), corresponding to dis-
tance, in meters, from the measurement position to the origi-
nal cave entrance.
The significant variables are once again inversely corre-
lated to the presence of motifs. The added observation from
this analysis is that one is less likely to find motifs in mea-
surement positions deeper into the cave (b
5
¼0.006,
e
b5
¼0.994, p<0.05). Controlling for each of the other vari-
ables, we can interpret that motifs are: 1.9 times more likely
to be found when dimension 1 decreases by one unit; 1.6
times more likely to be found when dimension 2 decreases
by one unit; 2.1 times more likely to be found when the
square of dimension 1 decreases by one unit; and 1.006
times more likely if the distance to the original entrance
decreases by 1 m.
A curious result is that both dimension 1 and its squared
transformation are now significant. That indicates that, in
this model, dimension 1 contains both linear and quadratic
components. The conclusion from this result is that motifs
are found where RTs are low, but not extremely low, sugges-
ting a “bliss point” in the data (Moskowitz, 1981).
The inclusion of the distance variable and the consequent
increase in the explanatory power of this model suggest that
factors other than acoustic response will be significant in
explaining an organized positioning of the motifs.
VI. DISCUSSION OF RESULTS
A. Acoustic response
The general acoustic response measured within the five
caves studied here reveals reverberation and EDTs within a
range from about 0.2 s to an average of 1.5 s with a few sec-
tions, particularly large in volume, revealing values above
2.5 s. Despite the general belief that caves sustain very long
RTs, the spaces we measured did not show any particularly
long values. An explanation for this might be associated
with the various passageways and rough surfaces that are
found in most of the caves and sections we studied.
The ranges measured for acoustic metrics within these
caves show spaces with favorable conditions for speech and
music (as defined according to modern criteria) indicating
that any acoustic activity would have been accompanied by
acoustic effects such as reverberation and levels of intelligi-
bility that were neither limited nor excessive.
Reduction of variables from the 23 acoustic metrics
extracted from the impulse responses collected in these
caves has revealed that acoustic data are distributed along
three major orthogonal dimensions:
•Dimension 1, explaining 72% of the variance, describes a
measure of energy decay with large positive values repre-
senting higher reverberation (T30, EDT) and large nega-
tive values representing high values of clarity (C80),
definition (D50), and STI.
•Dimension 2, explaining 11% of the variance, describes a
measure of low frequency response merit with large posi-
tive values along this dimension corresponding to spaces
approaching optimal low frequency behavior (as defined
for modern sound reproduction in rooms) and negative
values representing resonant behavior in the response.
•Dimension 3, explaining 4.5% of the variance in the data,
describes evidence for audible echoes.
B. Association between acoustic response and motifs
Statistical associations between the positioning of
motifs and acoustic response were found in several of our
analyses. These include statistically significant associations
between the presence of dots and lines, the earlier type of
motifs, and dimensions 1 and 2. The analysis showed that
lines and/or dots are more likely to be found at places with
low reverberation and high clarity/definition and STI, and
TABLE VII. Logistic regression model for data where motif presence is
coded at individual positions. The variable “distance to entrance” has been
included in the model. B is beta coefficient, S.E. is standard error, df is
degrees of freedom, Sig. is significance, and Exp(B) is the odds ratio.
Variables in the Equation B S.E. Wald df Sig. Exp(B)
Dimension 1 0.635 0.215 8.710 1 0.003 0.530
Dimension 2 0.505 0.198 6.479 1 0.011 0.604
Dimension 3 0.015 0.180 0.007 1 0.935 1.015
Dimension 1 squared 0.768 0.212 13.115 1 0.000 0.464
Dist. to entrance 0.006 0.002 10.372 1 0.001 0.994
Constant 1.594 0.339 22.076 1 0.000 4.921
1346 J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al.

where there is evidence for low frequency resonances. The
effect size for this association was small at (Nagelkerke)
R
2
¼0.181 and the odds ratios calculated, giving a sense of
effect size, were all in the small range (i.e., <3.5).
A statistically significant association was found between
the presence of motifs in general, regardless of type, color,
or period, and acoustic response. The significant variables in
these associations were again associated with dimensions 1
and 2, i.e., the degree of reverberation, intelligibility, clarity,
and definition and the degree of low frequency resonance in
the response. In line with results for dots and lines, it was
found that any motif is more likely to be located at places
where reverberation is low and intelligibility, clarity, and
definition are high and where low frequency resonances
might be audible. Here again, the odds ratio calculated was
found to be in the small range, always below 2.5.
Perhaps more intriguingly, our best model suggests that
motifs are more likely to be found at places where indices of
reverberation are moderate, rather than too high or too low,
suggesting an optimal region. The explanatory power of the
best statistical model fitted to this data is 30.1%, which is
not very high, and might warn against inferring very strong
conclusions from these results. This statistical model con-
tains variables accounting for the behavior of the two main
dimensions representing the acoustic metrics as well as a
variable representing the distance from the acoustic mea-
surement to the original entrance of the cave.
The results presented here both confirm and contradict
some of the arguments made in previous studies by Waller
(1993a,b) and Reznikoff and Dauvois (1988). On the one
hand, there seems to be weak evidence of statistical associa-
tion supporting the notion that motifs, and in particular lines
and dots, are more likely to be found at places with resonan-
ces. This was Reznikoff’s most confident conclusion
(Reznikoff, 2006: p. 79). On the other hand, according to our
analyses, motifs in general, regardless of type, color, or
period, are less likely to be found at places with high rever-
beration. The effect size of this result was in the small range,
which means the evidence of association exists but only
weakly. Also, there is no evidence to suggest that echoes
might have played a part, although this result is strongly
influenced by the fact that we have not found any positions
within these caves that sustained clearly audible echoes.
Employing a systematic and robust methodology, our
study presents evidence that there is some statistical associa-
tion between the positions of motifs and the acoustic
response measured close to them, albeit at a weak statistical
level. What has become clear is that if an appreciation of
sound played a part in determining the position of motifs in
these caves, it was only a part, since other aspects such as
distance from the original cave entrance appear to have a
significant relative weight, raising the explained variance in
the model from 23% to 30%. Furthermore, the demonstra-
tion that distance from an entrance makes a significant con-
tribution to the statistical model, suggests that a complex
interaction of relationships is taking place.
No significant associations were found between chronol-
ogy, or type or color of motifs, and the distribution of acous-
tic responses.
There are a number of possible aspects that affect the
analysis and may play some part in explaining the weak sta-
tistical significance and effect sizes observed: there is a diffi-
cult archaeological context, with a 15 000 to 40 000 year
distance to some of the material, the potential for not identi-
fying positions with motifs due to deterioration, and the diffi-
culty of working underground, in restricted time, within sites
of archaeological significance, all producing significant chal-
lenges; the acoustic metrics used have been designed as
descriptors of acoustic response in a modern built environ-
ment and while some have been shown to correlate to human
response, they might not be the optimal metrics that can
describe the experience of our ancestors in the context of
caves; finally, the statistical models are sparse in terms of
other architectural (contextual) factors that might have
affected placement of motifs, such as porosity of the rock
face and its accessibility.
VII. CONCLUSIONS
Blesser and Salter (2009) observe that, “cave wall
images are tangible, enduring manifestations of (…) early
humans,” and that in contrast sound “has no enduring mani-
festation, nor of course could it have for any pre-technical
peoples,” meaning that as a result, “available data are too
sparse to draw strong conclusions.” Our contribution makes
this data less sparse for the first time in a methodical and
repeatable manner.
In our work, a statistical association has been estab-
lished between acoustic response and the positions of
Palaeolithic visual motifs found in these caves. Our primary
conclusion is that there is statistical, although weak, evi-
dence, for an association between acoustic responses mea-
sured within these caves and the placement of motifs. We
found a statistical association between the position of motifs,
particularly dots and lines, and places with low frequency
resonances and moderate reverberation.
Importantly, we must reiterate that the statistically sig-
nificant association does not necessarily indicate a causal
relationship between motif placement and acoustic response.
In other words, our evidence does not suggest that the posi-
tioning of motifs can be explained simply through relation-
ships with acoustics, and we are not suggesting that motif
positioning was based solely on an appreciation of sound
properties. Indeed, we also found that motifs are statistically
less likely to be found further into the caves, away from its
original entrance, and this result further illustrates the com-
plex relationship between early human behavior and features
of these caves.
Rather than such simple associations, we suggest the
interaction evidenced is subtle and complex, not one of basic
causality, and that additional data are required for it to be
fully understood. This is the first systematic study of this
type, and further study is encouraged. Future research should
aim to increase the size and quality of the dataset, by explor-
ing more caves in Spain and France, particularly those vis-
ited by Reznikoff and Dauvois, as well as other cave systems
in the world where this type of material culture exists; col-
lecting a better balance between target and control positions,
J. Acoust. Soc. Am. 142 (3), September 2017 Fazenda et al. 1347

particularly for under-represented motifs such as hand sten-
cils; investigating other aspects such as area and material
properties of stone surface or volume of cave sections, which
are directly related to acoustic response, but might also influ-
ence the decision to place a motif; and to further investigate
aspects of the acoustic low frequency response in proximity
to dots.
Musical instruments that have been found by archaeolo-
gists in caves that feature Palaeolithic motifs have provided
some suggestions that ritualized musical activity might have
been present in these spaces in prehistory in the same period
when early human visual motifs were being created (Conard
et al., 2009;Buisson, 1990;Garc
ıa Benito et al., 2016;
Iba~
nez et al., 2015). Our analysis presents empirical evi-
dence that may be used to further investigate the suggestion
of an appreciation of sound by early humans in caves that
feature Palaeolithic visual motifs. The methodological chal-
lenge was to move beyond that general claim—that an
appreciation of sound was relevant to cave rituals—and pro-
vide a methodology to evaluate the claim on a statistical
basis.
The data collection and data analysis that we present
here provide a new and robust approach, linking the physical
properties of caves to early human behavior in a more rigor-
ous and measurable way.
ACKNOWLEDGMENTS
This work was supported by the Arts and Humanities
Research Council and Engineering and Physical Sciences
Research Council (Grant No. AH/K00607X/1) as part of the
Science and Heritage Programme. Access to the caves was
only possible because of the support and commitment of the
Gobierno de Cantabria and Gobierno Del Principado de
Asturias, the local regional governments in the area. The
project team also acknowledges the assistance of Dr.
Alastair Pike (University of Southampton), Professor Jian
Kang (University of Sheffield), Professor Pablo Arias Cabal
(University of Cantabria), Professor Philip Scarf (University
of Salford), and Dr. Jonathan Sheaffer, formerly at
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