"What" and "where" in auditory sensory processing: a high-density electrical mapping study of distinct neural processes underlying sound object recognition and sound localization.

Abstract

Functionally distinct dorsal and ventral auditory pathways for sound localization (WHERE) and sound object recognition (WHAT) have been described in non-human primates. A handful of studies have explored differential processing within these streams in humans, with highly inconsistent findings. Stimuli employed have included simple tones, noise bursts, and speech sounds, with simulated left-right spatial manipulations, and in some cases participants were not required to actively discriminate the stimuli. Our contention is that these paradigms were not well suited to dissociating processing within the two streams. Our aim here was to determine how early in processing we could find evidence for dissociable pathways using better titrated WHAT and WHERE task conditions. The use of more compelling tasks should allow us to amplify differential processing within the dorsal and ventral pathways. We employed high-density electrical mapping using a relatively large and environmentally realistic stimulus set (seven animal calls) delivered from seven free-field spatial locations; with stimulus configuration identical across the "WHERE" and "WHAT" tasks. Topographic analysis revealed distinct dorsal and ventral auditory processing networks during the WHERE and WHAT tasks with the earliest point of divergence seen during the N1 component of the auditory evoked response, beginning at approximately 100 ms. While this difference occurred during the N1 timeframe, it was not a simple modulation of N1 amplitude as it displayed a wholly different topographic distribution to that of the N1. Global dissimilarity measures using topographic modulation analysis confirmed that this difference between tasks was driven by a shift in the underlying generator configuration. Minimum-norm source reconstruction revealed distinct activations that corresponded well with activity within putative dorsal and ventral auditory structures.

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INTEGRATIVE NEUROSCIENCE
ORIGINAL RESEARCH ARTICLE
published: 22 June 2011
doi: 10.3389/fnint.2011.00023
“What” and “Where” in auditory sensory processing: a
high-density electrical mapping study of distinct neural
processes underlying sound object recognition and
sound localization
Victoria M. Leavitt1,2†, Sophie Molholm1,3,4, Manuel Gomez-Ramirez1†and John J. Foxe1,2,3,4*
1The Cognitive Neurophysiology Laboratory, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NewYork, NY, USA
2Program in Neuropsychology, Department of Psychology, Queens College of the City University of NewYork, Flushing, NY, USA
3The Cognitive Neurophysiology Laboratory, Children’s Evaluation and Rehabilitation Center, Department of Pediatrics, Albert Einstein College of Medicine,
Bronx, NY, USA
4Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
Edited by:
Patricia M. Di Lorenzo, Binghamton
University, USA
Reviewed by:
Klaus Mathiak, RWTH Aachen
University, Germany
Richard Pastore, Purdue University,
USA
*Correspondence:
John J. Foxe, Departments of
Pediatrics and Neuroscience, Albert
Einstein College of Medicine, Van
Etten Building –Wing 1C, 1225 Morris
Park Avenue, Bronx, NY 10461, USA.
e-mail: john.foxe@einstein.yu.edu
†Present address:
Victoria M. Leavitt, Neuropsychology
and Neuroscience Laboratory, Kessler
Foundation Research Center, West
Orange, NJ, USA;
Manuel Gomez-Ramirez, Zanvyl
Krieger Mind/Brain Institute, Johns
Hopkins University, Baltimore, MD,
USA
Functionally distinct dorsal and ventral auditory pathways for sound localization (WHERE)
andsoundobjectrecognition(WHAT)havebeendescribedinnon-humanprimates.Ahand-
ful of studies have explored differential processing within these streams in humans, with
highly inconsistent findings. Stimuli employed have included simple tones, noise bursts,
and speech sounds, with simulated left–right spatial manipulations, and in some cases
participants were not required to actively discriminate the stimuli. Our contention is that
these paradigms were not well suited to dissociating processing within the two streams.
Our aim here was to determine how early in processing we could find evidence for disso-
ciable pathways using better titratedWHAT andWHERE task conditions.The use of more
compelling tasks should allow us to amplify differential processing within the dorsal and
ventral pathways.We employed high-density electrical mapping using a relatively large and
environmentally realistic stimulus set (seven animal calls) delivered from seven free-field
spatial locations; with stimulus configuration identical across the “WHERE” and “WHAT”
tasks. Topographic analysis revealed distinct dorsal and ventral auditory processing net-
works during the WHERE and WHAT tasks with the earliest point of divergence seen
during the N1 component of the auditory evoked response, beginning at approximately
100ms.While this difference occurred during the N1 timeframe, it was not a simple mod-
ulation of N1 amplitude as it displayed a wholly different topographic distribution to that of
theN1.Globaldissimilaritymeasuresusingtopographicmodulationanalysisconfirmedthat
this difference between tasks was driven by a shift in the underlying generator configura-
tion. Minimum-norm source reconstruction revealed distinct activations that corresponded
well with activity within putative dorsal and ventral auditory structures.
Keywords: auditory, event-related potential, electrophysiology
INTRODUCTION
There is growing evidence for functionally and anatomically dis-
tinct auditory processing pathways. Findings in non-human pri-
mates (Romanski et al., 1999; Rauschecker and Tian, 2000; Tian
et al., 2001) and humans (Weeks et al., 1999; Clarke et al., 2000,
2002; Anourova et al., 2001; Zatorre et al., 2002; Poremba et al.,
2003) point to a WHAT/WHERE distinction analogous to that
seen in the visual system (Mishkin et al., 1982). These data sug-
gest a system whereby auditory inputs are communicated dorsally
from primary auditory cortex for the extraction of informa-
tion regarding spatial localization in parietal cortical regions (the
WHERE pathway), and ventrally from primary auditory cortex
along medial and inferior temporal cortex for the processing of
the specific object features of the signal such as spectral content
and temporal integration (the WHAT pathway).
Much of what we know about WHAT and WHERE audi-
tory pathways comes from studies in non-human primates,which
suggest that the earliest evidence for separable and functionally
distinct pathways can already be found in the medial geniculate
nucleus(MGN)of thethalamus(KaasandHackett,1999;Roman-
ski et al., 1999; Rauschecker and Tian, 2000). Rauschecker and
Tian (2000) summarized work utilizing histochemical techniques
and anatomical tracer dyes to track the trajectory of neuronal
subpopulations from the MGN to primary auditory cortex (A1).
Adjacent to A1 are a rostral area (R) and a caudomedial area
(CM; Figure 1). While A1 and R have been shown to receive
input from the ventral portion of the MGN, CM is the target
of dorsal, and medial divisions of MGN. The lateral belt areas
receiving input from A1 and R may represent the beginning of an
auditory pattern or object stream, as the neurons here respond to
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Leavitt et al.Dorsal/ventral auditory processing in humans
FIGURE 1 |Anatomical regions of non-human primate (monkey) brain
involved in processing of spatial and non-spatial stimuli. (A) Lateral
belt region (in color) comprises A1, primary auditory cortex; CL,
caudolateral belt region; AL, anterolateral belt region; ML, medial lateral belt
region. (B)The same region, enlarged to show the locations of the medial
and lateral belt (yellow), primary auditory cortex (purple) and the parabelt
cortex on the superior temporal gyrus. Adapted from Romanski et al.
(1999), with permission.
species-specific vocalizations and other complex sounds (Roman-
ski et al.,1999; Rauschecker and Tian,2000). Conversely,neurons
in caudal regions CM and CL (caudomedial and caudolateral
regions respectively) show a good degree of spatial selectivity (see
Tian et al., 2001). Romanski et al. (1999) traced the streams fur-
therinrhesusmonkeys,findingdistincttargetsfortheventraland
dorsal pathways in non-spatial and spatial domains of prefrontal
cortex,respectively. Similarly,human lesion studies have provided
additionalsupportforthedivisionoftheauditorysystemintodor-
sal and ventral pathways with clear functional dissociations seen
for patients with dorsal versus ventral lesions (Clarke et al., 2000,
2002; Clarke and Thiran, 2004).
In humans, there is also mounting functional evidence for
WHAT and WHERE divisions of the auditory system. Arnott
et al. (2004) compiled a meta-analysis of neuroimaging studies
of ventral and dorsal auditory processing in humans. By review-
ing 11 spatial studies and 27 non-spatial studies, these authors
concluded that physiologically distinct areas for processing spa-
tial and non-spatial features of sounds could be identified. For
the studies classified as “spatial,” centers of activation clustered
around the inferior parietal lobule, the temporal lobe posterior
to primary auditory cortex, and the superior frontal sulcus. For
the non-spatial studies, centers of activation were seen clustered
around inferior frontal cortex. While there was substantial over-
lap in areas of activation, the clusters of activation corresponded
well with the notion that WHERE processing occurs in a dorsally
positionedprocessingstreamandWHAT processinginaventrally
positioned processing stream (see Figure2). The studies included
in this analysis all utilized positron emission tomography (PET)
and/or functional magnetic resonance imaging (fMRI). Thus far,
however, there have been relatively few electroencephalographic
(EEG)/magnetoencephalographic (MEG) investigations of pro-
cessing within these pathways in the human auditory system.
Therefore, while we have some idea of the anatomical localiza-
tion of the processing of these distinct types of information, our
understanding of the temporal dynamics of these processes is still
quite sparse.
FIGURE 2 | Schematic representation of auditory pathways as
evidenced by a meta-analysis of 11 spatial (dorsal) and 27 non-spatial
(ventral) studies. Regions showing greatest activation in each study are
indicated. Adapted from Arnott et al. (2004) with permission.
Furthermore, the paradigms that have been employed to tap
into functional capacities of auditory processing have varied
greatly. For instance, a large portion of the non-spatial studies
examinedintheArnottmeta-analysisusedhumanspeechsounds,
and this may be problematic insofar as speech represents a highly
specialized auditory information class for humans, with a spe-
cific set of speech processing regions that may not necessarily lend
themselveseasilytothisdorsal/ventraldistinction.Ithasalsobeen
proposed that the dorsal pathway in humans not only processes
auditory space,but also plays an important role in speech percep-
tion(BelinandZatorre,2000),raisingtheissueof possibleoverlap
of presumptive dorsal and ventral pathway functions.
HumanEEGandMEGstudieshavecertainlyattemptedtoshed
some light on the timing of dorsal/ventral processing dissocia-
tions, but the findings have been highly inconsistent in terms of
how early in the processing hierarchy information begins to be
preferentially processed by a specific pathway. In the context of a
match-to-sample task for location (right and left) and frequency
(220,440,and 880Hz) that varied by memory load,Anurova et al.
(2003)foundverylateeffectsof taskdifferencesoccurringapprox-
imately 400ms after stimulation. In an fMRI/EEG study, Alain
et al. (2001) also found late occurring task-related effects from
300ms onwards using a match-to-sample task. Stimuli in their
studywerefivepitch-variednoiseburstsdeliveredviaheadphones
to five simulated spatial locations. In contrast to these findings of
late processing differences, Anourova et al. (2001) using simulta-
neously recorded EEG and MEG, found peak latency differences
between WHAT and WHERE tasks at considerably earlier time-
points during the so-called N1 component of theAEP (∼100ms),
but there were no amplitude differences seen. In that study, iden-
tical blocks of tone stimuli were presented to subjects who were
instructed to attend either to sound location or frequency. Stimuli
weresimpletonesoftwofrequencies(1000and1500Hz)delivered
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Leavitt et al.Dorsal/ventral auditory processing in humans
viaheadphonestotwosimulatedlocations(aninterauralintensity
difference was applied; perceived location was either left or right).
Utilizing MEG and fMRI and a match-to-sample task with speech
stimuli,Ahveninenetal.(2006)foundbothamplitudeandlatency
effectsat∼70–150ms(duringtheN1component).DeSantisetal.
(2007a) also argued for “WHAT/WHERE” related effects on the
amplitude of the auditory N1, but it is important to point out
that in this study, subjects were involved in a passive listening
task and did not explicitly perform spatial or object judgments.
Stimuli were bandpass filtered noise bursts (250 or 500Hz) deliv-
ered to two perceived locations (interaural time difference was
used to simulate left and right locations). Although the aim of
this study was for a setup where the stimuli used in the WHAT
and WHERE conditions were “identical,” this was not in fact the
case,andthedesignresultedinuniquestimulusconfigurationsfor
eachcondition.FortheWHAT condition,80%of thestimuliwere
of one frequency and 20% were of a second frequency, while the
spatial position was equi-probable (50/50). A second counterbal-
ancedblockof thesamepassivelypresentedWHAT conditionwas
run in which the 80/20 ratio was reversed, and for analysis, the
authors appropriately collapsed responses over both blocks. An
analogous80/20designwasusedforthetwoblocksof theputative
WHERE condition, with the frequency aspect of the stimulated
stream varied equally (50/50). There appear to be two potential
issues with this design that complicate a straightforward interpre-
tationoftheresults.First,the80/20–50/50ratiosforonecondition
were flipped for the other and so the stimulation sequences were
notidentical.Secondly,andmoreimportantly,thisdesigntakesthe
form of a standard oddball paradigm,and would necessarily have
producedthewell-knownmismatchnegativity(MMN)tothelow-
probability stimuli (see e.g.,Molholm et al.,2005; Näätänen et al.,
2007). To circumvent this issue only the frequent tones were com-
pared between conditions. Unfortunately, it has been shown that
thefirstfrequent“standard”tonefollowinganinfrequent“deviant”
tone also receives differential processing under such designs, and
a robust MMN is often seen to this presumed“standard”tone (see
e.g.,Nousaketal.,1996;Roeberetal.,2003).Uponcarefulreading,
responses to these first standards appear to have been included in
theDeSantisanalysis(DeSantisetal.,2007b).Inallfiveofthepre-
vious EEG/MEG studies that aimed to study the WHAT/WHERE
distinction in audition, stimuli were tones, noise bursts, or vowel
sounds, and all utilized simulated three-dimensional (3D) envi-
ronments delivered via headphones. None of these studies used
sounds delivered in the free field and one could certainly question
the “objectness” of simple tones and noise bursts. There is also a
clear discrepancy between the two studies that show only late dis-
sociations (>300ms: Alain et al., 2001; Anurova et al., 2003) and
those that suggest considerably earlier effects during the N1 pro-
cessing period (∼100ms: Anourova et al., 2001; Ahveninen et al.,
2006; De Santis et al.,2007a).
Apriorinvestigationof mid-latencyauditoryevokedresponses
(MLRs) from our group suggested that there was constitutive
divergence of activation into separable pathways in the context of
apassivelisteningtask,adivergencethatisalreadyobservablevery
earlyinprocessingduringtheauditoryPacomponent,whichpeaks
at just 35ms (Leavitt et al., 2007). At this latency, source-analysis
pointed to distinct responses with sources from more dorsal and
more ventral areas of auditory cortex. While that study was not
designedtoexpresslytestthefunctionalityofthedorsalandventral
auditorypathways,automaticsensoryactivationofthesepathways
is likely an inherent result of receiving any auditory stimulation
or performing any auditory task. By analogy, visual stimuli con-
stitutively activate both the dorsal and ventral visual streams even
when no explicit object or spatial processing tasks are required
(see, e.g., Schroeder et al., 1998). That is, both pathways are acti-
vated by typical visual inputs,but it is possible to activate them in
relativeisolationbymanipulatingstimulusproperties(e.g.,theuse
of isoluminant chromatic contrast; see Lalor et al., 2008). Indeed,
in non-human primate studies,the animals are often anesthetized
during recordings and yet both the dorsal and ventral pathways
are activated (e.g., Schmolesky et al., 1998). Our aim here was to
determinejusthowearlyinauditoryprocessingwecouldfindevi-
dencefordissociablepathwaysunderspecificWHAT andWHERE
task conditions. Our premise was that functionally distinct and
ecologically valid tasks would allow us to amplify any differen-
tial processing within the dorsal and ventral pathways, and that
there were likely earlier dissociations of WHAT/WHERE process-
ing than previously reported (e.g., Alain et al., 2001; Anourova
et al.,2001;Anurova et al.,2003;Ahveninen et al.,2006; De Santis
et al., 2007a). To that end, we employed animal calls as stimuli,
avoiding any interference from language-specific areas that may
be activated in response to human speech sounds, as well as tak-
ing advantage of the broadened extent of auditory cortex that is
activatedbycomplexacousticstimuli(asopposedtosimpletones;
see for example Rama et al., 2004). In using a larger set of com-
plex animal calls and thereby attaching semantic meaning to our
stimuli, our intention was to maximize object-processing while
avoiding possible confounds induced by the use of speech stimuli.
Similarly, by using free-field spatial stimuli delivered from a rel-
atively large array of seven possible locations, we intended to get
away from simple left–right judgments over headphones that may
have been less effective at invoking spatial mechanisms. Identical
stimuliweremaintainedforbothtasks,andonlythetaskitself was
varied.
MATERIALS AND METHODS
The procedures for this study were approved by the Institu-
tional Review Board of The Nathan S. Kline Institute for Psychi-
atric Research/Rockland Psychiatric Center and the Institutional
Review Board of The Graduate Center of the City University of
NewYork.
SUBJECTS
Informed consent was obtained from 12 (10 male) healthy con-
trol subjects aged 23–56years (mean=33±11.4). All reported
normal hearing. One subject was left-handed, as determined by
the Edinburgh Test for Handedness (Oldfield, 1971). None of the
participants had current or prior neurological or psychiatric ill-
nesses, nor were any currently taking psychotropic medications.
All subjects were paid a modest fee for their participation.
STIMULI
Precisely the same stimuli were used for both the WHAT and
WHERE conditions: seven animal sounds (cow, dog, duck, ele-
phant, frog, pig, and sheep), adapted from Fabiani et al. (1996).
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Leavitt et al.Dorsal/ventral auditory processing in humans
These sounds were of uniquely identifiable vocalizations. They
were modified using Cool Edit Pro Version 2 such that each had
a duration of 250ms, and were presented over seven Blaupunkt
PCx35290watt3.5??2-waycoaxialsoundsourcesatacomfortable
listening level of approximately 80dB sound pressure level. Inter-
stimulus interval (ISI) was variable to avoid anticipatory effects;
stimuli in both conditions were delivered every 2500–3500ms.
PROCEDURE
Participants were seated in a comfortable chair in a dimly illumi-
nated, sound-attenuated, electrically shielded (Braden Shielding
Systems) chamber and asked to keep head and eye movements to
a minimum. Auditory stimuli were presented from seven spatial
locations via sound sources arranged in the participant’s median
plane along a frontal arc of ±90˚ (−90˚, −60˚, −30˚, 0, +30˚,
+60˚, +90˚) and centered on the participant’s head (1.0m dis-
tance; see Figure 3). This arc was adjustable on a pulley system
such that it could be positioned level with the horizontal plane
of each individual subject at head height. Sound sources were
concealed behind a black curtain to prevent participants from
forming a visual representation of sound source positions. The
apparatus was precisely centered within the 2.5m×2.5m square
chamber, equidistant from the left and right walls of the cham-
ber.A 12??×12??digitizedWacom graphics tablet was mounted in
front of and aligned with the participant’s median sagittal plane
of the trunk. It consisted of a stylus that the participant used to
draw a freehand line toward the source of the sound as it was
perceived (during the localization task). The tablet was obscured
from view by a partition. There were two blocked conditions,
which were counterbalanced for order across subjects. The two
conditions were equated for motor (response) requirements, to
minimize motor confounds. In addition, the exact same stimuli
were used in both conditions, to minimize potential confounds.
In order to insure sufficient power,we collected a very large num-
ber of sweeps for each subject (upward of 2000 trials on average
in both conditions, an order of magnitude more than is typical,
thereby ensuring very high SNR). The number of blocks varied
in the WHERE condition due to individual subject factors (i.e.,
fatigue, breaks, time constraints). (1) WHERE task: the WHERE
task was to indicate the location that a sound came from; subjects
completed a minimum of 13 and not more than 15 blocks. Each
blockcontained168stimuluspresentations(24persoundsource).
Stimuli were animal calls, as in the WHAT task. Spatial presenta-
tionofthestimulifollowedarandomorder,asdidtheanimalcalls.
Subjects responded by drawing a line on the graphic tablet out to
theradiusinthedirectionof eachanimalcallafteritwasdelivered.
The angle and trajectory of the line was recorded by the computer
in Presentation 4.0. Then, the participant returned the stylus to
the midpoint of the tablet; the resting position. This position was
indicatedbyadepressioninthetablet.(2)WHAT task:theWHAT
taskwastoidentifyatargetanimalfromsevenrandomlydelivered
animalcalls.Thetargetanimalforeachrunwasrandomlyselected
and randomized across blocks. There were 14 blocks. At the start
of each block,the subject was given an auditory cue:“This is your
target...” followed by one of the seven randomly selected target
animalcalls.Afterthat,allsevenanimalcallswererandomlydeliv-
ered throughout the block in equal distributions, precisely as in
the WHERE task. In this task subjects were to respond to a desig-
natedtargetwithoneresponse,andtoallnon-targetswithanother
response. There were two response types, drawing either a line or
a circle on the tablet. The pairing of response type to stimulus
type (target or non-target) was counterbalanced across subjects.
Thetargetstimulioccurredon15.6%of trials.Eachanimalserved
as target in 2 of 14 runs, in randomized order both within and
between subjects. To address a potential stimulus confound, an
alternate WHAT task, WHAT II, was run on a subset of subjects
(6 of the 12 subjects). The concern was that in the WHERE task,
each stimulus was processed by subjects as a target requiring a
response. However,the WHAT task stimuli were targets and non-
targets. Therefore, in WHAT II subjects were instructed: “Please
say the name of each sound you hear,”thereby equating all stimuli
across conditions as targets. (Note that while subjects were never
explicitlytoldtheywouldhear“animal”calls,soasnottobiasthem
tosemanticcategory,itwasveryclearthatallsubjectsimmediately
FIGURE 3 | Graphic representation of sound source locations. Seven sound sources were arranged in the participant’s median plane along a frontal arc of
±90˚ (−90˚, −60˚, −30˚, 0, +30˚, +60˚, +90˚) and centered on the participant’s head (1.0m distance).
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Leavitt et al.Dorsal/ventral auditory processing in humans
understoodwhatthesoundswere,asevidencedbytheirhighrates
of accuracyonthetask.)Araterwaspresentintheroomrecording
each response on a laptop. Therefore, each stimulus was verbally
identified by the participant immediately after stimulus delivery.
Asbefore,thesevenanimalcallswererandomlydeliveredthrough-
out the block. This second control variant of the WHAT task was
always run on a separate day. In our analyses, WHAT II will be
compared to WHAT I through generation of a statistical cluster
plotanalysistodeterminewhetherthetwoparadigmsyieldsignif-
icant differences at any electrodes across all time points. WHAT II
will also be compared to WHERE to determine whether a similar
pattern of results is seen between WHERE and the two respective
WHAT conditions.
DATA ACQUISITION
Continuous EEG was acquired through the ActiveTwo Biosemi™
electrode system from 168 scalp electrodes, digitized at 512Hz.
For display purposes, data were filtered with a low-pass 0-
phase shift 96dB 40Hz filter after acquisition. The reference
electrode was assigned in software after acquisition. BioSemi
replaces the“ground”electrodes that are used in conventional sys-
tems with two separate electrodes: common mode sense (CMS)
active electrode and driven right leg (DRL) passive electrode.
These two electrodes form a feedback loop, thus rendering them
“references.” For a detailed description of the referencing and
grounding conventions used by the Biosemi active electrode
system, the interested reader is referred to the following web-
site: http://www.biosemi.com/faq/cms&drl.htm. All data were re-
referenced to the nasion after acquisition,for analysis (in one sub-
ject,thesupranasionwasusedforre-referencing,asthenasionwas
contaminated by artifact).After each recording session,before the
electrode cap was removed from the subject’s head, the 3D coor-
dinatesof all168electrodeswithreferencetoanatomiclandmarks
on the head (nasion,pre-auricular notches) were digitized using a
Polhemus Magnetic 3D digitizer. EEG was averaged offline. Data
were epoched (−100ms pre-stimulus to 500ms post-stimulus)
and then averaged. Baseline was defined as the mean voltage over
−100to0msprecedingtheonsetofthestimulus.Trialswithblinks
and large eye-movements were rejected offline on the basis of
horizontal and vertical electro-oculogram recordings. An artifact
rejection criterion of ±100μV was used at all other electrode sites
to exclude periods of muscle artifact and other noise-transients.
Fromtheremainingartifact-freetrials,averageswerecomputedfor
each subject. These averages were then visually inspected for each
individual to ensure that clean recordings with sufficient numbers
of trials were obtained and that no artifacts were still included.
Across both WHAT and WHERE conditions, the average number
of accepted sweeps was over 2000 trials. Prior to group averag-
ing,data at electrodes contaminated by artifacts were interpolated
for each subject. As described by Greischar et al (2004), spherical
spline interpolation represents a method for increasing power at
electrode sites where data has been contaminated. Data were re-
baselined over the interval −50 to 20ms after interpolating. Data
wereultimatelyaveragedacrossallsubjects(grandmeanaverages)
for visual comparison at the group level and for display purposes.
The reader should note that throughout this paper, we use the
familiar nomenclature of the modified 10–20-electrode system to
refer to the positioning of electrode sites. Since our montage con-
tains considerably more scalp-sites than this nomenclature allows
for, in some cases, we will be referring to the nearest neighboring
site within the 10–20 system.
ANALYSIS STRATEGY
For each electrode, the data for all non-target trials from all sub-
jects were collapsed into a single average waveform for each of
the sound sources and averaged over all seven locations. These
group-averaged waveforms were then visually inspected across all
scalp-sites. To constrain our analyses, we first identified the well-
characterizedauditorycomponentspriortoandincludingtheN1:
P20, Pa, P1, and N1. Components were identified on the basis
of their expected latencies, over scalp regions previously iden-
tified as those areas of maximal amplitude for auditory evoked
componentry (see, for example, Picton et al., 1974; Leavitt et al.,
2007). Note that the latencies and topographies of the basic AEP
components reported here were entirely typical of those previ-
ously reported. Analyses were performed across three pairs of
scalp electrodes for each component; we chose three electrodes
at homologous locations on each side of the scalp (for a total of
six electrodes) that best represented the maximal amplitude of
the component of interest in a given analysis and averaged across
the three electrodes. Statistical analyses were conducted with the
SPSS software package (SPSS version 11.5). Limiting our analyses
to those electrodes where the components are maximal represents
a conservative approach to the analysis of high-density ERP data
andraisesthelikelihoodofmissedeffects(so-calledTypeIIerrors)
in these rich datasets. Therefore, an exploratory (post hoc) analy-
sis phase was also undertaken (described below under Statistical
Cluster Plots).
For each component of interest (P20, Pa, P1, N1), we calcu-
lated the area under the waveform for an epoch centered on the
peak of the grand mean (the time window used for each epoch
was either 10 or 20ms depending on the width of the respec-
tive component’s peak). We then averaged these measures across
the three representative electrode sites for each hemisphere. These
area measures were then used as the dependent variable. To inves-
tigate differences between the AEPs of subjects in the WHAT and
WHERE conditions, we tested each identified component with a
2×2 analysis of variance (ANOVA). The factors were condition
(WHAT versus WHERE) and hemi-scalp (right versus left).
To verify that our object identification task was effective, we
compared the average waveform for responses to targets to the
same for the standard (i.e., non-target) stimuli. The Nd compo-
nent(alsoreferredtoastheresponsenegativity)hasbeenshownto
beelicitedwhensubjectsattendtorelevantauditorystimuli,when
relevanceisdefinedalongaphysicaldimension(HansenandHill-
yard, 1980; Molholm et al., 2004). The presence of a robust Nd
component would provide confirmation that our subjects were in
fact actively engaged in the object detection task.
It is important to note that our first level of analysis was per-
formed using an average of all seven sound sources,collapsed into
a single waveform for each condition. However, the experiment
was designed such that every subject was exposed to hundreds of
trials from each individual sound source, therefore enabling us to
analyze the ERPs generated in response to each spatial location
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separately. As a second level of analysis, we inspected seven sepa-
rate waveforms within each condition. We compared these seven
averages for our subjects both within and across condition.
SOURCE RECONSTRUCTION
Sourcereconstructionsweregeneratedbyapplyingtheminimum-
norm least squares method as implemented in the BESA software
suite (version 5.1; Gräfelfing, Germany). The minimum-norm
approach is a commonly used method to estimate a distributed
electrical current image in cortex as a function of time (see e.g.,
Hamalainen and Ilmoniemi, 1994). The source activities of 1426
regional sources are computed. Since the number of sources is
much larger than the number of sensors, the inverse problem is
highly underspecified and must be stabilized by a mathematical
constraint (i.e., the minimum norm). Out of the many current
distributions that can account for the recorded sensor data, the
solution with the minimum L2 norm, i.e. the minimum total
power of the current distribution, is displayed in BESA. To com-
pute the minimum norm here,we utilized an idealized three-shell
spherical head model with a radius of 85mm and assumed scalp
and skull thickness of 6 and 7mm, respectively. The minimum
normwasappliedtothedataacrossthelatencyinterval90–130ms,
spanning the N1 timeframe. Baseline period was used to compute
mean noise levels. Weightings were applied to the data as follows:
(1) weighting at the individual channel level with a noise scale
factor of 1.00; (2) depth-weighting such that the lead field of each
regional source was scaled with the largest singular value of the
singular value decomposition of the source’s lead field; and (3)
spatio-temporal weighting using the signal subspace correlation
method of Mosher and Leahy (1998) (dimension setting=6).
STATISTICAL CLUSTER PLOTS
As described above,we took a conservative approach to the analy-
sis of the high-density ERP data in order to limit the number of
statistical tests performed, with the spatio-temporal properties of
the componentry delimiting the tests. Our conservative approach
raises the likelihood of missed effects. We therefore performed
an exploratory analysis as a means of fully exploring the rich-
ness of our data set and as a hypothesis-generating tool for future
research. We have devised a simple method for testing the entire
data matrix for possible effects, which we term statistical cluster
plots (see Molholm et al., 2002; Murray et al., 2002). These clus-
ter plots were derived by calculating point-wise,paired,two-tailed
t-tests between the WHAT and WHERE conditions. The results
were then arrayed on a single grid, with scalp regions (electrode
positions) plotted on the y axis and post-stimulus time plotted
on the x axis, thus providing a snapshot overview of significant
differences between conditions across scalp regions over time. In
the present data treatment, periods of significant difference were
only plotted if an alpha criterion of 0.05 was exceeded and then
only if this criterion was exceeded for at least five consecutive data
points(∼10ms;seeWetherillandLevitt,1965;FoxeandSimpson,
2002).
TOPOGRAPHIC MODULATIONS
To statistically identify periods of topographic modulation, we
calculate the global dissimilarity (GD; Lehmann and Skrandies,
1980, 1984) between WHAT and WHERE responses for each
time point of each subject’s data. GD is an index of configura-
tion differences between two electric fields, independent of their
strength.Thisparameterequalsthesquarerootof themeanof the
squared differences between the potentials measured at each elec-
trode (versus the average reference), each of which is first scaled
to unitary strength by dividing by the instantaneous global field
power (GFP). GD can range from 0 to 2, where 0 indicates topo-
graphic homogeneity and 2 indicates topographic inversion. To
derive statistical significance, the observed GD score at each time
pointwascomparedagainstaMonteCarlodistributioncomposed
of 5000 iterations (see Strik et al.,1998 for a description). Statisti-
cal significance was determined when the observed GD score (i.e.,
themeanGDacrossparticipants)was ±2SDawayfromthemean
of the Monte Carlo distribution. Since electric field changes are
indicative of changes in the underlying generator configuration
(Fender, 1987; Lehmann, 1987, this non-parametric test, in com-
bination with the scalp distributions, provides a reliable measure
for determining if and when the brain networks activated by the
WHAT and WHERE conditions differ significantly.
RESULTS
BEHAVIORAL RESULTS
WHAT condition:group-averagedaccuracyacrossalltrialsforthe
sound identification task was 85.4%. WHERE condition: for the
sound localization task, 11 participants’ responses were analyzed
(one subject’s responses had to be excluded from the behavioral
analysisastheywerenotcapturedduetoacomputermalfunction).
Each participant’s responses were averaged separately for each of
the seven sound source locations; group-averaged responses are
displayed in Figure 4. The average offset of responses collapsed
over all seven locations was 10.6˚±12.0˚. The average offset to
sound source 4, the midline sound source (where we might well
expect a very high degree of accuracy) was 4.5˚; moreover, all 11
subjects erred to the left of center. Greater accuracy was seen for
responses to sound sources located in participants’ left hemifield
(Sound sources 1, 2, 3), as compared to right hemifield (Sound
sources 5, 6, 7). The average offset for responses to left sound
sources(averagingoversoundsources1,2,and3)was3.6˚±11.0˚.
Average offset to sound sources in subjects’ right hemifield (aver-
aging over 5, 6, and 7) was 11.7˚±9.3˚. This between hemifield
difference was significant (p =0.006).
ELECTROPHYSIOLOGICAL RESULTS I: GENERAL DESCRIPTION
In both tasks, stimuli elicited clear ERPs, which contained P20,
Pa, P1, and N1 components (Figure 5) with typical scalp dis-
tributions (Figure 6). The time period 15–25ms was selected
around the P20 peak in our dataset. An ANOVA revealed no sig-
nificantmaineffectof task,WHAT versusWHERE,(F1,11=0.006,
p =0.938), nor was there a significant interaction effect for con-
dition by hemisphere (F1,11=2.509, p =0.141). The Pa peak in
our data was at approximately 42ms; analysis of 10-ms time win-
dow around the peak of the Pa found neither a main effect of
condition (F1,11=1.061, p =0.325), nor for condition by hemi-
sphere (F1,11=0.079, p =0.783). The P1 peak was found at
approximately 62ms. Analysis of a 10-ms time window around
the peak of the P1 yielded neither a main effect of condition
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FIGURE 4 | Behavioral results for the “WHERE” condition. Blue lines identify the average of responses across 11 participants (one had to be excluded due to
a technical problem capturing responses) for each of the seven sound source locations. Average offset of responses for all seven locations collapsed was
10.6˚±12˚.
(F1,11=0.966,p =0.347),noraconditionbyhemisphereinterac-
tion(F1,11=0.298,p =0.596).Inourdata,theN1wasevidentasa
negativitythatpeakedfronto-centrallyatapproximately124msin
bothconditions.Toaccountforthebroaderdeflectionofthiscom-
ponent,a 20-ms time window was selected around the peak of the
N1(114–134ms).Here,wefoundasignificantmaineffectof con-
dition (F1,11=5.811, p =0.035), reflecting substantially greater
amplitude of the N1 in the WHAT condition. The effect size of
this difference was 0.64 (using the criteria of Cohen’s d). There
wasnoconditionbyhemisphereinteractionfortheN1component
(F1,11=0.455, p =0.514).
To rule out the possibility of order effects, subjects who com-
pleted the WHERE condition on Day 1 were compared to those
who completed the WHAT condition on Day 1 (Figure 5B). As
described in the text, the figure demonstrates that both groups
(small N of 6 notwithstanding) showed an N1 difference between
conditions that is consistent with that shown by the full group,
and is detailed in our main results for the full group. Importantly,
the direction of the difference is consistent with the full group
analysis.
To ensure that subjects’responses actually reflected attentional
processingof relevantauditorystimuli,wecomparedtheresponse
evoked by target stimuli with that of non-target stimuli.As shown
in Figure 7, the response elicited by stimuli that included a target
auditory element became more negative-going than the responses
elicited by the stimuli without a target auditory element over
central/fronto-central scalp. This difference extended to about
350ms. This response pattern is consistent with elicitation of the
auditory selective attention component,the Nd (see,for example,
Hansen and Hillyard, 1980; Molholm et al., 2004), thereby pro-
viding us with compelling evidence that our subjects were truly
engaged in a task of sound object recognition.
To better detect missed effects between conditions, we com-
putedastatisticalclusterplotforallsevensoundsourcescollapsed
into one waveform for each condition (see Materials and Meth-
ods). This served as a hypothesis-generating tool; it provided us
withasnapshotofwhereandwhensignificantdifferencesoccurred
between conditions that may have been missed in our planned
analyses.Consistentwithourpreviousanalysis,weobservedadis-
tinct cluster that coincided with the N1 (Figure 8). What proved
to be rather striking was the almost complete absence of any other
differences, particularly at later time-points where higher-order
cognitive effects generally tend to become evident in ERPs.
A topographic map of the differences between conditions for
the N1 timeframe (Figure9) reveals a topography that is not con-
sistent with auditory components generated in primary auditory
cortex. Indeed, it is suggestive of substantial contributions from
frontal generators, thereby reflecting processes that more likely
involve ventral auditory pathway.
ELECTROPHYSIOLOGICAL RESULTS II: LOCATION SPECIFIC AVERAGES
We examined each of the seven separate sound source averages
within each condition to determine whether evidence of an inter-
action between sound source location and task could be seen.
When we compared between tasks, the N1 difference was consis-
tent; that is,the N1 amplitude in the WHAT task was consistently
greater than in the WHERE task (Figure 10A). To determine the
significance of this and to probe for any further effects,individual
statistical cluster plots for each sound source location were gener-
ated.A significant difference between conditions emerged around
the time window for the N1 at each sound source location with
the exception of Sound source 4 (the midline sound source). It is
important to note that the latency of the difference was shifted,
depending on the spatial orientation of the sound source. That is,
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FIGURE 5 | (A) Auditory evoked potentials in participants generated during
the WHAT task (red) and the WHERE task (black). Data from eight electrodes
spanning frontal, central, and centro-parietal regions are presented. (B)
Representative electrode from subgroups of the six subjects who participated
in the WHAT condition first (Day 1) and the six who participated in the
WHERE condition first.
forSoundsource1,thebetween-conditiondifferenceemergedthe
earliest, prior to 100ms (∼80ms), while the difference at Sound
source 7 became significant after 100ms (∼120ms). A closer
inspectionof theindividualsoundsourcewaveformsrevealedthat
theN1peakwasactuallylaterforresponsestoSoundsource7than
Sound source 1 in both conditions, by approximately 10ms. This
determination was made by a visual inspection of midline elec-
trodes where the N1 was maximal; therefore, the effect cannot be
due to laterality effects. For both the WHAT and WHERE tasks,
the N1 responses to each individual sound source location from
one representative electrode where the N1 component is maximal
(Fz)wereoverlaidandthesearepresentedinFigure10B.Whereas,
in the object task, the N1 response elicited to each sound source
was highly stable in that the amplitude was very similar across
sound sources, the spatial task evoked responses to each sound
sourcelocationthatshowedgreatervariability.Totestthisstatisti-
cally,a 2×2×7ANOVA was conducted with factors of condition
(WHAT versus WHERE), sound source (1–7), and sound source
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FIGURE 6 |Topographic maps of ERPs recorded duringWHAT andWHERE tasks. Group-averaged data are displayed for peak of each auditory component:
P20, Pa, P1, and N1.
FIGURE 7 | Group-averaged waveforms for responses to the sound
object recognition (WHAT) task for target stimuli (red) versus
non-target stimuli (blue). Representative electrode shown is FCz.
hemifield (left versus right) to determine whether there was a sig-
nificant interaction between sound source location and task. We
took the average N1 amplitude from three fronto-central elec-
trodes (around FCz) using a 40-ms time window around the N1
peak (importantly, this window was shifted such that it centered
on the peak of the N1 for each sound source, as these latencies
varied). No significant interaction was found for sound source
by condition [F(2,10)=1.057, p >0.1], nor was there a three-
way interaction between hemifield, sound source, and condition
[F(2,10)=0.410, p >0.1].
Topographic maps of the difference between conditions
(WHAT minus WHERE) were created for an average of the left
hemifield sound sources (1–2–3) and the right hemifield sound
sources (5–6–7). Subtle but distinct differences can be seen for
these, suggesting contributions from unique underlying genera-
tor configurations to stimuli that come from the left hemifield
compared to those from the right (Figure10C).
TOPOGRAPHIC MODULATION ANALYSIS
Averagingacrossallsevenspatiallocations,theGDmetricallowed
ustoidentifyadistinctperiodofstatisticallysignificantdifferences
in topographic modulations between the WHAT and WHERE
conditions over the N1 period, indicative of the activation of
distinct configurations of intracranial brain generators for each
experimental condition (Figure11). That is,the observed effect at
N1 was not only due to a change in the amplitude of activation
within a given generator configuration,but crucially,to actual dif-
ferences in the configurations of neural generators underlying the
twotasks(eitherintheactualneuralgeneratorsinvolved,orinthe
relative contributions from the same set of neural generators).
CORTICAL ACTIVATION: MINIMUM-NORM MAPS
As described in the Methods, the baseline period was used to
compute mean noise levels for the source reconstructions, result-
ing in a signal-to-noise ratio for the selected epoch of 8.67.
Source reconstructions at 10ms intervals, starting from 90ms,
suggested a strong left hemisphere bias for differential process-
ing between conditions (see Figure 12). Two stable clusters of
activation, suggestive of separable processing streams, can be
seen. At 90ms, the strongest activation is seen in a cluster
over left inferior and superior parietal areas, and this activa-
tion continues to be apparent throughout the sampled epoch,
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FIGURE 8 | Statistical cluster plot of the results of the point-wise
running two-tailed t-tests comparing the amplitudes of participants’
auditory evoked potentials in theWHAT versusWHERE conditions.
Time with respect to stimulus onset is presented on the x axis and
topographic regions of 168 electrode positions on the y axis. Color
corresponds to t values. Periods of significant difference are only plotted
if a strict alpha criterion of <0.05 was exceeded for at least five consecutive
data points.
FIGURE 9 |Topographic map of the differences between conditions
(WHAT minusWHERE) during the N1 timeframe.
although it weakens as time progresses. This cluster is anatom-
ically compatible with the dorsal auditory pathway. The sec-
ond stable cluster, compatible with the ventral auditory path-
way, emerges at approximately 100ms over anterior temporal
regions and peaks at 110ms, after which point it dissipates
substantially. As such, these two activation clusters appear to
have some WHAT different time courses. Activation in the
right hemisphere suggests much less involvement of underlying
generators.
RESULTS FOR ALTERNATE WHAT PARADIGM: WHAT II
Given that only a subset of participants (n =6) completed the
WHAT II paradigm, results should be interpreted cautiously.
However,a very specific prediction was being tested here,namely,
that a manipulation of the task would not affect the N1 differ-
enceseenbetweentheWHAT andWHERE conditions.Behavioral
performance accuracy in the WHAT II condition did not differ
significantly from WHAT I. A statistical cluster plot analysis com-
paring the two WHAT paradigms across the entire epoch (−100
to 500) failed to find any areas of significance, suggesting that the
differences between WHAT I and WHAT II were negligible (plots
not shown). These results support the notion that one paradigm
was just as effective as the other in characterizing neural processes
underlying sound identification. Next, we conducted an analysis
comparingWHAT II andWHERE.Wereasonedthatif differences
betweenthesetaskswerefounditwouldsupporttheviewthatboth
WHATI andWHATII aretappingprocessesof soundobjectiden-
tification, distinct from processes of sound localization. We used
a one-tailed t-test, guided by our hypothesis that the difference
between these conditions would replicate the direction seen for
WHATI versusWHERE.TheN1differencewasapparentbetween
conditions. Comparing the waveforms, we saw that the direction
of the effect was the same: WHAT II resulted in a greater deflec-
tion of the N1 relative toWHERE,as seen inWHAT I (Figure13).
While the absolute magnitude of the difference between WHAT II
and WHERE is not the same as that of WHAT I versus WHERE,
giventhatthenumberof subjectsinWHATII wasonlyasubsetof
the full sample (n =6), this was likely a power issue and as such,
not an unexpected finding.
DISCUSSION
The present data showed a robust difference in processing over
a surprisingly delimited time frame for subjects performing a
task of sound object recognition versus sound localization using
exactly the same stimulus setup. This difference was characterized
by greater negative-going amplitude during the N1 processing
timeframe for the WHAT condition compared to the WHERE
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FIGURE 10 | (A) WHAT (red) and WHERE (blue) group-averaged
waveforms taken from electrode Cz are shown for seven individual sound
source averages. In the figure, each waveform pair is shown at a location
consistent with the sound source from which it was derived. (B) Separate
waveforms for each of the sound sources are overlaid from each condition
at one representative electrode (Fz). (C)Topographic maps of the N1
difference between conditions (WHAT minus WHERE) showing distinct
topographies for sound sources on the left (Sound sources 1–2–3)
compared to the right (Sound sources 5–6–7).
condition. This effect was seen consistently at electrode sites over
frontal, central, centro-parietal and parietal scalp regions. While
this difference occurred during the N1 timeframe, it was clearly
not a simple modulation of N1 amplitude as it had a completely
different topographic distribution to that of the N1. GD measures
FIGURE 11 | Statistical plot of topographic modulations. Areas of
non-significance appear in blue; areas where significance exceeds p <0.03
are displayed in a graduated color scheme according to the legend shown.
Note that this significance cluster falls in a time frame coincident with the
N1 component.
using topographic modulation analysis confirmed that the differ-
ence between tasks during this timeframe was driven by a shift
in the underlying generator configuration. Additionally, applying
a minimum-norm source reconstruction algorithm to the differ-
encewaverevealeddistinctactivationsthatcorrespondedwellwith
activity within putative dorsal and ventral auditory structures.
Themaingoalof thepresentinvestigationwastofindtheearli-
est discernible point of differential processing in the context of
spatial and non-spatial auditory tasks. However, a crucial dis-
tinction must be made here, and that is that feature processing
pathways in cortex are not laid out as one-way highways. Namely,
when auditory information is received, it comprises both spatial
andnon-spatialobjectfeatures.Whiletheorganismmaybebiased
to preferentially process one or the other of these aspects at any
given time, this is not to say that the stimulus is stripped of its
unattended features. Findings from studies of MMN make clear
that both location and object-based properties of auditory stimuli
are automatically processed even when attention is directed away
fromtheauditorymodality(e.g.,Paavilainenetal.,1989;Molholm
etal.,2005;TataandWard,2005;Ritteretal.,2006;Näätänenetal.,
2007); indeed, MMNs to these features are automatically elicited
in people when they are asleep (e.g., Ruby et al., 2008) or even
in a coma (e.g., Fischer et al., 2000; Wijnen et al., 2007). More-
over, there is a clear suggestion from our prior investigation of
mid-latency auditory evoked responses that there is a constitutive
divergence of activation into separable pathways in the context of
a passive listening task (Leavitt et al., 2007). While that study was
not designed to expressly test the functionality of the dorsal and
ventral auditory pathways, automatic sensory activation of these
pathways is an inherent result of receiving any auditory stimula-
tion or performing any auditory task. Here, we asked subjects to
actively attend to one stimulus feature, location, or identity, in a
given condition. Clearly, however, neural processing of the“unat-
tended”featureoccurredtosomedegreeaswell.Thereisagrowing
body of functional imaging literature showing that irrelevant fea-
tures of objects seem to be processed by the brain, despite having
no current task relevance (e.g., O’Craven et al., 1999; Wylie et al.,
2004). This strong bias for attention to spread to non-primary
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FIGURE 12 | Cortical maps displaying the spatio-temporal minimum-norm topographies forWHAT minusWHERE difference waveforms.
FIGURE 13 |Waveforms from participants (n =6) comparing performance
inWHAT II andWHERE conditions.The direction of the N1 amplitude
difference between conditions mirrors that seen in WHAT I versus WHERE;
that is, the N1 amplitude for WHAT II is greater than WHERE.
stimulusfeatureshasalsobeenshowncross-modally,usingaudio-
visualtasks(Molholmetal.,2004,2007;Fiebelkornetal.,2010a,b).
This spread of attention is further evidenced by findings in the
visual modality that directing spatial attention to one part of an
object results in the facilitation of sensory processing across the
entire extent of that object (e.g., Egly et al., 1994; Martinez et al.,
2007). Therefore, while in the present investigation we have iden-
tified a point of task-specific divergence at approximately 120ms,
it is extremely unlikely that this represents the first point at which
processing for these qualities truly begins.
Two previous studies of object versus spatial processing have
also shown amplitude modulation during the auditory N1 time-
frame,but in both of these,the direction of the effect was actually
opposite to that seen here, with responses in the WHERE condi-
tion showing larger amplitude (Ahveninen et al., 2006; De Santis
et al., 2007a). A third MEG study found no amplitude differences
for N1 (Anourova et al., 2001), but rather, a latency difference
between tasks. These differences are likely explained by paradig-
matic differences. The stimuli used by these investigators were
simple tones (Anourova et al., 2001; De Santis et al., 2007a) and
vowel pairs (Ahveninen et al., 2006) and it could be argued that
these stimuli might be relatively less effective at activating object-
processingregions.Here,byemployingcomplex,andconsiderably
moreecologicalsounds,itislikelythatgreateractivationofWHAT
processing regions was invoked. Whereas sound localization may
be more automatic, and engaged more quickly (as suggested by
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Leavitt et al.Dorsal/ventral auditory processing in humans
Anourova et al.,2001),object recognition and identification relies
more heavily on accessing a memory trace and it is a reasonable
assumption that invoking a memory trace of a complex sound
such as the animal calls used here, would employ a greater neural
network than that of a simple tone. Similarly, in all of these prior
investigations, only a limited number of spatial locations were
used during the spatial condition. In most, only two positions
were used, and in only one case were three used (Anurova et al.,
2003). Here,sevendiscretelocationswereused,andthereforeper-
forming the current task would have required a great deal more
effort,andconsequently,greaterinvolvementof underlyingneural
networks for spatial discrimination. Moreover, our subjects were
not explicitly aware that there were seven discrete sound sources,
and so the number of sound sources was unknown to them and
likely created higher demands on them relative to past studies uti-
lizing less sound sources. In sum, we suggest that the paradigm
employed in the current study was both: (1) more challenging
in both the WHAT and WHERE conditions than prior studies
employing less complex, and fewer stimulus types, and (2) more
ecologicallyvalid,andthereforeprovidedresultswhichmoreaccu-
rately reflect the way we actually process auditory information in
the complex world around us.
It could be considered surprising that while we found signifi-
cant task-related differences during the N1 processing timeframe,
there were no subsequent time-points where significant differ-
ences were seen. With such different tasks as localizing sound and
making a sound object identification, one might well predict that
distincthigher-ordercognitiveprocessesmightbeengagedatlater
latencies, such as the P300 component, which has been shown
to index attention-related information processing (e.g., Polich,
1986; Linden, 2005). On the other hand, the lack of differences
during later timeframes here provides good confirmation that
higher-order attentional processes were not differentially engaged
betweenthetwotasks,suggestingthatthelocationandrecognition
tasks were well-matched for difficulty. This suggestion, however,
mustbeconsideredinthecontextofournotionthattheuseofani-
mal calls in the present study is what made our design both more
ecologically valid, and more accurate in identifying a greater allo-
cation of neural resources as evidenced exclusively by the higher
amplitude N1 component seen in theWHAT condition. Whether
such a focal deployment of attentional resources (that is, limited
only to the N1 period) can be measured in an experimental para-
digm engaging sensory modalities other than the auditory system
remains to be investigated by future studies.
The behavioral findings for the WHERE task are noteworthy,
in that they appear to represent an auditory analog to the pattern
of pseudoneglect that has been consistently observed in the visual
system of healthy controls. Specifically, pseudoneglect refers to a
leftwardattentionalbiasthatoccursinnormallyfunctioningindi-
viduals and causes them to bisect visually presented lines slightly
to the left of their true center (e.g., McCourt, 2001; Foxe et al.,
2003; McCourt et al., 2008). This spatial bias has been attrib-
uted to the well-known right hemisphere specialization for spatial
attention (see e.g., Mesulam, 2000), and indeed, both functional
imaging(Finketal.,2001andERPstudies(Foxeetal.,2003)show
thatvisuospatialjudgmentsduringline-bisectionaresubservedby
right parietal regions. Here, subjects consistently mislocated the
centrally presented sound approximately 4.5˚ leftward of veridical
center. This leftward bias can also be seen for the pair of closest
flankingsoundsources(soundsources3and5).Anotherinterest-
ing aspect of these data was the observation of significantly more
accurate responses to the three sound sources in the left hemi-
sphere (Sound sources 1, 2, 3) than to the three sound sources
in the right hemisphere (Sound sources 5, 6, 7). This is consis-
tent with what is known about hemispheric differences in sound
localization in humans;namely,that auditory spatial processing is
more accurate for sounds in the left hemifield (Burke et al.,1994),
again presumably because of the right hemisphere bias for spa-
tialattention.Indeed,theseinvestigatorsinterpretedtheirfindings
to suggest a pivotal role for right hemisphere in auditory spatial
acuity.Inagreement,otherstudieshaveimplicatedgreaterinvolve-
mentof therighthemisphereinsoundlocalization(seee.g.,Farah
et al., 1989; Hausmann et al., 2005; Tiitinen et al., 2006; Mathiak
et al.,2007).
It is of note that we did not find specific lateralization of the
main effect reported here between the WHAT and WHERE tasks,
withnoevidenceforahemispherebyconditioninteractioninour
main ERP analysis. However, the ensuing source-localization did
suggest that there may in fact be a left hemisphere bias, which
would be in accord with literature providing evidence for a left
lateralization effect of the WHERE pathway during speech sound
processing (Mathiak et al., 2007), as well as evidence for involve-
ment of language-dominant inferior dorsolateral frontal lobe in
processingnon-verbalauditoryinformation(Mathiaketal.,2004).
It will fall to future work to specifically test whether there is
hemispheric specialization for processing of WHAT and WHERE
auditory stimuli.
HUMAN LESION STUDIES
Human lesion studies provide additional support for the division
of the auditory system into dorsal and ventral pathways (Clarke
etal.,2000,2002;ClarkeandThiran,2004).Inaseriesof casestud-
ies,Clarkeetal.(2000)investigatedauditoryrecognitionandlocal-
izationinfourpatientswithcircumscribedlefthemispherelesions.
Twooffourpatientsshowedimpairedrecognitionofenvironmen-
tal sounds but intact ability to localize sounds. In one, the lesion
includedpostero-inferiorportionsof thefrontalconvexityandthe
anteriorthirdofthetemporallobe.Intheother,thelesionincluded
left superior, middle and inferior temporal gyri, and lateral audi-
tory areas, but spared Heschl’s gyrus, the acoustic radiation, and
the thalamus. The third patient was impaired in auditory motion
perception and localization, but had preserved environmental
sound recognition; here, the lesion included the parieto-frontal
convexity and the supratemporal region. The fourth patient was
impaired in both tasks,with a lesion comprising large portions of
the supratemporal region, temporal, postero-inferior frontal, and
antero-inferior parietal convexities.A subsequent investigation by
thesamegroupexamined15patientswithrightfocalhemispheric
lesions (Clarke et al., 2002). Again, sound recognition and sound
localization were shown to be disrupted independently. In this
study, patients with selective sound localization deficits tended
to have lesions involving inferior parietal and frontal cortices, as
well as superior temporal gyrus, whereas patients with selective
recognition deficits had lesions of the temporal pole, the anterior
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Leavitt et al.Dorsal/ventral auditory processing in humans
partof fusiform,andtheinferiorandmiddletemporalgyrus.This
double dissociation clearly supports somewhat independent pro-
cessing streams for sound recognition and sound localization in
humans.
CONCLUSION
In this study, we asked participants to make judgments about
the spatial location of sounds versus the object-identity of those
same sounds, with an eye to assessing whether varying task set in
this manner would engage dissociable neural circuits within audi-
tory cortices. These tasks were chosen to tap so-called WHERE
and WHAT processes, which have been associated with dor-
sal and ventral regions of the auditory system respectively. Our
high-density electrical mapping data revealed a robust difference
in the ERP during the timeframe of the auditory N1 component
and topographic analysis pointed to differential engagement
of underlying auditory cortices during this timeframe. Source-
analysissupportedthemainthesisinthatthisdifferentialprocess-
ing was generated in dissociable regions of the dorsal and ventral
auditory processing stream.
ACKNOWLEDGMENTS
Support for this work was provided by a grant from the US
National Institute of Mental Health (RO1-MH65350 to John J.
Foxe; RO1-MH85322 to Sophie Molholm and John J. Foxe) and
by a Ruth L. Kirschstein pre-doctoral fellowship to Victoria M.
Leavitt (NRSA – MH74284). The authors thank Daniella Blanco,
Megan Perrin,and Jennifer Montesi for their invaluable assistance
with data collection.
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Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any
commercial or financial relationships
that could be construed as a potential
conflict of interest.
Received: 10 August 2010; paper pending
published: 17 September 2010; accepted:
17 May 2011; published online: 22 June
2011.
Citation:Leavitt VM, Molholm
Gomez-Ramirez M and Foxe JJ (2011)
“What”and“Where”inauditorysensory
processing: a high-density electrical map-
ping study of distinct neural processes
underlying sound object recognition and
soundlocalization.
Integr. Neurosci.
10.3389/fnint.2011.00023
Copyright © 2011 Leavitt, Molholm,
Gomez-Ramirez and Foxe. This is an
open-access article subject to a non-
exclusive license between the authors and
Frontiers Media SA, which permits use,
distribution and reproduction in other
forums,providedtheoriginalauthorsand
source are credited and other Frontiers
conditions are complied with.
S,
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