EEG potentials associated with artificial grammar learning in the primate brain

Abstract

Electroencephalography (EEG) has identified human brain potentials elicited by Artificial Grammar (AG) learning paradigms, which present participants with rule-based sequences of stimuli. Nonhuman animals are sensitive to certain AGs; therefore, evaluating which EEG Event Related Potentials (ERPs) are associated with AG learning in nonhuman animals could identify evolutionarily conserved processes. We recorded EEG potentials during an auditory AG learning experiment in two Rhesus macaques. The animals were first exposed to sequences of nonsense words generated by the AG. Then surface-based ERPs were recorded in response to sequences that were ‘consistent’ with the AG and ‘violation’ sequences containing illegal transitions. The AG violations strongly modulated an early component, potentially homologous to the Mismatch Negativity (mMMN), a P200 and a late frontal positivity (P500). The macaque P500 is similar in polarity and time of occurrence to a late EEG positivity reported in human AG learning studies but might differ in functional role.

Full text

EEG potentials associated with artificial grammar learning in the primate
brain
Adam Attaheri
a,b
, Yukiko Kikuchi
a,b
, Alice E. Milne
a,b
, Benjamin Wilson
a,b
, Kai Alter
a
,
Christopher I. Petkov
a,b,
⇑
a
Institute of Neuroscience, Henry Wellcome Building, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
b
Centre for Behaviour and Evolution, Henry Wellcome Building, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
article info
Article history:
Accepted 8 November 2014
Available online xxxx
Keywords:
Monkey
mMMN
Primate
Comparative neurobiology
Electroencephalography (EEG)
Event Related Potentials (ERPs)
Communication
Language
Statistical learning
abstract
Electroencephalography (EEG) has identified human brain potentials elicited by Artificial Grammar (AG)
learning paradigms, which present participants with rule-based sequences of stimuli. Nonhuman animals
are sensitive to certain AGs; therefore, evaluating which EEG Event Related Potentials (ERPs) are
associated with AG learning in nonhuman animals could identify evolutionarily conserved processes.
We recorded EEG potentials during an auditory AG learning experiment in two Rhesus macaques. The
animals were first exposed to sequences of nonsense words generated by the AG. Then surface-based
ERPs were recorded in response to sequences that were ‘consistent’ with the AG and ‘violation’ sequences
containing illegal transitions. The AG violations strongly modulated an early component, potentially
homologous to the Mismatch Negativity (mMMN), a P200 and a late frontal positivity (P500). The
macaque P500 is similar in polarity and time of occurrence to a late EEG positivity reported in human
AG learning studies but might differ in functional role.
Ó2014 The Authors. Published by Elsevier Inc. This is an open access article underthe CC BY license (http://
creativecommons.org/licenses/by/3.0/).
1. Introduction
To better understand the neurobiology of human language
requires distinguishing language-specific processes from cognitive,
domain-general processes not restricted to language (Bickerton &
Szathmary, 2009; Fedorenko, Duncan, & Kanwisher, 2012;
Friederici, 2011; Hagoort, 2005). Certain domain-general processes
may be evolutionarily conserved (Bozic, Tyler, Ives, Randall, &
Marslen-Wilson, 2010; Fitch & Friederici, 2012). Thus insights into
how the human brain has specialised for language could come
from cross-species comparative neurobiology. However, all
comparative efforts depend on first finding evidence for shared
abilities on tasks thought to be associated with language-related
processes in humans and second, testing nonhuman animals using
neurobiological techniques commonly used in humans.
Artificial Grammars (AG) regulate the relationships between
stimuli in a sequence (Friederici, Bahlmann, Heim, Schubotz, &
Anwander, 2006; Gomez & Gerken, 1999; Marcus, Vijayan, Bandi
Rao, & Vishton, 1999; Reber, 1967;Saffran, 2002;Saffran,
Johnson, Aslin, & Newport, 1999). AG learning paradigms have been
used to explore the abilities of adult humans, pre-verbal infants and
nonhuman animals to process different AGs (Bahlmann, Schubotz,
& Friederici, 2008; Fitch & Hauser, 2004; Gentner, Fenn,
Margoliash, & Nusbaum, 2006; Murphy, Mondragon, & Murphy,
2008; Saffran et al., 2008; van Heijningen, de Visser, Zuidema, &
ten Cate, 2009; Wilson et al., 2013). Typically, in AG learning studies
the participant is exposed to exemplary sequences of stimuli gener-
ated by the AG. In a subsequent testing phase, the participant’s
responses to ‘consistent’ sequences that follow the AG are
evaluated relative to those that violate it. Different responses to
‘violation’ versus consistent sequences can provide evidence that
the participant is sensitive to the properties of the AG.
The behavioural literature has highlighted potentially con-
served capabilities associated with AG learning in humans and
other animals. Nonhuman animals, including primates, have been
shown to be sensitive to a range of simple and moderate complex-
ity AGs (Fitch & Hauser, 2004; Gentner et al., 2006; Hauser &
Glynn, 2009; Murphy et al., 2008; Saffran et al., 2008; van
Heijningen et al., 2009; Wilson et al., 2013). In this regard, even
relatively simple AGs (such as ‘finite-state’ AGs, which generate a
finite set of sequences based on adjacent transitions between
stimuli; Chomsky, 1957; Fitch & Friederici, 2012) have been
informative about evolutionarily conserved sequence processing
http://dx.doi.org/10.1016/j.bandl.2014.11.006
0093-934X/Ó2014 The Authors. Published by Elsevier Inc.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
⇑
Corresponding author at: Institute of Neuroscience, Henry Wellcome Building,
Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United
Kingdom. Fax: +44 (0) 191 208 5227.
E-mail address: chris.petkov@ncl.ac.uk (C.I. Petkov).
Brain & Language xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Brain & Language
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Please cite this article in press as: Attaheri, A., et al. EEG potentials associated with artificial grammar learning in the primate brain. Brain & Language
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capacities that predated human language evolution. Concurrently,
human brain imaging studies with functional Magnetic Resonance
Imaging (fMRI) have demonstrated that much of the perisylvian
language network can also be engaged during AG sequence pro-
cessing (Bahlmann et al., 2008; Friederici, 2011; Friederici et al.,
2006; Petersson, Folia, & Hagoort, 2012).
Although fMRI can identify brain regions associated with AG
learning, the temporal resolution of EEG is better suited for identi-
fying the time course of the neural response to AG sequences. In
humans, the polarity and general topography of EEG Event Related
Potentials (ERPs) associated with AG learning have been identified
and related to neurophysiological markers of natural language-
related processes (Friederici, 2002, 2005). Moreover, EEG studies
in humans and other animals have identified ERP components
associated with ‘oddball’ or change detection paradigms, which
can also be modulated by AG learning paradigms. Specifically,
unexpected auditory oddball stimuli elicit a Mismatch Negativity
(MMN; or its monkey homolog: mMMN), which is an enhanced
negativity at 150 ms thought to be generated by regions includ-
ing auditory cortex (Bekinschtein et al., 2009; Fishman &
Steinschneider, 2012; Gil-da-Costa, Stoner, Fung, & Albright,
2013; Javitt, Schroeder, Steinschneider, Arezzo, & Vaughan, 1992;
Naatanen & Alho, 1995;Ueno et al., 2008; Uhrig, Dehaene, &
Jarraya, 2014; Ulanovsky, Las, & Nelken, 2003). The human P200
is an ERP component that can be modulated by attention to infre-
quently presented auditory targets (Garcia-Larrea, Lukaszewicz, &
Mauguiere, 1992; Novak, Ritter, & Vaughan, 1992). A positivity at
300 ms (P3a) elicited in humans and its homolog in nonhuman
primates is thought to be a more general marker of change detec-
tion that seems to involve dorsal fronto-central brain regions
(Arthur & Starr, 1984; Gil-da-Costa et al., 2013; Molholm,
Martinez, Ritter, Javitt, & Foxe, 2005; Paller, Zola-Morgan, Squire,
& Hillyard, 1988; Pineda, Foote, Neville, & Holmes, 1988; Zevin,
Yang, Skipper, & McCandliss, 2010). The MMN and P3a components
in humans can also be elicited by AG paradigms, especially if vio-
lation sequences are presented infrequently (Baldwin & Kutas,
1997; Mueller, Friederici, & Männel, 2012). Moreover, in humans
AG learning paradigms can influence other ERP components also
associated with natural language processes (Friederici, 2004;
Friederici, Steinhauer, & Pfeifer, 2002). Namely, violations to
adjacent AG relationships (local violations) elicit more negative
potentials at 200–350 ms, such as the Early Left Anterior
Negativity (ELAN), which is stronger in frontal electrodes of the left
hemisphere (Friederici, 2005). More complex AGs (such as those
having non-adjacent relationships) can also elicit late centro-pari-
etal positivities (P600: Friederici, 2004; Friederici et al., 2002).
Despite behavioural evidence in humans and other primates for
finite-state AG learning, EEG studies associated with AG learning
were not previously available in nonhuman primates. Therefore,
the question posed is which macaque ERP components would be
elicited by AG sequences? If certain neural processes for AG learn-
ing were evolutionarily conserved, we might expect to find maca-
que ERP responses similar to those reported for comparable AG
processing in humans, including homologs of the MMN and ELAN
potentials, some of which might be stronger in frontal electrodes.
2. Methods
2.1. Properties of the current AG and summary of prior macaque
behavioural results
In a previous behavioural study we showed that Rhesus maca-
ques are sensitive to a moderately complex finite-state AG (Wilson
et al., 2013), based on an AG developed by Saffran et al. (2008).
Such findings suggest that the cognitive abilities required for this
form of AG learning are neither unique to humans nor to language.
The AG consists of branching relationships between several oblig-
atory and optional elements, all of which contribute to the struc-
ture of the AG (Fig. 1A). Such AGs allow the generation of less
predictable (non-deterministic) sequences of varying length: a
property of both sequences of natural events and of most linguistic
sequences (Hurford, 2012; Petkov & Wilson, 2012). After a period
of exposure to exemplary AG sequences, Rhesus macaques (two
of which participated in the current EEG experiment) showed
stronger orienting responses to novel sequences that violated the
AG, relative to those that were ‘consistent’ with the AG (Wilson
et al., 2013). In the behavioural work we also show evidence that
the macaque AG learning results cannot be explained by simple
learning strategies (Wilson et al., 2013).
2.2. Participants
Two adult male Rhesus monkeys (Macaca mulatta) from a group
housed colony participated in this experiment (ages, Macaque 1
Fig. 1. Artificial Grammar (AG), stimuli, sequence comparisons and macaque EEG electrode placement. (A) Schematic of the AG used. Following arrows from ‘start’ to ‘end’
creates a legal, consistent sequence. Not following the arrows (e.g., a ‘D’ to ‘F’ transition) creates a violation. (B) Spectrograms of the acoustic nonsense word sound elements
(A, C, D, F, G) in the sequences. For example, the nonsense word ‘‘YAG’’ took the position of element A in the AG sequences. (C) Exemplary matching consistent vs. violation
comparison sequence pair (see Suppl. Fig. S3 for all comparison pairs). The red box highlights the first illegal sound element in the violation sequence. The sequences are
aligned so that acoustically identical elements can be compared (e.g., ‘F’, ‘C’ and ‘G’). (D) Illustrates the approximate location of the eight scalp surface EEG electrodes on the
macaque, including ground and reference electrodes.
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(M1) = 15 years, Macaque 2 (M2) = 7 years; weight: M1 = 9.8 kg,
M2 = 16 kg). All the procedures performed were approved by the
UK Home Office and comply with the Animal Scientific Procedures
Act (1986) on the care and use of animals in research and also with
the European Directive on the protection of animals used in
research (2010/60/EU). We support the Animal Research Reporting
of In Vivo Experiments (ARRIVE) principles on reporting animal
research. All persons involved in this project were Home Office cer-
tified and the work was strictly regulated by the U.K. Home Office.
2.3. Stimuli
Each of the stimulus sequences (see Fig. 1) were made by
digitally combining recordings of naturally spoken nonsense words
produced by a female speaker. The recordings were made with an
Edirol R-09HR (Roland Corp.) sound recorder. The amplitude of the
recorded sounds was root-mean-square (RMS) balanced and the
nonsense word stimuli were combined into sequences using
customised Matlab scripts (150 ms inter-stimulus intervals). The
experiments were coded in Matlab (Psychophysics Toolbox:
http://psychtoolbox.org/) and Cortex software (Salk Institute). All
sounds were presented at 75 dB SPL (calibrated with an XL2
sound level meter, NTI Audio). The durations of the spoken
nonsense word stimuli were subsampled from a corpus so that
all the stimuli (YAG, LEK, KEM, PAV, ROP) were 413 ms long. For
additional details on the behavioural paradigm see Wilson et al.
(2013). The experimental paradigm is shown in Fig. 1 and Suppl.
Figs. S1–S3.
2.4. EEG recordings
The macaques were individually tested in a custom made pri-
mate chair within an acoustically insulated room (IAC). Once in
the acoustic chamber the macaque was placed 60 cm in front of
a monitor (24
00
Samsung, LCD) on which a fixation spot was pre-
sented and the animal completed trials of fixation (Suppl.
Fig. S1). The sounds were presented free-field from two audio
speakers (Creative Inspire T10) placed horizontally at ±30°on
either side of the monitor. A head post was used for immobilising
the head during testing. EEG signals were recorded using eight Ag/
AgCl (Silver/Silver-Chloride) electrodes held in place by a custom
made cap (Fig. 1C). Signals were sampled at a rate of 1000 Hz
through an EEG head stage and amplifier (Neuroscan).
2.5. Time course of a recording session
The macaques had previously been slowly acclimated to periods
of head immobilisation and were trained using operant training and
positive reinforcement to conduct a visual fixation task during
sound stimulation. During the first phase of the experiment the ani-
mals were exposed for 30 min with the exemplary consistent AG
sequences (Suppl. Fig. S2A). The exposure phase was followed by
a30 min testing phase (240 completed test sequence trials)
where randomly selected consistent and violation testing
sequences were individually presented (Suppl. Figs. S1–2). A test
sequence trial was initiated by the animal fixating a spot at the cen-
tre of the screen, measured with an infra-red eye tracker (Arrington
Research). To minimise eye movements, fixation to the centre of the
screen had to be maintained throughout the test sequence presen-
tation for a trial to be correctly completed (Suppl. Fig. S1). After the
end of the test sequence trial there was a 1000 ms interval before
the fixation spot was removed and the juice reward was given for
a completed trial. Only completed trials were included for analysis
with each session having a minimum of 240 completed trials. M1
broke fixation on average in 7% of the trials, M2 broke fixation on
average in 10% of the trials. After the inter-trial-interval (minimally
4500 ms; Suppl. Fig. S1) the fixation spot re-appeared and the next
testing trial began when the macaque engaged the fixation spot. See
Suppl. Figs. S1–3 for illustrations of the trial timing and further
details on the testing sequences used.
We had more testing sequences than we could test in a given
session, as each testing session was completed at the macaque’s
own pace. So to avoid undersampling the EEG data collection for
any of the pairs of sequences we split the experimental data collec-
tion into two blocks, Block A and Block B. Each block contained the
testing sequences illustrated in Suppl. Fig. S2 and consisted of a
number of separate daily testing sessions (total number of sessions
74, M1 = 37 sessions, 20 with Block A, 17 with Block B; M2 = 37
sessions, 18 with Block A, 19 with Block B; see Suppl. Fig. S2).
We counterbalanced the order of these two blocks of data collec-
tion between the two macaques (e.g., one macaque was tested first
on Block A and the other on Block B).
2.6. EEG data analysis
2.6.1. Pre-processing
Data analysis was conducted in Matlab R2011a (The Mathworks)
using the EEGLAB toolbox (http://sccn.ucsd.edu/). Pre-processing
was applied to the data from every channel in each recording ses-
sion. First a high pass filter at 0.3 Hz and a notch filter at 50 Hz were
applied to remove line noise effects. Manual inspection identified
other noisy periods in the EEG trace which were removed. This
resulted in on average 117 trials (±37 trials, standard deviation)
remaining for further analysis, out of the available 240 trials per ses-
sion. Following this, an independent-components algorithm
(‘runica’ in EEGLAB) was used to identify any other artefacts, which
in turn were extracted from the data.
The EEG activity elicited by each sequence trial within a session
was epoched from 200 ms before the sequence onset through to
3250 ms after sequence onset. For each session an average wave-
form was created per channel for each of the sixteen sequences
(Block A = four consistent and four violation sequences, Block
B = four consistent and four violation sequences). This procedure
was repeated for each macaque. Then for each average sequence
waveform the 200 ms pre-sequence silent period was used for
baseline correction. This baseline correction was repeated sepa-
rately for each of the sixteen sequences. Subsequently, Block A
and Block B sequences were treated as a combined set of sixteen
sequences (eight consistent and eight violation) in all further anal-
yses, unless specified otherwise. Finally, the average waveforms
across sessions in the channels of interest were averaged creating
a sequence average waveform.
2.6.2. Analysis of consistent vs. violation sequence effects
Next each of the EEG waveforms in response to the violation
sequences were analysed relative to the EEG responses to the
matching consistent sequence pairs. This allowed us to compare
the effects in response to a violation element in the violation
sequences to an acoustically identical element in the consistent
sequence (Fig. 1C). For this analysis some sequences had to be
aligned so that acoustically matched periods of the sequences
could be compared (Suppl. Fig. S3). After this alignment of
sequences, the EEG responses to the comparison (violation or con-
sistent) sequences were separately averaged (Fig. 3A). Then the
consistent and violation sequence response waveforms were sub-
tracted from each other to create a difference plot (violation minus
consistent; Fig. 3B). From this difference waveform we computed
the lower and upper bounds of the 95% confidence interval of the
baseline period. This was defined based on the variability in the
613 ms time period, including the 200 ms before the onset of
the sound sequence, through to the offset of the first sound
element in the sequence, which was always the same ‘A’ element
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(200 to 413 ms: 613 ms window). Therefore, the confidence
interval (CI) reflects the 95% upper and lower bounds of the vari-
ability in the difference waveform during the baseline period
(i.e., reflecting the 2.5% and 97.5% levels of the null difference
waveform distribution). The CI analysis was used to identify signif-
icant differences between the matched violation and consistent
sequence pairs (Fig. 3B).
To investigate the topographical distribution of the results, such
as whether the effects identified with the CI analyses are more left
or right hemisphere lateralised, or more anterior or posterior, we
submitted the effects to a Repeated Measures ANOVA. First we
obtained the time of the peak difference for the mMMN, P200
and P500, where the ERP components of interest maximally
breached the CIs (e.g., Fig. 3B and Suppl. Fig. S4B). A 40 ms analysis
window was centred on these time points (analysis windows used:
mMMN: 128–168 ms; P200: 161–201 ms; P500: 480–520 ms).
These analysis windows were then used to calculate the maximum
EEG response value within the window, session-by-session. Next,
the EEG voltage potentials in response to the ‘consistent’ and
‘violation’ analyses were submitted to the RM-ANOVA as a
within-subject (repeated-measures) factor of ‘condition’ (consis-
tent or violation). The RM-ANOVA also included the between-
subjects factor of ‘Macaque’ (M1 or M2), and two other
between-subjects factors of ‘Hemisphere’ (left or right) and
‘Antero-posterior axis’ (anterior or posterior). The total number
of sessions numbered 74, M1 = 37 sessions (20 with Block A, 17
with Block B); M2 = 37 sessions (18 with Block A, 19 with Block
B; Suppl. Fig. S3). Each session had two comparison sequence pairs
(Suppl. Fig. S3) and data from the 8 channels. The ANOVA was con-
ducted separately for the mMMN, P200 and P500 ERP components.
The Supplementary Materials reports additional supporting
analyses and results as follows: (1) grand average responses in
all electrodes (Suppl. Fig. S4); (2) ERP effects to the violating sound
and a lack of such effects to the subsequent sound in the sequence
(Suppl. Fig. S5); (3) analyses on whether the effects remain in
sequences including no shifting between consistent and violation
pairs and those that were balanced in the direction of shifting
(Suppl. Fig. S6); (4) analyses showing that violation-related effects
do not seem to depend on the EEG responses to the sound prior to
the violation (Suppl. Fig. S7); and (5) ERP results shown separately
by macaque (Suppl. Fig. S8) shown also in Table S1 as maximum
voltages across sessions for the two macaques separately.
3. Results
We first exposed the macaques to the exemplary AG sequences
for 30 min (Suppl. Fig. S2). Then in the subsequent testing phase,
we recorded surface-based EEG potentials in the animals
(Fig. 1D; Suppl. Fig. S1) as the animals listened to randomly
presented testing sequences (Suppl. Fig. S2). The macaques con-
ducted a fixation task during sequence presentation and EEG data
acquisition (Suppl. Fig. S1). The testing sequences were either ‘con-
sistent’ sequences generated by the AG or ‘violation’ sequences,
which violated the AG structure by having an illegal transition
between nonsense word ‘elements’ (Fig. 1C shows one consistent
and violation matching sequence pair; see Suppl. Figs. S2-3 for
all sequences used). Critically, the first illegal sound element in a
violation sequence was identical to one in the matching consistent
sequence, allowing us to analyse acoustically identical parts of the
consistent and violation sequences (Suppl. Fig. S3).
We first evaluated the EEG response to each of the 5 elements in
the sequence and observed the presence of several typical Event
Related Potential components (ERPs) to each of the elements in
the sequence (Fig. 2). For this, we calculated the grand average
ERP response to each of the elements (across all testing sequences,
sessions, macaques and electrodes; Macaque 1, 37 testing sessions,
Macaque 2, 37 testing sessions). The results show the presence of
early positivities in the macaque brain (P100 at 80 ms; P200 at
200 ms) and a negativity (N100 at 110 ms), see Fig. 2. We also
observe that even the response to the last element showed clear
N100, P100, and P200 ERP components (Fig. 2C).
Next we evaluated effects related to the AG sequencing condi-
tion (consistent or violation) by analysing ERP differences to the
corresponding consistent and violation comparison sequence pairs.
Namely, the first violating sound present in the violation sequence
had an acoustically identical match in its consistent sequence pair
(Fig. 1C). Fig. 3A shows the ERP components to the violation and
consistent sequences in response to the violating sound in the
violation sequences, for the frontal electrodes where we expected
certain ERP components to be more prominent (Suppl. Fig. S4
shows the grand average ERPs for all electrodes). To identify statis-
tically significant effects we first created a difference waveform
(violation minus consistent; Fig. 3B). We then determined the
lower and upper bounds of the 95% confidence interval (CI) from
the difference waveform variability during a baseline period; the
baseline period was defined as the silent period before the start
of the sound sequences and the first element in the sequences,
which was always element ‘A’ (Suppl. Figs. 2 and 3). Projecting
the CI over the period during the violation element and its
corresponding consistent sound pair (Fig. 3) was used to identify
significant waveform differences that deviate in preference for
either the violation or consistent condition. This analysis identified
Fig. 2. Event-Related Potentials (ERPs) in response to the sounds in the sequence.
Grand average ERP from all electrodes (FP1, FP2, F3, F4, P3, P4, C3, C4) in response to
all recorded sequences, all having five elements in a sequence. (A) Expanded ERP to
the first element of the five within the sequences. (B) Grand average ERP to the five
element long sequence. (C) Expanded ERP to the final element of the five in the
sequence. Yellow boxes depict the periods of the ERP where the nonsense word
elements were presented.
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both positive and negative differences between the violation and
matched consistent sequence elements. In the four frontal elec-
trodes (FP1, FP2, F3, F4), the violation condition elicited a stronger
early negativity peaking at 148 ms (see red trace in Fig. 3A and the
first breach in the difference waveform in Fig. 3B). This early ERP
component resembles a macaque homologue of the human MMN
(which we will refer to as the mMMN; Fig. 3B). In these frontal
set of electrodes, the violation condition also elicited a later more
positive ERP peaking at 497 ms (Fig. 3A and B). The grand average
ERP across all of the electrodes also show a strong mMMN (Suppl.
Fig. S4), although in the results with all electrodes combined the
P500 is weaker and we identify another early component (P200).
Interestingly, these effects were specific to the first violation sound
because no obvious differences between the violation and consis-
tent sequences were evident for the subsequent sound after the
violation (Suppl. Fig. S5; note here that all three ERP components,
the mMMN, P200 and P500 are evident in this analysis only includ-
ing the sequences which had at least two sounds after the violation
that could be comparably analysed between the violation and con-
sistent sequences).
We evaluated whether there was a session-by-session ERP
response preference either for the violation or consistent condi-
tions. Here, we analysed the session-by-session breaches across
the confidence interval (CI) over the period including the violation
sound and its matching consistent sound element (563 ms). We
measured the number and average area (using trapezoid method)
of the CI breaches in favour for either the violation or consistent
condition. We observed that the distribution of average area
breaches across the CI was shifted towards significantly higher
areas for the violation, relative to the consistent sequences
(Fig. 3C; Wilcoxon signed-rank test, p= 0.013; mean CI breach area
(standard error of the mean, SEM), for violation: 12.81, (±1.96); for
consistent: 8.73 (±1.17)). The violation sequences also elicited a
significantly greater number of breaches above the confidence
intervals than the consistent sequences (Wilcoxon signed-rank
test, p< 0.001; mean number of breaches, for violation: 2.85
(±0.18); consistent: 2.07 (±0.16)).
We used a Repeated Measures Analysis of Variance (RM-ANOVA)
to investigate whether the identified ERP components (mMMN,
P200 and P500) were left or right hemisphere lateralized, or distrib-
uted more anterior or posterior on the head. First we identified the
time windows of interest for each of these components based on our
results with the CI analyses (Fig. 3B, Suppl. Fig. S4B). Next a 40 ms
response window was centred on the position of each of these iden-
tified ERP components (MMN: 128–168 ms; P200: 161–201 ms;
P500: 480–520 ms). Within these windows, the maximum value
of the EEG response to both the consistent and violation sequences
was measured. These values were submitted to the RM-ANOVA
containing a within subjects factor of Condition (consistent or
violation). Also between subjects factors of ‘monkey’ and two addi-
tional between subjects factors identifying the position of the elec-
trodes were modelled (‘Antero-posterior axis’ and ‘Hemisphere’
(left/right)).
With the RM-ANOVA results, first we confirmed that there were
significant main effects for Condition for all of the noted ERP
components (mMMN: F
(1,1176)
= 9.607, p< 0.001; P200:
F
(1,1176)
= 5.392, p< 0.001; P500: F
(1,1176)
= 5.058, p= 0.025). The
amplitude of the EEG responses differed by macaque for many of
the factors, as might be expected (main effect of Macaque; mMMN:
F
(1,1176)
= 9.443, p= 0.002; P200: F
(1,1176)
= 75.008, p< 0.001; P500:
F
(1,1176)
= 144.503, p< 0.001). Notably however the P500 and P200
component did not have a significant Condition by Macaque inter-
action, suggesting that the macaques did not differ in the effects for
these components (P200: F
(1,1176)
= 1.731, p= 0.188; P500:
F
(1,1176)
= 0.872, p= 0.351). The mMMN did show a Condition by
Macaque interaction (mMMN F
(1,1176)
= 9.607, p= 0.02). However,
the polarity of all ERP components (mMMN, P200 and P500) was
consistent across the macaques (Suppl. Fig. S8). Regarding topo-
graphical distribution, only the mMMN component was signifi-
cantly different between left- and right-hemisphere channels:
the mMMN was stronger in the left (FP1, F3, C3, P3) than right elec-
trodes (FP2, F4, C4, P4), (F
(1,1176)
= 4.199, p= 0.041). Regarding
anterior or posterior head distribution, the RM-ANOVA showed
that the mMMN, P200 and P500 components were all significantly
stronger in the more anterior electrodes (electrodes FP1, FP2, F3,
Fig. 3. Artificial grammar sequence processing effects in the frontal electrodes
following violation sound onset. (A, B) Grand average ERPs across the frontal
electrodes (FP1, FP2, F3, F4), aligned to the onset of a violation within the 5 element
sequence (see Fig. 1 and Suppl. Figs. S1–3). ERPs shown are for the first illegal sound
element and its matching consistent sequence element. (A) Grand average ERP for
consistent responses (blue line) and violation responses (red line). (B) Difference
plot (violation minus consistent, see A). Red lines correspond to the upper (solid)
and lower (dashed) bounds of the 95% confidence interval (2.5% and 97.5%
respectively), defined by the variability in the difference waveform during a 613 ms
baseline period including the 200 ms prior to the start of the first sound in the
sequence through the end of the first element in the sequences, which was always
element ‘A’ (see Section 2for further details). Black horizontal line shows the mean
of the baseline period difference waveform, reflecting some variability in the
difference waveform in response to the violation minus consistent sequence
elements. Areas that breach the confidence interval are filled in red. (C) Histogram
showing the distributions of average area of CI breaches (2 comparison pairs per
session, for all the sessions with each macaque; n= 148 data points).
A. Attaheri et al. / Brain & Language xxx (2014) xxx–xxx 5
Please cite this article in press as: Attaheri, A., et al. EEG potentials associated with artificial grammar learning in the primate brain. Brain & Language
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F4; mMMN F
(1,1176)
= 48.893, p< 0.001; P200, F
(1,1176)
= 6.649,
p= 0.01; P500, F
(1,1176)
= 5.033, p= 0.025).
To match the violation sequences with the same elements in
consistent sequences involved shifting the alignment of some of
the sequences. We confirmed that the reported effects were evident
in the grand average ERP response including sequences that were
not shifted and those that were balanced in the direction of shifting
(Suppl. Fig. S6). Also, since we required that the sounds after a vio-
lation were acoustically matched, the experimental design necessi-
tated that the sounds preceding the violation were different
between consistent and violation sequences (Fig. 1C). Thus we
asked whether the magnitude of the response difference to these
sounds prior to the violation was associated with the size of the
mMMN and P500 ERP components seen in response to the violation
sound. This could identify an interesting contextual effect whereby
the strength of the acoustically-related EEG response to the consis-
tent vs. violation sequence element prior to the violation was asso-
ciated with the strength of the effect to the violation sound and its
acoustically matched consistent sequence sound (illustrated in
Suppl. Fig. S7C). However, there was no significant association with
the magnitude of the response difference to the acoustically differ-
ent sounds prior to the violation and the magnitude of the mMMN
and P500 effects to the violation sound (Suppl. Fig. S7).
4. Discussion
This macaque EEG study provides evidence that certain ERP
components are modulated by violations to a moderately complex,
finite-state AG, which macaques appear able to implicitly learn
(Wilson et al., 2013). We observed that violations to adjacent
relationships in the AG modulated positivities and negativities
(from 150–500 ms), with the strongest modulations occurring
for the macaque mMMN, P200 and a later frontal positivity
(P500). We next separately discuss each of these observed ERP
components in relation to the literature, including ERPs reported
in human EEG studies of AG learning.
Our experimental design ensured that effects related to a viola-
tion sound were analysed in relation to an acoustically identical
sound in a matched comparison consistent sequence. Thus the
results cannot easily be attributed to acoustical differences and
instead reflect the sequencing condition in which the nonsense
word elements occurred (i.e., whether the order of the preceding
elements in the sequence lead to the analysed element being
consistent with or in violation of the AG). We observed that for a
violation element, certain ERP components were modulated to a
greater extent than for their acoustically matched elements in
the consistent sequence. The effects were restricted to the violating
element, as none of the effects persisted for the next sound
following the violation element. Moreover, the reported effects
are evident in sequences with no shifting and balanced shifting
between consistent and violation sequences (Suppl. Fig. S6). Fur-
thermore, the reported effects do not appear to be associated with
the acoustically-related EEG response to the sound prior to the vio-
lation (Supp. Fig. S7).
We had hypothesised that AG violations would modulate a
number of components, such as the monkey homologs of the
MMN (Bekinschtein et al., 2009; Fishman & Steinschneider, 2012;
Gil-da-Costa et al., 2013; Javitt et al., 1992; Naatanen & Alho,
1995 ;Ueno et al., 2008; Uhrig et al., 2014; Ulanovsky et al.,
2003), human Early Left Anterior Negativity (ELAN: Friederici,
2004; Friederici et al., 2002), P200 (Garcia-Larrea et al., 1992;
Novak et al., 1992) and P3a (Arthur & Starr, 1984; Baldwin &
Kutas, 1997; Gil-da-Costa et al., 2013; Molholm et al., 2005;
Mueller et al., 2012; Paller et al., 1988; Pineda et al., 1988; Zevin
et al., 2010). Two of these predictions were met. We observed
mMMN and P200 ERP components. The mMMN is of interest
because human EEG studies using AGs with similar levels of com-
plexity to the AG used here (e.g., those with adjacent relationships)
can elicit an MMN (Baldwin & Kutas, 1997; Mueller et al., 2012).
However, such AGs also often elicit an ELAN (Friederici, 2004;
Friederici et al., 2002), for which we found no strong evidence of
a macaque homolog using our significance criteria, although care
is needed when interpreting topographical distributions from our
limited set of electrodes. Interestingly, we observed a prominent
late P500 component, which was not predicted because such a late
positivity in human AG learning studies is usually associated with
more complicated (e.g., non-adjacent) AG relationships (Friederici,
2004; Friederici et al., 2002).
Some of the earlier macaque ERP components show consider-
able similarities to those that have been reported in the EEG
literature, in relation to ERP components modulated by violations
of expectancy. Prior human and nonhuman animal work studying
neuronal responses or ERPs, associated with oddball sounds or
change detection, have also noted effects on early components.
For example, the nonhuman animal homolog of the MMN response
is an extended negativity occurring at 150 ms after stimulus
onset, which is elicited by an acoustically deviant sound
(Bekinschtein et al., 2009; Fishman & Steinschneider, 2012; Gil-
da-Costa et al., 2013; Javitt et al., 1992; Naatanen & Alho, 1995 ;
Ueno et al., 2008; Ulanovsky et al., 2003). Human EEG studies of
AG learning have also reported effects on the MMN or P300 when
the violation sequences are presented infrequently (Baldwin &
Kutas, 1997; Mueller et al., 2012). Thus, our observed enhance-
ment of the macaque mMMN and P200 by the AG violation condi-
tion could relate to violations of expectancy. However, our study
ensured balanced presentation of violation and consistent
sequences, which is also the case for many other AG learning stud-
ies (Friederici, 2004; Friederici et al., 2002). Thus, the violation of
expectancy in this experiment was established by the period of
exposure (lasting for 30 min) prior to testing.
The P500 component that we observed was unexpected but
appears to be a robust effect. The later positivity to AG violations
in humans (P600) might be near enough in time to be a homolog
of the component that we see in the macaque P500. However, the
P600 in humans is thought to be elicited by more complex AGs
including those that have hierarchical relationships of the forms
that are only present in human language (Friederici, 2002, 2005)
and for which there is currently no clear evidence that any nonhu-
man animal can learn (Berwick, Okanoya, Beckers, & Bolhuis, 2011).
Therefore, our macaque P500 is unlikely to be a direct homolog of
the P600 reported in humans for complex AG or syntax-related pro-
cesses. It also seems unlikely that the observed macaque P500 is a
later macaque homolog of the P3a since the macaque P3a in
response to oddball sounds does not seem to persist through to
500 ms after sound onset (Gil-da-Costa et al., 2013). Furthermore,
during the time period where we might expect a macaque P3a com-
ponent (200–350 ms), we see, if anything, a stronger negativity for
the violation sequences (Fig. 3). This observation is inconsistent
with our observed P500 being a remnant of a prior sub-threshold
P3a effect. Thus, the correspondences in the polarity and general
time of occurrence of the macaque P500 to the reported human
P600 might reflect evolutionarily conserved processes involved in
evaluating sequences for consistency with previously learned
sequencing relationships. The differences in the functional role for
the macaque P500 and the human P600 may reflect the differenti-
ation that has occurred in humans to support language-specific
processes.
In conclusion, we identified a number of expected effects on
early macaque ERP positivities and negativities associated with
AG learning, such as prominent effects on the macaque mMMN
and P200. We did not identify a corresponding homolog of the
human ELAN response to adjacent AG violations. However, we note
6A. Attaheri et al. / Brain & Language xxx (2014) xxx–xxx
Please cite this article in press as: Attaheri, A., et al. EEG potentials associated with artificial grammar learning in the primate brain. Brain & Language
(2014), http://dx.doi.org/10.1016/j.bandl.2014.11.006

a prominent later frontal positivity (P500), which although
unexpected is similar in polarity and relative time of occurrence,
but likely differs in its functional role, to the P600 that has been
reported in human EEG studies of more complex forms of AG
learning. This first macaque EEG study of AG learning raises the
possibility that certain processes associated with auditory
sequence analysis are evolutionarily conserved as reflected in the
ERP responses that were measured here. Some, like the macaque
P500, might have further functionally differentiated in humans.
The conserved aspects of the ERP components can now be studied
at the neuronal level in macaques, as a primate model system, and
related to humans using comparative EEG and fMRI.
Author contributions
A.A. and C.I.P. designed research; A.A. and A.M. performed
research; A.A. analysed data; B.W., K.A., Y.K., and C.I.P. provided
materials and intellectual contributions; A.A. and C.I.P. wrote the
paper with input from the co-authors.
Acknowledgments
We thank B. Malone and two anonymous reviewers for
constructive comments on a previous version of the manuscript.
We thank A. Hanson for assistance with EEG recordings, V. Willey
for custom machine work, and P. Flecknell and members of the
Comparative Biology Centre staff for expert veterinary and
husbandry support. Supported by the Wellcome Trust – United
Kingdom (to CIP; Project Grant WT092606/Z/10/Z; Investigator
Award WT102961MA).
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.bandl.2014.11.
006.
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