Received: 4 March 2018
DOI: 10.1111/psyp.13292
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Revised: 18 July 2018
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Accepted: 28 August 2018
ORIGINAL ARTICLE
Visuospatial sequence learning on the serial reaction time task
modulates the P1 event‐related potential
Jarrad A. G. Lum1
Ian Fuelscher1
|
|
Imme Lammertink2
Christian Hyde1
1
Cognitive Neuroscience Unit, School of
Psychology, Deakin University, Geelong,
Australia
2
Amsterdam Center for Language and
Communication, University of Amsterdam,
Amsterdam, The Netherlands
3
Department of Neuroscience, Georgetown
University, Washington, District of
Columbia
Correspondence
Jarrad A. G. Lum, School of Psychology,
Deakin University, 221 Burwood Highway,
Burwood, Victoria 3125, Australia.
Email: jarrad.lum@deakin.edu.au
Funding information
ARC Future Fellowship (FT160100077)
(to P.G.E.)
|
|
Gillian M. Clark1
Peter G. Enticott1
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|
Michael T. Ullman3
Abstract
This study examined whether the P1, N1, and P3 ERP components would be sensitive to sequence learning effects on the serial reaction time task. On this task, participants implicitly learn a visuospatial sequence. Participants in this study were 35
healthy adults. Reaction time (RT) data revealed that, at the group level, participants
learned the sequence. Specifically, RT became faster following repeated exposure to
the visuospatial sequence and then slowed down in a control condition. Analyses of
ERP data revealed no evidence for sequence learning effects for the N1 or P3 component. However, sequence learning effects were observed for the P1 component.
Mean P1 amplitude mirrored the RT data. The analyses showed that P1 amplitude
significantly decreased as participants were exposed to the sequence but then significantly increased in the control condition. This suggests that visuospatial sequence
learning can modulate visual attention levels. Specifically, it seems that, as sequence
knowledge is acquired, fewer demands are placed on visual attention resources.
KEYWORDS
attention, ERPs, implicit memory
1
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IN T RO D U C T ION
Sequence learning has been implicated in the acquisition
and use of motor skills (Ashe, Lungu, Basford, & Lu, 2006;
Doyon et al., 2009) and higher order operations such as language and social skills (Hamrick, Lum, & Ullman, 2018; Van
Overwalle, 2009). To date, only a handful of studies have
used EEG to study sequence learning. As a consequence, little is known about the time course of the neurological activity
associated with this type of learning. In this study, ERPs were
used to examine the time course of neurological events associated with implicit sequence learning on the serial reaction
time (SRT) task.
1.1
|
The SRT task
The SRT task was developed by Nissen and Bullemer (1987)
and has been widely used to study the implicit learning of
Psychophysiology. 2019;56:e13292.
https://doi.org/10.1111/psyp.13292
a visuospatial sequence in clinical and nonclinical populations (e.g., Clark & Lum, 2017; Janacsek, Fiser, & Nemeth,
2012; Robertson, 2007). In the standard version of the task,
a visual stimulus repeatedly appears in one of four horizontal locations on a computer display. The participant’s
task is to press one of four horizontally arranged buttons
on a response panel that matches the location of the visual
stimulus. Once a response is made, the stimulus appears in
another location, and the participant is prompted to respond,
and so on. In the “standard” Nissen and Bullemer (1987)
version of the task, stimulus presentations are grouped into
blocks comprising anywhere from around 50 (Menghini,
Hagberg, Caltagirone, Petrosini, & Vicari, 2006) to over
100 trials (Gabay, Schiff, & Vakil, 2012). Unbeknownst to
participants, on some blocks, the order in which the visual
stimulus appears in the four locations follows a predetermined sequence. These blocks are hereafter referred to as
wileyonlinelibrary.com/journal/psyp
© 2018 Society for Psychophysiological Research
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| sequence blocks. After participants have completed multiple
sequence blocks, a block of trials is then presented in which
the stimulus randomly appears in one of the four positions.
That is, the stimulus no longer follows the sequence. These
are hereafter referred to as random blocks, but in the SRT
literature they are sometimes referred to as control blocks.
In nonclinical groups (Thomas & Nelson, 2001; Thomas
et al., 2004), reaction times (RTs) become faster as participants are exposed to the sequence blocks and then slow down
when a random block is presented at the end of the task. This
slowdown in RT indicates that knowledge about the sequence
has been obtained. If it were the case that RT on the sequence
blocks were only influenced by perceptual motor skills and not
sequence knowledge, one would expect manual responses to
become even faster or reach asymptote once the final random
block is presented. Importantly, changes in RT across the SRT
task can occur without participants being aware that a sequence
was present (e.g., Bo, Jennett, & Seidler, 2011). Under these
conditions, sequence learning is considered to be implicit.
1.2
|
ERP studies of the SRT task
A number of studies have used ERPs to examine sequence
learning on the SRT task (Eimer, Goschke, Schlaghecken,
& Stürmer, 1996; Ferdinand, Mecklinger, & Kray, 2008;
Ferdinand, Rünger, Frensch, & Mecklinger, 2010; Ferdinand,
Weiten, Mecklinger, & Kray, 2010; Kopp & Wolff, 2000;
Miyawaki, Sato, Yasuda, Kumano, & Kuboki, 2005;
Rüsseler, Hennighausen, Münte, & Rösler, 2003; Rüsseler,
Hennighausen, & Rösler, 2001; Rüsseler, Kuhlicke, &
Münte, 2003; Rüsseler & Rösler, 2000; Schlaghecken,
Stürmer, & Eimer, 2000). It should be noted that, in this literature, none of the studies have used the standard version of
the SRT task described by Nissen and Bullemer (1987; i.e.,
trials presented in blocks comprising sequence stimulus presentations followed by a block comprising random stimulus
presentations). Thus, potential ERP components associated
with the more widely used Nissen and Bullemer (1987) version of the task have yet to be investigated.
In past ERP studies of sequence learning, the SRT task
has been presented as an oddball paradigm designed to
elicit N2 and P3 components (Näätänen, Pakarinen, Rinne,
& Takegata, 2004). In oddball versions of the SRT task, the
highly frequent standard trials comprised stimulus presentation that followed the sequence. The infrequent deviant
trials were created by replacing a trial in which a stimulus
presentation did not follow the sequence (e.g., Rüsseler,
Hennighausen et al., 2003). In this literature, deviant trials
have been found to elicit larger N2 and P3 components compared to standard trials. Thus, it seems that both components
are sensitive to sequence learning on the oddball version
of the SRT task. The N2 component appears to be elicited
LUM et al.
when sequence learning is implicit or explicit (Ferdinand et
al., 2008; Ferdinand, Rünger et al., 2010; Schlaghecken et
al., 2000). However, there is evidence to suggest that the P3
component is only observed when sequence learning is explicit (Ferdinand et al., 2008; Rüsseler et al., 2001; i.e., when
participants become aware of the sequence or are informed of
its existence prior to commencing the task).
1.3 | P1 and N1 ERP components and
sequence learning on the SRT task
In the current study, we examined whether the visual P1 and
N1 components might also be sensitive to sequence learning
on the Nissen and Bullemer (1987) version of the SRT task.
The P1 and N1 components appear as early as 80 and 140 ms
poststimulus onset, respectively, and amplitudes are generally
highest at occipital‐parietal scalp recording sites (Hillyard
& Anllo‐Vento, 1998; Luck & Kappenman, 2011). The amplitude of these two components is modulated by attention.
Specifically, allocating attention to a visual stimulus will increase the amplitude of the P1 and N1 components (Mangun,
Hopfinger, Kussmaul, Fletcher, & Heinze, 1997; Vogel &
Luck, 2000). Studying the P1 and N1 components in relation
to the SRT task may provide new insights into the role of attention in implicit sequence learning. Indeed, the extent to
which attentional processes are necessary for implicit learning
have been an area of ongoing debate (Curran & Keele, 1993;
Jiménez & Méndez, 1999; Staels & Van den Broeck, 2017).
The P1 and N1 components most likely capture different attentional processes (Coull, 1998; Kotchoubey, 2006;
Luck & Kappenman, 2012; Natale, Marzi, Girelli, Pavone, &
Pollmann, 2006). Supporting this position is research showing dissociations between these two components. For example, a decrease in the amplitude of the P1 component, but not
N1, has been observed when a visual stimulus unexpectedly
appears in one spatial location that is not being attended (e.g.,
Luck et al., 1994). The N1 component can be independently
modulated by varying the demands associated with evaluating a visual stimulus (Mangun & Hillyard, 1991). For example, evaluating whether a visual stimulus is of a certain color
can lead to an N1 component that is larger in amplitude compared to evaluating whether a visual stimulus is present in a
visual field (Vogel & Luck, 2000). These findings have led to
the suggestions that the P1 component is modulated by attentional processes related to visuospatial processing and the N1
component by evaluation of a visual target (Vogel & Luck,
2000; Warbrick, Arrubla, Boers, Neuner, & Shah, 2014).
Two proposals from the SRT task literature suggest that
sequence learning may modulate visuospatial attention and,
as a consequence, the P1 component. According to one
view, as participants acquire information about the sequence,
they can anticipate forthcoming stimulus locations and shift
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spatial attention to its expected location (Marcus, Karatekin, &
Markiewicz, 2006; Nissen & Bullemer, 1987; Stadler, 1989).
It has been repeatedly demonstrated (for a review, see Luck &
Kappenman, 2012) that P1 amplitude increases when attention
has been directed to a location where a visual stimulus will appear. Conversely, a decrease in P1 amplitude is observed when
a visual stimulus appears in a noncued spatial location. Based
on this literature, as participants complete more sequence
blocks on the SRT task, P1 amplitude may increase as attention
is already directed at forthcoming stimulus locations. However,
on the random block, P1 amplitude should decrease. This is
because sequence knowledge can no longer be used to cue participants to the location where the visual stimulus will appear.
A second view predicts P1 amplitude might decrease over
sequence blocks on the SRT task. In a fMRI study of SRT
task performance, Thomas et al. (2004) found parietal cortex
activation was lower for sequence blocks compared to random
blocks in adults. There is considerable evidence indicating
that the parietal cortex plays a key role in visual‐attentional
processes (Corbetta, Shulman, Miezin, & Petersen, 1995). In
light of this literature, Thomas et al. suggested that sequence
learning might lead to a decrease in levels of visual attention
needed to provide a manual response on the SRT task. This
may occur because, once the knowledge of the sequence has
been acquired, manual responses to the visual stimulus become automatized and less contingent on the need to attend
to the visual stimulus. Based on these findings, one would expect sequence learning on the SRT task to lead to a decrease in
P1 amplitude. There may also be a decrease in N1 amplitude
since this component captures visual attentional processes. In
responding to trials on the random block, attention would be
needed to respond to the visual stimulus. As a consequence,
P1 and N1 amplitude should increase on this part of the task.
1.4
|
Current study
The aim of the current study was to examine whether changes
in P1 and N1 ERP components would be sensitive to implicit learning of a visuospatial sequence on the SRT task.
In this study, healthy adults completed a version of the SRT
task described by Nissen and Bullemer (1987). Specifically,
blocks of trials were presented to participants comprising sequence or random stimulus presentations. Participants were
not informed of the sequence prior to testing. Given this, the
study examined ERPs related to implicit sequence learning.
The hypothesis put forward in this study was that implicit
sequence learning on the SRT task would modulate the P1 or
both P1 and N1 components. An exploratory arm of the study
examined whether a version of the Nissen and Bullemer SRT
task would elicit N2 and/or P3 ERP components. As noted
earlier, previous research using oddball versions of the SRT
task report that N2 and P3 components are sensitive to sequence learning effects (e.g., Eimer et al., 1996; Ferdinand,
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Rünger et al., 2010; Rüsseler et al., 2001; Schlaghecken et
al., 2000). However, to our knowledge whether the Nissen
and Bullemer version of the SRT task also elicits these components has yet to be investigated.
2
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2.1
M ETHOD
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Participants
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Materials
The final sample comprised 35 healthy adults (25 female) aged
between 18 and 34 years (M = 22.7, SD = 3.4). The participants did not have visual or hearing impairments, psychiatric
illnesses, or neuromotor diseases as indicated by a prescreening questionnaire. All participants had completed a minimum
of secondary education. Participants provided written consent
before taking part in the study and received a $30 voucher.
Prior to completing the SRT task, participants completed
tasks that measured handedness and general cognitive functioning. Handedness was quantified using the Edinburgh
Handedness Inventory (Oldfield, 1971). On this instrument,
handedness is quantified using a laterality quotient that ranges
from −1 to 1. Positive values indicate greater tendency to
be right‐handed and negative values, left‐handed. All participants in this sample had laterality quotients greater than
zero (M = 0.88, SD = 0.20). General nonverbal functioning
was assessed using the matrices subtest from the Wechsler
Abbreviated Scale of Intelligence (WASI; Wechsler, 1999).
Performance on the matrices subtest is quantified as a T score,
which is standardized to a mean of 50 and standard deviation
of 10. The mean T score for the sample was 61.8 (SD = 5.7).
2.2
2.2.1
|
SRT task
The SRT task used in this study was based on the version
described by Nissen and Bullemer (1987). Participants were
seated in front of a computer display and presented with a
four‐button response panel. The task consisted of one practice
block and six test blocks, with 60 trials in each block. A single
trial consisted of a visual stimulus appearing in one of four
horizontal locations on the computer display. Each location
was vertically centered on the computer display. Each trial
commenced with a blank screen (colored gray) for 500 ms. A
visual stimulus then appeared in one of the four locations for
650 ms. Once the stimulus appeared, participants’ task was
to press one of four buttons on a response box that matched
the location of the visual stimulus. All participants used their
right hand to make a response. Specifically, the first digit (or
index finger) was used to operate the left‐most response button, the second digit was used to operate the adjacent button,
and so on. The stimulus remained on the screen for 650 ms
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LUM et al.
| LUM et al.
F I G U R E 1 Schematic overview of the serial reaction time task. Each trial commenced with a blank screen for 500 ms. A visual stimulus then
appeared for 650 ms. Participants used their right hand to press a button on a response box that matched the location of the visual stimulus [Colour
figure can be viewed at wileyonlinelibrary.com]
irrespective of whether a response was made. If a response
was not made during this time, the trial was marked as incorrect and the next trial commenced. No feedback was provided
to participants as they completed the task. A schematic overview of the SRT task and stimuli is presented in Figure 1.
On the practice block, the visual stimulus appeared in each
of the four locations an equal number of times, but in a pseudorandom order. The only constraint was that the stimulus could
not appear in the same location on two consecutive trials. The
purpose of the practice block was to familiarize participants
with making a manual response following stimulus onset. Data
from the practice block were not used in the analyses.
On the first five test blocks, unbeknownst to participants,
the location that the stimulus appeared in the four spatial locations followed a predetermined sequence. These five test
blocks are hereafter referred to as S1, S2, S3, S4, and S5.
Between each block there was a 3‐s rest period. Labeling the
left‐most location that the stimulus could appear as 1 and the
right‐most position as 4, the sequence used was 3–4‐1–2‐4–
1‐3–4‐2–1. This sequence can be considered a first‐order conditional sequence. In this type of sequence, the position that
a stimulus appears on a single trial is predicted, to varying
degrees of probability, by the position of the stimulus in the
preceding trial. For example, if the visual stimulus appears in
Position 2, there is a 50% probability the next stimulus will appear in Position 4 or Position 1. There is 0% probability the visual stimulus will appear in Position 3. On the final test block,
a visual stimulus appeared in pseudorandom positions. This
final test block is hereafter referred to as R6. The constraints
for this block were that a visual stimulus (a) appeared in each
location the same number of times as for S1–S5, and (b) could
not appear in the same location on two consecutive trials.
Additionally, none of the first‐order transitions in the random
sequence matched those found in the repeating sequence.
Unlike most versions of the SRT task, boxes or borders were
not used to mark the four horizontal locations where the visual
stimulus could appear. This modification was made to avoid
the boxes acting as an external cue to indicate where the visual
stimulus might appear and to reduce awareness of the sequence.
Thus, the screen was blank between stimulus presentations.
The only potential cue to indicate the location of the visual
stimulus on any given trial was its location in the previous trial.
Also, unlike the standard SRT task, a different visual stimulus
appeared on each trial. A total of 60 different visual stimuli
were created for this experiment. The stimuli comprised 60
different shapes (e.g., square, triangle, odd‐shaped polygons)
of different colors (green, blue, magenta, pink, purple, yellow,
brown). Each stimulus subtended approximately 4.6° × 4.6° of
visual angle. The contrast between the stimuli and background
screen color was reduced to the point that no visual aftereffects
occurred between stimulus presentations. Example stimuli are
presented in Figure 1. On each trial within a block, one stimulus was randomly selected, without replacement, to appear in
one of the four visual locations on the screen. Thus, on each
block, participants were presented with the same stimuli, but
the order they appeared between blocks differed. This manipulation to the stimuli was also used to mask the sequence. The
stimuli were presented using E‐Prime 2.0 software (Schneider,
Eschman, & Zuccolotto, 2002).
2.3 | Data preprocessing and a
summary of the dependent variables
2.3.1 | Accuracy and RTs from
manual responses
Both accuracy and RT were recorded as participants made
a manual response on the SRT task. A correct response was
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4 of 12
recorded when participants pressed the button on the response
pad that matched the location of the visual stimulus. For each
participant, the proportion of correct responses was computed
for each block (i.e., S1–S5, R6). RT, recorded in milliseconds
(ms), measured the time taken to press the correct button on
the response pad following stimulus onset. Only RTs from
correct responses were included in the analyses. For each participant, the median RT for each block was computed.
2.3.2 | EEG: Recording and data
preprocessing
As participants completed the SRT task, EEG was recorded
using 20 Ag‐AgCl electrodes that were embedded in an
elastic cap (Easycap, Herrsching, Germany). The electrodes
were placed in the following positions of the EEG 10–20
system: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4,
P8, P3, Pz, P4, P8, O1, O2, and also on the left and right
mastoids. Electrooculogram (EOG) was acquired from electrodes placed on the left outer canthus and right outer canthus
as well as above and below the left eye. The EEG was acquired using a TMSi RefA system (Twente Medical Systems
International, The Netherlands) at a sampling rate of 2048 Hz
using a common average as the online reference. Prior to recording, impedances were reduced to below 5 kΩ.
Offline EEG data processing was undertaken using
EEGLAB 13.6.5b (Delorme & Makeig, 2004) and ERPLAB
6.0 (Lopez‐Calderon & Luck, 2014). The EEG data were
first downsampled to 250 Hz and high frequency noise reduced using a 0.1 to 30 Hz FIR band‐pass filter. EEG data
were then rereferenced to the average of the left and right
mastoids. Stimulus‐locked epochs were created from the continuous EEG commencing −400 prior to stimulus onset to
650 ms postonset. Epochs with anticipatory eye movements
were first identified and rejected from further analysis. This
was achieved by analyzing the data acquired from horizontal EOG (HEOG) channels. A DC offset correction was then
applied only to epochs from the HEOG channels. An epoch
was excluded if voltage exceeded ±50 μV from either of the
HEOG channels. This threshold aimed to capture horizontal
eye movements initiated before the stimulus appeared. This
led to an average rejection of 14% of trials from each block
(S1 = 14%; S2 = 14%; S3 = 10%; S4 = 14%; S5 = 13%; R6
= 15%). A one‐way repeated measures analysis of variance
(ANOVA) revealed no significant effect of block on the number of trials rejected from each block, F(3.493, 115.272) =
1.240, p = 0.293, ηp2 = 0.036.
Epochs without anticipatory eye movements were baseline
corrected using the mean amplitude of the 200 ms prestimulus data. Separate averaged ERP waveforms were computed
for each block from the SRT task (i.e., blocks S1–S5 and R6).
Removal of further ocular and nonocular artifacts was achieved
by running independent components analysis in EEGLAB and
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then correcting the EEG signal using the ADJUST algorithm
(Mognon, Jovicich, Bruzzone, & Buiatti, 2011). An epoch
was discarded from further analyses if amplitude exceeded
±150 μV and/or if it was associated with an incorrect manual
response. Separate averaged ERP waveforms were computed
for each block from the SRT task (i.e., blocks S1–S5 and R6).
Latency ranges for the ERP components were derived using a data‐driven procedure outlined by Brooks,
Zoumpoulaki, and Bowman (2017). This procedure has been
shown to reduce Type I error rates with respect to identifying
regions of interest on ERP waveforms. This approach aims to
find regions of interest on an ERP waveform while balancing
the need to accommodate specific experimental conditions
but avoid the problem of overfitting the data. In the context
of the current study, this procedure involved computing a
grand‐averaged ERP aggregated across all trials and conditions. Latency ranges were then defined using this waveform.
Aggregate grand averages from trials collapsed across block
are presented in Figure 2a. Discernible P1, N1, and P3 components were found at P7, P3, Pz, P4, P8, O1, and O2 electrode sites. Figure 2a shows the ERP components of interest
to be present across left and right electrode sites within the
parietal‐occipital region. It should be noted that typically the
amplitude of P1 and N1 components vary depending upon in
which visual field a stimulus appears (Luck & Kappenman,
2012). The symmetrical response of the P1 and N1 components in this study most likely reflect that the visual stimulus
appeared an equal number of times in each visual field. Given
the distribution of the ERP response across hemispheres and
that visual stimuli appeared in both visual fields, data from
the midline Pz site were used in the analyses (e.g., Oken,
Chiappa, & Gill, 1987).
Mean amplitude was used as the dependent variable for
the ERP analyses. P1 and N1 latency ranges for the mean
amplitude were computed over a 40‐ms window centered
on the peak using the aggregate grand average from trials.
These peaks are identified in Figure 2b. Using this criterion,
the latency ranges for the P1 and N1 were 152–192 ms and
196–236 ms, respectively. Mean amplitude for the P3 component was computed over 280–368 ms time interval. These
two values corresponded to two discernible peaks marking
durations in which amplitude was highest for this component
(see Figure 2b). For each participant, ERP amplitude was averaged over the aforementioned latencies.
2.4
|
Statistical analyses
The effect of block (S1, S2, S3, S4, S5, R6) on the behavioral
and electrophysiological data was examined using a one‐way
repeated measures ANOVA. The Greenhouse–Geisser adjustment for nonsphericity was used when appropriate. Next,
planned comparisons comprising paired samples t tests were
undertaken to examine sequence learning effects. The first
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LUM et al.
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F I G U R E 2 (a) Aggregate grand‐averaged ERP waveforms from trials (collapsed across blocks). (b) Aggregate grand‐averaged ERP
waveforms at Pz site showing peak amplitude for P1, N2, and P3 components
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comparison tested for a difference between the first and last
sequence blocks (i.e., S1 vs. S5). The second comparison examined whether there was a significant change from the final
sequence blocks (S5) to the following random block (R6). For
the planned comparisons, observed p values were corrected
using the Bonferroni procedure (i.e., unadjusted p values were
multiplied by two). For all statistical tests, alpha was set at
0.05.
2.5
|
Procedure
Participants were presented with the prescreening instrument, handedness measure, and WASI prior to being administered the SRT task. We assessed implicit learning via
self‐report. Specifically, after participants completed the
SRT task, they were asked whether they were aware that
the visual stimulus followed a pattern. Initially, 39 participants were tested for this study. Of this total, four participants indicated that they were aware of the sequence after
they completed the SRT task. Data from these participants
were excluded from the study. The final sample comprised
35 participants.
FIGURE 3
3
3.1
|
| 7 of 12
RESULTS
|
Behavioral data
3.1.1 | Accuracy and RT from
manual responses
The mean proportion of correct responses for all blocks approached ceiling (S1: M = 0.94, SE = 0.01; S2: M = 0.93,
SE = 0.01; S3: M = 0.94, SE = 0.01; S4: M = 0.93, SE =
0.01; S5: M = 0.92, SE = 0.01; R6: M = 0.90, SE = 0.01).
An arcsine transformation was applied to the data prior to
running the analysis to correct for nonnormality. The effect
of block (S1, S2, S3, S4, S5, R6) on accuracy was statistically significant, F(3.771, 128.209) = 5.671, p < 0.001, ηp2
= 0.143. The first planned comparison revealed that there
was no significant change in accuracy between Blocks
S1 and S5 (p = 0.096). The second planned comparison
showed a significant decrease in accuracy from B5 to R6
(p = 0.006).
Figure 3a presents summary data reporting RT by block.
The general trend observed was a decrease in RT (i.e., faster
(a) Manual reaction times. (b) Mean P1 amplitude. (c) Mean N1 amplitude. (d) Mean P3 amplitude
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LUM et al.
| responses) across the sequence blocks (S1–S5) followed by
an increase in RT in R6 (random block). The ANOVA revealed a significant main effect of block on RT, F(4.019,
136.635) = 6.894, p < 0.001, ηp2 = 0.169. Planned comparisons revealed RT in S5 were significantly faster compared to
S1 (p = 0.023). There was also a significant increase in RT
from S5 to R6 (p < 0.001). This pattern of results shows participants’ RT became significantly faster when exposed to the
sequence blocks, but then slowed down when random trials
were presented.
LUM et al.
3.2
|
Analyses of ERP data
Figure 4a shows grand‐averaged ERP waveforms for each
block. Highlighted in Figure 4b,c,d are peaks for the P1, N1,
and P3 components, respectively.
3.2.1
|
Analyses of mean amplitude
Separate ANOVAs tested the effect of block on the mean amplitude of the P1, N1, and P3 components. For the P1 component, the effect of block on mean amplitude was significant,
F I G U R E 4 Grand‐averaged ERP waveforms for sequence (S1–S5) and random (R6) blocks (a) recorded at Pz. Highlighted in the figure are
P1 (b), N1 (c), and P3 (d) components. P1 amplitude decreases across blocks S1 to S5 and increases for R6. Y axes on all panels show μV changes
in increments of 0.5 [Colour figure can be viewed at wileyonlinelibrary.com]
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F(5, 170) = 2.798, p = 0.019, ηp2 = 0.076. Planned comparisons revealed mean amplitude was significantly lower in S5
compared to S1 (p = 0.012). There was also a significant increase in mean amplitude from S5 to R6 (p = 0.003). This
pattern of amplitude change mirrors the RT data. This is seen
in Figure 3b, which plots mean P1 amplitude by block.
There was no significant effect of block on mean amplitude for the other studied components. Figure 3c presents
mean amplitude for the N1 component by block. While
changes in N1 mean amplitude over the blocks are similar to
the RT data, the effect of block on N1 mean amplitude was
not statistically significant, F(5, 170) = 0.975, p = 0.435, ηp2
= 0.028. Also, both planned comparisons were not statistically significant (S1 vs. S5: p = 0.127; S5 vs. R6: p = 0.125).
Figure 3d presents mean amplitude for the P3 component by
block. For this component, the main effect of block was also
not statistically significant, F(5, 170) = 1.620, p = 0.157, ηp2
= 0.045. There was no significant difference in mean amplitude between S1 and S5 blocks (p = 0.823) or between S5
and R6 (p = 0.282).
4
|
D IS C U SSION
This study examined attentional‐related ERP components
associated with implicit sequence learning on a version of
the Nissen and Bullemer (1987) SRT task. In the first instance, we found that this task elicited discernible P1, N1,
and P3 components. However, unlike the oddball version of
the SRT task previously used (Eimer et al., 1996; Ferdinand,
Rünger et al., 2010; Miyawaki et al., 2005; Schlaghecken et
al., 2000), we did not observe an N2 component. The data
indicated that changes in P1 amplitude were sensitive to sequence learning. Analyses showed that RT decreases across
sequence blocks (S1–S5) were paralleled by a reduction in P1
mean amplitude. Also, when RT increased from sequence to
random blocks (S5–R6), P1 amplitude also increased. Based
on this pattern of results, it is suggested that visuospatial sequence learning leads to a decrease in the levels of attention
needed to complete the SRT task. This effect is specific to the
P1 component and therefore visuospatial attention. Sequence
learning effects were not found to significantly influence N1
or P3 mean amplitude.
Increases in P1 amplitude have been proposed to reflect
an increase in the allocation of visuospatial attention to a
target (Hillyard & Anllo‐Vento, 1998). The reduction in P1
amplitude over the sequence blocks (S1–S5) is interpreted to
indicate a reduction in visuospatial attention. This reduction
was found only to occur after repeated exposure to the sequence. We argue that this modulation is related to sequence
learning effects rather than general task demands. An alternative possibility is that changes in P1 amplitude might be due
to general arousal level or mental fatigue, given the repetitive
| 9 of 12
nature of the task. However, this proposal does not fit the data
because sequence learning effects were not observed for the
N1 or P3 components (Luck, 2014). Also, if mental fatigue
did cause the reduction in P1 amplitude, it would be expected
that this reduction would continue through to the final random block, which was not the case.
The pattern of P1 amplitude change may indicate that sequence learning on the SRT task leads to a reduction in the
levels of visual attention. This proposal is in line with the
fMRI data presented by Thomas et al. (2004) showing that
sequence learning is associated with a decrease in parietal activation. A reduction in attention levels on the SRT task may
occur at the same time that the basal ganglia assert greater
influence on manual responses. Substantial research shows
that basal ganglia support the implicit learning of sequences
(Ashby, Turner, & Horvitz, 2010; Bar‐Gad, Morris, &
Bergman, 2003; Bradshaw, 2001; Hardwick, Rottschy, Miall,
& Eickhoff, 2013). Once knowledge of the sequence has
been acquired by the basal ganglia, manual responses to the
stimulus may become increasingly more automatized. As a
consequence, fewer demands are placed on visual attentional
resources leading to a decrease in P1 amplitude. On the final
random block (Block R6), however, sequence knowledge can
no longer be used to provide a correct response to the visual
stimulus. This may lead to a greater reliance on visuospatial
attentional resources to process the visual stimulus that is reflected by an increase in P1 amplitude. We note that our hypothesized relationship between basal ganglia activation and
P1 amplitude will need to be tested in future research using
combined fMRI‐EEG methodology.
Our finding that sequence learning had a significant effect on P1 but not N1 amplitude is consistent with past research showing these two components reflect different
attentional processes (Coull, 1998; Kotchoubey, 2006; Luck
& Kappenman, 2012). In previous research, N1 amplitude
has been modulated by changing the demands associated
with evaluating a characteristic of a visual stimulus (Vogel
& Luck, 2000). The nonsignificant main effect of block on
N1 amplitude most likely indicates that the demands associated with evaluating the visual stimulus remained constant
throughout the task. Thus, a new finding arising from this
study is that sequence learning on the SRT task can lead to
dissociations between P1 and N1 components.
One limitation of this study is the extent to which sequence learning can be considered implicit. After completing
the SRT task, we asked participants to indicate whether they
became aware of the sequence. In this study, only those participants who were not aware of the sequence were included
in the analyses. Analyses of P3 amplitude provide some evidence that sequence learning was implicit. In previous research, the P3 component has been found to be sensitive to
sequence learning effects in groups who explicitly learned
the sequence (Ferdinand et al., 2008; Miyawaki et al., 2005;
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LUM et al.
LUM et al.
Rüsseler, Hennighausen et al., 2003; Rüsseler et al., 2001;
Rüsseler & Rösler, 2000; Schlaghecken et al., 2000). The
nonsignificant effects of block on P3 amplitude might confirm the implicit nature of sequence learning in this study.
However, there are limitations with the approach used in
this study (and others) to determine whether the sequence
learning was implicit. Eimer et al. (1996) points out that tests
of awareness are undertaken after participants complete the
SRT task. These tests occur after a block of trials is presented
in which the stimulus appears in random positions. It is possible that random stimulus presentations may interfere with
participants’ recollection of whether a sequence was present.
Alternative methods for evaluating the effect of awareness on
implicit learning paradigms have been proposed (Cleeremans,
Destrebecqz, & Boyer, 1998). For example, one approach
(Jacoby, 1991) involves directly comparing explicit and implicit learning in the same participants. Future research could
use these approaches to study the extent to which P1 amplitude
is affected by implicit and explicit learning. Thus, while the
results of the current study show sequence learning can modulate the P1 component, the relative contribution of implicit
and explicit processes will need to be tested in future research.
4.1
|
Conclusions
This study revealed that the P1 ERP component could be
modulated by learning a visuospatial sequence on the SRT
task. The implication of this finding is that implicit sequence
learning influences visual attention. To our knowledge, this
is the first study to identify that sequence learning on the
SRT task modulates the P1 component. More generally, the
study demonstrates that ERP can be used to study implicit
sequence learning on the standard version the SRT task in
which sequence and random trials are presented in blocks.
ACKNOWLEDGMENTS
Peter G. Enticott is supported by an ARC Future Fellowship
(FT160100077).
ORCID
Jarrad A. G. Lum
http://orcid.org/0000-0003-2098-2403
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How to cite this article: Lum JAG, Lammertink I,
Clark GM, et al. Visuospatial sequence learning on
the serial reaction time task modulates the P1 event‐
related potential. Psychophysiology. 2019;56:e13292.
https://doi.org/10.1111/psyp.13292
14698986, 2019, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/psyp.13292 by New York University, Wiley Online Library on [11/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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