Abstract
Rare events (oddballs) produce a variety of enhanced physiological responses relative to frequent
events (standards), including the P3b component of the event-related potential (ERP) waveform.
Previous research has suggested that the P3b component is related to working memory, which
implies that working memory representations will be enhanced for rare stimuli. To test this
hypothesis, we devised a modified oddball paradigm in which the target was a disk presented at
one of 16 different locations, which were divided into a rare set and a frequent set. Participants
made a binary response on each trial to report whether the target appeared in the rare set or the
frequent set. As expected, the P3b was much larger for stimuli appearing at a location within the
rare set. We also included occasional probe trials in which the subject reported the exact location
of the target. We found that these reports were more accurate for locations within the rare set
than for locations within the frequent set. Moreover, the mean accuracy of these reports was
correlated with the mean amplitude of the P3b. We also applied multivariate pattern analysis to
the ERP data to “decode” the remembered location of the target. Decoding accuracy was greater
for locations within the rare set than for locations within the frequent set. These behavioral and
electrophysiological results demonstrate that although both frequent and rare events are stored in
working memory, the representations are enhanced for rare events.
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Enhanced Rare Working Memory Representations
Significance Statement
For many decades, researchers have observed that rare events elicit a broad range of
physiological responses, and there has been much speculation about the functional significance
of these responses. One such response is the P3b component, which is a large voltage deflection
in scalp EEG recordings. Over 40 years ago, the P3b was hypothesized to reflect “context
updating” (now often called “working memory updating”). However, there has been no direct
evidence that working memory is actually enhanced for rare, P3b-eliciting events. In the present
study, we found that both behavioral and electrophysiological measures of working memory
were enhanced for rare events. This is potentially related to the release of norepinephrine across
the cortex.
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Enhanced Rare Working Memory Representations
Introduction
Rare task-relevant events (often called oddballs) generate a variety of physiological
responses, including increased firing of noradrenergic neurons in the locus coeruleus, phasic
pupil dilation, increased blood-oxygen-level dependent (BOLD) activity in a variety of cortical
areas, and a large P3b event-related potential (ERP) component (Bledowski et al., 2004; Clark et
al., 2000; Johnson, 1988; Krebs et al., 2018; Linden et al., 1999; Murphy et al., 2011; Polich,
1986; Soltani & Knight, 2000). There has been considerable speculation about the functional
significance of these physiological changes (Kim, 2014; Linden et al., 1999; Nieuwenhuis et al.,
2011; Paller et al., 1992). A common hypothesis is that the P3b activity elicited by oddballs is
related to working memory encoding, although the details vary across theories (Donchin &
Coles, 1988; Kok, 2001; Polich, 2007, 2012). Moreover, oddballs elicit phasic increases in
attention (Aston-Jones & Cohen, 2005; Katayama & Polich, 1998; Kim, 2014; Murphy et al.,
2011), which might also enhance working memory. However, we know of no direct evidence
that working memory is actually enhanced for relatively rare events compared to relatively
frequent events. The goal of the present study was to test this hypothesis, using both behavioral
and electrophysiological measures.
Typical oddball paradigms do not provide a sensitive assessment of working memory.
For example, a typical paradigm would involve presenting a sequence of stimuli in which 90%
are the letter X and 10% are the letter O, and the task would be to press one button for Xs and
another button for Os. The responses are made immediately, so it is not necessary to store the Xs
and Os in working memory. Moreover, the Xs and Os are so easily discriminable that memory
performance would likely be at ceiling if tested after a brief delay.
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Enhanced Rare Working Memory Representations
We therefore developed the modified oddball paradigm shown in Figure 1. On each trial,
a target disc appeared briefly near one of eight locations around a circle, and the main task was
to press one button if the disc appeared near one of the cardinal axes (up, down, left, or right) and
a different button if it appeared near one of the diagonal axes (upper left, upper right, lower left,
or lower right). One of these two categories was rare (12.5%), and the other was frequent
(87.5%). This was much like a traditional oddball task, in which participants make an immediate
response to indicate whether the stimulus belonged to the rare category or the frequent category.
We assumed that stimuli belonging to the rare category (the oddballs) would elicit a larger P3b
component than stimuli belonging to the frequent category (the standards). To provide a
sensitive measure of the working memory representation of the disk, we also included occasional
probe trials, on which participants were asked to click on the exact location of the disc from that
trial after a brief delay. If working memory is enhanced for rare stimuli, then the target
localization response on probe trials should be more accurate following an oddball than
following a standard. Moreover, if the P3b component elicited by oddballs is associated with
working memory updating, then participants with greater P3b amplitudes for the oddballs should
exhibit more accurate memory on the probe trials than individuals with smaller P3b amplitudes.
In addition, we applied multivariate pattern analysis (MVPA) to the ERP data to decode
the remembered location of the disc in the delay period following each stimulus. This provided a
means of monitoring the working memory representation of the stimuli during the delay period
following each target, independently of the decision and response processes that are involved in
the behavioral responses. Specifically, we decoded which of the four locations within a category
was presented (e.g., up, down, left, or right for the cardinal category). We predicted that the
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Enhanced Rare Working Memory Representations
within-category decoding accuracy would be greater for a given category when that category was
rare than when it was frequent.
Figure 1. (A) Overview of the oddball task. On every trial, participants made a speeded keypress
following the presentation of a target disc to indicate whether it appeared near a cardinal axis or near a
diagonal axis (as shown in B). For a given trial block, one axis was rare and the other was frequent. On
12.5% of trials (probe trials), the fixation point turned red at the offset of the intertrial interval, which
signaled participants that they should use the mouse to click on the remembered location of the target disk
from that trial. (B) Possible locations of the target disc, which could appear 1° counterclockwise or 1°
clockwise from a cardinal or diagonal axis. Note that the distance of the targets from the axes is slightly
exaggerated in this image.
Methods
Participants
Twenty-two human participants from the UC Davis community completed the study (14
women, 7 men, 1 unreported gender; mean age = 20, SD = 1.69). All participants were
neurotypical and had normal or corrected-to-normal vision with no history of neurological
conditions. Twenty participants were right-handed, and two were left-handed. We chose to
Probe: 12.5% occurrence
1200-1400 ms
200 1200-1400 1200-1400200 200
...
Time (ms)
OpenEnded ResponseWindow
A) B)
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Enhanced Rare Working Memory Representations
collect a somewhat higher N than is typical of multivariate pattern analysis EEG studies (e.g., N
= 16 in Bae & Luck, 2019; Bae, 2021; Bae & Luck, 2018) because of the relatively small
number of oddball trials in the present study. Monetary compensation was provided at a rate of
$15/hr. All participants provided informed consent, and the study was approved by the
University of California, Davis Institutional Review Board.
Stimuli and Task
Figure 1 illustrates the stimuli and task. The experiment presentation script can be
downloaded at doi.org/10.17605/OSF.IO/HV7JU.
Stimuli were presented using Psychopy (Peirce et al., 2019) on an LCD monitor (Dell
U2412M) with a gray background (27.7 cd/m2) at a viewing distance of 100 cm. A 0.05° black
fixation dot was continuously visible in the center of the monitor, surrounded by a black circle
with a radius of 2.17°.
On each regular trial, a black target disc (0.2°) appeared for 200 ms, followed by an
interstimulus interval of 1200-1400 ms (rectangular distribution). The target appeared on the
black circle, near one of the cardinal axes (0°, 90°, 180°, 270°) or near one of the diagonal axes
(45°, 135°, 225°, 315°), centered either -1 or +1 degrees from one of these axes (e.g., 44°, 46°,
89°, 91°). Thus, there were 16 possible target locations, 8 near the cardinal axes and 8 near the
diagonal axes. Placing the target ±1° from an axis was designed to require participants to
remember the precise location of the target rather than relying on simple categories such as “top”
or “lower left”. In addition, this made it possible to examine biases away from the cardinal axes
(Bae, 2022).
For half of the trial blocks, the target appeared near a cardinal axis on 12.5% of trials
(oddballs) and appeared near a diagonal axis on the remaining 87.5% of trials (standards). This
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Enhanced Rare Working Memory Representations
was reversed for the other trial blocks. Each of the 8 locations within the cardinal or diagonal
category occurred with equal probability. Participants were instructed to make a speeded
response on a computer keyboard to indicate whether the immediately preceding target was near
a cardinal axis or near a diagonal axis. Half the participants pressed the left arrow key for
cardinal and the right arrow key for diagonal; this was reversed for the other half.
Although most oddball studies use only one rare stimulus (e.g., a high-pitched tone) and
one frequent stimulus (e.g., a low-pitched tone), the P3b component is sensitive to the probability
of the task-defined category rather than the probability of the physical stimulus (Luck, 2014;
Mecklinger & Ullsperger, 1993). Oddball studies have used abstract categories such as the ones
used here for decades (e.g., Kutas et al., 1977).
Each block contained 256 trials (32 oddballs and 224 standards). The blocks alternated
between cardinal-oddball/diagonal-standard and diagonal-oddball/cardinal-standard, with the
starting condition counterbalanced across participants. Each participant received 256 oddballs
and 1792 standards, with each of the 16 locations occurring equally often within each of these
categories (2048 trials total). Thus, there were 112 trials for a given location when that location
was in the standard category and 16 trials for a given location when that location was in the
oddball category.
Working memory for the exact target location was probed following a random 12.5% of
targets. When a target was probed, the fixation dot changed from black to red at the end of the
interstimulus interval (i.e., after the participant indicated whether the target had been near a
cardinal or diagonal axis). Once it turned red, the participant could use the mouse to move the
red dot. They were instructed to move the red dot to the remembered location of the target and
then click the mouse button (with no time pressure). The red dot then disappeared and was
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Enhanced Rare Working Memory Representations
replaced by the black fixation dot in the center of the monitor. The stream of targets then
resumed after a delay of 1200-1400 ms. For each participant, oddballs were probed on 32 trials
and standards were probed on 224 trials.
Note that participants could not predict whether a given trial would be a probe trial. Probe
trials were just like any other trials until the end of the intertrial interval following a given target.
Thus, the working memory representations and neural activity during the intertrial interval
following a target could not be systematically different on probe and non-probe trials.
We probed on only a small subset of trials for two reasons. First, we wanted the task to be
more like a traditional oddball task, in which exact memories are not probed. Second, probe
responses took considerable time, and we could obtain more targets in a session of a reasonable
duration if we probed infrequently. A very large overall number of trials was needed to obtain a
sufficient number of trials per location for the EEG decoding (which used the data from all trials,
not just probe trials). Far fewer trials were needed to obtain robust measures of behavioral
accuracy.
EEG Recording and Preprocessing
The continuous EEG was recorded using a Brain Products actiCHamp recording system.
We recorded EEG signals from 27 standard 10/20 sites: FP1, Fz, F3, F7, Cz, C3, Pz, P3, P5, P7,
P9, PO7, PO3, O1, POz, Oz, FP2, F4, F8, C4, P4, P6, P8, P10, PO4, PO8, O2. We also recorded
signals from horizontal electrooculogram (HEOG) electrodes lateral to the left and right external
canthi, from a vertical electrooculogram (VEOG) electrode located under the right eye, and from
electrodes over the left and right mastoids. Single-ended voltages were recorded relative to a
ground electrode located at AFz. All electrode impedances were kept < 50 KΩ. The signals were
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filtered with a cascaded integrator-comb antialiasing filter (half-power cutoff at 130 Hz) and
digitized at 500 Hz.
All preprocessing steps were conducted in MATLAB using the EEGLAB and ERPLAB
toolboxes (Delorme & Makeig, 2004; Lopez-Calderon & Luck, 2014), following the standard
pipeline described by Luck (2023).We began by shifting the event codes to account for the
monitor delay (56 ms, which was measured using a photodiode). The signals were then
resampled at 250 Hz. The DC offset was removed, and the signals were high-pass filtered
(noncausal Butterworth impulse response function, half-amplitude cutoffs at 0.1 Hz, 12 dB/oct
roll-off). Time segments between the trial blocks were deleted, and the EEG data were
referenced to the P9 electrode site. A bipolar VEOG channel was created by subtracting the FP2
electrode from the VEOG electrode, and a bipolar HEOG channel was created by subtracting
HEOG-right from HEOG-left.
Independent component analysis (ICA) was then performed to correct for blinks and eye
movements (excluding the bipolar channels). The data used for the ICA decomposition were
filtered more aggressively (noncausal Butterworth impulse response function, half-amplitude
cutoffs at 1 – 30 Hz, 48 dB/oct roll-off) and resampled at 100 Hz. The ICA weights were then
transferred back to the original data, and independent components corresponding to blinks and
artifacts were removed from the data; typically, 1-2 components were removed per participant.
We used consistency between the shape, timing and spatial location of the component compared
to the bipolar HEOG and VEOG signals to determine which components were artifacts.
The ICA-corrected data were then re-referenced to the average of the left and right
mastoid electrodes. The data were then segmented from -500 to 1496 ms relative to stimulus
onset and baseline-corrected to the mean voltage from -500 to 0 ms. Finally, epochs were
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marked for rejection using standard ERPLAB routines if they contained large voltage deflections
in any channel (using a simple voltage threshold) or an eyeblink that would have prevented
perception of the target (using a moving window peak-to-peak function from 0 to 200 ms in the
bipolar VEOG channel). This led to an average rejection of 13.7% of trials across participants
(SE = 6.4%). We always exclude participants for whom more than 25% of trials were rejected;
no participants exceeded this threshold in the present study.
Behavioral Analyses
For the oddball categorization task, we computed the proportion correct and the mean
response time (RT) for each participant, separately for the oddball and standard categories.
For the probe task, the stimuli and responses were coded in terms of their angular
position around the circle of possible target locations. We excluded trials on which the reported
location was > 40° away from the true location (0.89% ± 0.31% of trials), because such large
errors presumably reflect lapses of attention. On the remaining trials, we computed the response
error, defined as the angular distance between the true target location and the reported location.
In the primary analyses, we took the absolute value of the response error on each trial and
averaged across trials for a given participant, separately for the oddball and standard categories.
Working memory representations tend to be biased away from the cardinal axes (Bae,
2022) and we examined these biases in a separate analysis of the probe data. Following prior
research, we expected that locations that were 1° clockwise from a cardinal axis would be
reported as being more than 1° clockwise, and that locations that were 1° counterclockwise from
a cardinal axis would be reported as being more than 1° counterclockwise. If working memory
representations are enhanced for oddballs compared to standards, then these biases should be
reduced for the oddballs. To analyze the biases, we took the response error on each trial and gave
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Enhanced Rare Working Memory Representations
it a negative sign if it was farther away from the nearest cardinal axis than the true stimulus
location (e.g., given a true location of 89°, which was near the cardinal axis at 90°, a reported
location at 87° was coded as a response error of -2°). Similarly, we gave a response error a
positive sign if it was biased in the opposite direction (e.g., given a true location of 89°, a
reported location at 91° was coded as a response error of +2°). These values were then averaged
across trials for a given participant, separately for oddball and standard trials. A consistent
repulsion away from the nearest axis would lead to a negative average value. Little or no bias of
this sort would be expected for locations near the diagonal axes for either oddballs or standards,
so trials with stimuli near the diagonal axes were excluded from this analysis.
P3b Scoring
We measured P3b amplitude at an a priori electrode size (Pz) and an a priori time
window which we defined as defined as ±150 ms around the P3b peak of the grand averaged
ERP. This peak was at 512 ms so our measurement window was from 362 to 662 ms. The P3b
amplitude was scored as the mean voltage during this time window at Pz, separately for oddballs
and standards.
Decoding Analysis
The decoding analysis collapsed across the two locations that were ±1° from a given axis
(e.g., 44° and 46°), which were too similar to be reliably differentiated by the decoding process.
This gave us four cardinal locations (0°, 90°, 180°, 270°) and four diagonal locations (45°, 135°,
225°, 315°). We performed the decoding separately for these two categories, separately, when a
category was the oddball and when it was the standard. For example, we decoded which of the
four cardinal locations was present on the cardinal trials when the cardinal category was the
oddball, and we separately decoded which of the four cardinal locations was present on the
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Enhanced Rare Working Memory Representations
cardinal trials when the cardinal category was the standard. This allowed us to compare the
decoding accuracy for the same stimulus locations when those locations were oddballs and when
they were standards. Decoding was performed separately at each time point (every 4 ms from -
500 to +1496 ms).
We followed the location decoding procedure developed by Bae and Luck (2018), as
implemented in ERPLAB Toolbox (Lopez-Calderon & Luck, 2014) using MATLAB’s fitcecoc()
function. The first step was to apply a 6 Hz lowpass filter (48 dB/octave roll-off), which
minimized contamination from alpha-band EEG oscillations. This filter reduced the temporal
resolution of the analysis, but that was not a major problem given that we were examining long-
duration working memory effects. The ocular channels were left out of the analysis, leaving 27
scalp channels.
Decoding was performed on averaged ERP waveforms using support vector machines
(SVM) with error-correcting output codes (Dietterich & Bakiri, 1995) and 3-fold cross-
validation. We conducted four separate decoding runs for each participant: a) cardinal oddball; b)
cardinal standard; c) diagonal oddball; and d) diagonal standard. For each run, there were four
possible stimulus locations (the four different cardinal locations or the four different diagonal
locations). The decoder attempted to determine which of these four locations was presented on
the basis of the pattern of voltage across electrode sites at a given time point.
Because decoding accuracy tends to increase when more trials are available, we
randomly subsampled a subset of trials from the standards to equate the number of trials for
oddballs and standards. This yielded an average across participants of 19.5 trials (SD = 4.5) at
each of the four locations for each decoding run. The available trials were randomly divided into
three subsets, and a separate averaged ERP waveform was created from each of these three
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subsets. This gave us a matrix of 4 classes (4 locations) ´ 3 averages ´ 27 electrode sites ´ 500
time points for each participant for each decoding run. Decoding was performed on the matrix of
classes ´ averages ´ electrode sites, separately at each time point for each participant for a given
run.
We performed a 3-fold cross-validation by training the decoder on two of the three
averages for each location and then having the decoder predict the location on the basis of the
remaining average for each location. We then repeated the process two more times with new
decoders, changing which averages were used for training and which were used for testing. This
process was then iterated 100 times, with different random subsets of trials used to create the
averaged ERPs for each iteration. Completely new decoders were trained for every fold,
iteration, time point, and participant.
For each fold, the decoder consisted of four separate SVMs, each of which was trained to
distinguish between one location and the other three locations on the basis of the pattern of
voltage across electrode sites. To test the decoder, the vector of voltages across electrode sites
for a given location from the test average was passed to all four SVMs. The decoder predicted
the location for that test average using MATLAB’s predict() function, which minimized the
average binary loss over the four SVMs. Decoding accuracy was computed as the proportion of
correct predictions by the decoder across the three folds and 100 iterations. Because there were
four locations, chance decoding accuracy was 25%.
We then averaged decoding accuracy across the cardinal and diagonal decoding runs,
separately for oddballs and standards. This gave us a decoding accuracy for each participant at
each time point for oddballs and for standards. To maximize statistical power, decoding accuracy
was then averaged across an a priori time window that began at the start of the P3b measurement
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window (362 ms) and extended until the end of the epoch (1496 ms). For each participant, this
gave us one decoding accuracy value for oddballs and another for standards.
Results
Behavioral results
Oddball task. We first examined the speed and accuracy for the buttonpress task, in
which participants indicated whether a given target was an oddball or a standard. As is typical in
oddball tasks, mean accuracy was lower for oddballs (80.5% ± 2.32%) than for standards (95.7%
± 0.512%), which was significant in a paired t test (t(21) = 6.85, p < 0.0001, dz = 1.46). In
addition, mean RTs were slower for oddballs (470.66 ± 19.48 ms) than for standards (346.04 ±
18.75 ms), which was also a significant difference (t(21) = 14.59, p < 0.0001, dz = 3.11). Note
that all statistical tests reported in this paper were two-tailed and used an alpha of .05. Effect
sizes are quantified as dz, which is the standard effect size metric corresponding to paired and
one-sample t tests (Cohen J, 1988; Lakens, 2013). When means are given, we also provide the
standard error of the mean (SEM).
Figure 2. Probe trial results. A) Mean absolute error in the report of the exact location of the target disc.
Error bars show ±1 SEM. B) Histograms of the single-trial bias values for target discs near a cardinal
axis. For oddballs, the distribution of errors was fairly symmetrical around zero (minimal bias). For
standards, the distribution is shifted toward negative values (repulsion away from the axis).
Probes:
p=0 . 0 0 3
Oddballs Standards
0
1
2
3
4
5Mean Absolute Error
A) B) Oddballs Standards
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Enhanced Rare Working Memory Representations
Probe task. We next examined accuracy on the probe trials, for which participants
clicked on the remembered location of the target. Figure 2a shows that the mean absolute
response error for the probe task was smaller for oddballs (2.99 ± 0.155°) than for standards
(3.36 ± 0.156°), which was a significant difference in a paired t test (t(21) = 3.30, p = 0.003, dz =
0.704). This result indicates that working memory representations were more accurate for rare
stimuli than for frequent stimuli.
We also asked whether working memory representations of rare stimuli are less subject to
systematic biases. In particular, we took advantage of the fact that stimuli presented near a
cardinal axis are typically remembered as being shifted away from that axis (Bae, 2022; Pratte et
al., 2017; Wei & Stocker, 2015). We asked whether this bias would be reduced for oddballs
relative to standards. Figure 2b shows histograms of the single-trial bias values, with negative
values indicating a bias away from the nearby cardinal axis and positive values indicating a bias
toward the axis. On standard trials, the distribution of values was shifted toward the negative side
of zero, indicating the typical finding of repulsion away from the axis. We computed an average
bias score for each participant, and we found that this bias score was significantly different from
zero for the standards in a one-sample t test (t(21) = 14.59 , p = 0.002, dz = 0.739). On oddball
trials, the distribution of bias values was more symmetrical around zero, and the average bias
score was not significantly different from zero (t(21) = 1.08 , p = 0.292, dz = 0.231). The key
finding was that the average bias score was significantly more negative for standards than for
oddballs in a paired t test (t(21) = 3.17 , p = 0.005, dz = 0.676). This indicates that working
memory representations were less biased for rare stimuli in addition to being more accurate.
P3b amplitude
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Figure 3. A) Grand average ERP waveforms for oddballs and standards at representative electrode sites.
The P3b component peaked shortly after 500 ms and was largest at central and parietal electrode sites. B)
Mean amplitude at site Pz measured across a 300-ms time window centered around the peak (362-662
ms). Error bars show ±1 SEM. C) Grand average scalp topography for the difference in amplitude
between oddballs and standards in the P3b time window. D) Scatterplots showing the correlation between
P3b amplitude (measured from 362-662 ms at the Pz electrode site) and behavioral accuracy on probe
trials (mean absolute error). Each dot shows an individual participant, and the line is the best-fit
regression line.
Figure 3a shows the grand average ERP waveforms for oddballs and standards at five
representative electrode sites. As is typical, a large P3b was visible for oddballs, peaking at 512
ms at the Pz electrode site. As shown in Figure 3b, the mean voltage during the measurement
window (362-662 ms) at Pz was significantly larger for oddballs than for standards in a paired t
test (t(21) = 8.10, p < 0.0001, dz = 1.43). The scalp distribution of the oddball-minus-standard
difference wave during this window showed the typical midline centroparietal maximum (Figure
3c). Thus, although the oddball paradigm used in the present study was somewhat unusual, it
yielded the typical pattern of a larger P3b for oddballs than for standards.
We also asked whether individuals with larger P3b amplitudes had more accurate
working memory representations. Specifically, we examined the correlation between P3b
A) Oddballs
Standards
-500 0 500 1000 1500
Time (ms)
-5
0
5
10
15µV
Avg Fp1/Fp2
-500 0 500 1000 1500
Time (ms)
-5
0
5
10
15
Fz
-500 0 500 1000 1500
Time (ms)
-5
0
5
10
15
Cz
-500 0 500 1000 1500
Time (ms)
-5
0
5
10
15
Pz
-500 0 500 1000 1500
Time (ms)
-5
0
5
10
15
Oz
Oddballs Standards
0
2
4
6
8
10
12µV
Pz p<0 . 0 0 0 1B) C) D)
µV
Oddballs - Standards
0 2 4 6
Mean Absolute Error
0
5
10
15
20
25Mean Amplitude (µV)
Oddballs
r = 0.457, p = 0.0327
0 2 4 6
Mean Absolute Error
0
5
10
15
20
25 Standards
r = 0.151, p = 0.5016
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Enhanced Rare Working Memory Representations
amplitude (mean voltage at Pz during the measurement window on all oddball or standard trials)
and the absolute response error (on oddball or standard probe trials). The scatterplots are shown
in Figure 3d. A statistically significant correlation was observed for oddballs (r = 0.457, p =
0.033), with a smaller error for participants with a larger P3b amplitude. A correlation in the
same direction was observed for standards, but it was weak and not significant (r = 0.151, p =
0.502).
Together, these results show that oddball stimuli produce both a larger P3b and more
accurate working memory representations, and that these effects are correlated with each other.
Note, however, that this does not demonstrate a causal relationship between the P3b and the
working memory enhancement. It is entirely plausible that both effects are results of a common
underlying factor (e.g., an increase in attention triggered by the oddballs, which separately
impacts P3b amplitude and working memory encoding).
Decoding Results
Our final analyses were designed to determine whether we could see evidence of
enhanced working memory for oddballs in the brain activity measured between the onset of the
P3b wave and the end of the trial. Toward that end, we attempted to decode the location of the
stimulus from the ERP activity at each moment in time across the recording epoch. We collapsed
across the two locations that were near a given axis (e.g., 44° and 46°), giving us four cardinal
locations and four diagonal locations. We then decoded which of the four cardinal locations was
presented when the cardinals were standards, which of the four cardinal locations was presented
when the cardinals were oddballs, which of the four diagonal locations was presented when the
diagonals were standards, and which of the four diagonal locations was presented when the
diagonal were oddballs. We then collapsed across the diagonal and cardinal categories to obtain
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Enhanced Rare Working Memory Representations
overall decoding accuracy for oddballs and for standards. Because there were four locations
within a category, chance decoding accuracy was 25%.
Figure 4. A) Timepoint-by-timepoint decoding of target location on oddball and standard trials. The dark
lines show mean decoding accuracy across participants, and the shading shows ±1 SEM. B) Mean
decoding accuracy between the start of the P3b measurement window (362 ms) and the end of the epoch
(1500 ms). Error bars show ±1 SEM.
Figure 4a shows decoding accuracy at each individual time point. Location decoding
accuracy was slightly greater for oddballs than for standards for much of the epoch, but most
noticeably from approximately 800-1400 ms. To maximize statistical power, we averaged the
decoding accuracy across an a priori time window that began at the start of the P3b measurement
window (362 ms) and extended through the end of the epoch (1496 ms). As shown in Figure 4b,
mean decoding accuracy was greater for oddballs than for standards, which was a significant
difference in a paired t test (t(21) = 2.10, p = 0.048, dz = 0.425). Thus, the representation of the
target was modestly but significantly enhanced for oddballs relative to standards during the
period of time following stimulus offset.
Discussion
-500 0 500 1000 1500
Time (ms)
0.2
0.3
0.4
0.5Decoding Accuracy
Oddballs
Standards
0.2
0.3
0.4
0.5
Oddballs
Standards
p = 0.048
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Enhanced Rare Working Memory Representations
The current study sought to provide direct evidence that working memory representations
are enhanced for relatively rare task-relevant events compared to relatively frequent task-relevant
events. Traditional oddball paradigms do not provide a sensitive assessment of working memory
representations, but we were able to assess the accuracy of target representations behaviorally by
including probe trials. We also assessed the neural representation of the target by decoding the
target’s location on the basis of the EEG activity following target offset.
As expected, oddballs elicited a much larger P3b wave than standards, with the
prototypical P3b scalp distribution. This indicates that the modifications we made to the oddball
paradigm did not disrupt its fundamental nature. The behavioral data from probe trials showed
that participants maintained a more accurate and less biased representation of the target for
oddballs than for standards. In addition, the EEG decoding results showed that the neural
representation of the target was enhanced for oddballs relative to standards in the period
following P3b onset. These findings are, to our knowledge, the first direct evidence that working
memory is enhanced for rare, task-relevant stimulus categories.
The present results do not indicate the nature of the neural mechanism underlying
enhanced working memory, but such an effect could potentially be a result of increased firing by
neurons in the locus coeruleus, which would lead to a phasic increase in norepinephrine release
throughout the cortex (Aston-Jones & Cohen, 2005; Krebs et al., 2018; Murphy et al., 2011;
Nieuwenhuis et al., 2005). This is consistent with prior findings that norepinephrine is critical for
signaling oddballs in humans and that norepinephrine depletion leads to working memory
deficits in nonhuman primates (Arnsten, 2006; Arnsten & Goldman-Rakic, 1985; Brozoski et al.,
1979; Cai et al., 1993; Franowicz et al., 2002; Strange & Dolan, 2007; Zhang et al., 2013).
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Enhanced Rare Working Memory Representations
The finding that working memory representations were enhanced for rare items—with
greater accuracy for participants with larger P3b amplitudes—might be interpreted as evidence
for the context updating hypothesis of the P3b wave, which was proposed over 40 years ago by
Donchin (1981). Although Donchin did not use the term working memory, more recent
researchers have interpreted the context updating hypothesis as stating that the P3b is related to
working memory encoding (Kok, 2001; Polich, 2007). However, there is not much evidence to
support the idea that the P3b specifically reflects the updating of working memory (Verleger,
2008). Indeed, using a reference-back task that was specifically designed to distinguish between
updating and other processes, Rac-Lubashevsky and Kessler (2019) found no evidence that a P3b
was triggered when working memory updating was required. Moreover, the P3b component in
the present study was more than three times larger for oddballs than for standards, and yet
behavioral accuracy on probe trials (a fairly direct measure of working memory) was only
slightly greater for oddballs than for standards. Similarly, although decoding accuracy greater for
oddballs than for standards, the difference in decoding accuracy was modest.
Thus, it is unlikely that the neural mechanisms that produce the P3b component are the
same mechanisms that encode information into working memory. The correlation between P3b
amplitude and working memory accuracy in the present study is more likely related to a shared
impact of attentional allocation on P3b amplitude and working memory encoding. By analogy,
pupil dilation is also enhanced for oddballs, correlated with working memory performance, and
linked with locus coeruleus activity (Aminihajibashi et al., 2019; Eckstein et al., 2019; Gilzenrat
et al., 2010; Murphy et al., 2011; Robison et al., 2023; Robison & Unsworth, 2019; Zokaei et al.,
2019). However, this does mean that the mechanisms that produce pupil dilation are the same as
the mechanisms that encode information into working memory. Instead, the P3b may be related
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Enhanced Rare Working Memory Representations
to models in which locus coeruleus activity provides a signal across the cerebral cortex that
predictions about the environment have been strongly violated (Jordan, 2023). However,
additional research would be needed to provide conclusive evidence of causal links between
locus coeruleus activity and the P3b wave.
Behavioral responses in working memory tasks can be influenced by decision processes
at the time of report in addition to encoding and maintenance processes. To provide additional
information about the nature of the differences in working memory between rare and frequent
stimuli, we decoded the location of the target being held in working memory using the scalp
topography of the ERP signal during the interval immediately following the target, prior to the
probe period. The finding of greater decoding accuracy for oddballs than for standards during
this period suggests that rare stimuli are encoded and maintained more accurately than frequent
stimuli (as opposed to an effect of rareness that is isolated to decision processes at the time of
response). However, we cannot be certain that these ERP decoding effects reflect the same
processes responsible for the improved behavioral performance for rare stimuli on probe trials.
Indeed, the ERP decoding analyses were designed to distinguish among the four different
locations within a given response category, which were separated by 90°, whereas the behavioral
responses were typically within 5° of the true target location (see Figure 2A). The coarseness of
the decoding process may partly explain why the differences in decoding accuracy between
oddballs and standards were so small. We are not yet at the point where we can decode
differences in stimulus location from scalp voltages with the same precision as the behavioral
responses.
We would like to emphasize that the present study used a behavioral task to create the
rare and frequent stimulus categories in the context of the behavioral task. These were not
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Enhanced Rare Working Memory Representations
categories that vary in their frequency or novelty outside of this task. Stimuli that are intrinsically
novel elicit a very different pattern of neural activity, including the novelty P3a component
(which has a much more frontal scalp distribution than the P3b component) and extensive BOLD
activity in temporal and inferior frontal cortex (Strobel et al., 2008). It is not known how intrinsic
novelty impacts working memory. Performance in visual working memory tasks is more
accurate for familiar than for unfamiliar stimuli under some conditions (Chen et al., 2006;
Jackson & Raymond, 2008; Ngiam et al., 2019), but this likely reflects the use of longer-term
memory representations to aid in task performance. Click or tap here to enter text.Novelty can
improve working memory under other conditions, particularly encoding processes (Mayer et al.,
2011). Additional research will be necessary to fully understand how this kind of intrinsic
novelty is related to the encoding and maintenance of information leading to enhancements in
working memories.
Proactive interference provides a potential explanation for the finding of differences in
working memory accuracy between the rare and frequent categories in the present study. In
studies using verbal materials, working memory accuracy is reduced when similar information is
presented across many trials (Baddeley, 1986; Keppel & Underwood, 1962). In the present
study, locations in the frequent category were, by definition, presented more frequently than the
locations in the rare category, which could have led to proactive interference. However, this kind
of interference is not typically seen in visual working memory tasks using brief retention
intervals like those in the current study (Lin & Luck, 2012; Oberauer et al., 2017). Future
research would be needed to provide a conclusive test of this explanation.
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Enhanced Rare Working Memory Representations
Data and Code Availability
All data will be available at: doi.org/10.17605/OSF.IO/HV7JU, upon publication.
Author Contributions
Carlos Daniel Carrasco: conceptualization, analyses, writing; Aaron Matthew Simmons:
software development, writing; John E. Kiat: analyses, writing; Steven J. Luck:
conceptualization, writing, development of methods, funding acquisition. Editing/Review was
performed by all authors prior to submission of the final version of the manuscript.
Acknowledgements
We thank the Luck lab for all the help and support always.
Funding
This study was supported by grants R01MH087450, R01EY033329, and R01MH076226 from
the National Institute of Mental Health.
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