Electrophysiological priming effects confirm that the extrastriate symmetry network is not gated by luminance polarity

Abstract

It is known that the extrastriate cortex is activated by visual symmetry. This activation generates an ERP component called the Sustained Posterior Negativity (SPN). SPN amplitude increases (i.e., becomes more negative) with repeated presentations. We exploited this SPN priming effect to test whether the extrastriate symmetry response is gated by element luminance polarity. On each trial, participants observed three stimuli (patterns of dots) in rapid succession (500 ms. with 200 ms. gaps). The patterns were either symmetrical or random. The dot elements were either black or white on a grey background. The triplet sequences either showed repeated luminance (black > black > black, or white > white > white) or changing luminance (black > white > black, or white > black > white). As predicted, SPN priming was comparable in repeated and changing luminance conditions. Therefore, symmetry with black elements is not processed independently from symmetry with white elements. Source waveform analysis confirmed that this priming happened within the extrastriate symmetry network. We conclude that the network pools information across luminance polarity channels.

Full text

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Eur. J. Neurosci.. 2021;53:964–973.
wileyonlinelibrary.com/journal/ejn
1
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INTRODUCTION
Visual symmetry has been studied extensively (Bertamini,
Silvanto, Norcia, Makin, & Wagemans,2018; Cattaneo,2017;
Treder,2010). Symmetry is an important cue for object rec-
ognition and figure-ground segmentation (Driver, Baylis,
& Rafal, 1992; Koffka, 1935). Here we consider reflec-
tional (mirror) symmetry only, although it not the only type
(Mach,1886). In reflectional symmetry, there is a correlation
between element position on either side of the axis (Barlow
& Reeves,1979). Therefore models of symmetry perception
consider how local element position signals are integrated to
Received: 5 June 2020
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Revised: 28 August 2020
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Accepted: 29 August 2020
DOI: 10.1111/ejn.14966
RESEARCH REPORT
Electrophysiological priming effects confirm that the extrastriate
symmetry network is not gated by luminance polarity
Alexis D. J.Makin1
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AndreaPiovesan2
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JohnTyson-Carr1
|
GiuliaRampone1
|
YiovannaDerpsch1
|
MarcoBertamini1
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original
work is properly cited.
© 2020 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd
Abbreviations: ANOVA, analysis of variance; CI, confidence interval; CLARA, classical LORETA recursively applied; ECD, equivalent current dipole;
EEG, electroencephalography; EOG, electrooculogram; ERP, event related potential; fMRI, functional Magnetic Resonance imaging; ICA, independent
components analysis; LCD, liquid crystal display; LOC, lateral occipital complex; LORETA, low resolution electromagnetic tomographic analysis; PCA,
principal component analysis; PNG, portable network graphics; SPN, sustained posterior negativity; TMS, transcranial magnetic stimulation.
1Department of Psychological Sciences,
University of Liverpool, Liverpool, United
Kingdom
2Department of Psychology, Edge Hill
University, Ormskirk, United Kingdom
Correspondence
Alexis D. J. Makin, Department of
Psychological Sciences, Eleanor Rathbone
Building, University of Liverpool,
Liverpool, Merseyside L69 7ZA, United
Kingdom.
Email: Alexis.makin@liverpool.ac.uk
Funding information
Economic and Social Research Council,
Grant/Award Number: ES/S014691/1
Abstract
It is known that the extrastriate cortex is activated by visual symmetry. This activa-
tion generates an ERP component called the Sustained Posterior Negativity (SPN).
SPN amplitude increases (i.e., becomes more negative) with repeated presentations.
We exploited this SPN priming effect to test whether the extrastriate symmetry re-
sponse is gated by element luminance polarity. On each trial, participants observed
three stimuli (patterns of dots) in rapid succession (500ms. with 200ms. gaps). The
patterns were either symmetrical or random. The dot elements were either black or
white on a grey background. The triplet sequences either showed repeated lumi-
nance (black>black>black, or white>white>white) or changing luminance
(black>white>black, or white>black>white). As predicted, SPN priming was
comparable in repeated and changing luminance conditions. Therefore, symmetry
with black elements is not processed independently from symmetry with white ele-
ments. Source waveform analysis confirmed that this priming happened within the
extrastriate symmetry network. We conclude that the network pools information
across luminance polarity channels.
KEYWORDS
luminance polarity, reflection, regularity, repetition enhancement, sustained posterior negativity

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MAKIN et Al.
form a global gestalt (van der Helm & Leeuwenberg,1996;
Wagemans, Van Gool, Swinnen, & Vanhorebeek,1993).
Early vision can be conceptualized as a retinotopic array
of spatial frequency and orientation tuned filters (Campbell
& Robson,1968). Building on this foundation, filter models
presume that the first stage in symmetry perception involves
low-pass filtering of the image. This extracts midpoint-co-
linear blobs that are aligned orthogonally to any axes of
reflection. Symmetry discrimination can then proceed by
estimating blob alignment (Dakin & Hess, 1997; Dakin &
Watt,1994; Osorio, 1996). Dakin and Hess (1998) suggest
that symmetry is extracted in this way from a narrow inte-
gration window elongated about the axis. Julesz and Chang
(1979) found that participants can ignore noise masks as long
as they differ in spatial frequency from the underlying sym-
metrical pattern. Likewise, Rainville and Kingdom (2000)
found that participants can ignore noise masks with different
orientations. These considerations suggest specific symmetry
representations are linked to specific spatial frequency filters.
Therefore, different symmetry representations may not over-
lap and perceptually interfere with each other.
Reflected elements can vary in their low-level visual prop-
erties, such luminance or colour. To accommodate these vari-
ations, filter models sometimes incorporate a full or half wave
rectification stage at the front end (e.g. Dakin & Hess,1997).
Among other things, such rectification eliminates the dif-
ference between black and white elements on a grey back-
ground. Global integration can then utilize contrast signals
rather than luminance signals. Early half-wave rectification is
also incorporated into some influential shape detection mod-
els (Poirier & Wilson, 2010; Wilson & Wilkinson, 2002).
These considerations suggest that there should be complete
overlap between symmetry representations with different lu-
minance polarity (even if there is no overlap between symme-
try representations with different spatial frequencies).
Furthermore, Glass pattern (Glass, 1969) aftereffects are
known to transfer perfectly between black and white exem-
plars (Clifford & Weston, 2005). This again suggests that
some types of global structure are coded independently of
element luminance polarity. This could be true of reflectional
symmetry as well.
Without referring to filter models directly, Tyler and
Hardage (1996) also addressed these topics. They distin-
guished first-order (luminance) mechanisms, which only find
symmetrical correspondences between elements of the same
luminance polarity (and operate over short distances), and
second-order (contrast) mechanisms, which can find sym-
metrical correspondences between elements of opposite lumi-
nance polarity (and can operate over long distances spanning
the whole visual field). Tyler and Hardage (1996) measured
symmetry sensitivity while varying density and eccentricity.
If second-order mechanisms dominate, then symmetry sen-
sitivity should improve at low densities, and this was indeed
the case. Tyler and Hardage (1996) also compared matched
luminance polarity conditions (black regions paired with
black, and white paired with white) and opposite luminance
polarity conditions (white paired with black, and black paired
with white). Symmetry detection could tolerate opposite lu-
minance polarity when density was low. This again empha-
sises the role of second-order mechanisms. In light of these
findings, Tyler and Hardage (1996) concluded that long-
range, second-order mechanisms predominate in symmetry
detection. This account again suggests reflectional symmetry
is coded independently of element luminance polarity.
Opposite luminance polarity symmetry is often termed
anti-symmetry. Symmetry and anti-symmetry are sometimes
perceptually equivalent, as found by Tyler and Hardage (1996).
However, there are many cases where anti-symmetry is per-
ceptually weaker. For instance, anti-symmetry discrimination
declines at high element density, when multiple greyscale lev-
els are used, and when elements are presented in the periphery
(Mancini, Sally, & Gurnsey,2005; Wenderoth,1996). It seems
that global symmetry detection mechanisms are sensitive to lu-
minance polarity (mis)matching across the axis. Apparently,
early filter-rectification and/or second order mechanisms do
not always render black and white elements informationally
identical and thereby abolish all anti-symmetry costs.
Following these themes, Gheorghiu, Kingdom, Remkes, Li,
and Rainville (2016) tested whether symmetry perception is se-
lective for low-level properties: For example, are symmetrical
arrangements of black dots coded by one neural mechanism,
and symmetrical arrangements of white dots coded by another
neural mechanism? In other words, we can ask whether sym-
metry perception mechanisms are gated by luminance polar-
ity. Wright, Mitchell, Dering, and Gheorghiu (2018) presented
evidence against selectivity. They claimed that “symmetry de-
tection mechanisms pool both luminance-polarities into one
channel, and thus, extrastriate visual areas sensitive to sym-
metry are not gated by luminance polarity” (page 487). This
non-selectivity hypothesis is partially anticipated by the work
mentioned above, such as the second-order predominance ac-
count of Tyler and Hardage (1996), and filter models with an
early rectification stage (Dakin & Hess,1997). For simplicity,
we use the term non-selectivity hypothesis to refer to this fam-
ily of related ideas.
The current project tested the non-selectivity hypothe-
sis. We measured an established ERP called the Sustained
Posterior Negativity (SPN): After 200 ms, amplitude is
lower at posterior electrodes when participants view sym-
metrical compared to asymmetrical stimuli. This pos-
terior negativity was first identified by Norcia, Candy,
Vildavski, and Tyler (2002), and has been replicated many
times (Jacobsen & Höfel,2003; Makin, Rampone, Morris,
& Bertamini, 2020; Makin, Wilton, Pecchinenda, &
Bertamini,2012; Makin etal.,2016). Source localization
shows that the SPN is generated in the extrastriate cortex.

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This is consistent with fMRI work, which has identified
symmetry activations in a network of extrastriate regions,
including V4 and the shape-sensitive Lateral Occipital
Complex (LOC). This extrastriate symmetry response
was first found in an fMRI study by Tyler et al. (2005),
and then replicated by others (Keefe etal.,2018; Kohler,
Clarke, Yakovleva, Liu, & Norcia,2016; Sasaki, Vanduffel,
Knutsen, Tyler, & Tootell, 2005; Van Meel, Baeck,
Gillebert, Wagemans, & Op de Beeck,2019). SPN local-
ization is also consistent with TMS research, which has
found that disruption of the LOC impairs symmetry de-
tection (Bona, Cattaneo, & Silvanto,2015; Bona, Herbert,
Toneatto, Silvanto, & Cattaneo,2014).
Recently, we found that SPN amplitude increases (that is,
becomes more negative) with repeated presentation of sym-
metrical patterns (Bertamini, Rampone, Oulton, Tatlidil, &
Makin, 2019). We term this repetition enhancement effect
SPN priming. Following an established research strategy (e.g.
Kim, Biederman, Lescroart, & Hayworth, 2009; Kourtzi &
Kanwisher,2001), our recent work has exploited SPN priming
to assess the independence of different symmetry representa-
tions (Makin, Tyson-Carr, Rampone, Derpsch, & Bertamini,
2020). Experiment 1 of Makin, Tyson-Carr, et al. (2020) found
SPN priming with repeated presentation of different exemplars,
but not repeated presentation of identical exemplars (Figure1).
Other experiments in Makin, Tyson-Carr, et al. (2020) demon-
strated that SPN priming survives changes in regularity type,
but not changes in retinal location, or unpredictable changes in
axis orientation. In the current work, we tested whether SPN
priming would survive changes in element luminance polarity,
as predicted by the non-selectivity account.
We used different exemplars (Figure2), known to pro-
duce SPN priming (red wave in Figure1). Following Makin,
Tyson-Carr, et al. (2020), we used a secondary task that
was unrelated to symmetry: Our participants discriminated
between normal sequences with three patterns, and oddball
sequences, with a blank in the middle (Figure2b).
In the Repeated luminance condition, polarity was held
constant across the three presentations in a trial (e.g.
black>black>black or white>white>white). Conversely,
in the Changing luminance condition, polarity alternated
(e.g. black>white>black or white>black>white). The
non-selectivity account predicts that SPN priming should be
equivalent in both Repeated and Changing luminance con-
ditions. These predictions were pre-registered (https://aspre
dicted.org/2rh7e.pdf).1
2
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METHOD
2.1
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Participants
Twenty-two participants were involved (mean age 20, range
18–50, 2 males, 1 left-handed). The pre-registered aim of
testing 24 participants was abandoned prematurely due to the
COVID19 pandemic. The experiment had local ethics com-
mittee approval and was conducted in accordance with the
Declaration of Helsinki (2008).
2.2
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Apparatus
Participants sat 57 cm from a 29 × 51 cm LCD monitor,
updating at 60Hz. A chin rest was used for head stabiliza-
tion. EEG data was recorded continuously at 512 Hz from
64 scalp electrodes (BioSemi Active-2 system, Amsterdam,
Netherlands). Horizontal and Vertical EOG external chan-
nels were used to monitor excessive blinking and eye move-
ments. The experiment was programmed in Python using
open source PsychoPy libraries (Peirce,2007).
1We also ran an Identical exemplars experiment on a different group of 22
participants. Here all three presentations were the same in terms of element
position. This experiment was also pre-registered. We predicted that there
would be no SPN priming, either in the Repeated or Changing luminance
conditions. However, the results were inconclusive. There was no SPN
priming when analysing the electrode cluster chosen a priori. However,
there was evidence of SPN priming in both Repeated and Changing
luminance conditions when a larger posterior cluster was used. There was
also SPN priming in the source waveforms. These results reduce our
confidence in the claim that SPN priming is eliminated when exemplars are
identical (i.e. the result shown in Figure1). However, they do not
contradict in our claim that SPN priming is independent of luminance
polarity, which is the main result reported in this paper.
FIGURE 1 Selective SPN priming effect when novel exemplars are shown. Data from Makin, Tyson-Carr, et al. (2020). Participants viewed a
sequence of three reflectional symmetries (left). The sequence could involve different reflections (red outline) or identical reflections (blue outline).
The SPN was the difference between reflection and random waves (middle). SPN amplitude increased (i.e. became more negative) with repeated
presentations of different reflections (red) but not with repeated presentations of the same reflection (blue). This is shown in the bar chart on the
right (error bars=95% CI)

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2.3
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Stimuli
Patterns were comprised of 160 non-overlapping black or
white Gabor elements on a circular grey background. The
circular disk was 8° in diameter. The regions where the ele-
ments could fall was 7.14° diameter. The visible dot at the
centre of each circular Gabor was approximately 0.25 de-
grees diameter. In PsychoPy RGB coordinates (which vary
from −1 to 1), the background was dark grey [−0.25, −0.25,
−0.25], the circular region mid-grey [0, 0, 0], the black ele-
ments were black [−1, −1, −1] and the white elements were
white [1, 1, 1]. Stimuli were generated offline and saved as
PNG files. The reflection patterns had horizontal and verti-
cal axes. We used 2-fold reflection in this study to increase
signal strength (SPN amplitude almost doubles as we go up
from 1 to 2 folds, e.g. Makin etal.,2016). All experiments
used the same set of images, but these were shuffled so they
played a different role in for each participant.
2.4
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Procedure
The timeline of a single trial was based on Makin, Rampone,
Morris, et al. (2020). The 1,500ms fixation baseline was fol-
lowed by three 500ms patterns with 200ms gaps. In 80% of
trials, all three presentations in the sequence were reflection
or random patterns (Figure 2b top row). In the remaining
20% there was blank oddball between two random patterns
(Figure2b bottom row). The participant's task was to dis-
criminate normal from oddball trials (by pressing buttons
labelled “All patterns” or “Blank in the middle”). The re-
sponse mapping switched between trials, so sometimes the
left (A) key was used to report ‘Blank in middle’ and some-
times the right (L) key was used to report “Blank in middle.”
Each participant completed 600 trials in total. 480 were
normal trials, 120 were oddball trials. Only the normal trials
were analysed. Of the 480 trials, there were 60 in each of the 8
triplet types shown in Figure2a. There were thus 120 trials in
FIGURE 2 (a) Triplet sequences
used in this experiment. Colour coding
matches the ERP results in Figures 4 and
5. These are just examples, in the real
experiment, every trial involved different
patterns. (b) Example trial structure. The
participant's task was to discriminate
common “all pattern” trials, (top row, 80%)
from infrequent “blank in the middle” trials
(bottom row, 20%)

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the 4 crucial conditions [(Reflection, Random) X (Repeated
luminance, Changing luminance)]. The experiment was bro-
ken into 30 blocks of 20 trials. Within each block, conditions
were presented in a randomized order. A single practice block
was presented before the experiment began.
2.5
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ERP analysis (sensor level)
Pre-processing conventions were chosen a-priori and pre-
registered. EEG data from 64 channels was analysed of-
fline using EEGLAB 13.3.4b toolbox in Matlab (Delorme &
Makeig,2004). The data were re-referenced to the scalp aver-
age, low pass filtered at 25Hz, downsampled to 128Hz and
segmented into −0.5 to+2.1 s epochs with a −200ms pre-
stimulus baseline. Eye blinks and other large artefacts were
removed using Independent Components Analysis (Jung
etal.,2000). An average of 10.05 (min 4, max 16) compo-
nents were removed. Trials where amplitude exceeded±100
microvolts were removed from all analysis (11%–12%).
Oddball trials were not included in the ERP analysis. We did
not remove ERP trials if participants entered an incorrect re-
sponse because these infrequent mistakes probably happen
during response entry and excluding error trials would make
only a negligible difference to ERP waveforms.
The SPN was computed as the difference between reflec-
tion and random waves at posterior electrode cluster [PO7,
O1, O2 and PO8]. SPN in the Repeated luminance condition
was defined as Repeated luminance reflection – Repeated
luminance random (averaging over black > black > black
and white>white>white sequences). SPN in the Changing
luminance condition was defined as Changing luminance
reflection – Changing luminance random (averaging over
black>white>black and white>black>white sequences).
Three windows were chosen a-priori for statistical analysis
of SPN priming: First window=250–600ms, Second win-
dow=950–1300ms, Third window=1650–2000ms.
SPN was analysed with repeated measures ANOVAs. There
were 2 within subject factors [Sequence position (first, second
third) X 2 Sequence type (Repeated luminance, Changing lumi-
nance)]. The assumption of sphericity was met (Mauchly's test
p>.159) and none of SPNs in this analysis violated the assump-
tion of normality according to Shapiro Wilk test (p>.366).
2.6
|
Source waveform analysis (source level)
Source waveform analysis (implemented in BESA v. 7.0,
MEGIS GmbH, Munich, Germany) was used to investigate the
spatiotemporal dynamics of the SPN priming effect. Accurate
localisation of cortical sources requires data with a large signal-
to-noise ratio. Hence, the average difference wave (symme-
try—random) was computed across the repeated and changing
luminance conditions, thus producing the average waveform
representing symmetry-specific activity.
The construction of a source dipole model requires that
equivalent current dipoles (ECDs) are fitted to describe the
3-dimensional source currents from cortical regions contrib-
uting maximally to the observed data. To identify the num-
ber of contributing sources, principle component analysis
(PCA) was used. In accordance with previous fMRI literature
identifying the extrastriate cortex as being the origin of the
SPN response (Keefe etal.,2018; Kohler etal.,2016; Sasaki
etal.,2005; Tyler etal.,2005; Van Meel etal., 2019), two
ECDs were fitted bilaterally within the extrastriate cortices.
Classical LORETA analysis recursively applied (CLARA)
was used as an independent source localisation method to
confirm and adjust the locations of the ECDs. A source di-
pole model including bilateral ECDs within the extrastriate
cortices explained 91.9% of the variance in the observed
data. Since the PCA identified no other significantly con-
tributing sources, no further ECDs were fitted. Finally, the
orientation of the ECDs had to be determined. Due to differ-
ences in gyral anatomy between subjects, ECD orientation
was determined on an individual subject basis but with the
constraint of fixed ECD location between subjects. A 4-shell
ellipsoid head volume conductor model was employed using
the following conductivities (S/m = Siemens per meter):
Brain=0.33S/m; Scalp = 0.33 S/m; Bone =0.0042S/m,
Cerebrospinal Fluid = 1 S/m. Source waveforms for each
experiment and condition were exported and analysed using
repeated-measures ANOVAs.
3
|
RESULTS
3.1
|
ERP analysis (sensor level)
Grand average ERP waves are shown in Figure3a. SPN dif-
ference waves are shown in Figure3b. SPN amplitude in the
three intervals are shown in Figure3c.
All six SPNs in Figure 3c constitute significant brain
responses to symmetry (amplitude < 0, one sample t tests,
p < .001). As expected, SPN amplitude increased over the
three presentations in both Repeated and Changing lumi-
nance conditions. Repeated measures ANOVA found a main
effect of Sequence position (F(2,42) = 3.624, p = .035,
ηp
2 = 0.147, linear contrast, F(1,21) = 4.722, p = .041,
ηp
2=0.184), but no effect of Sequence type (F<1), and no
interaction (F<1).
Additional analysis found no difference between SPNs
generated by black and white reflections (collapsing over
Sequence position and Sequence type, −1.89 vs. −1.97 mi-
crovolts, t (21)=0.427, p=.674). This replicates previous
work, where SPN amplitude was independent of luminance,
contrast and colour, at least when magnitudes are matched in

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MAKIN et Al.
reflected locations (Martinovic, Jennings, Makin, Bertamini,
& Angelescu,2018; Wright etal.,2018).
3.2
|
Individual participant level analysis,
effect size and power
The left column in Figure3 shows ERP data in a standard
format. The right column in Figure3 shows the same ERP
data, but with a richer visualization of between participant
variation, as recommended by Rousselet, Foxe, and Bolam
(2016). Note individual subject waves behind grand averages
in Figure3a (and the necessarily extended vertical scale to ac-
commodate), 95% CI around difference waves in Figure3b,
and violin plots which represent distribution of individual-
participant SPNs in Figure3c.
Depending on condition, between 19/22 and 21/ 22 par-
ticipants had an SPN (reflection < random). This is sig-
nificantly more than the 11/22=0.5 expected by chance
(p=.001, binomial test). Just 16/22 participants demon-
strated an SPN priming effect (defined by a negative se-
quence position slope). This was only marginally greater
than 0.5 (p=.052, binomial test). This marginal non-para-
metric effect suggests that our experiment was statistically
underpowered. Furthermore, observed power for the sig-
nificant parametric main effect of Sequence position was
just 0.638, while the observed power of the linear contrast
was just 0.545. However, SPN priming was replicated by
Makin, Tyson-Carr, et al. (2020) in all the expected con-
ditions (plus some unexpected conditions) across five ex-
periments with 48 participants in each. Here effect size
ranged from 0.110 to 0.290. We are thus confident that
FIGURE 3 ERP results. Left and right
columns show the same data; however,
the right column has enriched visual
representation of between-participant
variability. (a) Left: Grand average ERP
waves from posterior electrode cluster [PO7,
O1, O2 and PO8]. Right: individual waves
are presented behind the grand averages,
and scale is expanded to accommodate
this variance. (b) Left: SPN as a difference
wave. The three intervals when the pattern
was presented are marked. Right: 95% CI
waves are presented behind the difference
waves (dashed lines). (c) Left: SPN in first,
second and third intervals (Error bars=95%
CI based on procedure of Morey,2008).
Right: a violin plot which represents density
of individual participant data points. Black
horizontal dashed lines indicate repeated
and changing condition averages. SPN
priming manifests as the widest bulge of the
violin moving from above to below the line.
Note that amplitude increases by around
0.5 microvolts in both repeated luminance
(red) and changing luminance (green)
conditions

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SPN priming is a real effect, although future research that
exploits SPN priming should obtain larger samples than
our 22.
3.3
|
Source waveform analysis (source level)
As discussed in Makin, Tyson-Carr, et al. (2020), the SPN
priming effect is ambiguous when analysed at the sen-
sor level. Does the increase in amplitude reflect increase
in activation at extrastriate sources (as we assume) or later
activation of additional dipoles elsewhere in the cortex?
As in Makin, Tyson-Carr, et al. (2020), we ran additional
source-level analysis to confirm that the observed SPN
priming effect does indeed reflect increasing amplitude of
the extrastriate symmetry response itself. This analysis also
allowed us to assess hemispheric differences. This is impor-
tant, because previous work has reported some right later-
alization of the extrastriate symmetry response (Bertamini &
Makin,2014; Bona etal.,2015; Verma, Van der Haegen, &
Brysbaert,2013; Wright, Makin, & Bertamini,2015).
A source dipole model was constructed comprised of two
bilateral ECDs within the extrastriate regions. This model
explained 91.9% of variance in the average difference wave
across the repeated and changing conditions. Both the left
ECD1 (Brodmann area 19; Talairach – x=−27.7, y=−61.9,
z=9.9) and the right ECD2 (Brodmann area 19; Talairach
– x=31.2, y=−61.8, z=−7.2) were located within the fu-
siform gyrus (see Figure4a). The source waveforms for each
ECD are illustrated in Figure4b. It can be seen that the SPN
priming effect is bilateral, and comparable in Repeated and
Changing luminance conditions.
A three-way repeated measures ANOVA [Sequence type
(Repeated luminance; Changing luminance) X Sequence
position (First; Second; Third) X Hemisphere (Left ECD1;
Right ECD2)] was carried out on the source waveforms.
There was a main effect of Sequence position (F(1.371,
28.790)=5.455, p=.018, ƞp
2=0.206). Although the right
hemisphere response was numerically stronger, there were no
other effects or interactions (p≥.096).
4
|
DISCUSSION
One branch of previous work has discovered SPN priming:
SPN amplitude increases with repeated presentation of novel
symmetrical exemplars (Figure1). Another branch of previ-
ous work supports the non-selectivity hypothesis, and sug-
gests that the extrastriate symmetry network pools element
position information across low-level channels (Gheorghiu
etal.,2016; Wright etal.,2018). We combined both branches
of previous work and found new support for the non-selectiv-
ity hypothesis with SPN priming.
SPN priming was comparable when luminance polarity
was repeated or changed in the triplet sequences. This sug-
gests that black and white symmetries are not coded by inde-
pendent neural systems.2 Furthermore, source waveform
analysis confirmed the SPN priming results at the source
level and found no additional cortical sources. Therefore,
SPN enhancement was due to increased activation within the
extrastriate symmetry network. This is consistent with simi-
lar analysis in Makin, Tyson-Carr, et al. (2020).
TMS work has found that the disruption of LOC reduces
symmetry repetition effects as measured behaviourally
(Cattaneo, Mattavelli, Papagno, Herbert, & Silvanto,2011).
This also suggests repetition effects are mediated within
the extrastriate symmetry network, in line with our source
level analysis. However, the study by Cattaneo etal.(2011)
raises some caveats here that require further discussion.
In Cattaneo et al. (2011), participants were slower to dis-
criminate target symmetry when it was congruent with
the adaptor (e.g. Vertical > Vertical) than when it was
2Our experiment did not distinguish between a) element luminance and b)
element luminance polarity (i.e., the direction of luminance difference from
the grey background). Here white dots were a luminance increment
compared to the grey background (positive polarity), and black dots were a
luminance decrement compared to the grey background (negative polarity).
It would be possible to devise an experiment like ours, but where both dot
luminance levels constituted an increment [e.g. a black background with
dark grey dots (increment 1) or light grey dots (increment 2)]. Likewise, it
would be possible to devise an experiment like ours where both dot
luminance levels constituted a decrement [e.g. a white background with
light grey dots (decrement 1) or dark grey dots (decrement 2)]. We are
confident that SPN priming would be transfer across large changes in
element luminance even when these changes do not constitute a polarity
reversal. However, this has not been empirically established by the current
experiment.
FIGURE 4 Source model. (a) Final
source dipole model comprising two
extrastriate sources. (b) Source waveforms
for each ECD and mean amplitude within
the defined intervals. Error bars = ± 1SD.
(c) Mean scalp map for each sequence type
and latency interval

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971
MAKIN et Al.
incongruent (Vertical > Horizontal). It is not clear why
Cattaneo etal.(2011) found this when our results would pre-
dict the opposite (a congruence advantage). It could be that
Cattaneo et al.’s participants had to inhibit the task-irrelevant
adaptor, and this was more difficult when it matched the
target orientation. In our study, the repeated trials were pre-
sented passively, so the symmetry response could accumulate
without such cognitive complications.
It has been shown that SPN amplitude is similar for lumi-
nance defined (achromatic) and colour defined (isoluminant)
stimuli when contrast greatly exceeds threshold (Martinovic
etal.,2018). This suggests that the symmetry network is in-
different to luminance and colour, as the non-selectivity hy-
pothesis claims. However, the ERP similarity demonstrated
by Martinovic etal.(2018) is only an approximate indicator
of neural similarity. Transfer of repetition effects, as demon-
strated in the current study, is more convincing evidence for
the non-selectivity hypothesis.
The non-selectivity hypothesis follows from other work.
For instance, Tyler and Hardage (1996) found that sec-
ond-order, polarity insensitive mechanisms are predominant
during some symmetry discrimination tasks. Furthermore,
filter models of symmetry perception can incorporate early
half or full wave rectification (Dakin & Hess,1997; Wilson
& Wilkinson, 2002). This work also suggests that regular-
ity coding should transcend element luminance polarity and
predicts priming should transfer across changes in luminance
polarity.
The current results are also in line with those of Clifford
and Weston (2005), who found that Glass pattern aftereffects
survive changes in luminance polarity. There are many sim-
ilarities between neural coding of reflectional symmetry and
Glass patterns (Rampone & Makin, 2020), so it is perhaps
unsurprising that both are indifferent to element luminance
polarity.
While the extrastriate symmetry network is not lumi-
nance polarity selective, we stress that it is sometimes sen-
sitive to luminance polarity (mis)matching across the axis.
This is revealed by experiments on anti-symmetry. As men-
tioned, some psychophysical work has found that anti-sym-
metry discrimination thresholds are elevated, especially
when element density is high (Gheorghiu et al., 2016;
Mancini etal., 2005; Wenderoth,1996). The SPN is gen-
erated by anti-symmetry, but amplitude is reduced (Makin,
Rampone, & Bertamini,2020; Makin etal.,2016; Wright
et al., 2018). Therefore, any future models of symmetry
perception must account for both sensitivity to luminance
polarity mismatching, AND the fact that extrastriate sym-
metry response is not gated by luminance polarity. The
future theoretical challenge is to accommodate both these
robust empirical findings.
Finally, we note that the visual effects of illumination and
shading are typically unstable in real environments. When
looking at a real object, the wavelengths reflected from its
surfaces change quickly, while the spatial relationships be-
tween its parts change slowly. It is perhaps adaptive for vi-
sual object recognition mechanisms to be tuned to spatial
relationships between parts, and ignore relatively superficial
variability in luminance, contrast and colour.
ACKNOWLEDGMENTS
This project was part funded by an ESRC grant (ES/
S014691/1). We would like to thank University of Liverpool
3rd year students Claudia Barchieri and Sophie McCullough
who helped with data collection.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
AUTHOR CONTRIBUTIONS
Alexis Makin designed the experiment, analysed the re-
sults and wrote the manuscript. John Tyson-Carr collected
some data and conducted the source localization analysis.
Yiovanna Derpsch and Andrea Piovesan helped with data
collection. Giulia Rampone assisted with EEG analysis and
interpretation of results. Marco Bertamini programmed the
stimuli, helped with interpretation of results and writing of
the manuscript. All authors contributed to the final report.
PEER REVIEW
The peer review history for this article is available at https://
publo ns.com/publo n/10.1111/ejn.14966
DATA AVAILABILITY STATEMENT
The experiments, stimuli and raw pre-processed EEG data are
available on Open Science Framework (https://osf.io/2yjus/).
We are happy for other researchers to use this material.
ORCID
Alexis D. J. Makin https://orcid.
org/0000-0002-4490-7400
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How to cite this article: Makin ADJ, Piovesan A,
Tyson-Carr J, Rampone G, Derpsch Y, Bertamini M.
Electrophysiological priming effects confirm that the
extrastriate symmetry network is not gated by luminance
polarity. Eur. J. Neurosci.2021;53:964–973. https://doi.
org/10.1111/ejn.14966