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\begin{document}

\title{Explicit Face Memory Abilities are positively related to the
non-intentional Encoding of Faces: Behavioral and ERP Evidence.}



\author[1]{Werner Sommer}%
\author[2]{Krzysztof Kotowski}%
\author[3]{Yang Shi}%
\author[2]{Adam Switonski}%
\author[4]{Andrea Hildebrandt}%
\author[2]{Katarzyna Stąpor}%
\affil[1]{Humboldt-Universitat zu Berlin}%
\affil[2]{Silesian University of Technology}%
\affil[3]{Zhejiang Normal University}%
\affil[4]{Carl von Ossietzky Universität Oldenburg}%


\vspace{-1em}



  
  \date{May 15, 2024}


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\selectlanguage{english}
\begin{abstract}
Individual differences in face memory abilities have been shown to be
related to individual differences in brain activity. The present study
investigated brain-behavior relationships for the N250 component in
event-related brain potentials, which is taken as a neural sign of face
familiarity. We used a task in which a designated, typical target face
and several (high- and low-distinctive) nontarget faces had to be
distinguished during multiple presentations across a session.
Separately, face memory/recognition abilities were measured with easy
versus difficult tasks. We replicated an increase of the N250 amplitude
to the target face across the session and observed a similar increase
for the non-target faces, indicating the build-up of memory
representations also for these faces. On the interindividual level,
larger across-session N250 amplitude increases to low-distinctive
non-target faces were related to faster face recognition as measured in
an easy task. These findings extend the present knowledge about
brain-behavior relationships in face memory/recognition and indicate
that an advantage in non-intentional encoding of low-distinctive
non-target faces into memory goes along with the swift recognition of
explicitly learned faces.%
\end{abstract}%



\sloppy


\textbf{1. Introduction}

Individuals differ greatly in their face memory/recognition abilities
(e.g., Wilhelm et al., 2010). For example, whereas super-recognizers
(Russell et al., 2009) remember a vast number of faces, individuals with
developmental or acquired prosopagnosia may have a hard time recognizing
even the faces of their family members (Behrmann \& Avidan, 2005).
Although the general outlines of the functional neuroanatomy of face
processing are understood (Haxby et al., 2000), research on the neural
correlates of individual differences in face processing is still scarce.
In the present study, we explored whether the amplitude of the N250
component in the event-related potential (ERP) is related to individual
differences in face recognition and whether this relationship depends on
the distinctiveness of the faces to be memorized.

In their influential face processing model describing its functional
neuroanatomy, Haxby et al. (2000) distinguished between a core system
and an extended system of facial information processing. The core system
includes the fusiform face area (FFA), the occipital face area (OFA),
and the posterior superior temporal sulcus. This core system is held to
process facial features, face identity, and facial expressions,
respectively. The extended system includes brain regions that encode
biographical and semantic information gleaned from the face. In the ERP,
various components in response to faces have been described. Best known
is the N170, an occipito-temporal negativity peaking around 170 ms after
stimulus onset, which is larger to faces than to most common objects and
has therefore been interpreted as reflecting structural encoding of
faces. Following the N170 and peaking between 200 to 300 ms after
stimulus onset a repetition effect can be observed, consisting in a
temporo-occipital negativity to repeated relative to non-repeated famous
or personally familiar faces. This N250r has been related to a transient
activation of facial representations for recognition (Schweinberger \&
Burton, 2003). Immediately thereafter, starting around 300 ms, a second
repetition effect for faces has been described (e.g., Schweinberger et
al., 1995). This has been related to the activation of multimodal
biographical and semantic person representation (for a review see
Schweinberger \& Neumann, 2016).

Tanaka et al. (2006) investigated the N250 component in the context of
long-term repetition. A specific face was designated as a target and had
to be distinguished from several non-target faces. The target face was
repeatedly shown multiple times but widely spaced and interspersed
between other (non-target) faces. The N250 amplitude to the target face
increased from the first to the second half of the experimental session,
indicative of the build-up of face representations. The findings of
Tanaka et al. (2006) were replicated by Sommer et al. (2021). Gosling
and Eimer (2011) reported that the N250 can distinguish already familiar
faces from unfamiliar faces. Since the N250 increases after repeated
presentations of a face also when different images are used across
presentations (Andrews et al., 2017; Kaufmann et al., 2009), the
evidence suggests that the N250 reflects facial representations at a
certain level of abstraction (Wiese et al., 2021). The neural generators
of the N250 and N250r have been reported to be in or near the fusiform
gyrus (Kaufmann et al., 2009; Schweinberger et al., 2002; Schweinberger
et al., 2007).

For assessing the neural correlates of face processing-related
abilities, knowledge about the psychometric structure of these abilities
at the behavioral level becomes crucial. Fairly comprehensive
psychometric work based on multivariate test batteries applied to large
participant samples and analyzed by confirmatory factor analyses has
shown that face processing-related abilities are robustly separable from
general intelligence and are multi-faceted (Hildebrandt et al., 2010;
Wilhelm et al., 2010). In difficult (accuracy) tasks, face perception
and face memory abilities can be dissociated, but in easy (speed) tasks,
there is only a single ability of face recognition speed. The separation
of face perception and face memory has been also applied to the
single-task Cambridge face memory test (Duchaine \& Nakayama, 2006)
versus the Cambridge face perception test (Duchaine et al., 2007).

Given the structure of face processing-related abilities derived from
psychometric work, research on the neural correlates of individual
differences in domain specific face processing abilities has recorded
ERPs in independent testing sessions for EEG recordings versus
psychometric measurements. Thus, in two independent large samples,
Herzmann et al. (2010) and Kaltwasser et al. (2014) showed that both the
accuracy of face perception and memory (in difficult tasks) and the
speed of face processing (in easy tasks) are negatively associated with
the latency of the N170 component and with the amplitude of the N250r.
In other words, the shorter the N170 latency of an individual and the
larger the priming effect in the N250r, the better are the mentioned
face processing abilities. The negative association between the N170
latency and the accuracy of face perception and memory was further
confirmed by Nowparast Rostami et al. (2017). These ERP studies were
concerned with explicit memory tasks where individuals attempted to
intentionally memorize and recognize faces during the EEG session as
well as in most of the tasks administered in the psychometric test
session.

Several studies with somewhat heterogeneous approaches and results were
designed to test associations between face processing performance and
fMRI data. Furl et al. (2011) compared a group of participants with
developmental prosopagnosia and normal controls (total \emph{n} = 35).
Performance differences in a test battery of face identification were
mirrored in the face selectivity of the fusiform gyrus during an
incidental task; there was no relationship with repetition suppression
(often likened to priming effects) in fMRI. Huang et al. (2014) used a
localizer task in fMRI with passive viewing in two large samples. In
both samples, the face selectivity of fMRI activation in both FFA and
OFA were positively correlated with the accuracy of recognizing
previously learned faces (controlling for the recognition performance
for objects). Li et al. (2017) showed that face selectivity in the right
and left FFA correlated with two different aspects of holistic face
perception. Ramot et al. (2019) investigated resting state connectivity
and related it to performance in the Cambridge Face Memory Test. They
found no relationship for the connectivity with the face network but
with connectivity between the face network and hubs outside of this
network.

Altogether, ERP and fMRI studies show brain-behavior relationships. For
ERPs, the most consistent findings concern the N170 and N250r.
Considering that both the N170 and the N250r have been suggested to be
generated in the fusiform gyrus, these findings are largely in line with
the reports that fusiform face selectivity in fMRI is predictive of
individual differences in face memory/recognition.

An important variable in face processing tasks is the typicality or
distinctiveness of a face relative to the average of the faces
experienced by a person. This average face is viewed as a prototype in a
multidimensional face space in which any known or perceived individual
face can be located (e.g., Leopold et al., 2001; Valentine, 1991;
Valentine et al., 2016). If a face is located close to the prototype it
is ``typical'' and if it is far away from the prototype it is referred
to as an untypical or distinctive face. Distinctive faces are usually
easier to perceive and recognize than typical faces (e.g., Bartlett et
al., 1984; Sommer et al., 1995; Valentine \& Endo, 1992).

\textbf{1.1. Aims of the present study}

Our overarching aim was to investigate how changes of the N250 component
in ERPs to faces across repeated exposures are associated with face
memory/recognition abilities on the individual differences level. More
specifically, we distinguished between an easy versus a difficult test
of face memory, following the distinction established by Wilhelm et al.
(2010). Due to limit testing time, we measured face memory/recognition
on a manifest rather than a latent level. ERPs, in particular the N250,
were derived in a paradigm first employed by Tanaka and colleagues
(2006). In this paradigm, a designated target face is presented,
randomly mixed with several initially unfamiliar nontarget faces and the
own face of the participant. As a novelty, the nontarget faces in the
present study consisted of two sets of high- and low-distinctive
unfamiliar faces, as determined by an independent sample of raters.

On the group level, we tested the N250 amplitudes for changes from the
first to the second half of the EEG session, expecting to replicate the
increase for target faces reported by Tanaka et al. (2006) and Sommer et
al. (2021). We also tested this change across session halves in the N250
to the non-target faces and differentiated between high- and
low-distinctive faces. On the individual differences level, we
correlated the ERP differences between the first and second half of the
session for the N250 to target faces, and to high- and low-distinctive
non-targets faces with the performance estimates from the two memory
tests. We expected more negative N250 amplitudes in the second half of
the experiment to go along with better face memory/recognition. It was
of special interest to study how this relationship depends on the
target-status and the distinctiveness of the N250-eliciting stimuli and
on the difficulty of the memory test.

\textbf{2. Methods}

The study consisted of a single session with two parts, a psychometric
part, employing a test from the test battery of Hildebrandt et al.
(2010) in a speed and an accuracy version and an EEG part modelled after
the study of Tanaka et al. (2006). We always conducted the psychometric
part first, followed by a break of 40 minutes before the EEG part
started.

\textbf{2.1. Participants}

Participants were 40 female students of the Zhejiang Normal University,
Jinhua, Zhejiang Province, China. Participants reported normal or
corrected-to-normal vision. Because one participant had to be excluded
from the data analysis due to noisy EEG, the final sample consisted of
39 women (\emph{M} \textsubscript{age} = 20.41 \selectlanguage{ngerman}± 1.30 years; range: 18
-- 23 years). The experimental protocol was approved by the ethical
committee of Zhejiang Normal University.

\textbf{2.2. Psychometrics}

\textbf{2.2.1. Materials}

The face stimuli for the psychometric test were taken from Cepulic et
al. (2018). They were grayscale frontal images of Caucasian face models
taken from Hildebrandt et al. (2010) and Asian models taken from Gao et
al. (2008) (cf. Fig.1). All images were fitted within an ellipse of 200
× 300 pixels and showed no external features or non-face cues, such as
clothing, glasses, beards, hair, or piercings.

\textbf{2.2.2. Procedure}

The psychometric test procedure (Fig. 1), lasting about 30 min, was
adapted from Cepulic et al. (2018) and conducted individually before the
EEG experiment. After the overall test instruction, six practice trials
with correctness feedback were provided. The tasks were designed for low
versus high difficulty levels; either 25 or 40 s were available to
memorize a matrix of either four or 15 target faces, respectively. All
target faces in a given matrix were either all Asian or all Caucasian.
Each of these four conditions was repeated once with different faces.
The faces in these matrices served as target faces in the recognition
phase of the task, which was conducted after an intermediate mental
speed task aiming to hamper contributions of working memory to face
recognition performance.

\textbf{Figure 1} .

Example of an easy condition in the face memory task with Asian faces.
After memorizing a matrix with four previously unfamiliar faces and an
intervening mental speed task, the memorized faces were presented one by
one, in random order, and intermixed with novel distractor faces,
requiring an old/new decision by pressing one of two buttons.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

In the mental speed task, taking 1-1.5 min, one of four different
2-choice decisions to visual stimuli presented at the center of the
screen were required by means of button presses about whether or not (1)
a letter out of eight possible letters (A Q Z T S P R W ) was an ``A'',
(2) a one-digit number out of eight was ``3'', (3) a line-drawn face was
smiling (out of smiling, neutral and sad expression), and (4) whether
two symbols, letters or numbers shown side by side were identical.

In the recognition phase of the face memory task, single faces were
presented that were to be classified as quickly and accurately as
possible by choice-response button presses (without feedback) as to
whether or not they had been present in the study matrix. In easy tasks,
each of the four target faces was presented four times, randomly mixed
with 16 unknown same-race distractor faces (32 trials per run). In
difficult tasks, 15 target faces were mixed with 15 new same-race
distractor faces (30 trials per run). The task order was fixed for all
participants, as follows: (1) Asian, easy, Run 1, (2) Asian, easy, Run
2, (3) Asian, hard, Run 1, (4) Asian, hard, Run 2, (5) Caucasian, easy,
Run 1, (6) Caucasian, easy, Run 2, (7) Caucasian, hard, Run 1, and (8)
Caucasian, hard, Run 2. None of the faces shown in the matrices of the
learning phases was used in any of the other matrices and none of the
distractors in the recognition phase was used twice.

\textbf{2.2.3. Scoring and Data Treatment}

The data treatment followed the procedure of Cepulic et al. (2018) and
was conducted via a Python script, using Numpy and Pandas packages. For
obtaining accuracy data, we excluded fast guesses with reaction times
(RT) \textless{} 200 ms and calculated the average accuracy for each
block. For calculating RT data, we excluded incorrect trials (15.04\% of
trials in the whole dataset). Then, the data was winsorized (i.e., Tukey
correction; Tukey, 1977) at the within-person level, that is, for each
block, all RTs longer than the third quartile plus 1.5 times the
interquartile range were set to this limit value (7.9\% of trials in the
whole dataset). Based on the winsorized data, average RTs for each
condition in each participant were calculated. We also inspected the
averaged data across all conditions for interindividual outliers based
on the same criterion; no participants fell outside of this limit.
Finally, the mean RTs per condition and participant were transformed by
multiplying the reciprocal of the average RT (ms) by 1000 (1000/RT) to
normalize the distribution. The transformed RT data reflect the number
of correct responses per second, that is, larger values indicate greater
speed.

\textbf{2.3. EEG Session}

\textbf{2.3.1. Materials}

The stimuli for the EEG experiment were taken with permission from the
face database of Xi'an Jiaotong University
(http://www.aiar.xjtu.edu.cn/info/1015/1639.htm). The faces were
selected based on an initial distinctiveness rating. A total of 35
female face pictures were rated by 21 female students of Zhejiang Normal
University, different from the participants of the EEG experiment.~The
instructions for the distinctiveness ratings were: ``Imagine that you
are in a dining hall, and this person is in the crowd. How hard is it
for you to notice the person?'' Ratings were on a 5-point scale from (1)
very easy to (5) very hard. In addition, the faces were rated on
attractiveness (``In your opinion, how attractive is this face?'') on a
5-point scale from (1) very attractive to (5) very unattractive, and
emotionality of facial expression (``In your opinion, what expression
does this face show?''), also on a 5-point scale from (1) very negative
to (5) very positive.

On the basis of these ratings, we selected a low-distinctive face as the
target face; this was done to increase the difficulty of target face
recognition. According to these ratings the target face was hard to
notice, \emph{M} = 3.90 (\emph{SD} = 0.97), attractiveness was
average,\emph{M} = 2.33 (\emph{SD} = 0.89), and emotionality was
slightly positive, \emph{M} = 3.14 (\emph{SD} = 0.47).

From the other faces, we selected a set of 6 easy-to-notice
(high-distinctive) faces and 4 hard-to-notice (low-distinctive, typical)
faces. High- and low-distinctive faces differed in distinctiveness as
intended, \emph{M} = 2.80 (\emph{SD} = 0.98) vs. \emph{M} = 3.99
(\emph{SD} = 0.85), facial expressions were rated as more positive in
more distinctive than in more typical faces, \emph{M} = 3.48 (\emph{SD}=
0.69) vs. \emph{M} = 2.63 (\emph{SD} = 0.69), and attractiveness was
rated lower in more distinctive than in more typical faces, \emph{M} =
2.90 (\emph{SD} = 0.95) vs. \emph{M} = 2.13 (\emph{SD} = 0.87).
According to a permutation test, the differences in all three dimensions
were significant, \emph{p} = .005, .019, and .007, respectively. The
confound of distinctiveness with attractiveness was to be expected
because faces close to the average of faces are usually considered more
attractive and it is also not surprising that more attractive faces are
perceived as showing a more positive expression.

\textbf{2.3.2. Apparatus and EEG Recordings}

The EEG experiment was conducted in an electrically-shielded, quiet, and
dimly-lit room. Participants were seated about 60 cm away from the
computer monitor. The experimental procedure was implemented in E-Prime
3.0 software (Psychology Software Tools, USA). Using conductive gel, the
EEG signal was recorded from 64 active electrodes, placed according to
the 10-10 system in a BrainProducts actiCap; impedances were kept below
10 k\selectlanguage{greek}Ω. \selectlanguage{english}The electrodes were connected to a BrainProducts actiChamp
amplifier; sampling rate was 1 kHz; no online filtering or active noise
canceling was applied. Online reference was the Cz electrode, and
bipolar recording of AFz versus the outer canthus of the left eye served
as EOG channel.

\textbf{2.3.3. EEG preprocessing}

The EEG preprocessing was based on the procedures described in Kotowski
et al. (2023). The code was written in Python 3.9.7 using the MNE 0.24.0
package (Gramfort et al., 2013) and will be publicly available after
publication. In short, any manually identified corrupted channel
(various individual channels in 17 participants) were interpolated using
spherical splines (Perrin et al., 1989). The EEG channels were
recalculated to common-average reference and band-pass filtered between
2 and 40 Hz using a third-order zero-phase forward-backward digital
Butterworth filter (Gustafsson, 1996). ERP epochs between -100 to 500 ms
relative to stimulus onset in correct trials were extracted. Ocular
artifacts were corrected by removing independent components (ICs)
correlated with eye blinks in EOG signal by more than 2~\emph{SD} s
higher than others. We manually removed some additional artifactual ICs
for 16 participants (on average 1.4 ICs per participant, as reported in
the code provided above) that were not detected by the algorithm due to
weak EOG signals or due to artifacts of non-ocular origin. Finally, the
ERPs were corrected with respect to the 100 ms pre-stimulus baseline.

\textbf{2.3.4. ERP Amplitude Analysis}

The ERP component of central interest was the N250. In order to quantify
this component, ERP epochs were averaged separately for the first and
second half of the EEG session and per condition. Conditions were the
target face and the non-target faces divided into high- versus
low-distinctive ones. Since the own face ERPs were not relevant for the
present questions, we will not report them here. The N250 component was
measured as in Sommer et al. (2021) but limited to the subset of
electrodes available, that is, to the averaged channels TP9, P7, PO7,
O1, TP10, P8, PO8, and O2, in which the N250 amplitude was measured
between 230 and 320 ms.

\textbf{2.4. Permutation test for correlation coefficients}

The classic approach to multiple hypothesis testing, for example, the
Bonferroni correction, is very conservative in some cases, increasing
the risk of Type 2 errors. In order to adequately test the six
correlations for which we formulated hypotheses, we applied a
simultaneous permutation test as described in the following. Let us
denote \emph{X\textsubscript{1}} (N250 targets)\emph{,
X\textsubscript{2}} (N250 low-distinct non-targets)\emph{,
X\textsubscript{3}} (N250 high-distinct non-targets)\emph{,
X\textsubscript{4}} (recognition accuracy)\emph{,
X\textsubscript{5}}(recognition speed). We were interested in six
correlation coefficients\(\rho_{i}\) \emph{i = 1,\ldots{},6}
(between each of the three variables \emph{X} \textsubscript{1},
\emph{X} \textsubscript{2},\emph{X} \textsubscript{3} and each of the
two variables:\emph{X} \textsubscript{4}, \emph{X} \textsubscript{5}).
The problem can be addressed by verifying the hypothesis:

\(H_{0}:\ \operatorname{}{|\rho_{i}}|=\ 0\)

\(H_{1}:\ \operatorname{|}\rho_{i}|>\ 0\)

Hence, we took the maximum from the (absolute) values of the six
mentioned correlation coefficients as a test statistic:

\(T=\operatorname{}{|r}_{i}|\) \emph{i = 1,\ldots{}, 6}

where r\textsubscript{i} is a sample Pearson correlation coefficient. To
perform the permutation test we calculated the value
T\textsubscript{0}of statistics for our tested sample from the
experiment. Assuming that the null hypothesis is true, we performed
\emph{N} = 10,000 random permutations (shuffles) of the data in each of
the five variables, calculated the value of the test statistic for each
permutation and created the empirical distribution of
\emph{T\textsubscript{j}} ,\emph{j =1, 2,\ldots{},N} .

If the calculated ASL (Achieved Significance Level) (Good, 1994):

\begin{equation}
\text{ASL}\approx\frac{\text{card}\{T_{i}\geq T_{0})}{N}\nonumber \\
 \end{equation}

is less than the significance level \emph{p} = 0.05, the null hypothesis
is rejected. Please note that apart from being less conservative than
Bonferroni correction, the permutation test of statistical significance
has the advantage of not assuming normal distribution of the data.

\textbf{3. Results}

\textbf{3.1. Psychometric Test}

For the 39 participants with ERP data, we merged the psychometric
results from all blocks and across Asian and Caucasian faces. In the
difficult task, accuracy was \emph{M} = 74.23\% correct (\emph{SD} =
7.55; range 60.00 -- 92.50\%) and mean inverted RTs (1/s) were \emph{M}=
0.90 (\emph{SD} = 0.18; range: 0.56 -- 1.18). In the easy task, accuracy
was \emph{M} = 95.17\% correct (\emph{SD} = 4.49; range 80 -- 100\%) and
mean inverted RTs (1/s) were \emph{M} = 1.14 (\emph{SD} = 0.17; range:
0.68 -- 1.46). Paired \emph{t} -tests revealed that in the easy task
accuracy was significantly higher than in the hard task
(\(t_{\left(61.89\right)}=14.88,p<0.001\)); similarly, inverted RTs showed higher speed in the
easy task (\(t_{\left(38\right)}=11.17,p<0.001\)).These results demonstrate that the two
tasks differ in accuracy and response speed as intended, yielding easy
versus difficult versions indeed.

In order to assess the reliability of the psychometric tasks, we
calculated the correlations of the performance between Run 1 and Run 2
(after averaging across Asian and Caucasian faces) for each test
versions. For the easy task, inverted RTs in Run 1 and Run 2 were
correlated \emph{r} = 0.820, corresponding to a split-half reliability
of 0.901 after Spearman-Brown correction. For the difficult tasks,
accuracy in Run 1 and Run 2 was correlated \emph{r} = 0.541,
corresponding to a split-half reliability of 0.702 after Spearman-Brown
correction. These results are similar as those of Cepulic et al. (2018;
online supplement), where the inverted RTs in the two runs in easy
Caucasian and Asian faces correlated \emph{r} = 0.71 and \emph{r} =
0.56, respectively, and for the accuracy in the hard versions the
correlations were \emph{r} = 0.59 and \emph{r} = 0.58, respectively.
Moreover, the present results indicate excellent reliability for the
easy task and sufficient reliability for psychometric purposes for the
difficult task.

\textbf{Table 1}

Sample average results of the psychometric tasks\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Condition & \emph{M} accuracy & \emph{M} inverted RTs\tabularnewline
\midrule
\endhead
Asian Easy & 95.39\% \selectlanguage{ngerman}± 5.38\% & 1.17 \selectlanguage{ngerman}± 0.18\tabularnewline
Caucasian Easy & 94.95\% \selectlanguage{ngerman}± 6.07\% & 1.10 \selectlanguage{ngerman}± 0.19\tabularnewline
Asian Hard & 73.16\% \selectlanguage{ngerman}± 7.64\% & 0.92 \selectlanguage{ngerman}± 0.21\tabularnewline
Caucasian Hard Easy (Average) Hard (Average) & 75.30\% \selectlanguage{ngerman}± 9.08\% 95.17\%
± 4.49\% 74.23\% ± 7.55\% & 0.88 \selectlanguage{ngerman}± 0.17 1.14 ± 0.17 0.90 ±
0.18\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\textbf{3.2. ERP Test}

Accuracy of recognizing the target face was very good with \emph{M} =
95.69\%~~correct responses (\emph{SD} = 3.72; range 86 -- 100).

\textbf{3.2.1. Group level analyses}

We first aimed to replicate that the mean N250 amplitude to target
trials increases from the first to the second part of the experiment as
reported by Tanaka et al. (2006) and Sommer et al. (2021). This was
indeed the case: N250 amplitude became significantly more negative from
the first half of the session, \emph{M} = 0.81, \emph{SD} = 1.16 \selectlanguage{greek}µ\selectlanguage{english}V, to
the second half, \emph{M} = 0.38, \emph{SD} = 0.98 \selectlanguage{greek}µ\selectlanguage{english}V, as confirmed by a
one-sided Wilcoxon signed-rank test for H\textsubscript{1} alternative
(\emph{p} = 0.0007). ERP waveforms were obtained by averaging over all
electrodes within each ROI, per condition and each half of the
experiment (see Fig. 2).

For the N250 components to the non-target faces, we asked whether these
responses changed across the experiment and how this depends on face
distinctiveness. A repeated measures ANOVA indicated a significant
amplitude change towards more negative (less positive) values from the
first to the second half of the experiment, \emph{M} = 1.03 \selectlanguage{greek}µ\selectlanguage{english}V,\emph{SD}
= 1.03) vs. \emph{M} = 0.77 \selectlanguage{greek}µ\selectlanguage{english}V, \emph{SD} = 0.71,\emph{F} (1,38) = 7.52,
\emph{p} \textless{} .05, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = .17 . In contrast to
the P200, there was no main effect of distinctiveness in the N250,
\emph{F} (1,38) = 1.70, \emph{p}\textgreater{} .05, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2}
= .043, nor did it interact with experiment half, \emph{F} (1,38) =
.774, \emph{p} \textgreater{} .05, \selectlanguage{greek}η\selectlanguage{english}\textsuperscript{2} = .020.

\textbf{Figure 2}

ERPs in the N250 ROI separated for hemisphere and the first and second
half of the experiment. Superimposed are the responses to the target
face, the high- and low-distinctive non-target faces, and the
participant's own face.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3/Fig3}
\caption{{This is a caption
{\label{894585}}%
}}
\end{center}
\end{figure}

To estimate the reliability of the N250, we correlated the amplitudes of
the first and second half of the experiment. For the target
face-elicited N250 this correlation was \emph{r} = 0.748, yielding good
reliability after Spearman-Brown correction (0.855). For
high-distinctive non-target faces the correlation was \emph{r} = 0.876
and for low-distinctive non-target faces it was \emph{r} = 0.864,
yielding excellent Spearman-Brown corrected reliabilities of 0.934 and
0.927, respectively.

\textbf{3.2.2. Brain-behavior relationships}

In the next step, we correlated N250 amplitude differences between the
first and second part of the experiment with the performance in the easy
versus difficult face memory tests. As common in psychometrics, we used
accuracy as a memory measure in the difficult task and the inverted RTs
as a performance indicator in the easy tasks.

For the N250 amplitude differences to target faces the correlations were
small for both the easy versus the difficult memory tasks, \emph{r} =
-0.035 and \emph{r} = -0.070, respectively. For the N250 in response to
non-target faces, we calculated amplitude differences between
experimental halves separately for high- and low-distinctive faces. The
correlations with recognition performance of high-distinctive faces with
easy versus difficult task performance were \emph{r} = -0.201
and\emph{r} = -0.181, respectively. The corresponding correlations for
low-distinctive faces were \emph{r} = -0.355 and \emph{r} = -0.163,
respectively.

According to the permutation test described in Section 2.4, we can
reject the null hypothesis with a probability of 5\% that all six
considered pairs of variables are independent. Hence, for at least one
pair of variables (i.e., the largest N250 low-distinct non-targets X2
vs. memory speed X5) there is a statistically significant linear
relationship. The same permutation test was used to test the H1 that~the
second largest correlation coefficient (for the modulus) is greater than
zero. The corresponding null hypothesis could not be rejected even at a
level of 0.1. Thus, there is only one significant correlation, which is
between the change in N250 amplitude to low-distinctive non-target faces
and the psychometric face recognition speed (r = -0.355). The scatter
plot of corresponding data of this distribution is shown in Figure 3. Of
note, there are at least four data points where performance in the face
recognition test was rather slow. Although, upon closer inspection these
participants did not show any unusual behavior, we excluded these four
participants to assess the stability of the association. As a
consequence, this particular correlation dropped to \emph{r} = -.25 and
the permutation test of all 6 correlations of interest failed
significance.

\textbf{Figure 3}

Association of the change in N250 amplitude to low-distinctive
non-target faces from the first to the second half of the session and
the psychometric face recognition speed.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{4. Discussion}

The main aim of the present study was to investigate the relationship
between the N250 component and face recognition performance on the
individual differences level by applying the paradigm of Tanaka et al.
(2006), where participants had to identify a single target face among
several non-target faces. We tested this relationship separately for
target faces and non-target faces, while distinguishing -- within
non-target faces -- between high and low distinctive faces. For target
faces, we replicated the increase of N250 amplitude across the first and
second half of the experiment, but did not find a relationship to
recognition performance. The N250 to non-target faces showed -- in
contrast to previous reports -- an increase across experiment parts.
Importantly, the increase of the N250 to low-distinctive non-target
faces was correlated with memory performance in the easy task version.

\textbf{4.1. Psychometric measurements}

We should first point out that the psychometric task applied to Chinese
participants successfully yielded an easy and a difficult version,
markedly differing in the percentage of correct responses (95 vs. 74\%
correct). Therefore, the test, albeit a single task in each version
fulfills the criterion of a speed test, where the variance is mainly in
RTs, and a difficult version, where individuals differ in the proportion
of correct responses. The relative independence of the speed and
accuracy tasks is reflected in their low correlation (\emph{r} = 0.36).
Importantly, both task versions had shown sufficient to excellent
reliability.

\textbf{4.2. ERPs}

Extending reports of Tanaka et al. (2006) and Sommer et al. (2021), the
N250 to target faces was larger in the second than in the first part of
the experiment. The N250 has been related to facial representations at a
certain level of abstraction (Wiese et al., 2021) and the increase
across the experiment likely reflects the increasing build-up of these
representations. Since our stimulus materials consisted of Asian faces
and our participants were Chinese students, the results indicate the
culture independence of this increasing face representations.

Interestingly, also the non-target elicited N250 increased from the
first to the second half of the experiment. The non-target faces did not
have to be explicitly identified at an individual level but only had to
be rejected as not being the target face. These results go beyond
findings of both the studies of Tanaka et al. (2006) and Sommer et al.
(2021), which did not find such effects in the N250 amplitude to
non-target faces. We presume that a crucial factor in bringing out the
N250 effects to non-targets is the nature of these faces. In contrast to
the previous studies, we had controlled the stimulus materials for
distinctiveness and can therefore claim that the target face was of
low-distinctiveness. Low-distinctive target faces are likely harder to
recognize than high-distinctive target faces (e.g., Light et al., 1979).
Therefore, it is possible that in order to detect the target face, our
participants were forced to scrutinize the non-target faces more than
the participants of the previous studies and therefore encoded them
incidentally. Attention to a stimulus is a crucial factor in memory
encoding.

\textbf{4.3. Brain-Behavior Relationships}

Contrary to our expectations, at the inter-individual level the amount
of change of the target-elicited N250 from the first to the second
experiment half did not correlate with memory performance. Since these
correlations were essentially zero, we suspect that this null result is
not due to a lack of power or a low signal-to-noise ratio, as indicated
by the high split-half reliabilities of the target-elicited N250 and the
good to excellent reliability of the face memory test results. Rather it
seems that the robust increase in N250 amplitude across the experiment,
although possibly reflecting increasing familiarity with the designated
target face, is not related to individual differences in face
recognition. One conceivable reason is the ease of recognizing the
target face, leading to a ceiling effect.

The relationship of the across-session changes of the N250 to non-target
faces and memory performance was significant only between N250 to
low-distinctive faces and performance in the easy but not in the
difficult memory task. Wilhelm et al. (2010) and Hildebrandt et al.
(2010) have demonstrated in large samples that face cognition in easy
and difficult tasks must be distinguished, similar to the need for such
a distinction when measuring general cognitive abilities. Therefore, it
is an interesting finding that also the brain-behavior relationship
seems to be specific for the kind of memory task -- the association with
the easy task was double in effect size than with the hard task. The
simplest explanation for the unverifiable association between the
across-session change of the N250 and performance in the difficult
memory test is the lower reliability of the accuracy than of the
recognition speed test. However, since our sample size was only modest
and memory task performance was measured on the manifest level, it
remains important to replicate these findings with a larger sample and
to investigate a set of indicators for testing these associations at the
latent level.

Interestingly, the brain-behavior relations of the easy task performance
were found for across-session N250-changes in ERPs to low-distinctive
rather than to high-distinctive faces. Intuitively, one might expect a
stronger relationship between the neural signal of building-up a memory
trace of high-distinctive faces and face recognition performance than
for the harder to process low-distinctive faces. However, the stronger
relationship found for across-session increases in low-distinctive
face-elicited N250 may become plausible when considering that the ERP
task required the discrimination of the low-distinctive target face from
the likewise low-distinctive non-target faces. Individuals with better
face recognition ability may have solved this task by a faster build-up
of a representation of these non-target faces, as manifested in a larger
N250 amplitude increase across the session. In contrast, memory
representations for high-distinctive non-target faces may not have been
necessary to make the distinction from the non-distinctive target face.

To the best of our knowledge this is the first study that shows a
brain-behavior relationship between the N250 to faces and face memory.
Hence, the present results extend previous findings from the N250r
(Herzmann et al., 2010; Kaltwasser et al. 2014) to a condition where the
N250 amplitude evolves across multiple presentations. Since the
difference of the N250 across experimental halves can also be considered
a kind of repetition effect, it would be of interest, whether the
difference between experimental halves in the N250 is related to the
N250r. This would be of particular interest, considering the close
conceptual relations of these components.

\textbf{4.4. Limitations}

In the present study, following the original design of Tanaka et al.
(2006) we have used the same image for all individuals serving as
non-target faces and also the target face image was always the same.
Hence, the present study may be criticized as tapping into (face) image
recognition rather than face recognition, which is characterized by high
variability of the images associated by the same person (Burton et al.
2016). Although a recent publication by Olderbak et al. (2022) has
demonstrated that the psychometric structure of face cognition does not
depend on the usage of the same or different images, one of the future
research tasks should be a replication with different images of the same
individuals. In this research, larger samples, more elaborated test
batteries and explicit tasks for ERP recordings might be used to follow
up the present findings.

Further limitations are the small number of participants, which make the
association observed susceptible to outliers (see Fig. 3), and the
restriction to the manifest levels of single tasks. Future studies
should extend the present approach to a larger sample and to the latent
level by including multiple indicators on the brain and behavioral
sides.

\textbf{4.5. Conclusions}

In conclusion, the present study showed that the within-experiment
increase of the N250 component of the ERP to incidentally encoded
low-distinct non-target faces is related to the speed of face processing
at an individual differences level. This finding -- although in need of
further elaboration -- extends the present knowledge about
brain-behavior relationships for faces. Specifically, it indicates that
individuals with better abilities in the speed of face processing in
memory tasks have an advantage in familiarization (memory trace
formation) for low-distinctive, hard to recognize faces.

\textbf{Author Contributions}

\textbf{Werner Sommer} : Conceptualization, Methodology,
Writing.\textbf{Krzysztof Kotowski} : Data Analysis, Visualization,
Software.\textbf{Yang Shi} : Experimental set up, Data Collection, Data
Analysis.\textbf{Adam Switonski} : Data Analysis. \textbf{Andrea
Hildebrandt} : Methodology, Editing. \textbf{Katarzyna Stapor} :
Methodology, Data Analysis, Writing

\textbf{Acknowledgements}

We thank Xiaohua Cao for generously providing his lab for conducting
this research at a time when Polish and German labs were unavailable to
the Covid 19 pandemic.

\textbf{References}

Andrews, S., Burton, A. M., Schweinberger, S. R., \& Wiese, H. (2017).
Event-related potentials reveal the development of stable face
representations from natural variability. \emph{Quarterly Journal of
Experimental Psychology, 70} (8), 1620-1632.
https://doi.org/10.1080/17470218.2016.1195851

Bartlett, J. C., Hurry, S., \& Thorley, W. (1984). Typicality and
familiarity of faces. \emph{Memory \& Cognition, 12} , 219-228.

Behrmann, M., \& Avidan, G. (2005). Congenital prosopagnosia: face-blind
from birth. \emph{Trends in Cognitive Sciences, 9} (4), 180-187.
https://doi.org/10.1016/j.tics.2005.02.011

Burton, A. M., Kramer, R. S., Ritchie, K. L., \& Jenkins, R. (2016).
Identity from variation: Representations of faces derived from multiple
instances. \emph{Cognitive Science, 40} (1), 202-223.
https://doi.org/10.1111/cogs.12231

Cepulic, D. B., Wilhelm, O., Sommer, W., \& Hildebrandt, A. (2018). All
categories are equal, but some categories are more equal than others:
The psychometric structure of object and face cognition. \emph{Journal
of Experimental Psychology-Learning Memory and Cognition, 44} (8),
1254-1268. https://doi.org/10.1037/xlm0000511

Duchaine, B., \& Nakayama, K. (2006). The Cambridge Face Memory Test:
Results for neurologically intact individuals and an investigation of
its validity using inverted face stimuli and prosopagnosic
participants.\emph{Neuropsychologia, 44} (4), 576-585.
https://doi.org/10.1016/j.neuropsychologia.2005.07.001

Duchaine, B., Germine, L., \& Nakayama, K. (2007). Family resemblance:
Ten family members with prosopagnosia and within-class object
agnosia.\emph{Cognitive Neuropsychology, 24} (4), 419-430.
https://doi.org/10.1080/0264329070138049

Furl, N., Garrido, L., Dolan, R. J., Driver, J., \& Duchaine, B. (2011).
Fusiform Gyrus face selectivity relates to individual differences in
facial recognition ability. \emph{Journal of Cognitive Neuroscience, 23}
(7), 1723-1740. https://doi.org/10.1162/jocn.2010.21545

Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., \& Zhao, D.
(2008). The CAS-PEAL large-scale Chinese face database and baseline
evaluations. \emph{IEEE Transactions on Systems, Man, and
Cybernetics-Part A: Systems and Humans, 38} (1), 149-161.
https://doi.org/10.1109/tsmca.2007.909557

Good P. I. (1994). \emph{Permutation tests: A practical guide for
testing hypotheses} . Springer, New York

Gosling, A., \& Eimer, M. (2011). An event-related brain potential study
of explicit face recognition. \emph{Neuropsychologia, 49} (9),
2736-2745. https://doi.org/10.1016/j.neuropsychologia.2011.05.025

Gramfort, A., Luessi, M., Larson, E., Engemann, D., Strohmeier, D.,
Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., \&
H\selectlanguage{ngerman}ämäläinen, M. (2013). MEG and EEG data analysis with
MNE-Python.\emph{Frontiers in Neuroscience 7} , 267.
https://doi.org/10.3389/fnins.2013.00267

Gustafsson, F. (1996). Determining the initial states in
forward-backward filtering. \emph{IEEE Transactions on Signal Processing
44} , 988--992. https://doi.org/10.1109/78.492552

Haxby, J. V., Hoffman, E. A., \& Gobbini, M. I. (2000). The distributed
human neural system for face perception. Tren\emph{ds in Cognitive
Sciences, 4} (6), 223-233. https://doi.org/10.1016/S1364-6613(00)01482-0

Herzmann, G., Kunina, O., Sommer, W., \& Wilhelm, O. (2010). Individual
Differences in Face Cognition: Brain-Behavior
Relationships.\emph{Journal of Cognitive Neuroscience, 22} (3), 571-589.
https://doi.org/10.1162/jocn.2009.21249

Hildebrandt, A., Sommer, W., Herzmann, G., \& Wilhelm, O. (2010).
Structural invariance and age-related performance differences in face
cognition. \emph{Psychology and Aging, 25(4)} , 794-810.
https://doi.org/10.1037/a0019774

Huang, L., Song, Y., Li, J., Zhen, Z., Yang, Z., \& Liu, J. (2014).
Individual differences in cortical face selectivity predict behavioral
performance in face recognition. \emph{Frontiers in Human Neuroscience,
8,} 483. https://doi.org/10.3389/fnhum.2014.00483

Kaltwasser, L., Hildebrandt, A., Recio, G., Wilhelm, O., \& Sommer, W.
(2014). Neurocognitive mechanisms of individual differences in face
cognition: A replication and extension. \emph{Cognitive, Affective, \&
Behavioral Neuroscience, 14} , 861-878.
https://doi.org/10.3758/s13415-013-0234-y

Kaufmann, J. M., Schweinberger, S. R., \& Burton, A. M. (2009). N250 ERP
Correlates of the Acquisition of Face Representations across Different
Images. \emph{Journal of Cognitive Neuroscience, 21(4)} , 625-641.
https://doi.org/10.1162/jocn.2009.21080s

Kotowski, K., Ochab, J., Stapor, K., \& Sommer, W. (2023). The
importance of ocular artifact removal in single-trial ERP analysis: The
case of the N250 in face learning. \emph{Biomedical Signal Processing
and Control, 79} , 104115.
https://doi.org/https://doi.org/10.1016/j.bspc.2022.104115

Leopold, D. A., O'Toole, A. J., Vetter, T., \& Blanz, V. (2001).
Prototype-referenced shape encoding revealed by high-level after
effects. \emph{Nature Neuroscience, 4(1)} , 89-94.
https://doi.org/10.1038/82947

Li, J., Huang, L., Song, Y., \& Liu, J. (2017). Dissociated neural basis
of two behavioral hallmarks of holistic face processing: The whole-part
effect and composite-face effect. \emph{Neuropsychologia, 102} , 52-60.
https://doi.org/10.1016/j.neuropsychologia.2017.05.026

Light, L. L., Kayra-Stuart, F., \& Hollander, S. (1979). Recognition
memory for typical and unusual faces. \emph{Journal of Experimental
Psychology: Human Learning and Memory, 5} , 212-228.

Nowparast Rostami, H., Sommer, W., Zhou, C., Wilhelm, O., \&
Hildebrandt, A. (2017). Structural encoding processes contribute to
individual differences in face and object cognition: Inferences from
psychometric test performance and event-related brain
potentials.\emph{Cortex, 95,} 192-210.
https://doi.org/http://dx.doi.org/10.1016/j.cortex.2017.08.017

Olderbak, S., Pollak, K., Sanchez, J. F., \& Hildebrandt, A. (2022).
Assessing individual differences in face recognition: No impact of task
design features on the structure of this ability. \emph{Psychological
Assessment, 34} , 1172-1179. https://doi.org/10.1037/pas0001173

Perrin, F., Pernier, J., Bertrand, O., Echallier, J.F. (1989). Spherical
splines for scalp potential and current density
mapping.\emph{Electroencephalography and Clinical Neurophysiology 72} ,
184--187. https://doi.org/10.1016/0013-4694(89)90180-6

Ramot, M., Walsh, C., \& Martin, A. (2019). Multifaceted integration:
Memory for faces is subserved by widespread connections between visual,
memory, auditory, and social networks. \emph{Journal of Neuroscience,
39(25)} , 4976-4985. https://doi.org/10.1523/JNEUROSCI.0217-19.2019

Russell, R., Duchaine, B., \& Nakayama, K. (2009). Super-recognizers:
People with extraordinary face recognition ability. \emph{Psychonomic
Bulletin \& Review, 16} (2), 252-257.
https://doi.org/10.3758/PBR.16.2.252

Schweinberger, S. R., \& Burton, A. M. (2003). Covert recognition and
the neural system for face processing. \emph{Cortex, 39} , 9-30.
https://doi.org/10.1016/S0010-9452(08)70071-6

Schweinberger, S. R., Pfütze, E. M., \& Sommer, W. (1995). Repetition
priming and associative priming of face recognition: Evidence from
event-related potentials. Jour\emph{nal of Experimental
Psychology-Learning Memory and Cognition, 21,} 722-736.
https://doi.org/10.1037//0278-7393.21.3.722

Schweinberger, S. R., Kaufmann, J. M., Moratti, S., Keil, A., \& Burton,
A. M. (2007). Brain responses to repetitions of human and animal faces,
inverted faces, and objects -- An MEG study. \emph{Brain Research, 1184}
, 226-233.

Schweinberger, S. R., Pickering, E. C., Jentzsch, I., Burton, A. M., \&
Kaufmann, J. M. (2002). Event-related brain potential evidence for a
response of inferior temporal cortex to familiar face
repetitions.\emph{Cognitive Brain Research, 14(3)} ,
398-409.\emph{https://doi.org/10.1016/S0926-6410(02)00142-8}

Sommer, W., Heinz, A., Leuthold, H., \& Matt, J. (1995). Metamemory,
distinctiveness, and event-related potentials in recognition memory for
faces. \emph{Memory \& Cognition, 23,} 1-11.
https://doi.org/10.3758/BF03210552

Sommer, W., Stapor, K., Ko\selectlanguage{polish}ń\selectlanguage{english}czak, G., Kotowski, K., Fabian, P., Ochab,
J., Bere\selectlanguage{polish}ś, \selectlanguage{english}A., \selectlanguage{polish}Ś\selectlanguage{english}lusarczyk, G. (2021). The N250 event-related potential
as an index of face familiarity: a replication study. \emph{Royal
Society Open Science, 8} , 202356. https://doi.org/10.1098/rsos.202356

Tanaka, J.W., Curran, T., Porterfield, A.L., Collins, D. (2006).
Activation of preexisting and acquired face representations: The N250
event-related potential as an index of face familiarity. \emph{Journal
of Cognitive Neuroscience 18} , 1488--1497.
https://doi.org/10.1162/jocn.2006.18.9.1488

Valentine, T. (1991). A unified account of the effects of
distinctiveness, inversion, and race in face recognition.\emph{Quarterly
Journal of Experimental Psychology, 43A} , 161-204.
https://doi.org/10.1080/14640749108400966

Valentine, T., \& Endo, M. (1992). Towards an exemplar model of face
processing: The effects of race and distinctiveness. \emph{Quarterly
Journal of Experimental Psychology, 44A} , 671-703.

Valentine, T., Lewis, M. B., \& Hills, P. J. (2016). Face-space: A
unifying concept in face recognition research. \emph{Quarterly Journal
of Experimental Psychology, 69(10} ), 1996-2019.
https://doi.org/10.1080/17470218.2014.990392

Wiese, H., Anderson, D., Beierholm, U., T\selectlanguage{ngerman}üttenberg, S.C., Young, A.W.,
Burton, A.M. (2021). Detecting a viewer's familiarity with a face:
Evidence from event-related brain potentials and classifier
analyses.\emph{Psychophysiology, 59} , e13950.
https://doi.org/10.1111/psyp.13950

Wilhelm, O., Herzmann, G., Kunina, O., Danthiir, V., Schacht, A., \&
Sommer, W. (2010). Individual differences in perceiving and recognizing
faces: One element of social cognition. \emph{Journal of Personality and
Social Psychology, 99(3)} , 530-548. https://doi.org/10.1037/a0019972

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