Context Matters: Human Faces Hinder Face Pareidolia

Laura Bourgaux, Diane Rekow, Arnaud Leleu, Adélaïde de Heering

► To cite this version:

Laura Bourgaux, Diane Rekow, Arnaud Leleu, Adélaïde de Heering. Context Matters: Human Faces Hinder Face Pareidolia. Journal of Cognitive Neuroscience, 2026, 38 (2), pp.213-225. 10.1162/JOCN.a.94 hal-05501189

HAL Id: hal-05501189

https://hal.inrae.fr/hal-05501189v1

Submitted on 9 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Context Matters: Human Faces Hinder Face Pareidolia

Laura Bourgaux<fnref n="1" />,², Diane Rekow², Arnaud Leleu³, and Adélaïde de Heering<fnref n="1" />

**Abstract**

■ The human visual system readily processes illusory faces (IFs) as faces, a phenomenon known as face pareidolia. Building on evidence that IF processing elicits face-like neural activity and is sensitive to contextual cues, we investigated, via two experiments, whether and how the presence of human faces as a visual context to IFs influences IF categorization. In Experiment 1, we exploited the frequency-tagging approach in EEG to display IFs within rapid sequences of various object categories, interleaved with either human faces (face context, FC) or houses (nonface context, NC). The IF-selective neural response was significantly weaker and less face-like in FC compared to NC, with

different topographical and temporal patterns. In Experiment 2, another group of participants performed an explicit IF detection task and exhibited slower RTs and lower detection accuracy in FC than in NC, consistent with the neural findings from Experiment 1. These results suggest that, rather than facilitating IF categorization, the presence of human faces interferes with IF categorization, likely because they compete for the same face-selective resources. Overall, this research highlights the critical role of context in shaping visual categorization by demonstrating earnestly how the visual environment dynamically influences the neural and perceptual processing of ambiguous stimuli. ■

**INTRODUCTION**

Humans possess the remarkable ability to rapidly detect and categorize faces (Jacques, Retter, & Rossion, 2016; Rossion, Torfs, Jacques, & Liu-Shuang, 2015; Crouzet, Kirchner, & Thorpe, 2010). This proficiency relies on a distributed face-selective cortical network located within the ventral occipito-temporal cortex (VOTC) typically showing a right-hemispheric dominance (Grill-Spector, Weiner, Kay, & Gomez, 2017; Jonas et al., 2016; Zhen et al., 2015; Meng, Cherian, Singal, & Sinha, 2012). Interestingly, it has been suggested that human face categorization processes have evolved to favor sensitivity over specificity: Minimal facial cues, such as a pair of eyes or a mouth, are sufficient to capture attention and trigger rapid face detection, thereby ensuring that potential faces are rarely missed (Jakobsen, Hunter, & Simpson, 2023; Caruana & Seymour, 2022; Keys, Taubert, & Wardle, 2021; Omer, Sapir, Hatuka, & Yovel, 2019).

An illustration of this rapid and broadly tuned machinery is our ability to perceive faces in a variety of visual inputs despite the absence of genuine facial features, a phenomenon known as *face pareidolia*. A large body of research has documented the strong inclination of the human visual system to process illusory faces (IFs) as faces (e.g., Rekow, Baudouin, Brochard, Rossion, & Leleu, 2022; Wardle, Taubert, Teichmann, & Baker, 2020). It appears that the face-selective network in the VOTC is activated

by IFs of different types such as Mooney faces (Rossion, Dricot, Goebel, & Busigny, 2011; Andrews & Schluppeck, 2004; Dolan et al., 1997), Arcimboldo paintings (Rossion et al., 2011), and face silhouettes (Davidenko, 2007) as well as naturalistic objects configured as faces (Cerrahoğlu et al., 2025; Decramer et al., 2021; Wardle et al., 2020; Hadjikhani, Kveraga, Naik, & Ahlfors, 2009). Like faces, IFs also elicit judgments about the perceived sex (Lipp & Taubert, 2024; Wardle, Paranjape, Taubert, & Baker, 2022; Davidenko, 2007), emotional expression (Alais, Xu, Wardle, & Taubert, 2021), age, or attractiveness (Davidenko, 2007).

EEG and magnetoencephalographic measures further support the similarity between IF and human face categorization, with face-like brain responses to IF stimuli emerging as early as 100–150 msec after their onset (Cerrahoğlu et al., 2025; Wardle et al., 2020; Churches, Nicholls, Thiessen, Kohler, & Keage, 2014; Liu et al., 2014; Churches, Baron-Cohen, & Ring, 2009), and particularly over the right occipito-temporal cortex (ROT) (Caharel et al., 2013; Hadjikhani et al., 2009; Sagiv & Bentin, 2001) as for human faces (Rekow, Baudouin, Brochard, et al., 2022; Wardle et al., 2020). Notably, IFs elicit measurable face-like neural activity even under challenging visual conditions such as ultra-fast presentation (i.e., stimuli presented for as short as 17 msec; Rekow, Baudouin, Brochard, et al., 2022) within streams of other naturalistic objects (see also Rekow, Baudouin, Durand, & Leleu, 2022; Rekow et al., 2021). This robust neural response to IFs, despite their limited resemblance to human faces, highlights the strong sensitivity of face-selective neural regions to face-like patterns.

<footnote n="1">¹Université libre de Bruxelles, Belgium, ²University of Hamburg, Germany, ³Université Bourgogne Europe, Institut Agro, CNRS, INRAe, UMR CSGA, France</footnote>
Despite the apparent ubiquity of IF perception, face par-
eidolia is not systematically reported (Rekow, Baudouin,
Brochard, et al., 2022; Rekow, Baudouin, Durand, et al.,
2022; Shafto & Pitts, 2015) because of diverse factors.
On the one hand, internal factors such as religious beliefs,
culture, neurological disorders, or personality traits are
known to play a role in individuals' experience of face par-
eidolia (Hull, Van Hedger, & Van Hedger, 2024; Hamza,
Kéri, Csigó, Bedewy, & Moustafa, 2021; Pavlova, Heiz,
Sokolov, Fallgatter, & Barisnikov, 2018; Ryan, Stafford, &
King, 2016; Riekki, Lindeman, Aleneff, Halme, & Nuortimo,
2013). On the other hand, some external factors such as the
contextual information provided to the participant have
also been identified to modulate IF perception. For
instance, individuals who are primed to perceive IFs as faces
further exhibit a beforehand-absent face-like EEG activity
(Shafto & Pitts, 2015; Bentin & Golland, 2002), and even a
congruent multisensory context induced by human body
odors (in adults: Rekow, Baudouin, Durand, et al., 2022;
in infants: Rekow et al., 2021) facilitates the perception of
visually presented IFs, speculatively because they evoke
the presence of a person.

In summary, we know that IFs and human faces are
processed similarly by the human brain and the percep-
tion of IFs as faces seems to be facilitated by congruent
contextual information. It therefore appears that, because
their inherent ambiguity (from which we can perceive an
IF or the object composing it) makes them particularly
sensitive to external factors, IFs are ideal to explore con-
textual influences on visual categorization. However, we
do not know yet whether the presence of human faces
in the visual context could either facilitate IF categoriza-
tion, that is, by serving as congruent cues, or rather inter-
fere with it, that is, because of competition for the same
face-selective neural resources.

To address this issue, we conducted two distinct exper-
iments designed to capture both neural (Experiment 1)
and behavioral (Experiment 2) measures of the impact
of a visual context containing human faces on IF categori-
zation as faces. In Experiment 1, we relied on a frequency-
tagging EEG approach (FT-EEG; for the advantages of this
approach, see Kabdebon, Fló, de Heering, & Aslin, 2022;
Norcia, Appelbaum, Ales, Cottereau, & Rossion, 2015) and
presented a first group of participants with sequences of
natural images depicting various objects, using an inter-
laced design adapted from Quek and de Heering (2024).
In each 40-sec sequence, stimuli were presented at a rapid
rate of 6 Hz (i.e., six images per second), with naturalistic
IF instances (from the set used in Rekow, Baudouin,
Brochard, et al., 2022; Rekow, Baudouin, Durand, et al.,
2022) appearing every fifth item (i.e., at 6/5 = 1.2 Hz). To
manipulate the visual context in which IF stimuli were pre-
sented, we concurrently inserted in the sequence either
human faces or houses (appearing every fourth item,
i.e., at 6/4 = 1.5 Hz) as the context-defining stimuli.
Sequences with faces defined the face context (FC) and
were compared to sequences with houses, which served

as a control nonface context (NC). By presenting stimuli
at three distinct frequencies, we isolated three distinct
responses within the EEG spectrum: a general visual
response to the fast train of all images at 6 Hz (six images
per second) and harmonics (i.e., integer multiples), a
context-selective response to faces or houses at 1.5 Hz
(6 Hz/4) and harmonics, and critically, an IF-selective
response at 1.2 Hz (6 Hz/5) and harmonics, indexing
how the human brain responds to IFs within each visual
context. To relate contextual effects on face-like neural
activity to face pareidolia (i.e., the subjective perception
of a face in IFs), we conducted a second experiment. In
Experiment 2, another group of participants was exposed
to similar visual sequences as in Experiment 1, with IFs
appearing sporadically rather than periodically to avoid
participants building expectations as they were this time
instructed to explicitly detect the IF stimuli, while we mea-
sured their accuracy and RTs in detecting IFs.

Two potential directions for the contextual influence of
human faces on IF categorization were foreseen. The first
one roots in research showing a facilitating effect of con-
gruent contextual cues on the visual categorization of IFs
as faces (Rekow, Baudouin, Durand, et al., 2022; Rekow
et al., 2021). If this applied to the visual context created
by human faces, we expected a stronger IF-selective
response (i.e., higher amplitudes in Experiment 1) and more
face pareidolia (i.e., faster and more accurate IF detection
in Experiment 2) in FC, based on the rationale that the congru-
ent presence of human faces facilitates the detection of a
face-like pattern within the IF, thereby enhancing IF catego-
rization (“facilitation hypothesis”). Alternatively, the second
scenario posits an interfering effect of the presence of human
faces on IF categorization, resulting in a weaker IF-selective
response (i.e., lower amplitudes in Experiment 1) and less
face pareidolia (i.e., slower and less accurate IF detection
in Experiment 2) in FC, based on the rationale that the
high salience of human faces draws attention (Quek &
de Heering, 2024; Morrisey, Hofrichter, & Rutherford,
2019; Crouzet et al., 2010; Peelen, Fei-Fei, & Kastner,
2009) and engages face-selective resources, thereby
reducing IF categorization (“interference hypothesis”).

METHODS

This study is composed of an EEG experiment (Experiment 1) and a behavioral experiment (Experiment 2). All testing was conducted in accordance with the Declaration of Helsinki and approved by the local Erasmus-ULB Faculty Ethics Committee (CCB: B4062021000234). Experiment 2 was preregistered (https://osf.io/dnpy7) before data collection.

Experiment 1: EEG

Participants

A power analysis was run a priori for sample size estima-
tion, using G*Power software (Faul, Erdfelder, Lang, &
Buchner, 2007). We expected an effect half the size of the one found in previous work by Rekow, Baudouin, Durand, and colleagues (2022), that is, *d* = 0.75 (one-tailed, *α* = .05), resulting in *n* = 20 to achieve 95% power. Twenty-four undergraduate students from the Université libre de Bruxelles took part in the experiment, and four of them were excluded for technical reasons (*n* = 2) or because their EEG data were too noisy (*n* = 2). The final sample thus consisted of 20 participants (right-handed, 19.4 ± 1.3 [SD] years old, range: 18–23 years old, two men). All participants reported normal or corrected-to-normal visual acuity, and none reported neurological or psychiatric disorder. All provided written informed consent before the experiment and were compensated for their participation through university course credits.

## Stimuli

A set of 296 natural color images (200 × 200 pixels) described in previous studies (Jacques et al., 2016; de Heering & Rossion, 2015; Rossion et al., 2015) was used. It included 48 faces, 48 houses, and 200 other nonface visual categories (animals, plants, manufactured objects, and food items). In addition, 48 IF stimuli depicting various nonface objects configured as a face (objects eliciting face pareidolia) were selected from a set built by Rekow, Baudouin, Brochard, and colleagues (2022) for their high ranking of face likeness. All stimuli depicted their item within its original background and varied in terms of color, viewpoint, and low-level properties (e.g., luminance), such that stimulus heterogeneity was high both within and across categories.

## Apparatus and EEG Recording

The experiment took place in a single EEG recording session in a darkened room. Participants sat at a viewing distance of ~57 cm in front of a 1920 × 1080 pixel computer monitor with a 60-Hz refresh rate. Images were displayed at 6° of visual angle against a uniform gray background using a custom-made software (https://osf.io/53hdb) running on Psychtoolbox for MATLAB 2021b (MathWorks Inc.). Participants' scalp electrical activity was recorded using a 64-channel BioSemi ActiveTwo system. Electrode offset was held below ±30 µV, and data were sampled at 1024 Hz. During recording, the Common Mode Sense active electrode was used as the reference and the Driven Right Leg passive electrode was used as ground. The experimenter monitored the participant's gaze direction to the screen throughout the experiment by means of an external camera attached to the computer screen.

## Visual Stimulation and Procedure

Participants were instructed to passively watch the sequences of images flickering on the computer screen.

Each stimulation sequence started with a “Ready?” message. Once the experimenter confirmed that the EEG trace was artifact-free and ensured that the participant looked at the screen, they launched the sequence. At the beginning of each sequence, a black fixation cross (size: 6 × 6 mm, thickness: 1 mm) appeared on the screen, for a variable duration of 2–5 sec, and then disappeared as the stimulation started. Each sequence lasted 40 sec during which participants watched a rapid stream of 240 images, all flickering centrally at a strict periodic rate of six images per second (i.e., 6 Hz). The contrast of each image was sinusoidally modulated from 0%–100%–0% over the 167-msec stimulus duration. To minimize eye blinks elicited by the sudden appearance/disappearance of flickering stimuli, each sequence was preceded and followed by a transition period of 1.667 sec during which the maximum contrast of 10 images progressively ramped up (i.e., fade-in) or down (i.e., fade-out). These fade-in/fade-out periods were not included in the analyses. The stimulation consisted of 50 sequences divided into two blocks of 25 sequences that defined the visual context in which participants viewed the IF (see below). Block order was counterbalanced across participants. Overall, participants were presented with 36 min of visual stimulation (breaks excluded).

In each sequence, stimuli were randomly selected from their respective sets. IFs were presented in every sequence, whereas faces and houses were used to define the visual context (FC and NC, respectively; Figure 1). Base objects and IFs were identical across contexts, to specifically isolate the influence of the context-defining stimuli (faces and houses). Within the 6-Hz stream of stimuli, the categories of interest (faces/houses and IFs) were interlaced at distinct embedded frequencies: Faces or houses appeared every four stimuli, at a frequency of 6 Hz/4th image = 1.5 Hz, whereas IFs appeared every five stimuli, at a frequency of 6 Hz/5th image = 1.2 Hz. Every 20 stimuli, these frequencies overlapped (i.e., every 3.333 sec, i.e., 12 times per sequence; see Figure 1), in which case an IF was always presented, thus skipping the face/house instance to equalize the total number (*n* = 48) of IFs and faces/houses in each sequence. Given this interlaced FT-EEG design (Quek & de Heering, 2024), we expected distinct brain responses at distinct frequencies in the EEG spectrum. A general visual response was expected at 6 Hz and harmonics (i.e., integer multiples) in both FC and NC, reflecting the synchronization of the visual system to the rapid stream of images. Context-selective responses (face- or house-selective for FC and NC, respectively) were also expected at 1.5 Hz and harmonics, marking the effective categorization of the context stimuli. Finally, and critically, an IF-selective response was expected at 1.2 Hz and harmonics in both contexts, but with a different amplitude, topography, and timing between contexts if the presence of human faces indeed interferes with IF categorization.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Frequency-tagging interlaced design. (A) Example stimuli used in the interlaced design. Various objects (O) constitute the base stimuli of each 40-sec sequence. Faces (F), houses (H), and IFs are interleaved among the base objects (O). (B) Images are presented at 6 Hz with face items (FC) or control items (i.e., houses; NC) interlaced as every fourth image and with IFs (in both FC and NC) appearing as every fifth image, so at the slower frequencies of 1.5 and 1.2 Hz, respectively. Because IF and F/H presentations overlap every 20 images, IF always wins over F/H to equate IF and F/H instances within each sequence.</figcaption></figure>

## EEG Preprocessing

EEG data were analyzed offline using Letswave6 (https://www.letswave.org/) running on MATLAB 2021b (Mathworks Inc.). A 4th-order Butterworth high-pass filter (cutoff: 0.1 Hz) was first applied before the data were resampled to 250 Hz. Data were then segmented to include the fade-in and fade-out periods, which resulted in 43.3-sec-long epochs (i.e., 1.667-sec fade-in + 40-sec epoch + 1.667-sec fade-out). Noisy electrodes across the scalp were linearly interpolated using two immediately adjacent clean channels (no more than four channels interpolated per participant). All channels were then rereferenced to a common average of the 64-scalp channels, before cropping each epoch to exactly 40 sec, corresponding to the full-contrast sequence (i.e., excluding the fade-in/fade-out period). This produced 50 epochs per participant (25 for the FC and 25 for the NC). For each participant, the 25 epochs obtained for each context were averaged to reduce the EEG activity that was non-phase-locked to the stimuli, resulting in a single 40-sec epoch per context.

## Frequency-domain Analyses

Individual data were subjected to a fast Fourier transform to extract amplitude spectra with a high resolution of 1/40 = 0.025 Hz. To establish the presence of significant periodic EEG responses at the relevant stimulation frequencies (1.2, 1.5, and 6 Hz) and to determine the harmonic range to quantify these responses, *z* scores were calculated on grand-averaged data (averaged across channels, contexts, and participants) by subtracting the mean amplitude of the 20 surrounding bins (i.e., 10 bins on each side, excluding the immediately adjacent bins and the two extreme [i.e., both minimum and maximum] bins; Rekow, Baudouin, Brochard, et al., 2022), divided by their standard deviation. Harmonics were considered significant when *z* scores were above 1.64 (*p* < .050, one-tailed, signal > noise; Rekow, Baudouin, Brochard, et al., 2022) and until two consecutive *z* scores were no longer significant. As a result, eight harmonics were included for the general visual response (from 6 to 48 Hz); 11 harmonics, for the context-selective response (from 1.5 to 21 Hz); and seven harmonics, for the IF-selective response (from 1.2 to
9.6 Hz), excluding the harmonics corresponding to the
general response (e.g., 6 Hz) for both the context- and
IF-selective responses (Supplemental Table S1).

To quantify the overall magnitude of the brain
responses, a baseline correction was first applied to indi-
vidual amplitude spectra by subtracting, at each frequency
bin, the mean amplitude of the 20 surrounding bins (10 on
each side, excluding the two immediately adjacent and the
two extreme bins; Jacques et al., 2016; Retter & Rossion,
2016). Then, these baseline-corrected amplitudes (BCAs)
were summed across significant harmonics (Retter &
Rossion, 2016). The following analyses will refer to the
brain responses summed across harmonics.

We considered three ROIs defined a priori, based on the
results from Rekow, Baudouin, Brochard, and colleagues
(2022) who tested participants with similar stimuli. The
middle occipital region (MO) encompassed the three con-
tiguous channels O1, Oz, and O2, and the left (LOT) and
right (ROT) occipito-temporal regions encompassed P7,
PO7, and P9 and P8, PO8, and P10, respectively.

Statistical analyses were conducted separately for the
three responses of interest. First, *z* scores (see above) were
calculated on raw amplitudes to determine the significance
of both group and individual responses in each context
(Supplemental Table S2). Then, individual BCAs were
submitted to a repeated-measures ANOVA using Context
(FC vs. NC) and ROI (LOT vs. ROT vs. MO) as within-
participant factors. Greenhouse–Geisser corrections are
reported (e) whenever the assumption of sphericity deter-
mined by a Mauchly’s test was violated. Pairwise *t* tests were
applied for post hoc comparisons, and unless specified
otherwise, all *p* values were two tailed. False discovery rate
corrections were used to control for multiple comparisons.
Effect sizes are reported as partial eta squared (η²_p). Finally,
to analyze the topography of the brain responses irrespec-
tive of their amplitude, individual BCAs were normalized by
their scalp-wide power (McCarthy & Wood, 1985) before
being submitted to a repeated-measures ANOVA using
the same factors as above. We focused on the Context ×
ROI interaction to determine whether the distribution of
the responses across ROIs differed between the two con-
texts. We also estimated in which context the topography
of the IF-selective response was the most face-like by calcu-
lating Pearson’s correlations between the face-selective
response and the IF response in each context using their
BCAs at the 29 posterior channels (i.e., behind the mid-
line). Correlations were computed for both group and indi-
vidual responses. For the latter, means across participants
were compared between contexts using a *t* test.

Time-domain Analyses

Participants' preprocessed epochs were first subjected to a
4th-order Butterworth low-pass filter (cutoff: 30 Hz). A fast
Fourier transform multinotch filter (width of 0.02 Hz and
slope of 0.075 Hz) was then applied on the 6-Hz response
and its first five harmonics (i.e., 6, 12, 18, 24, and 30 Hz) to

filter out the general visual response. We then created
three distinct sub-data sets per individual (faces, IF in FC
and IF in NC) and applied a multinotch filter again to iso-
late the response to faces and IFs uncontaminated by the
IF- and context-selective responses, respectively. Hence,
the first 25 harmonics of the IF-selective response (i.e.,
1.2 Hz up to 30 Hz) were filtered out to isolate the face-
selective response, and the first 20 harmonics of the
context-selective responses (i.e., 1.5 Hz up to 30 Hz) were
filtered out to isolate the IF-selective response in each con-
text. EEG data were then segmented from 167 msec before
each face or IF stimulus onset up to 667 msec after, result-
ing in 1200 epochs per participant in each sub-data set
(i.e., 48 instances × 25 sequences). Epochs with artifacts
(amplitudes over 100 µV) were rejected, and a baseline
correction was performed by subtracting the mean ampli-
tude in the -67- to 0-msec time window from the entire
epoch, before averaging the epochs within each sub-data
set. Finally, these mean epochs were grand-averaged
across participants for visualization of the three mean
responses (faces, IF in FC and IF in NC).

To investigate the time course of each response, indi-
vidual epochs were first pooled across channels within
the ROIs, and amplitude was normalized (by dividing each
waveform by its maximal absolute value) to remove ampli-
tude differences between conditions. Next, two analyses
were conducted to determine the time windows of signif-
icant deviation relative to zero and the temporal unfolding
of the differences between faces and IFs in each context.
Both analyses were performed using nonparametric
cluster-based permutation testing (Maris & Oostenveld,
2007) with *t* tests (*p* < .050, two-tailed) calculated at each
time bin (between 0 and 167 msec) for cluster size defini-
tion and percentiles from the distribution of mean cluster
sum obtained after 10,000 permutations as cluster signifi-
cance threshold (*p* < .050, two-tailed). Because we con-
sidered averaged channels within ROIs, we did not explore
the topographical pattern of the response so that clusters
were defined only for time series.

**Experiment 2: Behavior**

Participants

A power analysis was run a priori for sample size estima-
tion, using G*Power software (Faul et al., 2007). Given that
no previous study measured a similar effect, we planned to
achieve 95% power to detect a medium effect size of 0.25
(α = .05), resulting in *n* = 54. Fifty-five undergraduate stu-
dents from the Université libre de Bruxelles (Belgium)
took part in the experiment. Six of them were excluded
because of outlying data (see Statistical Analyses for outlier
detection). The final sample thus consisted of 49 partici-
pants (right-handed, 21.1 ± 5.2 years old, range: 18–48
years old, 11 men). All participants reported normal or
corrected-to-normal visual acuity, and none reported neu-
rological or psychiatric disorder. All provided written
informed consent before the experiment and were com-
pensated for their participation through university course
credits.

Stimuli and Apparatus

Stimuli and apparatus were the same as for Experiment 1.

Visual Stimulation and Procedure

To design an explicit IF detection task, we used the same stimulation sequences as in Experiment 1 (Visual Simulation and Procedure), except that IF stimuli appeared non periodically. In addition, to reduce the occurrence of IFs in this detection task, between four and eight IFs were used in each of the 25 sequences of each context (FC and NC). They appeared at random positions with a minimal inter val of 2.5 sec between two consecutive IFs, for a total of 144 IFs in each context, that is, three repetitions of each of the 48 IF images from the original stimulus set. Each stimulation sequence was preceded by a written reminder of the instructions.

Before testing started, participants were given a defini-
tion of face pareidolia (“the phenomenon of seeing a
face in an object or pattern that is not a human face”)
and were shown four examples of IF images (which were
not included in the stimulation set). They were then
instructed to watch the screen and press the spacebar,
as quickly and accurately as possible with their dominant
hand, whenever they detected an IF. Before starting the
task and after the instructions, participants were exposed
to two training sequences (same duration and equivalent
content as the stimulation sequences) to familiarize them
with the task. Then, the experimental task started with
either the FC or the NC (block order was counterbalanced
across participants).

Statistical Analyses

Hits (correct detections), false alarms (incorrect detec-
tions), and RTs were recorded. To define hits and false
alarms for each individual participant, we considered the
distribution of their RTs (calculated in milliseconds from
the onset of each IF) and determined outliers as falling
outside the interval from 150 msec to the third quartile
(Q3) plus 1.5 interquartile range (i.e., IQR = Q3 – Q1).
Outlying responses were defined as false alarms, whereas
responses falling inside the interval were defined as hits.
Mean percentage (%) of hits, false alarms, and RTs for cor-
rect detection were then calculated for each participant
and each context. Six participants were then defined as
outliers (see Participants) as they fell outside the interval
defined by Q1 – 1.5 IQR and Q3 + 1.5 IQR calculated from
the distribution of all participants for at least one measure
in one context. Pairwise *t* tests were finally performed on
the percentage of hits, false alarms, and RTs of the rest of
the sample. On the basis of the EEG results, we expected a

faster and better IF detection in NC and thus relied on
one-tailed *p* values for statistics. Cohen's *d* was used to
estimate effect sizes.

RESULTS

**Experiment 1: EEG**

*Amplitude of the General, Context-, and IF-selective*
*Visual Responses in Both Contexts*

As expected, the fast 6-Hz train of stimuli elicited a strong general visual response at 6 Hz and harmonics (group *z* score > 40 for both contexts, *p* < .001; all individual *z* scores > 18, *p* < .001; Supplemental Table S2). This general response was of similar amplitude in FC (1.63 ± 0.12 [*SEM*] µV) and NC (1.66 ± 0.16 µV), as revealed by a nonsignificant main effect of Context, *F*(1, 19) = 0.14, *p* = .71, η<sup>2</sup><sub>p</sub> = .008 (Figure 2, left), suggesting that the rapid visual stimulation elicited comparable levels of basic visual processing across both contexts. There was also a significant main effect of ROI, *F*(2, 38) = 8.71, *p* < .001, η<sup>2</sup><sub>p</sub> = .314, on this data explained by a larger response at MO (1.92 ± 0.16 µV) and ROT (1.73 ± 0.19 µV) than LOT (1.28 ± 0.13 µV, *ps* < .050).

Importantly, clear evidence of category-selective activity
also appeared for all interlaced categories (face/house items
and IF items) in the form of significant responses in both
contexts (group *z* scores > 20 for all responses;
Supplemental Table S2). Individually, all participants exhib-
ited significant face- and house-selective responses in both
contexts (all *z* scores > 2.30, *p* < .050), and the IF-selective
response was significant (i.e., *z* > 1.64, *p* < .050) for 19 and
20 of 20 participants in FC and NC, respectively
(Supplemental Table S2). For 17 of 20 participants, *z* scores
were also higher for the face- than house-selective response
and higher for the IF-selective response in NC than in FC.

For the context-selective response, there was a signifi-
cant main effect of Context, *F*(1, 19) = 31.92, *p* < .001,
*η*<sub>p</sub><sup>2</sup> = .627, reflecting an overall stronger activation in
response to faces (2.02 ± 0.20 μV) than houses (0.87 ±
0.10 μV; Figure 2, center). The main effect of ROI also
reached significance, *F*(2, 38) = 21.10, *p* < .001, *η*<sub>p</sub><sup>2</sup> =
.526, and was qualified by a significant Context × ROI
interaction, *F*(1.50, 28.53) = 4.95, *ε* = 0.751, *p* = .022,
*η*<sub>p</sub><sup>2</sup> = .207. The difference between the responses to
faces and houses was significant at every ROI (all *ps* <
.001) but was particularly large at ROT (+1.44 μV) and
LOT (+1.28 μV) compared to MO (+0.75 μV).

For the IF-selective response, the main effect of Context
was also significant, *F*(1, 19) = 5.66, *p* = .028, *η*<sub>p</sub><sup>2</sup> = .230,
and qualified by a Context × ROI interaction, *F*(2, 38) =
7.55, *p* = .002, *η*<sub>p</sub><sup>2</sup> = .284. The response was also signifi-
cantly weaker in FC than NC at ROT (FC: 0.61 ± 0.09 µV,
NC: 0.98 ± 0.13 µV, *p* = .015), marginally at LOT (FC:
0.48 ± 0.08 µV, NC: 0.78 ± 0.14 µV, *p* = .060), and not
at MO (FC: 0.69 ± 0.09 µV, NC: 0.68 ± 0.12 µV, *p* = .90;
Figure 2, right).
**Figure 2.** Frequency-domain EEG results. Grand-averaged summed BCA of the general (gray), context-selective (brown), and IF-selective (cyan) responses in FC (left halves) and NC (right halves) averaged across the three ROIs. Horizontal bars represent the mean amplitude for each response, and vertical bars represent the SEMs. Individual data points are represented next to the corresponding violin plot. All responses were significant, and the context- and IF-selective responses differed between the visual contexts. Scalp topographies are illustrated by 3-D head maps (posterior views).

<figure><img src="image_3.png" /></figure>

In summary, the general visual response was not differ-
ent between the two contexts, whereas faces elicited a
larger context-selective response than houses, especially
at occipito-temporal sites (LOT and ROT). Critically, we
also found that IFs elicited a weaker brain response in
FC compared to NC, especially over ROT.

Contextual Effects on the Topography of the
IF-selective Response

Because the context-selective response was larger to
human faces (FC) than houses (NC) whereas the IF-
selective response was larger in NC than FC, we normal-
ized their amplitudes to characterize their topographies.
For the context-selective response, there was a marginal
Context × ROI interaction, *F*(2, 38) = 2.74, *p* = .078,
*η*<sub>p</sub><sup>2</sup> = .126. Faces (FC) elicited a larger brain response at
ROT and LOT than MO (*ps* < .01; 41% and 35% of the
response at ROT and LOT, respectively, vs. 24% at MO;
Figure 3A), and houses (NC) elicited a larger brain
response at ROT than LOT and MO (*ps* < .050, 39% of
the response at ROT vs. 31% and 30% at LOT and MO,
respectively).

In addition, the Context × ROI interaction was signifi-
cant for the IF-selective response, *F*(2, 38) = 8.34, *p* <
.001, η<sub>p</sub><sup>2</sup> = .305, indicating that IFs elicited a more medial
activity in FC (MO > LOT, *p* = .030) compared to a strong-
ger right-lateralized response in NC (ROT > MO, *p* = .015;
Figure 3A). Hence, whereas ROT, LOT, and MO contrib-
uted to 34%, 27%, and 39% of the response in FC, respec-
tively, their contributions were 42%, 30%, and 28% in NC.

These results point toward a more face-like scalp distribu-
tion of the response to IFs in NC compared to FC, with a
greater contribution of right occipito-temporal channels
and a lower contribution of MO to their scalp topography.

Finally, correlations between the group amplitudes for
the IF-selective response and the face-selective response
at posterior electrodes (Figure 3B) confirmed that the
topography of the response to IF was significantly less
face-like in FC (r = .73) than in NC (r = .94), t(26) =
5.34, p < .001. Importantly, this result was not driven by
a small subset of participants as the same correlations cal-
culated for each individual participant revealed a signifi-
cant difference between the mean correlations obtained
in FC (0.38 ± 0.06) and in NC (0.60 ± 0.04), t(19) =
4.37, p < .001.

Contextual Effects on the Temporal Unfolding of the
IF-selective Response

To investigate how the visual context influenced the IF-
selective response over time, we inspected the normalized
stimulus-locked responses to human faces, IF in FC and IF
in NC averaged across ROIs. The face-selective waveform
was highly consistent with previous FT-EEG studies (e.g.,
Quek & de Heering, 2024; Jacques et al., 2016; Rossion
et al., 2015), with a multicomponent brain response start-
ing around 100 msec after stimulus onset and lasting about
500 msec (Figure 4A). Cluster-based permutation testing
revealed four periods of significant face-selective activity:
a short positive component peaking at around 100 msec
(70–120 msec), a short negative peak at 170 msec (140–
<figure><img src="image_4.png" /><figcaption>**Figure 3.** (A) Normalized grand-averaged summed corrected amplitudes of the responses to IFs (in FC, dark cyan; in NC, light cyan) and to faces (F; yellow). (B) Group (left) and individual (right) Pearson's correlations between the amplitude of the face-selective response and the IF-selective response in each context at the 29 posterior channels (i.e., behind the midline). Horizontal bars on the violin plots represent the mean correlations across participants, and vertical bars represent the SEMs. Individual data points are represented next to the corresponding violin plot.</figcaption></figure>

190 msec), a slower positive component peaking at 335 msec and unfolding from 280 to 450 msec, and a weaker negative peak at around 550 msec (530–590 msec).

Strikingly, the two contexts (FC and NC) led to different IF-selective waveforms, although both contexts included the same IF (Figure 4A). First, like the response to faces, the response to IFs in NC presented a significant positive peak around 100 msec after stimulus onset as opposed to a nonsignificant negative peak within the same time window for the response in FC. Then, no significant activity

emerged at 170 msec for both contexts; however, a weak face-like component was visible for IFs in NC, but not in FC. Both responses to IFs were then significant from 240 msec, but this positive component was shorter in FC, stopping at around 330 msec and followed by another positive peak (420–490 msec), whereas it remained significant along a 240- to 500-msec time window in NC. Finally, whereas there was a last face-like negative component (550–610 msec) for the response in NC, no other significant time windows were identified for the IF response in FC.

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Normalized mean amplitude of the (A) responses to IFs in FC (dark cyan), IFs in NC (light cyan), and faces (yellow) shown as a function of time from stimulus onset and (B) their differences. Waveforms are averaged across ROIs. Color red gradient points show significant *p* values following cluster-based permutation over time for (A) the three responses against zero and (B) the two IF-selective responses against the face-selective response.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 5.** (Left) RTs to correctly detect IF in milliseconds (msec) in FC (dark cyan) and NC (light cyan). (Right) Percentage of IF correct (hits) and incorrect (false alarms) detections in each context. Horizontal bars inside each violin plot represent the means, and vertical bars represent the SEMs. Individual data points are represented next to their corresponding violin plot.</figcaption></figure>

In addition, pairwise comparisons between the IF- and face-selective responses (Figure 4B) revealed the dissimilarity between the IF response in FC and the face-selective response, as they differed during three time windows after stimulus onset (145–190, 330–410, and 530–580 msec). Conversely, the IF response in NC only significantly differed from the face-selective response during the 430- to 500-msec window after stimulus onset and with smaller difference values. In summary, time-domain analyses revealed distinct time patterns for IFs in both contexts, despite that they were composed of the same stimuli. Specifically, presenting IFs in NC led to a response that unfolded like the response to human faces, contrary to the response to the same IF in FC.

## Experiment 2: Behavior

Experiment 2 was conducted to determine whether the more face-like IF-selective neural response found in NC was associated with an actual faster and better detection of IFs. The results of Experiment 2 were consistent and indeed revealed the corresponding contextual influence on both hits and RTs (Figure 5): Participants detected less IFs in FC (80.4 ± 1.9%) than in NC (82.7 ± 1.4%), t(48) = 1.82, *p* = .037, *d* = .260, a performance that was associated with a slower detection of IFs in FC (588 ± 6.5 msec) compared to NC (580 ± 6.3 msec), t(48) = 2.44, *p* = .009, *d* = 0.348. The number of false alarms was, however, not significantly different between FC (9.1 ± 0.9%) and NC (8.9 ± 0.7%), t(48) = 0.35, *p* = .36, *d* = 0.050.

## DISCUSSION

In this study, we leveraged in two separate experiments the inherent ambiguity of IFs to investigate how a visual context influences visual categorization. Building on evidence that the human visual system readily responds to and processes IFs as faces engaging face-like resources

(e.g., Cerrahoğlu et al., 2025; Rekow, Baudouin, Brochard, et al., 2022; Wardle et al., 2020; Caharel et al., 2013) and that the perceptual ambiguity of IFs makes them particularly sensitive to contextual cues such as priming (Shafto & Pitts, 2015; Bentin & Golland, 2002) and congruent multi-modal sensory inputs (e.g., olfactory; Rekow, Baudouin, Durand, et al., 2022; Rekow et al., 2021), we questioned whether and how the presence of human faces as a visual context influences IF categorization. In Experiment 1, we took advantage of a robust FT-EEG interlaced design (Quek & de Heering, 2024) and presented various IF instances within rapid sequences of naturalistic images to assess whether the neural response to these stimuli varied between a face (FC) or nonface (NC) context defined by the presence of human faces or houses, respectively. We found that the IF-selective response changed both quantitatively and qualitatively as a function of the context, being weaker and less face-like in terms of topography and time course in FC than in NC. In Experiment 2, we used a similar paradigm with an explicit IF detection task and found, in line with the characteristics of the FL-selective neural response of Experiment 1, that participants were slower and less accurate to detect IFs in FC compared to NC. Altogether, these findings support the interpretation of an interfering effect of human faces on IF categorization.

## Measuring Distinct Categorization Responses within a Single Stimulation Sequence

Owing to the FT-EEG interlaced design in Experiment 1, we were able to tag several neural responses that we compared across the visual contexts (FC and NC). First, both contexts elicited a similar general response at the stimulation frequency (6 Hz) and harmonics, indicating that none of the contextual effect we observed on the IF-selective response was driven by low-level image differences (Norcia et al., 2015; Kim, Grabowewcky, Paller, Muthu, &
Suzuki, 2007). Second, we found significant face- and house-selective responses (at 1.5 Hz and harmonics), marking the effective categorization of the context stimuli, with higher amplitudes and typical face-selective topographies for faces, thereby replicating a previous study comparing these two categories (Jacques et al., 2016). Finally, and importantly, we found that every participant exhibited significant IF-selective activity (at 1.2 Hz and harmonics) over the occipito-temporal cortex, albeit always of weaker amplitude compared to human faces, as previously observed (e.g., Cerrahoğlu et al., 2025; Rekow, Baudouin, Brochard, et al., 2022). This IF-selective response, together with our behavioral findings in Experiment 2, indicates that IF stimuli were processed as a distinct visual category in both contexts, such that stimuli depicting very different objects (e.g., a face-like cup and a face-like broom) were generalized into a single category and differentiated from other stimuli depicting the same objects (e.g., cups and brooms). This is not a trivial achievement for the visual system at this speed (i.e., one stimulus presented every 167 msec), and this supports the strong inclination of the human brain to process IFs as faces (Jakobsen et al., 2023; Caruana & Seymour, 2022; Keys et al., 2021; Omer et al., 2019). Accordingly, in Experiment 2, IFs were detected with high accuracy (>80%) across contexts, indicating an effective IF categorization in both contexts.

## More Effective Categorization of IFs in the Absence of Faces

On the basis of previous findings, we hypothesized that this IF categorization would differ as a function of the visual context with mutually exclusive predictions. Either FC would act as a congruent context facilitating the detection of face-like patterns, as body odors do (Rekow, Baudouin, Durand, et al., 2022), and thereby enhancing IF categorization (“facilitation hypothesis”). Or, by drawing attention and engaging face-selective resources, the highly salient human faces (Quek & de Heering, 2024; Morrisey et al., 2019; Crouzet et al., 2010; Peelen et al., 2009) in FC would rather interfere with the face-like interpretation of IFs, thereby reducing IF categorization (“interference hypothesis”). Across both experiments, the results were consistent and showed a weaker neural response to, and a less effective detection of, IF stimuli in the presence of human faces (FC), supporting the interference hypothesis. In Experiment 1, the IF-selective activity was indeed weaker in amplitude in FC compared to NC, at the group level (−27%), and observed individually for most participants (75%), possibly because less IF instances were categorized in FC than in NC (see Retter, Jiang, Webster, & Rossion, 2020). Importantly, we ruled out that this effect reflects neural fatigue from adaptation (see Supplemental Figure S1). Indeed, when comparing early (1–5) and late (21–25) trials in a repeated-measures ANOVA using Response (IF

in FC vs. IF in NC vs. Faces vs. Houses) and Time (Early trials vs. Late trials) as within-participant factors, a main effect of Time was observed, $F(1, 16) = 29.07, p < .001$, $\eta_p^2 = .645$, reflecting an overall stronger response in late (1.07 ± 0.58 μV) compared to early (0.73 ± 0.55 μV) trials. In addition, the Response × Time interaction was significant, $F(3, 48) = 4.75, p = .006$, $\eta_p^2 = .229$, indicating that the neural responses to IFs in FC (+0.16 μV, $p = .046$) and to faces (+0.73 μV, $p < .001$) were stronger in late compared to early trials (Supplemental Figure S1). In other words, neural adaptation and, more specifically, passive fatigue of face-selective neurons do not seem to occur here. The behavioral findings from Experiment 2 aligned with Experiment 1, demonstrating not only lower IF detection rates but also slower RTs in FC compared to NC. Both neural and behavioral findings therefore supported the interference hypothesis.

## More Face-like Categorization of IFs in the Absence of Faces

Interestingly, the differential IF-response strength (FC < NC) found in Experiment 1 was particularly evident over the right (ROT) and left (LOT) occipito-temporal cortices, a bilateral pattern that typically signs for face perception (Rekow, Baudouin, Brochard, et al., 2022; Jacques et al., 2016; Rossion et al., 2015; Liu, Harris, & Kanwisher, 2002; Bentin, Allison, Puce, Perez, & McCarthy, 1996). More specifically, over LOT and ROT, the IF-selective brain activity was weaker in FC than in NC by 38% (FC < NC for 13/20 participants over LOT and for 18/20 participants over ROT). In addition, after data normalization, we found that the IF-selective scalp topographies considerably differed as a function of the visual context, although the same IF stimuli were shown to the participants. Where the brain signals associated with IFs in NC exhibited a typical face-like bilateral topography with a right-hemispheric advantage (40% of the occipital activity over ROT and about 35% larger compared to LOT or MO), the IF response in FC displayed a more distributed and MO-dominant (39% of the occipital activity) topography. Group and individual correlations between the IF- and face-selective responses confirmed that the response to IFs in NC was also more face-like than that in FC, suggesting that IFs recruit face-selective neural resources in the absence of human faces more than they do in the presence of human faces.

Time-domain data analyses confirmed this difference with a less face-like waveform for IFs in FC than in NC. Indeed, among the four significant components found in the face-selective waveform (replicating previous findings; e.g., Quek & de Heering, 2024; Jacques et al., 2016; Rossion et al., 2015), three were observed for IFs in NC, whereas only one was found for IFs in FC. Consequently, when amplitude was normalized, the IF-selective waveform in NC only differed from the face-selective waveform between 430 and 500 msec, whereas IFs in FC did at three distinct time windows: between 145–190, 330–410,
and 530–580 msec. In addition, although the IF-selective
response in NC emerged at the same early latency as faces
(around 70–80 msec), the response in FC was missing the
first two components and only emerged around 250 msec.
This suggests that the visual system takes more time to
categorize the face likeness of IFs in FC compared to NC
because of the interference of human face stimuli, thereby
exhibiting a partial and late face-like response, which
could also account for the topographical differences
described from the frequency-domain analyses.

Together, this evidence aligns with previous findings
showing the importance of early (<250 msec) face-like
activity for the effective categorization of IF stimuli
(Wardle et al., 2020; Caharel et al., 2013) and shows that,
although the IF-selective response was significant in
both contexts, the neural representation of IF measured
in both the frequency- and time-domain was markedly
less face-like when human faces were presented concur-
rently in the sequence of stimuli.

Conclusions and Perspectives

Overall, we demonstrate that the visual context matters
during IF perception, as we found a weaker and less
face-like categorization of IF stimuli when they were pre-
sented concurrently with human faces, suggesting that the
latter interfered with IF categorization. These findings
highlight, via the example of ambiguous patterns such as
face-like objects, the critical role of visual context in shap-
ing perceptual processes (Rekow, Baudouin, Brochard,
et al., 2022; Rekow, Baudouin, Durand, et al., 2022; Shafto
& Pitts, 2015; Klink, van Wezel, & van Ee, 2012; Bentin &
Golland, 2002). This context dependency indeed reveals
how the visual system dynamically adjusts its neural repre-
sentations based on environmental cues, as it does in
other nonface complex visual scenarios, such as crowding
(Chen, Bao, & Tjan, 2018), affective salience (Niu, Todd, &
Anderson, 2012), or social features in intricate naturalistic
scenes (End & Gamer, 2017).

Although our findings demonstrate contextual effects
on IF categorization, one limitation is the absence of for-
mal eye-tracking data in both experiments. However, the
rapid image presentation rate makes it unlikely that differ-
ential eye movements significantly influenced the results.
It is also worth noting that our sample consisted predom-
inantly of female participants (Experiment 1: 18/20;
Experiment 2: 38/49). Given previous evidence suggest-
ing gender differences in face pareidolia sensitivity
(Proverbio & Galli, 2016), future studies could aim to
recruit more gender-balanced samples to investigate
whether these contextual effects manifest differently
across genders. Future studies could also examine
stimulus-specific effects, for instance, whether the degree
of face likeness in an IF predicts its susceptibility to con-
textual influence or whether certain object categories
serving as IFs are more or less resistant to contextual
effects. A last intriguing avenue for future research would

be to explore whether the contextual modulation effects
that we found emerge early in life, before the maturation
of the face-selective network in the VOTC. Probing these
dynamics may indeed reveal how environmental cues
shape the maturation of the visual system, shedding light
on the foundations of human perceptual processes.

Acknowledgments

We thank Daniela Ferreira Mesquita, Sofia Bonsignore, and Manon Kohner for their help in recruiting participants and collecting the data, as well as Professor Fabienne Chetail for the FT-EEG script available on OSF (https://osf.io/53hdb/; https://doi.org/10.17605/OSF.IO/53HDB).

Corresponding author: Laura Bourgaux, Unité de Recherche en Neurosciences Cognitives (UNESCOG), Center for Research in Cognition & Neurosciences (CRCN), Université libre de Bruxelles, Avenue Franklin Roosevelt, 50-CP191, 1050 Brussels, Belgium, e-mail: laura.bourgaux@ulb.be.

Data Availability Statement

The data necessary to reproduce the analyses presented
here (anonymized preprocessed EEG and behavioral data
and participant information) are made permanently and
publicly accessible at https://osf.io/2v6fs/. Supplemental
Material can be accessed on this article's homepage:
https://doi.org/10.1162/JOCN.a.94.

Author Contributions

Laura Bourgaux: Conceptualization; Data curation; For-
mal analysis; Investigation; Methodology; Project
administration; Software; Validation; Visualization;
Writing—Original draft; Writing—Review & editing. Diane
Rekow: Formal analysis; Writing—Review & editing.
Arnaud Leleu: Formal analysis; Writing—Review & editing.
Adélaïde de Heering: Conceptualization; Formal analysis;
Funding acquisition; Investigation; Methodology; Project
administration; Resources; Software; Supervision; Valida-
tion; Writing—Original draft; Writing—Review & editing.
All authors have substantively reviewed the article and
approved the submitted version.

Funding Information

For this research, L. B. and A. H. were supported by the Fund for Scientific Research (PDR/F.R.S.-FNRS, T.0040.22), D. R. was supported by an Alexander von Humboldt Foundation fellowship/the Excellence Strategy of the Federal Government and the Länder (Z1-V5-099), and A. L. was supported by the French National Research Agency (Contract ANR-19-CE28-0009).

Diversity in Citation Practices

Retrospective analysis of the citations in every article pub-
lished in this journal from 2010 to 2021 reveals a persistent
pattern of gender imbalance: Although the proportions of
authorship teams (categorized by estimated gender
identification of first author/last author) publishing in the *Journal of Cognitive Neuroscience* (JOCN) during this period were M(an)/M = .407, W(oman)/M = .32, M/W = .115, and W/W = .159, the comparable proportions for the articles that these authorship teams cited were M/M = .549, W/M = .257, M/W = .109, and W/W = .085 (Postle and Fulvio, *JOCN*, 34:1, pp. 1–3). Consequently, *JOCN* encourages all authors to consider gender balance explicitly when selecting which articles to cite and gives them the opportunity to report their article’s gender citation balance. The authors of this article report its proportions of citations by gender category to be M/M = .447, W/M = .298, M/W = .149, and W/W = .106.

REFERENCES

Alais, D., Xu, Y., Wardle, S. G., & Taubert, J. (2021). A shared mechanism for facial expression in human faces and face pareidolia. *Proceedings of the Royal Society of London, Series B: Biological Sciences, 288*, 20210966. https://doi.org/10.1098/rspb.2021.0966, PubMed: 34229489

Andrews, T. J., & Schluppeck, D. (2004). Neural responses to Mooney images reveal a modular representation of faces in human visual cortex. *Neuroimage, 21*, 91–98. https://doi.org/10.1016/j.neuroimage.2003.08.023, PubMed: 14741646

Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G. (1996). Electrophysiological studies of face perception in humans. *Journal of Cognitive Neuroscience, 8*, 551–565. https://doi.org/10.1162/jocn.1996.8.6.551, PubMed: 20740065

Bentin, S., & Golland, Y. (2002). Meaningful processing of meaningless stimuli: The influence of perceptual experience on early visual processing of faces. *Cognition, 86*, B1–B14. https://doi.org/10.1016/s0010-0277(02)00124-5, PubMed: 12208653

Caharel, S., Leleu, A., Bernard, C., Viggiano, M.-P., Lalonde, R., & Rebaï, M. (2013). Early holistic face-like processing of Arcimboldo paintings in the right occipito-temporal cortex: Evidence from the N170 ERP component. *International Journal of Psychophysiology, 90*, 157–164. https://doi.org/10.1016/j.ijpsycho.2013.06.024, PubMed: 23816562

Caruana, N., & Seymour, K. (2022). Objects that induce face pareidolia are prioritized by the visual system. *British Journal of Psychology, 113*, 496–507. https://doi.org/10.1111/bjop.12546, PubMed: 34923634

Cerrahoğlu, B., Jacques, C., Rekow, D., Jonas, J., Colnat-Coulbois, S., Caharel, S., et al. (2025). The neural basis of face pareidolia with human intracerebral recordings. *Imaging Neuroscience, 3*, imag_a_00518. https://doi.org/10.1162/imag_a_00518, PubMed: 40800790

Chen, N., Bao, P., & Tjan, B. S. (2018). Contextual-dependent attention effect on crowded orientation signals in human visual cortex. *Journal of Neuroscience, 38*, 8433–8440. https://doi.org/10.1523/JNEUROSCI.0805-18.2018, PubMed: 30120209

Churches, O., Baron-Cohen, S., & Ring, H. (2009). Seeing face-like objects: An event-related potential study. *NeuroReport, 20*, 1290–1294. https://doi.org/10.1097/WNR.0b013e3283305a65, PubMed: 19641476

Churches, O., Nicholls, M., Thiessen, M., Kohler, M., & Keage, H. (2014). Emoticons in mind: An event-related potential study. *Social Neuroscience, 9*, 196–202. https://doi.org/10.1080/17470919.2013.873737, PubMed: 24387045

Crouzet, S. M., Kirchner, H., & Thorpe, S. J. (2010). Fast saccades toward faces: Face detection in just 100 ms. *Journal of Vision, 10*, 16. https://doi.org/10.1167/10.4.16, PubMed: 20465335

Davidenko, N. (2007). Silhouetted face profiles: A new methodology for face perception research. *Journal of Vision*, 7, 6. https://doi.org/10.1167/7.4.6, PubMed: 17461690

de Heering, A., & Rossion, B. (2015). Rapid categorization of natural face images in the infant right hemisphere. *eLife, 4*, e06564. https://doi.org/10.7554/eLife.06564, PubMed: 26032564

Decramer, T., Premereur, E., Zhu, Q., Van Paesschen, W., van Loon, J., Vanduffel, W., et al. (2021). Single-unit recordings reveal the selectivity of a human face area. *Journal of Neuroscience, 41*, 9340–9349. https://doi.org/10.1523/JNEUROSCI.0349-21.2021, PubMed: 34732521

Dolan, R. J., Fink, G. R., Rolls, E., Booth, M., Holmes, A., Frackowiak, R. S., et al. (1997). How the brain learns to see objects and faces in an impoverished context. *Nature, 389*, 596–599. https://doi.org/10.1038/39309, PubMed: 9335498

End, A., & Gamer, M. (2017). Preferential processing of social features and their interplay with physical saliency in complex naturalistic scenes. *Frontiers in Psychology, 8*, 418. https://doi.org/10.3389/fpsyg.2017.00418, PubMed: 28424635

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. *Behavior Research Methods, 39*, 175–191. https://doi.org/10.3758/bf03193146, PubMed: 17695343

Grill-Spector, K., Weiner, K. S., Kay, K., & Gomez, J. (2017). The functional neuroanatomy of human face perception. *Annual Review of Vision Science, 3*, 167–196. https://doi.org/10.1146/annurev-vision-102016-061214, PubMed: 28715955

Hadjikhani, N., Kveraga, K., Naik, P., & Ahlfors, S. P. (2009). Early (M170) activation of face-specific cortex by face-like objects. *NeuroReport, 20*, 403–407. https://doi.org/10.1097/WNR.0b013e328325a8e1, PubMed: 19218867

Hamza, E. G. A., Kéri, S., Csigó, K., Bedewy, D., & Moustafa, A. A. (2021). Pareidolia in schizophrenia and bipolar disorder. *Frontiers in Psychiatry, 12*, 746734. https://doi.org/10.3389/fpsyt.2021.746734, PubMed: 34955913

Hull, K., Van Hedger, K., & Van Hedger, S. C. (2024). Absorption relates to individual differences in visual face pareidolia. *Current Psychology, 43*, 4458–4474. https://doi.org/10.1007/s12144-023-04670-6

Jacques, C., Retter, T. L., & Rossion, B. (2016). A single glance at natural face images generate larger and qualitatively different category-selective spatio-temporal signatures than other ecologically-relevant categories in the human brain. *Neuroimage, 137*, 21–33. https://doi.org/10.1016/j.neuroimage.2016.04.045, PubMed: 27138205

Jakobsen, K. V., Hunter, B. K., & Simpson, E. A. (2023). Pareidolic faces receive prioritized attention in the dot-probe task. *Attention, Perception, & Psychophysics, 85*, 1106–1126. https://doi.org/10.3758/s13414-023-02685-6, PubMed: 36918509

Jonas, J., Jacques, C., Liu-Shuang, J., Brissart, H., Colnat-Coulbois, S., Maillard, L., et al. (2016). A face-selective ventral occipito-temporal map of the human brain with intracerebral potentials. *Proceedings of the National Academy of Sciences, U.S.A., 113*, E4088–E4097. https://doi.org/10.1073/pnas.1522033113, PubMed: 27354526

Kabdebon, C., Fló, A., de Heering, A., & Aslin, R. (2022). The power of rhythms: How steady-state evoked responses reveal early neurocognitive development. *Neuroimage, 254*, 119150. https://doi.org/10.1016/j.neuroimage.2022.119150, PubMed: 35351649
Keys, R. T., Taubert, J., & Wardle, S. G. (2021). A visual search advantage for illusory faces in objects. *Attention, Perception, & Psychophysics*, *83*, 1942–1953. https://doi.org/10.3758/s13414-021-02267-4, PubMed: 33768481

Kim, Y. J., Grabowucky, M., Paller, K. A., Muthu, K., & Suzuki, S. (2007). Attention induces synchronization-based response gain in steady-state visual evoked potentials. *Nature Neuroscience*, *10*, 117–125. https://doi.org/10.1038/nn1821, PubMed: 17173045

Klink, P. C., van Wezel, R. J. A., & van Ee, R. (2012). United we sense, divided we fail: Context-driven perception of ambiguous visual stimuli. *Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences*, *367*, 932–941. https://doi.org/10.1098/rstb.2011.0358, PubMed: 22371615

Lipp, O. V., & Taubert, J. (2024). The face pareidolia illusion drives a happy face advantage that is dependent on perceived gender. *Emotion*, *24*, 1781–1787. https://doi.org/10.1037/emo0001346, PubMed: 38842878

Liu, J., Harris, A., & Kanwisher, N. (2002). Stages of processing in face perception: An MEG study. *Nature Neuroscience*, *5*, 910–916. https://doi.org/10.1038/nn909, PubMed: 12195430

Liu, J., Li, J., Feng, L., Li, L., Tian, J., & Lee, K. (2014). Seeing Jesus in toast: Neural and behavioral correlates of face pareidolia. *Cortex*, *53*, 60–77. https://doi.org/10.1016/j.cortex.2014.01.013, PubMed: 24583223

Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. *Journal of Neuroscience Methods*, *164*, 177–190. https://doi.org/10.1016/j.jneumeth.2007.03.024, PubMed: 17517438

McCarthy, G., & Wood, C. C. (1985). Scalp distributions of event-related potentials: An ambiguity associated with analysis of variance models. *Electroencephalography and Clinical Neurophysiology*, *61*, S226–S227. https://doi.org/10.1016/0013-4694(85)90858-2

Meng, M., Cherian, T., Singal, G., & Sinha, P. (2012). Lateralization of face processing in the human brain. *Proceedings of the Royal Society of London, Series B: Biological Sciences*, *279*, 2052–2061. https://doi.org/10.1098/rspb.2011.1784, PubMed: 22217726

Morrisey, M. N., Hofrichter, R., & Rutherford, M. D. (2019). Human faces capture attention and attract first saccades without longer fixation. *Visual Cognition*, *27*, 158–170. https://doi.org/10.1080/13506285.2019.1631925

Niu, Y., Todd, R. M., & Anderson, A. K. (2012). Affective salience can reverse the effects of stimulus-driven salience on eye movements in complex scenes. *Frontiers in Psychology*, *3*, 336. https://doi.org/10.3389/fpsyg.2012.00336, PubMed: 23055990

Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015). The steady-state visual evoked potential in vision research: A review. *Journal of Vision*, *15*, 4. https://doi.org/10.1167/15.6.4, PubMed: 26024451

Omer, Y., Sapir, R., Hatuka, Y., & Yovel, G. (2019). What is a face? Critical features for face detection. *Perception*, *48*, 437–446. https://doi.org/10.1177/0301006619838734, PubMed: 30939991

Pavlova, M. A., Heiz, J., Sokolov, A. N., Fallgatter, A. J., & Barisnikov, K. (2018). Even subtle cultural differences affect face tuning. *PLoS One*, *13*, e0198299. https://doi.org/10.1371/journal.pone.0198299, PubMed: 29889882

Peelen, M. V., Fei-Fei, L., & Kastner, S. (2009). Neural mechanisms of rapid natural scene categorization in human visual cortex. *Nature*, *460*, 94–97. https://doi.org/10.1038/nature08103, PubMed: 19506558

Proverbio, A. M., & Galli, J. (2016). Women are better at seeing faces where there are none: An ERP study of face pareidolia. *Social Cognitive and Affective Neuroscience*, *11*, 1501–1512. https://doi.org/10.1093/scan/nsw064, PubMed: 27217120

Quek, G. L., & de Heering, A. (2024). Visual periodicity reveals distinct attentional signatures for face and non-face

categories. *Cerebral Cortex*, *34*, bhae228. https://doi.org/10.1093/cercor/bhae228, PubMed: 38879816

Rekow, D., Baudouin, J.-Y., Brochard, R., Rossion, B., & Leleu, A. (2022). Rapid neural categorization of facelike objects predicts the perceptual awareness of a face (face pareidolia). *Cognition*, *222*, 105016. https://doi.org/10.1016/j.cognition.2022.105016, PubMed: 35030358

Rekow, D., Baudouin, J.-Y., Durand, K., & Leleu, A. (2022). Smell what you hardly see: Odors assist visual categorization in the human brain. *Neuroimage*, *255*, 119181. https://doi.org/10.1016/j.neuroimage.2022.119181, PubMed: 35413443

Rekow, D., Baudouin, J.-Y., Poncet, F., Damon, F., Durand, K., Schaal, B., et al. (2021). Odor-driven face-like categorization in the human infant brain. *Proceedings of the National Academy of Sciences, U.S.A.*, *118*, e2014979118. https://doi.org/10.1073/pnas.2014979118, PubMed: 34001601

Retter, T. L., Jiang, F., Webster, M. A., & Rossion, B. (2020). All-or-none face categorization in the human brain. *Neuroimage*, *213*, 116685. https://doi.org/10.1016/j.neuroimage.2020.116685, PubMed: 32119982

Retter, T. L., & Rossion, B. (2016). Uncovering the neural magnitude and spatio-temporal dynamics of natural image categorization in a fast visual stream. *Neuropsychologia*, *91*, 9–28. https://doi.org/10.1016/j.neuropsychologia.2016.07.028, PubMed: 27461075

Riekki, T., Lindeman, M., Aleneff, M., Halme, A., & Nuortimo, A. (2013). Paranormal and religious believers are more prone to illusory face perception than skeptics and non-believers. *Applied Cognitive Psychology*, *27*, 150–155. https://doi.org/10.1002/acp.2874

Rossion, B., Dricot, L., Goebel, R., & Busigny, T. (2011). Holistic face categorization in higher order visual areas of the normal and prosopagnosic brain: Toward a non-hierarchical view of face perception. *Frontiers in Human Neuroscience*, *4*, 225. https://doi.org/10.3389/fnhum.2010.00225, PubMed: 21267432

Rossion, B., Torfs, K., Jacques, C., & Liu-Shuang, J. (2015). Fast periodic presentation of natural images reveals a robust face-selective electrophysiological response in the human brain. *Journal of Vision*, *15*, 18. https://doi.org/10.1167/15.1.18, PubMed: 25597037

Ryan, C., Stafford, M., & King, R. J. (2016). Brief report: Seeing the man in the moon: Do children with autism perceive pareidolic faces? A pilot study. *Journal of Autism and Developmental Disorders*, *46*, 3838–3843. https://doi.org/10.1007/s10803-016-2927-x, PubMed: 27696185

Sagiv, N., & Bentin, S. (2001). Structural encoding of human and schematic faces: Holistic and part-based processes. *Journal of Cognitive Neuroscience*, *13*, 937–951. https://doi.org/10.1162/089892901753165854, PubMed: 11595097

Shafto, J. P., & Pitts, M. A. (2015). Neural signatures of conscious face perception in an inattentional blindness paradigm. *Journal of Neuroscience*, *35*, 10940–10948. https://doi.org/10.1523/JNEUROSCI.0145-15.2015, PubMed: 26245958

Wardle, S. G., Paranjape, S., Taubert, J., & Baker, C. I. (2022). Illusory faces are more likely to be perceived as male than female. *Proceedings of the National Academy of Sciences, U.S.A.*, *119*, e2117413119. https://doi.org/10.1073/pnas.2117413119, PubMed: 35074880

Wardle, S. G., Taubert, J., Teichmann, L., & Baker, C. I. (2020). Rapid and dynamic processing of face pareidolia in the human brain. *Nature Communications*, *11*, 4518. https://doi.org/10.1038/s41467-020-18325-8, PubMed: 32908146

Zhen, Z., Yang, Z., Huang, L., Kong, X.-Z., Wang, X., Dang, X., et al. (2015). Quantifying interindividual variability and asymmetry of face-selective regions: A probabilistic functional atlas. *Neuroimage*, *113*, 13–25. https://doi.org/10.1016/j.neuroimage.2015.03.010, PubMed: 25772668