Snakes elicit specific neural responses in the human infant brain

Abstract

Detecting predators is essential for survival. Given that snakes are the first of primates’ major predators, natural selection may have fostered efficient snake detection mechanisms to allow for optimal defensive behavior. Here, we provide electrophysiological evidence for a brain-anchored evolved predisposition to rapidly detect snakes in humans, which does not depend on previous exposure or knowledge about snakes. To do so, we recorded scalp electrical brain activity in 7- to 10-month-old infants watching sequences of flickering animal pictures. All animals were presented in their natural background. We showed that glancing at snakes generates specific neural responses in the infant brain, that are higher in amplitude than those generated by frogs or caterpillars, especially in the occipital region of the brain. The temporal dynamics of these neural responses support that infants devote increased attention to snakes than to non-snake stimuli. These results therefore demonstrate that a single fixation at snakes is sufficient to generate a prompt and large selective response in the infant brain. They argue for the existence in humans of an inborn, brain-anchored mechanism to swiftly detect snakes based on their characteristic visual features.

Full text

1
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
Snakes elicit specic neural
responses in the human infant brain
J. Bertels1,2✉, M. Bourguignon2, A. de Heering1, F. Chetail3, X. De Tiège2, A. Cleeremans1 &
A. Destrebecqz1
Detecting predators is essential for survival. Given that snakes are the rst of primates’ major predators,
natural selection may have fostered ecient snake detection mechanisms to allow for optimal
defensive behavior. Here, we provide electrophysiological evidence for a brain-anchored evolved
predisposition to rapidly detect snakes in humans, which does not depend on previous exposure or
knowledge about snakes. To do so, we recorded scalp electrical brain activity in 7- to 10-month-old
infants watching sequences of ickering animal pictures. All animals were presented in their natural
background. We showed that glancing at snakes generates specic neural responses in the infant brain,
that are higher in amplitude than those generated by frogs or caterpillars, especially in the occipital
region of the brain. The temporal dynamics of these neural responses support that infants devote
increased attention to snakes than to non-snake stimuli. These results therefore demonstrate that a
single xation at snakes is sucient to generate a prompt and large selective response in the infant
brain. They argue for the existence in humans of an inborn, brain-anchored mechanism to swiftly detect
snakes based on their characteristic visual features.
Detecting predators is essential for survival. Over evolution and across species, natural selection may have there-
fore selected individuals equipped with perceptual systems tuned to detect predators quickly, so enabling better
defensive behavior. is hypothesis is at the core of the Snake Detection eory, which posits that the ancient
predator-prey relationship between snakes and primates played a substantial role in the evolution and expansion
of the latter’s visual system. e vital need to spot snakes rapidly would have shaped primates’ brain such that
they developed keen perceptual abilities, and, in particular, the ability to rapidly detect and process visual cues
suggestive of snakes1,2. e evolutionary pressure exerted by snakes would also have led to the development of
a “fear module” in the primate brain — a structure that is selectively sensitive to and automatically activated by
evolutionary threat-relevant stimuli, allowing their rapid detection3. Evidence for the existence of such a neurobi-
ological substrate for ecient detection of snakes in primates stems essentially from the identication of thalamic
neurons in the macaque brain that selectively respond to images of snakes4.
Primates would therefore be evolutionarily tuned to swily detect and process ancestrally threat-relevant
stimuli based on their visual features. Yet, it is unclear whether such detection mechanism is implemented in the
naive, immature brain.
Behaviorally, both human and non-human primates are remarkable snake detectors. When presented with
an array of pictures, human adults5–9 and children10–15, but also lab-reared monkeys16–18, are faster at detecting a
snake among threat-irrelevant pictures than vice versa. e coiled body shape of snakes would be a critical feature
in attracting participants’ attention12,14,15. Remarkably, infants under one year of age also show rapid detection
of snake pictures and preferential orienting toward these ancestrally threat-relevant stimuli. When presented
with pictures in the visual periphery, infants indeed shi attention faster towards snake pictures than towards
threat-irrelevant pictures19–23. is visual bias further impacts infants’ processing of subsequent stimuli19,21.
Infants’ sympathetic responses to videos, pictures and hissings of snakes likewise support the observation
that snakes capture their attention24–26. Importantly, these physiological studies, together with other research
examining infants’ and toddlers’ approach/avoidance behaviors toward snakes20,25,27 reported no fear reaction
or signs of distress in these participants. is is clear evidence that fear of snakes is not innate in humans, just as
1Consciousness, Cognition and Computation Group (CO3), Center for Research in Cognition and Neurosciences
(CRCN), ULB Neuroscience Institute (UNI), Université Libre de Bruxelles (ULB), Brussels, Belgium. 2Laboratoire
de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université Libre de Bruxelles
(ULB), Brussels, Belgium. 3Laboratoire Cognition Langage Développement (LCLD), Center for Research in Cognition
and Neurosciences (CRCN), ULB Neuroscience Institute (UNI), Université Libre de Bruxelles (ULB), Brussels, Belgium.
✉email: jbertels@ulb.ac.be
open
Content courtesy of Springer Nature, terms of use apply. Rights reserved

2
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
extensive work has previously demonstrated in monkeys (see, e.g.28). Primates would rather be prepared to learn
to fear snakes, so that fear of snakes (and of other evolutionary recurrent threats) is acquired more easily than
fear of other non-recurrent threats29. Perceptual biases toward snakes – which precede the development of fearful
behaviors – would thus act as a catalyst for fear learning30–32.
Infant and monkey studies have been taken as strong support in favor of an evolved predisposition to pref-
erentially process snakes. Factually, both infants and lab-reared monkeys are likely fully naive to real snakes,
even though infants might have been exposed to stued or cartoon versions of such reptiles. eir visual bias
towards snakes cannot therefore be attributed to any prior genuine exposure to these animals, and even less to
knowledge about their potential dangerousness. It would rather have an evolutionary origin1 since primate and
human brains would have evolved to detect physical attributes inherent to snakes in priority. ese studies are
nevertheless scarce and, as regards human studies, have thus far relied exclusively on eye gaze and physiological
measures (but see33).
In the present study, we used scalp electroencephalography (EEG) to unravel the neuronal mechanisms sub-
tending the evolved predisposition to preferentially and swily process snakes. We searched for objective and
reliable neural responses to snake images in 7- to 10-month-old infants. To do so, we leveraged fast periodic
visual stimulation (FPVS), consisting of trains of four base and one oddball stimuli, thereby tagging oddball
frequency at one h of base frequency34,35. Periodic stimulation is known to generate steady-state visual evoked
potentials (SSVEPs) exactly at the same fundamental frequency and harmonics as the driving stimulus36. When
oddball stimuli are tagged at a fraction of stimulus frequency, the ability of the brain to dierentiate between base
and oddball stimuli surfaces as periodic responses at oddball frequency. is methodology therefore provides
an implicit, objective and predictive measure of stimulus discrimination, and has recently proven its sensitivity
in infants exposed to complex visual stimuli37–39. Event-related potentials (ERPs) to oddball stimuli were also
examined as to uncover the temporal course of the discrimination response. Here, randomly selected color pic-
tures of various animals presented from dierent viewpoints in their natural background served as base stimuli
and were presented at a 6 Hz rate. Oddball stimuli consisting of snake or frog pictures (depending on the type
of sequence), equalized for contrast and luminance, were presented every ve stimuli (i.e., at 1.2 Hz). Frog pic-
tures were replaced by caterpillar pictures in a control study aiming at testing the specicity of the infant brain
response to snakes. Based on previous studies on infants’ visual categorization abilities40, we hypothesized that
snake-sensitive neural responses would be elicited in 7- to 10-month-old infants, mainly in the occipital region,
and would be of larger amplitude than any frog- or caterpillar-sensitive neural responses. e observation of such
dierential eects would constitute key evidence for the special status of snakes compared to similarly unfamiliar
and colorful, but threat-irrelevant animals such as frogs or caterpillars, and would thereby further support the
Snake Detection eory.
Results
Snake vs. frog-selective responses in the infant brain. Scalp EEG data were recorded in 26 7- to
9-month-old infants (18 females, mean age = 261 days, SD = 23 days), while viewing 20-s sequences of animal
pictures (sinusoidal contrast modulation at a rate F = 6 Hz) (s ee Fig.1). An oddball stimulus was presented every
h stimulus (i.e., F = 6/5 = 1.2 Hz) and consisted in a frog picture in frog sequences, and in a snake picture in
snake sequences. Frog and snake sequences were presented in alternation.
Figure 1. Schematic illustration of the experimental paradigm used. Animal pictures were presented by
sinusoidal contrast modulation at a rate of 6 per second (F = 6 Hz). Snake, frog or caterpillar pictures were
presented every h stimulus (F = 6/5 = 1.2 Hz), in dierent trial sequences. Snake and frog sequences were
used in the main study; snake and caterpillar sequences were used in the control study. e pictures diered
in terms of color, viewpoint and lighting conditions. Snake, frog, caterpillar and other animal pictures were
equalized in terms of luminance and contrast across the whole set. For copyright reasons, the pictures of snakes,
frogs and caterpillars displayed are dierent than those used in the actual experiment (originally coming from
Vanessa LoBue’s personal database), but the degree of variability across images is respected. Most of other
animal pictures come from CalPhotos (https://calphotos.berkeley.edu/fauna).
Content courtesy of Springer Nature, terms of use apply. Rights reserved

3
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
Infants viewed between 4 and 20 sequences each (M = 12.19, SD = 3.78). Of the total amount of sequences
(n = 317), 88 were excluded on the basis of predetermined criteria, and 11 snake sequences were randomly dis-
carded to ensure comparability of SNR measures between conditions (see Methods). Final analyses were therefore
run on 109 snake and 109 frog sequences.
Frequency domain analyses. Base frequency: In accordance with our selection of sequences (see Method),
grand-averaged signal to noise (SNR) spectra showed clear responses at the rst and second harmonics of animal
pictures presentation rate (6 and 12 Hz, respectively). ese responses were characterized by a medial occipital
topography peaking for both frog and snake sequences at electrode O1 at 6 Hz (mean SNR in the frog sequence =
16.26; mean SNR in the snake sequence = 20.52, see Fig.2A), and at Oz at 12 Hz (mean SNR in the frog sequence
= 11.14; mean SNR in the Snake sequence = 10.87).
Comparisons between SNR values in frog and snake sequences at 6 Hz at channel O1, and at 12 Hz at channel
Oz, did not reveal any signicant dierence (ps > 0.10; permutation statistics). ese results indicate that infants’
visual system synchronized successfully to the rapid presentation of animal pictures, and that this synchroniza-
tion did not dier signicantly between sequences.
Oddball frequency (discrimination response): Considering the SNR values averaged across the four harmonics
before base frequency (i.e., 1.2, 2.4, 3.6 and 4.8 Hz; see Methods for justications) for frog sequences, analyses
revealed a signicant response to frog pictures at electrodes PO3 and Oz (mean SNR = 1.84 and 2.27, ps < 0.05,
see Fig.2B).
Similar analyses revealed signicant responses to snake pictures at occipital (O1, Oz and O2; mean SNRs =
4.03, 4.11 and 4.17, ps < 0.005) and fronto-temporal channels (F7, FC5, T7, C3, CP5, and T8; mean SNRs between
1.84 and 2.68, ps < 0.05; see Fig.2B).
Examining the SNR values of each harmonic separately (see Fig.2A,B), a signicant response to frog pictures
was found at parieto-occipital sites (PO3 and O2) at the third harmonic (F = 3.6 Hz; mean SNRs = 4.60 and 3.18,
respectively, ps < 0.05). No other signicant response was observed at any electrode at the oddball frequency
and harmonics. Signicant responses to snake pictures were observed up to the fourth harmonic (i.e., at 1.2, 2.4,
3.6 and 4.8 Hz), at the medial occipital lobe (O1 and Oz, mean SNRs = 2.87–5.36 and 2.50–5.52, respectively;
ps < 0.05, see Fig.2A). At 1.2 Hz, signicant responses were also recorded at electrodes O2, C3 and P8 (mean
SNRs = 5.56, 4.24 and 3.47, respectively, ps < 0.05). At 3.6 Hz, a signicant response was also observed at elec-
trodes O2 and T7 (mean SNR = 5.01 and 4.15, p < 0.05).
To quantify dierences between frogs and snakes category-specic responses, we contrasted SNR values aver-
aged across the rst four oddball harmonics in frog and snake sequences. Signicant dierences emerged between
averaged SNR values in frog and snake sequences at electrodes O1, Oz, O2 and CP5 (ps < 0.03).
Time-domain analyses. We then explored EEG signals in the temporal domain to characterize the
spatio-temporal dynamics of selective responses to frogs and snakes.
e general response pattern for both frog and snake pictures was reminiscent of the typical pattern induced
by visual stimulation in infants, namely a negative deection at around 200–300 ms (i.e., the N290), followed by a
sustained positivity at around 400–600 ms (i.e., the P400), recorded over medial occipital regions41. Nevertheless,
Figure 2. Frequency-domain representation of frog and snake-selective responses during fast periodic visual
stimulation (le and right panels, respectively). (A) SNR spectra of each occipital electrode (O1, Oz, O2) and
topographical maps of SNR at the base frequency (6 Hz). Asterisks indicate signicant oddball responses. (B)
Topographical maps of SNR at each harmonic of the oddball frequency (1.2, 2.4, 3.6, and 4.8 Hz; le part), and
of SNR averaged on these rst four harmonics (right part). Asterisks indicate signicant responses at specic
channels.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

4
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
aer the appearance of frog pictures, only the time-window at around 450–500 ms proved to be signicant at O1
(p < 0.05) and peaked at 470 ms. Similarly, aer the appearance of snake pictures, only the time-window at around
390–570 ms proved to be signicant (ps < 0.05 for O1, Oz and O2, but also for FC5, C3, CP5, P8 and C4). is
positive component peaked at 460 ms over occipital channels.
Importantly, the amplitude of this component was signicantly larger at O2 when a snake than when a frog
picture was presented (p = 0.01, at around 420–490 ms). Figure3 displays the notch-ltered responses at O2 for
both frog and snake pictures.
Of note, the shape of the temporal response to frogs (i.e., 3 peaks and troughs of roughly similar amplitude)
explains why the third harmonic was predominant in SNR spectra.
Replication and clarication of the snake-specic visual features driving the eect. e aim of
this control study was two-fold. First, we wanted to ensure that the observed snake-specic infant brain response
was robust and not due to a novelty or pop-out eect of snakes (all having similar elongated limbless coiled body
shapes). Indeed, most of other presented animals, compared to snakes and despite their high variability, were
limbed (77% were quadrupeds) and had rather collected body shapes. Second, we wanted to specify the critical
features of snakes driving the specic brain response. In particular, we wanted to test the possibility that their
prototypical curvilinear coiled shape is important, as has been evidenced in previous behavioral studies12,14,15. To
do so, we exposed another smaller group of 7- to 10-month-old infants (n = 13, 8 females, mean age = 272 days,
SD = 43 days) to alternating snake and caterpillar sequences (i.e., in non-snake sequences, frog pictures from our
main study were replaced by caterpillar pictures, see Fig.1). Caterpillars were chosen as control stimuli since,
as snakes, they have long bodies and no prominent legs15. ey thereby visually dier from the animals used as
base stimuli, as snakes do. Crucially, the coiled posture is not characteristic of caterpillars; therefore none of our
pictures depicted coiled caterpillars. Hence, if the eect observed in the main study for snakes depends on specic
features of these animals such as their coiled shape – as previous studies have suggested and as predicted by the
Snake Detection eory, one might expect to replicate the eect observed in our main study, namely an increased
response to snakes than to non-snake animals (here, caterpillars). On the contrary, if the infant brain response
to snakes observed in the main study is due to their elongated shape (and thereby reects an eect of unspecic
features of snakes) or to the contrast between that shape and the collected shapes of the other animal pictures,
similar eects should be observed for snake and caterpillar pictures in the present experiment.
Figure 3. Time-domain representation of frog and snake-selective responses during fast periodic visual
stimulation. (A) Grand averages of the notch-ltered EEG responses relative to the onset of the frog and snake
stimuli, at O2. e red line below the waveforms represents the time-points at which the signal signicantly
deviates from baseline aer snake pictures. e grey area indicates the time-window at which the signal
signicantly diers between frog and snake pictures. Note that the amplitude scale is only approximate due to
the epoch-normalization scheme used (see methods). (B) Topographical maps of the P400 evoked by frog and
snake stimuli in the time-window at which the signal signicantly diers between them (i.e., 420–490 post-
stimulus onset).
Content courtesy of Springer Nature, terms of use apply. Rights reserved

5
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
Infants viewed between 9 and 23 sequences each (M = 12.92, SD = 3.68). Of the total amount of sequences
(n = 168), 57 were excluded on the basis of xed criteria, and one caterpillar sequence was randomly discarded to
ensure comparability of SNR measures between conditions (see Methods). Final analyses were therefore run on
55 snake and 55 caterpillar sequences.
Given the smaller sample of this control study compared to the main study, SNRs were obviously lower. We
therefore used a priori hypotheses based on results from the rst study. at is, for the appraisal of responses in
the frequency and time domains, we assessed only the responses at occipital electrodes, further averaged across
the four harmonics for the frequency domain.
Frequency domain analyses. Base frequency: In accordance with our selection of sequences (see Method),
grand-averaged SNR spectra showed clear responses at the rst and second harmonics of animal pictures pres-
entation rate (6 and 12 Hz, respectively). ese responses were characterized by a medial occipital topography
peaking at 6 Hz at electrode O2 for caterpillar sequences (mean SNR = 11.19) and at electrode Oz for snake
sequences (mean SNR = 12.14), and at 12 Hz at electrode Oz for both caterpillar and snake sequences (mean SNR
in the caterpillar sequences = 10.43, mean SNR in the snake sequences = 14.7, see Fig.4A).
Comparisons between SNR values in caterpillar and snake conditions at 6 Hz at channel O2 and Oz, and at
12 Hz at channel Oz, did not reveal any signicant dierence (ps > 0.50; permutation statistics).
Oddball frequency (discrimination response): Considering the SNR values averaged across the four harmonics
before base frequency (i.e., 1.2, 2.4, 3.6 and 4.8 Hz) for caterpillar sequences, analyses did not reveal any signif-
icant response to caterpillar pictures (all ps > 0.30, see Fig.4B). Similar analyses revealed signicant responses
to snake pictures at all three occipital channels (mean SNR at O1 = 1.97, mean SNR at Oz = 2.24 and mean
SNR at O2 = 1.95, ps < 0.05, see Fig.4B). Of note, when taking into account the complete set of electrodes as in
Experiment 1, analyses revealed very similar results: no signicant response to caterpillar pictures (all p > 0.10),
but signicant responses to snake pictures at Oz and O2 (ps < 0.05).
When contrasting SNR values averaged across the first four oddball harmonics in caterpillar and snake
sequences at occipital electrodes, a signicant dierence emerged between averaged SNR values in caterpillar and
snake sequences at electrode Oz (ps < 0.05). e dierence at electrodes O1 and O2 did not reach signicance
(p > 0.05). is analysis further supports the existence of snake-selective brain responses that are higher in ampli-
tude than (non-signicant) caterpillar responses.
Figure 4. Frequency-domain representation of caterpillar and snake-selective responses during fast periodic
visual stimulation (le and right panels, respectively). (A) SNR spectra of each occipital electrode (O1, Oz, O2)
and topographical maps of SNR at the base frequency (6 Hz). (B) Topographical maps of SNR averaged on the
rst four harmonics.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

6
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
Time-domain analyses. As in the rst study, although the general response pattern for snake pictures was rem-
iniscent of the typical pattern induced by visual stimulation in infants, only the time-window at around 380–
510 ms aer the appearance of snake pictures proved to be signicant (ps < = 0.001 for O1, Oz and O2). is
positive component peaked at 445 ms over occipital channels. e appearance of caterpillar pictures did not
evoke signicant EEG responses (all p > 0.25). Importantly, the amplitude of the response in the occipital region
was signicantly larger at O2 when a snake than when a caterpillar picture was presented (p = 0.004, at around
390–500 ms). e amplitude dierence at O1 and Oz did not reach signicance (ps = 0.09). Figure5 displays the
notch-ltered responses at O2 for both caterpillar and snake pictures.
Discussion
Recent behavioral and psychophysiological studies have shown that human infants, like their older peers, are
remarkable snake detectors19–23. Given infants’ innocence regarding the threat that snakes represent, these
findings support the idea that humans have a phylogenetic predisposition to rapidly detect evolutionarily
threat-relevant stimuli based on their physical attributes, originating from our ancestors’ vital need to spot pred-
atory snakes rapidly1. e present study provides novel electrophysiological evidence that this predisposition is
subtended by a neurobiological substrate that is functional early in development, independent of any prior expe-
rience with snakes, and sensitive to snake prototypical features.
Using a fast periodic visual stimulation approach, we examined infants’ neural responses to periodic oddball
pictures of snakes and non-snake control animals. Frogs were used as controls in the main study as they are
similarly unfamiliar, colorful and shiny, but threat-irrelevant compared to snakes15. Both types of stimuli gener-
ated a periodic response in the posterior region of the infant’s brain. Critically, glancing at snakes automatically
generated neural responses that were higher in amplitude and more widespread than those generated by frogs.
is dierential eect was replicated in a control study where we contrasted infant brain responses to snakes and
caterpillars, using the same design with a dierent smaller group of participants. ese ndings strongly support
the notion that humans have an early propensity – a possibly inborn predisposition – to rapidly detect specic
visual features of snakes.
Higher response to a specic stimulus category has been previously related to its relevance compared to
other visual categories42. Here, in line with the idea that the ancient predator-prey relationship between snakes
and primates shaped our visual system so that they are detected rapidly2,3,43, we argue that the evolutionary
threat-relevance of snakes compared to frogs and caterpillars is responsible for the enhanced response to snakes.
Figure 5. Time-domain representation of caterpillar and snake-selective responses during fast periodic visual
stimulation. (A) Grand averages of the notch-ltered EEG responses relative to the onset of the caterpillar
and snake stimuli, at O2. e red line below the waveforms represents the time-points at which the signal
signicantly deviates from baseline aer snake pictures. e grey area indicates the time-window at which
the signal signicantly diers between caterpillar and snake pictures. Note that the amplitude scale is only
approximate due to the epoch-normalization scheme used (see methods). (B) Topographical maps of the P400
evoked by caterpillar and snake stimuli in the time-window at which the signal signicantly diers between
both (i.e., 390–500 ms post-stimulus onset).
Content courtesy of Springer Nature, terms of use apply. Rights reserved

7
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
e fact that stronger neural activity was observed precisely in occipital areas strongly supports the idea that what
underlies this snake-selective discrimination response mainly relates to their visual physical attributes, echoing
results from behavioral studies14,15,23,44,45. Since infants are innocent regarding the snake matter, and given that
previous studies have clearly demonstrated that fear of snakes is not innate20,25,27, infants’ brain responses when
seeing a snake would not be linked to any fear or threat detection but rather to the detection of physical features
inherent to snakes that the primate and human brain would have developed to detect quickly and in priority. It
has been proposed that evolution shaped humans to develop visual templates for rapid recognition of animals that
represent a threat for their survival46. ese templates would integrate the low-level features and shapes of animals
that have been associated with danger throughout evolution, enabling their rapid identication.
e elongated coiled shape of snakes (common to all snakes in our study and characteristic of snakes in their
natural state) is most probably a distinctive and instrumental visual feature of these animals14,44. When responses
to coiled snakes were contrasted with responses to caterpillars – that also have elongated but not coiled body
shapes, snake-specic but not caterpillar-specic responses were observed in the occipital area. e absence of
responses to caterpillar pictures could be attributable to a lack of power since we had two times less sequences
for caterpillars than for frogs. In any case, responses to snakes diered from (non-signicant) responses to cat-
erpillars. ese results therefore evidence that infant brain responses to snakes are robust, and specic to snakes.
Importantly, they also demonstrate that snake-specic responses are not due to a novelty or “pop-out” eect of
their elongated body shape when presented among animals that, for the vast majority, have prominent limbs and
a collected shape. Otherwise, similar responses to caterpillar pictures would have also been observed.
Considering the high presentation rate and the heterogeneity (i.e., in terms of color, background and winding
style) of the snakes presented in this study, detection of snake prototypical attributes appears to be a highly e-
cient process. A possibility is that the infant brain actually responds to a combination of physical traits, or to even
more low-level features. For instance, recent studies have shown that snake scale patterns also are a determinant
factor in the fast detection of snakes by human and non-human primates47–49. In the pictures used in the present
study, snake scales are visible, although not close-up. At a more basic level, a core spectral feature of snakes that
is related to their coiled body shape – i.e., their high contrast energy in midrange spatial frequencies – has been
recently spotted as likely responsible for their conspicuity49,50. Interestingly, this feature is shared by many poison-
ous animals, which supports the adaptive value of a fast-acting visual mechanism responding to it50. Nevertheless,
as a counterpart, any stimulus that coincidently possesses this core spectral feature (although not being inherently
threat-relevant, such as holes50 or coiled wires14) might induce some form of aversion or fast detection, just as
snakes do, because of the survival value of such behavior50. In this view, the infant brain should also respond to
non-snake stimuli depicting prototypical snake features. Still, the fact remains that the infant brain reacts strongly
to snake-like features, not to frog- or caterpillar-like features, and this eect would most probably have evolution-
ary grounds.
Although diering in their amplitude, both snake and frog pictures generated a periodic response in the pos-
terior region of the infant brain, associated to a sustained positive ERP component. ese results provide further
evidence that 7- to 9-month-old infants can categorize stimuli at a basic level40 (in this case, animals according to
species). A single xation at these animals (i.e., pictures remained on screen less than 200 ms) was indeed su-
cient for the infants’ brain to discriminate these species from others, and to generalize across dierent exemplars
of the same species. However, caterpillar pictures did not evoke any signicant response. Although this null result
might be due to a lack of power (we had half of the sequences in Exp. 2 than in Exp. 1), it could also indicate that
the ability to categorize animals by species develops with age, with some species categorized more readily than
others. Future studies should examine this possibility further.
Examining the temporal dynamics of the neural responses revealed that snake pictures evoked a stronger
P400 component than frog and caterpillar pictures. e enhancement of an ERP component can generally be
interpreted as reecting an attentional eect51. is suggests that increased attention is devoted to snakes com-
pared to frogs or caterpillars, though at a rather late stage of neocortical processing. Crucially, it provides novel
electrophysiological correlates for prior behavioral ndings demonstrating that snakes capture infants’ atten-
tion19,21,22. In fact, as suggested elsewhere52, the higher P400 could reect feedback from anterior attentional
networks to posterior perceptual systems. Source analyses of the P400 component have indeed suggested that
anterior brain regions associated with attention contribute to a sustained P400 response in infants53. Periodic
responses to snakes recorded in fronto-temporal regions in the rst study could precisely reect the activation of
these anterior sustained attention networks.
e increase in P400 amplitude in response to snakes is also in line with previous ndings of a higher P400
response elicited by fearful than neutral and happy faces in 7-month-old infants54. Although faces dier from
snakes by their social nature and their high familiarity to infants, both are of great adaptive relevance for our
species, and preferential detection of face-like patterns has been shown to be independent from experience55,56.
Moreover, fearful faces, just as snakes, are evolutionarily threat-relevant stimuli. In this respect, both might pref-
erentially engage the subcortical visual system (involving the superior colliculus and the pulvinar for fast access
to the amygdala) that would be responsible for the rapid detection of and fast response to ancestral survival
threats43; although this view is still debated (see57 for further discussions). One possibility is that the observed
snake-sensitive increase in P400 amplitude would stem from the fast involvement of the subcortical visual system,
which would in turn trigger subsequent attentional biases towards snakes at the occipital level through extensive
connectivity with visual and associative neocortical sites57.
Admittedly, although we can be sure that none of our participants had previous experience with living snakes
nor were particularly familiar with these species (as conrmed by their parents), we cannot rule out the pos-
sibility that, despite their young age, they have been exposed to representations of snakes, such as in picture
books or through stued toys. However, it is very unlikely that, at that age, the caregiver pointed the potential
dangerousness of the animal depicted. Moreover, there is no reason to believe that such innocuous exposition to
Content courtesy of Springer Nature, terms of use apply. Rights reserved

8
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
animal representations would have occur more for snakes than for frogs or caterpillars. Replicating our ndings
in newborns would nevertheless provide unequivocal evidence that brain responses to snakes are inborn, and
functional from birth.
Overall, this study argues for the existence in humans of an inborn brain-anchored mechanism to swily
detect snakes, based on their perceptual features. It provides novel electrophysiological evidence supporting the
Snake Detection eory by demonstrating that neural systems enabling humans to detect evolutionarily rele-
vant threats are functional early in life, independent of any prior experience and sensitive to snake prototypical
features. More broadly, together with recent evidence that inexperienced chicks use visual cues to adequately
respond to dierent threats58, these results support the notion that the visual system of dierent species might
have been shaped by the need to detect predators and develop appropriate reactions.
Materials and methods
Experiment 1. Participants. Twenty-six full term 7- to 9-month-old infants (18 females) composed the
nal sample (mean age = 261 days, SD = 23 days). Five additional infants were tested but excluded from the
analysis (one due to excessive crying and four because their data indicated that they did not attend any of the
sequences in at least one condition, see below). e infants selected neither exhibited known developmental
diculties nor were they particularly familiar with frogs or snakes, as revealed by a questionnaire completed by
the accompanying parent (though we cannot exclude that they have been exposed to representations of frogs or
snakes, such as in picture books or through stued toys). e parents gave informed consent prior to testing. e
Psychological and Educational Sciences Faculty Ethics Committee of the Université libre de Bruxelles, and the
CUB Hôpital Erasme Ethics Committee approved the experimental protocol. e methods were carried out in
accordance with the approved guidelines and regulations.
Stimuli and procedure. Figure1 illustrates the experimental design. Infants were presented with visual stimuli
consisting of colored pictures of animals presented in their natural background, from dierent viewpoints. Base
stimuli consisted of 130 animal pictures from 13 dierent species (dogs, cats, shes, rabbits, horses, monkeys,
squirrels, cows, birds, gazelles, elephants, giraes and butteries; 10 exemplars of each) collected from the internet
(mostly from https://calphotos.berkeley.edu/fauna). Oddball stimuli consisted of 29 snake and 29 frog pictures
used in a previous study15. Snakes were all depicted coiled to maximize the animal/background ratio. None of
them was in a manifest attack posture. Frogs have oen been used as control non-snake animal stimuli15. ey
indeed resemble snakes in texture, brightness and color, and can be considered as similarly unfamiliar for infants
as snakes are. Pictures were resized to 200 × 200 pixels, and equalized in terms of luminance and contrast across
the whole set using Matlab (Mathworks, USA) to minimize low-level features.
Pictures were displayed at the center of a 60 Hz and 800 × 600 pixel resolution monitor, on a light grey back-
ground. At a looking distance of 40 cm, they subtended approximately 13 × 13 degrees of visual angle.
Stimuli were presented at a rate of 6 Hz (base stimulation frequency) using the Psychtoolbox for Windows in
Matlab 2013b (MathWorks Inc.). e stimulation cycle of each picture therefore lasted 166.7 ms (i.e., 1000 ms/6)
and began with a uniform grey background. Stimuli were presented through sinusoidal contrast modulation
(0–100%). Full contrast therefore reached its maximum at 83.35 ms (see Fig.1).
Stimuli were presented in sequences lasting 20.83 s, which were anked by a 1.67-s fade-in at the beginning of
the sequence, and by a 1.67-s fade-out at its end. is resulted in a total of 145 pictures per sequence, all of which
were dierent within sequences.
Each sequence consisted of successions of series of 4 base animal pictures and 1 oddball stimulus always
presented right aer. Oddball stimuli were either frog pictures for frog sequences, or snake pictures for snake
sequences. Pictures were randomly selected from each set. Frog and snake sequences were presented in alter-
nation. e rst sequence was chosen randomly. As a result, 16 in 26 infants started with a frog sequence. is
manipulation created a trial sequence containing changes at a frequency of 1.2 Hz (6 Hz/5) that could be directly
identied in the EEG spectrum as an index of discrimination by the infant’s visual system of frogs or snakes.
Infants were seated in a car seat in a dimly lit and quiet room. Parents were seated behind them and instructed
not to interact with their child. Infants viewed as many sequences as they were inclined to (M = 12.19, SD = 3.78,
range = 4–20). Looking behavior was monitored during the experiment by means of a webcam attached to the
computer screen. e experimenter initiated each sequence manually once the infant started looking at the
screen. When the infant looked away from the screen, the experimenter attracted his/her attention by means of
her voice or of a bell. If needed, breaks were provided between sequences. Testing took between 2 and 10 minutes
overall.
EEG acquisition. EEG signals were acquired at 1024 Hz using a BioSemi system (Amsterdam, Netherlands) with
32 electrodes arranged according to the standard 10–20 system locations and two additional reference electrodes.
Electrode oset was reduced to between ± 25 μV for each individual electrode by injecting the electrode with
saline gel.
Triggers were sent at the start of each sequence, indicating the type of sequence the participant was exposed to
(i.e. frog or snake sequence), and in between all successive images (i.e., when contrast is 0%).
EEG pre-processing. EEG pre-processing was carried out using Letswave 5 (http://letswave.org) running on
MATLAB R2017a (e Mathworks), following previously described procedures (see, e.g.34,38).
EEG data were rst ltered through 0.1–100 Hz using a FFT band-pass lter. Filtered data were then down-
sampled to 250 Hz to reduce data processing time. Trials were extracted from 2 s before sequence onset to 2 s aer
sequence oset (which served as baseline, see, e.g.38), resulting in 28.17 s segments. Sequences were further exam-
ined in the time domain for possible channel artifacts. Noisy channels were reconstructed by linear interpolation
Content courtesy of Springer Nature, terms of use apply. Rights reserved

9
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
of surrounding channels (for a maximum of three channels per infant). Electrode interpolation was applied in 15
infants. A common average reference computation was then applied to all channels. Pre-processed data segments
were then trimmed to exclude the fade-in and fade-out periods, resulting in 20.83-s stimulation sequences (25
cycles, 5210 bins in total).
A Fast Fourier Transform (FFT) was then applied to these segments. Frequency resolution (i.e., the inter-
val between adjacent frequency bins) was of 1/20.83 s = 0.048 Hz. For each sequence, signal-to-noise (SNR)
responses were computed as the ratio between the amplitude at each frequency bin and the average amplitude at
the 12 surrounding frequency bins (6 on each side, excluding the immediately adjacent bins, see37,39). SNR values
signicantly above 1 at oddball frequency would indicate specic discrimination of the oddball stimuli.
EEG sequences were discarded when the infant did not look at the screen for the majority of the sequence
(as noted online by the experimenter and further supported by the video recording). Only sequences with an
SNR above 2 at the base frequency in at least one of the medial occipital electrodes (O1, O2, Oz) were kept for
analyses (see37 and38 for similar procedures), the rationale being that the infant brain would not synchronize to
the stimulation frequency if (s)he is not looking at the screen. On average, ~3 sequences were excluded per infant
(M = 3.38, SD = 2.64, range 0–9). ere was no statistical dierence in the number of sequences kept in the frog
(M = 4.19, SD = 2.14, range = 1–8) and snake conditions (M = 4.62, SD = 1.86, range = 2–8; p > 0.10). We were
le with a total of 120 snake sequences and 109 frog sequences across all infants. Of the snake sequences, 11 were
randomly discarded to ensure comparability of SNR measures between conditions.
Frequency domain analyses. Further data processing was carried out with custom-made MATLAB code. A Fast
Fourier Transform (FFT) was applied to each of the kept sequences (109 snake and 109 frog sequences). For each
sequence, Fourier coecients were normalized by their mean amplitude across 0.6–1.8 Hz, a frequency range
that surrounds oddball frequency (~1.2 Hz). is procedure minimized the impact of epochs contaminated by
excessive movement artifacts by giving about the same weight to all sequences at ~1.2 Hz. For each condition
and electrode, amplitude spectra were obtained as the modulus of the averaged Fourier-transformed sequences.
Averaging was performed both across all sequences of a given type (frog or snake), yielding group-level amplitude
spectra, and within subjects, yielding subject-level amplitude spectra. Note that because the modulus was taken
aer averaging Fourier coecients, our derivation of amplitude spectra allowed for phase cancellation of activity
not phase-locked sequences. SNR responses were derived from the amplitude spectra as described above.
For the sake of statistical analysis, Z-scores were also calculated as the difference between amplitude
(group-level or subject-level) at each frequency bin and mean amplitude at the 12 surrounding frequency bins
(excluding the immediately adjacent bins, see below) divided by the standard deviation of the amplitude at these
12 surrounding bins. We hypothesized that snake pictures would generate stronger responses than frog pictures
at oddball frequency and harmonics.
Time-domain analyses. Periodic snake and frog responses were also investigated in the time domain, even
though the rapid presentation rate of the stimuli (resulting in overlapping evoked responses) and their diversity
do not provide ideal conditions for the investigation of event-related responses.
Preprocessed 20.83-s sequences were band-pass ltered through 0.5–29 Hz (zero phase shi Butterworth l-
ter, order 4), and notch ltered to selectively remove the contribution of the base stimulation frequency and its
rst four harmonics (6 to 24 Hz, FFT lter with a Hanning window of 0.1 Hz width, see42 for a similar proce-
dure). Sequences were then segmented into 24 epochs of exactly one cycle duration (5 pictures). Epochs started
166.7 ms before the onset of the category-specic event (−166.7 to 666.8 ms). Epochs were normalized by their
root-mean-square amplitude and averaged for each condition separately. Baseline correction was applied by sub-
tracting the mean amplitude in the −166.7 to 0 ms time-window.
We expected to observe larger amplitudes for snakes than frogs in the typical deections induced by visual
stimuli in the second half of the rst year of life, namely a negative deection at around 200–300 ms (i.e., the
N290), followed by a sustained positivity at around 400–600 ms (i.e., the P400), at occipito-temporal sites41.
Statistics. A non-parametric permutation-like test was used to estimate the statistical signicance of response
amplitude (group-level or subject-level) at oddball frequencies59. It specically tested the null hypothesis that
oddball stimuli elicit similar response as base stimuli, for each type of sequence separately. e test sought for
signicant response at all electrodes, with correction for multiple comparisons across electrodes. Such statistical
test was chosen because it can support claims of statistically signicant eect at each electrode separately, in
contrast with, e.g., cluster-based permutation tests60. Given that we did not expect multiple harmonics to have a
direct meaning in terms of underlying pathophysiological processes61,62, the response considered was the average
of the responses across the four rst harmonics of oddball frequency (1.2, 2.4, 3.6, and 4.8 Hz) in a rst step, and
the response at each of these individual frequency bins in a second step. Practically, the mean Z-scores across
tested frequencies (one value per electrode) were computed based on sequences in which either the rst or last
cycle was removed. A permutation distribution was then built by estimating 1000 times the maximum – across
tested electrodes – of the mean Z-score across tested frequency bins derived from sequences randomly trimmed
by a duration corresponding to the n = 0, 1, 2, 3, 4 rst images (n × 1/(1.2 Hz)) and 5–n last images. Trimming
the sequences randomized the position of the oddball images while preserving synchrony in image presentation
across sequences. Hence, this procedure destroys the phase locking – across sequences – of possible responses
specic to oddball images, that is needed for peaks at oddball frequencies to show in amplitude spectra. e sig-
nicance of the response at each tested electrode was computed as the proportion of values in the permutation
distribution that were above the observed value. is test, being akin to a permutation test59, is exact, and because
the permutation distribution was built on maximum values across electrodes, it intrinsically deals with the mul-
tiple comparison issue.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

10
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
A similar test was used to compare group-level responses between both types of sequences at oddball and
base frequencies separately. In that test, the Z-scores (oddball frequencies, mean across 1.2, 2.4, 3.6 and 4.8 Hz
and each of these frequencies in isolation; base frequency, 6 Hz and 12 Hz separately) – derived from untrimmed
sequences – were contrasted between conditions, and this contrast was compared to a permutation distribution
in which the maximum across all electrodes of such contrast value was obtained for 1000 random shues of frog
and snake sequences.
Permutation tests similar to those used in the frequency domain were used to assess the signicance of
group-level responses in the time domain. In these tests, responses in all electrodes were compared to a permu-
tation distribution in which the maximum across tested electrodes and all time points (from –167 to 667 ms) of
such response was obtained for 1000 random trimmings of the sequences.
Permutation tests were also used to assess the signicance of the contrast between conditions in the time
domain. To increase statistical power, the statistical assessment was performed on the mean of the contrast
response across the occipital electrodes. is response was compared to a permutation distribution in which
the maximum across time points of such response was obtained for 1000 random shues of frog and snake
sequences.
Experiment 2. Participants. irteen full term 7- to 10-month-old infants (8 females) composed the nal
sample (mean age = 272 days, SD = 43 days). ree additional infants were tested but excluded from the analysis
(one because his·her data indicated that he·she did not attend any of the sequences in at least one condition, and
two because more than three electrodes had to be interpolated). e infants selected neither exhibited known
developmental diculties nor were they particularly familiar with caterpillars or snakes. Prior to testing, written
informed consent was obtained from the parent for involving his·her child in the study and completing the ques-
tionnaire. Same Ethical Committees as in Experiment 1 approved the experimental protocol.
Overall procedure and analyses. Most of the procedure and analyses was identical to that used in Experiment 1.
Below, we list the dierences.
Frog pictures were replaced by caterpillar pictures. Stimuli were presented in sequences lasting 20 s, which
were anked by a 2-s fade-in at the beginning of the sequence, and by a 2-s fade-out at its end. Caterpillars were
chosen as non-snake control stimuli since, as snakes, they have long bodies and no prominent legs, but cannot
coil themselves15.
Infants viewed an average of 12.92 sequences (SD = 3.68, range 9–23). Testing took between 4 and 10 minutes
overall.
In EEG pre-processing, we extracted 20-s stimulation sequences that comprised 24 cycles of ve pictures (4
base and 1 oddball). e frequency resolution was of 1/20 s = 0.05 Hz. On average, ~4 sequences were excluded
per infant (M = 4.38, SD = 2.75, range 1–8). ere was no statistical dierence in the number of sequences kept
in the caterpillar (M = 4.31, SD = 2.06) and snake conditions (M = 4.23, SD = 2.05; p > 0.10). We were le with a
total of 55 snake sequences and 56 caterpillar sequences across all infants. One caterpillar sequence was randomly
discarded to ensure that SNR measures are comparable between conditions.
e datasets generated during and/or analyzed during the current study are available from the corresponding
author.
Received: 27 November 2019; Accepted: 27 March 2020;
Published: xx xx xxxx
References
1. Isbell, L. A. Snaes as agents of evolutionary change in primate brains. J. Hum. Evol. 51, 1–35 (2006).
2. Isbell, L. A. e Fruit, the Tree, and the Serpent. (Harvard University Press, 2009).
3. Öhman, A. & Minea, S. e malicious serpent: Snaes as a prototypical stimulus for an evolved module of fear. Curr. Dir. Psychol.
Sci. 12, 5–9 (2003).
4. Van Le, Q. et al. Pulvinar neurons reveal neurobiological evidence of past selection for rapid detection of snaes. Proc. Natl. Acad.
Sci. 110, 19000–19005 (2013).
5. Soares, S. C., Lindström, B., Esteves, F. & Öhman, A. e hidden snae in the grass: Superior detection of snaes in challenging
attentional conditions. Plos One 9, e114724 (2014).
6. Lipp, O. V., Derashan, N., Waters, A. M. & Logies, S. Snaes and cats in the ower bed: Fast detection is not specic to pictures of
fear-relevant animals. Emotion 4, 233–250 (2004).
7. LoBue, V. & Matthews, . e snae in the grass revisited: An experimental comparison of threat detection paradigms. Cogn. Emot.
28, 22–35 (2014).
8. LoBue, V., Matthews, ., Harvey, T. & Star, S. L. What accounts for the rapid detection of threat? Evidence for an advantage in
perceptual and behavioral responding from eye movements. Emotion 14, 816–823 (2014).
9. Ohman, A., Flyt, A. & Esteves, F. Emotion drives attention: Detecting the snae in the grass. J. Exp. Psychol. Gen. 130, 466–478
(2001).
10. Penunas, M. J. & Coss, . G. apid detection of visually provocative animals by preschool children and adults. J. Exp. Child Psychol.
114, 522–536 (2013).
11. Penunas, M. J. & Coss, . G. A comparison of rural and urban Indian children’s visual detection of threatening and nonthreatening
animals. Dev. Sci. 16, 463–475 (2013).
12. Masataa, N., Hayaawa, S. & awai, N. Human young children as well as adults demonstrate ‘superior’ rapid snae detection when
typical striing posture is displayed by the snae. Plos One 5, e15122 (2010).
13. Hayaawa, S., awai, N. & Masataa, N. e inuence of color on snae detection in visual search in human children. Sci. ep. 1, 80
(2011).
14. Lobue, V. & Deloache, J. S. What’s so special about slithering serpents? Children and adults rapidly detect snaes based on their
simple features. Vis. Cogn. 19, 129–143 (2011).
15. LoBue, V. & DeLoache, J. S. Detecting the snae in the grass: Attention to fear-relevant stimuli by adults and young children. Psychol.
Sci. 19, 284–289 (2008).
Content courtesy of Springer Nature, terms of use apply. Rights reserved

11
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
16. awai, N. & oda, H. Japanese moneys (Macaca fuscata) quicly detect snaes but not spiders: Evolutionary origins of fear-
relevant animals. J. Comp. Psychol. 130, 299–303 (2016).
17. Masataa, N., oda, H., Atsumi, T., Satoh, M. & Lipp, O. V. Preferential attentional engagement drives attentional bias to snaes in
Japanese macaques (Macaca fuscata) and humans (Homo sapiens). Sci. ep. 8 (2018).
18. Shibasai, M. & awai, N. apid detection of snaes by Japanese moneys (Macaca fuscata): An evolutionarily predisposed visual
system. J. Comp. Psychol. 123, 131–135 (2009).
19. Bertels, J., Bayard, C., Floccia, C. & Destrebecqz, A. apid detection of snaes modulates spatial orienting in infancy. Int. J. Behav.
Dev. 42, 381–387 (2018).
20. DeLoache, J. S. & LoBue, V. e narrow fellow in the grass: Human infants associate snaes and fear. Dev. Sci. 12, 201–207 (2009).
21. LoBue, V., Buss, . A., Taber-omas, B. C. & Pérez-Edgar, . Developmental dierences in infants’ attention to social and nonsocial
threats. Infancy 22, 403–415 (2017).
22. LoBue, V. & DeLoache, J. S. Superior detection of threat-relevant stimuli in infancy: reat detection in infancy. Dev. Sci. 13,
221–228 (2010).
23. aison, D. H. Do 5-month-old infants possess an evolved detection mechanism for snaes, shars, and rodents? J. Cogn. Dev. 19,
456–476 (2018).
24. Hoehl, S., Hellmer, ., Johansson, M. & Gredebäc, G. Itsy bitsy spider…: Infants react with increased arousal to spiders and snaes.
Front. Psychol . 8 (2017).
25. Erlich, N., Lipp, O. V. & Slaughter, V. Of hissing snaes and angry voices: Human infants are dierentially responsive to evolutionary
fear-relevant sounds. Dev. Sci. 16, 894–904 (2013).
26. rasher, C. & LoBue, V. Do infants nd snaes aversive? Infants’ physiological responses to “fear-relevant” stimuli. J. Exp. Child
Psychol. 142, 382–390 (2016).
27. LoBue, V., Bloom Picard, M., Sherman, ., Axford, C. & DeLoache, J. S. Young children’s interest in live animals. Br. J. Dev. Psychol.
31, 57–69 (2013).
28. Minea, S., eir, . & Price, V. Fear of snaes in wild- and laboratory-reared rhesus moneys (Macaca mulatta). Anim. Learn. Behav.
8, 653–663 (1980).
29. Seligman, M. E. P. Phobias and preparedness. Behav. er. 2, 307–320 (1971).
30. LoBue, V. What are we so afraid of? How early attention shapes our most common fears. Child Dev. Perspect. 7, 38–42 (2013).
31. LoBue, V., aison, D. H. & DeLoache, J. S. reat perception across the life span: Evidence for multiple converging pathways. Cu rr.
Dir. Psychol. Sci. 19, 375–379 (2010).
32. LoBue, V. &  aison, D. H. What we fear most: A developmental advantage for threat-relevant stimuli. Dev. ev. 33, 285–303 (2013).
33. Hoehl, S. & Pauen, S. Do infants associate spiders and snaes with fearful facial expressions? Evol. Hum. Behav. 38, 404–413 (2017).
34. Liu-Shuang, J., Norcia, A. M. & ossion, B. An objective index of individual face discrimination in the right occipito-temporal
cortex by means of fast periodic oddball stimulation. Neuropsychologia 52, 57–72 (2014).
35. ossion, B. Understanding individual face discrimination by means of fast periodic visual stimulation. Exp. Brain es. 232,
1599–1621 (2014).
36. egan, D. Human brain electrophysiology: Evoed potentials and evoed magnetic elds in science and medicine. (Elsevier, 1989).
37. Peyarjou, S., Hoehl, S., Pauen, S. & ossion, B. apid categorization of human and ape faces in 9-month-old infants revealed by fast
periodic visual stimulation. Sci. ep. 7 (2017).
38. de Heering, A. & ossion, B. apid categorization of natural face images in the infant right hemisphere. eLife 4 (2015).
39. Barry-Anwar, ., Hadley, H., Conte, S., eil, A. & Scott, L. S. e developmental time course and topographic distribution of
individual-level money face discrimination in the infant brain. Neuropsychologia 108, 25–31 (2018).
40. Peyarjou, S., Pauen, S. & Hoehl, S. How do 9-month-old infants categorize human and ape faces? A rapid repetition EP study:
Infants’ categorization of human and ape faces. Psychophysiology 51, 866–878 (2014).
41. de Haan, M., Johnson, M. H. & Halit, H. Development of face-sensitive event-related potentials during infancy: a review. Int. J.
Psychophysiol. 51, 45–58 (2003).
42. Jacques, C., etter, T. L. & ossion, B. A single glance at natural face images generate larger and qualitatively dierent category-
selective spatio-temporal signatures than other ecologically-relevant categories in the human brain. NeuroImage 137, 21–33 (2016).
43. Soares, S. C., Maior, . S., Isbell, L. A., Tomaz, C. & Nishijo, H. Fast detector/First responder: Interactions between the superior
colliculus-pulvinar pathway and stimuli relevant to primates. Front. Neurosci. 11 (2017).
44. LoBue, V. Deconstructing the snae: e relative roles of perception, cognition, and emotion on threat detection. Emotion 14,
701–711 (2014).
45. Souchet, J. & Aubret, F. evisiting the fear of snaes in children: the role of aposematic signalling. Sci. ep. 6 (2016).
46. aison, D. H. & Derringer, J. Do infants possess an evolved spider-detection mechanism? Cognition 107, 381–393 (2008).
47. Van Strien, J. W. & Isbell, L. A. Snae scales, partial exposure, and the Snae Detection eory: A human event-related potentials
study. Sci. ep. 7 (2017).
48. Isbell, L. A. & Etting, S. F. Scales drive detection, attention, and memory of snaes in wild vervet moneys (Chlorocebus
pygerythrus). Primates 58, 121–129 (2017).
49. awai, N. e fear of snaes: evolutionary and psychobiological perspectives on our innate fear. (2019).
50. Le, A. T. D., Cole, G. G. & Wilins, A. J. Assessment of trypophobia and an analysis of its visual precipitation. Q. J. Exp. Psychol. 68,
2304–2322 (2015).
51. Luc, S. J. & Hillyard, S. A. e operation of selective attention at multiple stages of processing: Evidence from human and money
electrophysiology. in e new cognitive neurosciences 687–700 (M. Gazzaniga, 2000).
52. Barry-Anwar, ., Hadley, H. & Scott, L. S. Dierential neural responses to faces paired with labels versus faces paired with noise at
6- and at 9-months. Vision es., https://doi.org/10.1016/j.visres.2018.03.002 (2018).
53. Guy, M. W., Z ieber, N. & ichards, J. E. e cortical development of specialized face processing in infancy. Child Dev. 87, 1581–1600
(2016).
54. Leppänen, J. M., Moulson, M. C., Vogel-Farley, V. . & Nelson, C. A. An EP study of emotional face processing in the adult and
infant brain. Child Dev. 78, 232–245 (2007).
55. Buiatti, M. et al. Cortical route for facelie pattern processing in human newborns. Proc. Natl. Acad. Sci. 116, 4625–4630 (2019).
56. eid, V. M. et al. e Human Fetus Preferentially Engages with Face-lie Visual Stimuli. Curr. Biol. 27, 1825–1828.e3 (2017).
57. Pessoa, L. & Adolphs, . Emotion processing and the amygdala: from a ‘low road’ to ‘many roads’ of evaluating biological
signicance. Nat. ev. Neurosci. 11, 773–782 (2010).
58. Hébert, M., Versace, E. & Vallortigara, G. Inexperienced preys now when to ee or to freeze in front of a threat. Proc. Natl. Acad.
Sci. 116, 22918–22920 (2019).
59. Nichols, T. E. & Holmes, A. P. Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum. Brain
Mapp. 15, 1–25 (2002).
60. Sassenhagen, J. & Draschow, D. Cluster-based permutation tests of MEG/EEG data do not establish signicance of eect latency or
location. Psychophysiology 56, e13335 (2019).
61. Heinrich, S. P. Some thoughts on the interpretation of steady-state evoed potentials. Doc. Ophthalmol. 120, 205–214 (2010).
62. Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. . & ossion, B. e steady-state visual evoed potential in vision
research: A review. J. Vis. 15, 4 (2015).
Content courtesy of Springer Nature, terms of use apply. Rights reserved

12
Scientific RepoRtS | (2020) 10:7443 | https://doi.org/10.1038/s41598-020-63619-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
Acknowledgements
is work was supported by a FRS – FNRS grant (F.4524.10). e rst author was a Post-Doctoral Researcher
of the Fonds de la Recherche Scientique-FNRS. Published with the support of the Fondation Universitaire
de Belgique. Note about animal pictures in Figure 1: For copyright reasons, the pictures of snakes, frogs and
caterpillars displayed in Figure 1 are dierent than those used in the actual experiment (originally coming from
Vanessa LoBue’s personal database), but the degree of variability across images is respected. Most of other animal
pictures come from CalPhotos (https://calphotos.berkeley.edu/). Credit for the photos are mentioned below.
Horse: Picture from Calphotos (https://calphotos.berkeley.edu/fauna). Credit: Gerald and Bu Corsi © California
Academy of Sciences Buttery: Picture from Calphotos (https://calphotos.berkeley.edu/fauna). Credit: T. W.
Davies © California Academy of Sciences Cow: Picture from Calphotos (https://calphotos.berkeley.edu/fauna).
Credit: © 2013 Simon J. Tonge Squirrel: Picture from Calphotos (https://calphotos.berkeley.edu/fauna). Credit:
Glenn and Martha Vargas © California Academy of Sciences Dog: Picture from Calphotos (https://calphotos.
berkeley.edu/fauna). Credit: © 2015 Simon J. Tonge Fish: Picture from Calphotos (https://calphotos.berkeley.
edu/fauna). Credit: Gerald and Bu Corsi © California Academy of Sciences Parrott: Copyright-free picture
from http://psitta.blogspot.com/2008/02/orange-winged-amazon.htmlElephant: Picture from Calphotos (https://
calphotos.berkeley.edu/fauna). Credit: H. Vannoy Davis © California Academy of Sciences Girae: Picture from
Calphotos (https://calphotos.berkeley.edu/fauna). Credit: Dr. Robert T. and Margaret Orr © California Academy
of Sciences Rabbit: Picture from Calphotos (https://calphotos.berkeley.edu/fauna). Credit: Gerald and Bu Corsi
© California Academy of Sciences Chimpanzee: Picture from Calphotos (https://calphotos.berkeley.edu/fauna).
Credit: Gerald and Bu Corsi © California Academy of Sciences Frogs, snakes and caterpillars: Copyright-free
picture from https://commons.wikimedia.org/.
Author contributions
J.B., A.D.H. and A.D. conceived the study. F.C. implemented the experiment. J.B. performed the main study, J.B.
and A.D.H. performed the control study. M.B. analyzed the data. J.B. and A.D.H. assisted in analyzing the data.
J.B. wrote the manuscript. X.D.T., A.D., M.B., A.C. and A.D.H. edited the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to J.B.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-
ative Commons license, and indicate if changes were made. e images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons license and your intended use is not per-
mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© e Author(s) 2020
Content courtesy of Springer Nature, terms of use apply. Rights reserved

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com