Developmental Science 18:6 (2015), pp 926–939

DOI: 10.1111/desc.12281

PAPER
Mapping the development of facial expression recognition
Helen Rodger, Luca Vizioli, Xinyi Ouyang and Roberto Caldara*
Department of Psychology, University of Fribourg, Switzerland

Abstract
Reading the non-verbal cues from faces to infer the emotional states of others is central to our daily social interactions from very
early in life. Despite the relatively well-documented ontogeny of facial expression recognition in infancy, our understanding of thedevelopment of this critical social skill throughout childhood into adulthood remains limited. To this end, using a psychophysicalapproach we implemented the QUEST threshold-seeking algorithm to parametrically manipulate the quantity of signals
available in faces normalized for contrast and luminance displaying the six emotional expressions, plus neutral. We thus
determined observers’ perceptual thresholds for effective discrimination of each emotional expression from 5 years of age up to
adulthood. Consistent with previous studies, happiness was most easily recognized with minimum signals (35% on average),whereas fear required the maximum signals (97% on average) across groups. Overall, recognition improved with age for all
expressions except happiness and fear, for which all age groups including the youngest remained within the adult range. Uniquely,
our findings characterize the recognition trajectories of the six basic emotions into three distinct groupings: expressions thatshow a steep improvement with age – disgust, neutral, and anger; expressions that show a more gradual improvement with age –sadness, surprise; and those that remain stable from early childhood – happiness and fear, indicating that the coding for these
expressions is already mature by 5 years of age. Altogether, our data provide for the first time a fine-grained mapping of thedevelopment of facial expression recognition. This approach significantly increases our understanding of the decoding of
emotions across development and offers a novel tool to measure impairments for specific facial expressions in developmental
clinical populations.

Research highlights

Introduction•

The ability to accurately decode complex emotional cuesin our social environment is a defining feature of humancognition and is essential for normative social development. How we recognize and process facial expressionsof emotion throughout development to reach maturity inadulthood is a pivotal question for developmental
psychologists, neuroscientists, educators, and caregivers
alike, who aim to trace both typical and atypicaltrajectories of this important social skill. Despite therelatively well-documented developmental course ofemotion recognition in infancy, it is acknowledged that
our understanding of the development of this importantsocial function throughout childhood, particularly after
the preschool years, remains limited (Mancini, Agnoli,
Baldaro, Bitti & Surcinelli, 2013; Thomas, De Bellis,
Graham & LaBar, 2007). This enduring gap in theliterature is surprising, especially for this stage ofdevelopment as opportunities for social learning increase•••

Our data provide a fine-grained mapping of thedevelopment of facial expression recognition for all
six basic emotions and a neutral expression.
Model fitting revealed that the developmental trajectories of facial expression recognition followed threetrends: Disgust, neutral and anger expressions
showed a steep improvement across development;
sadness and surprise showed a more gradualimprovement; whereas recognition of happiness andfear remained stable from early childhood, suggestingthat the coding for these expressions is alreadymature by 5 years of age.
Two main phases were identified in the developmentof facial expression recognition, ranging from 5 to
12 years old and 13 years old to adulthood.
This approach offers a novel psychophysical tool tomeasure impairments for specific facial expressions indevelopmental clinical populations.

Address for correspondence: Roberto Caldara, Department of Psychology, University of Fribourg, R. Faucigny 2, 1700 Fribourg, Switzerland;e-mail: roberto.caldara@unifr.ch© 2015 John Wiley & Sons Ltd

Mapping the development of facial expression recognition

greatly with the onset of school (Johnston, Kaufman,
Bajic, Sercombe, Michie et al., 2011) and evidence
suggests that the ability to recognize facial expressions
at age 5 predicts later social and academic competence(Izard, Fine, Schultz, Mostow, Ackerman et al., 2001).
In one of the most recent reviews of the development offacial expression recognition (FER) during childhood
and adolescence, Herba and Phillips (2004) specify theneed for normative data across this age range, not only
for a greater understanding of this vital social function
throughout development, but also to aid identification of
atypical emotional development. They further stress theneed for studies examining the continued development ofemotional expression recognition from childhood
through adolescence into early adulthood, as very little
is known about development across the full childhood
range up to adulthood.
Infant and childhood behavioral studies of facial
expression recognition have naturally employed diverse
methods according to the presence or absence of language,
making comparisons across these groups difficult. Two
common approaches employed during the stage of interest
here, from early childhood onwards, attempt to minimize
language ability confounds by using minimal verbal
communication. Matching and labeling tasks requireparticipants either to match emotional expressions froman array of expressions, or to label emotional expressions
in a forced choice paradigm or freely without constraints(Mondloch, Geldart, Maurer & Le Grand, 2003). Across
such studies there is general agreement that happiness ismost accurately recognized at the youngest age, while fear
is consistently one of the most difficult expressions torecognize. There is less agreement concerning the trajectory of the other basic emotions following mixed reports inthe literature; sadness and anger are frequently cited as
being recognized most accurately and earliest subsequent
to happiness, followed by surprise and disgust (Herba &Phillips, 2004; Widen, 2013). Some of the discrepancies
reported for developmental rates of expression recognition can be accounted for by task effects, as FER
performance has been shown to be task dependent (Vicari,
Snitzer Reilly, Pasqualetti, Vizzotto & Caltagirone, 2000;
Montirosso, Peverelli, Frigerio, Crespi & Borgatti, 2010;
Johnston et al., 2011). For example, even within the same
study very different results can be found for the same
expression depending on the task employed (Vicari et al.,2000). In this instance, performance for disgust across all
age groups in the labeling task was much lower than in thematching task. This difference is most likely attributed todiffering language demands of the labeling versus matching task, particularly as this trend was beginning to narrowin the oldest age group (9 to 10 years) possessing greater
language ability.

© 2015 John Wiley & Sons Ltd

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A third behavioral approach has more recently been
employed to examine children’s facial expression recognition accuracy as a function of expression intensity. The
motivation for such studies is derived from the fact that
we frequently see more subdued expressions of emotion
in daily life; therefore do older children recognize subtle
expressions of emotion more easily? Again, the results
reported in the literature have been mixed. One of theearliest studies found no association between age andintensity of expression as predicted (Herba, Landau,
Russell, Ecker & Phillips, 2006). Alternatively, inclusionof an adult group in a study investigating sensitivity tofear and anger expressions revealed that only adults had
significantly greater sensitivity to anger than bothchildren and adolescents, but for fear only differedsignificantly from children (Thomas et al., 2007). Quantitative differences determining the stage of development
when full maturity is reached for each emotional
expression can therefore only be established with theinclusion of an adult group, which has not previously
been adopted by all studies. A variety of emotional
expressions have also been included across behavioral
approaches, making some cross-study comparisons difficult. Only one study, Gao and Maurer (2010), has
included all six basic emotions in a non-computerizedtask to investigate sensitivity to expression intensity;
however, during the task the expressions were divided
into two subgroupings so all six emotions were notpresented at once together. Similarly, across studies there
has been variation in how developmental age groups aredefined, with most studies comparing age groups of
between 3 to 5 or more years difference, again making
some cross-comparisons of studies difficult. In sum,studies controlling for the continued development ofemotional expression recognition from childhood
through adolescence into early adulthood remain very
limited (Herba & Phillips, 2004). To the best of ourknowledge there is only one such developmental study
investigating relatively broad age groupings of between 3
to 5 years difference, from age 7 years up to adulthood(Thomas et al., 2007). However, this study was limited tofear and anger expressions only. Further, much of theresearch focuses on younger age groups, therefore
providing only a snapshot of differences at a particular
stage of development. Essentially, both empirical limitations leave the question of how the development of facial
expression recognition unfolds from early childhood up
to adulthood unresolved.
Targeting these prevailing gaps in the literature, theprimary aim of this study was to map for the first time
the continuous development of facial expression recognition in children aged 5 up to adulthood for each of thesix basic emotions and a neutral expression using a

928 Helen Rodger et al.

psychophysical approach. To the best of our knowledge,
this is the first time a psychophysical approach has beenused to investigate the development of facial expression
recognition. We used the QUEST adaptive staircase
procedure (Watson & Pelli, 1983) to establish eachparticipant’s recognition threshold for expression discrimination in a signal detection paradigm. The QUEST
procedure parametrically manipulated the quantity offace signals available in the stimulus to determine thethreshold signal strength at which an expression could be
categorized, with lower thresholds indicating more
effective discrimination. Based on previous developmental literature, we predicted a general improvement inrecognition thresholds with age, and distinct developmental trajectories for each of the expressions, withhappiness being recognized most easily at the lowest
threshold and fear with most difficulty at the highest
threshold.

Methods
Participants
One hundred and sixty individuals participated in the
study: 20 adults (M = 21.1 years, 18 females), 60 adolescents: 20 17–18-year-olds (M = 17.7 years, 13 females),20 15–16-year-olds (M = 15.7 years, 11 females), 20
13–14-year-olds (M = 13.5 years, 11 females), and 80
children: 20 11–12-year-olds (M = 11.5 years, 9 females),20 9–10-year-olds (M = 9.5 years, 10 females), 20 7–8year-olds (M = 7.5 years, 10 females) and 20 5–6-yearolds (M = 5.6 years, 10 females). Children were recruitedfrom local schools in the Fribourg and Glasgow regions,and parental consent was obtained for all children under
the age of 16. The study was approved by the Departmentof Psychology Ethics Committee at the University of
Fribourg.
Materials
The stimuli consisted of 252 grey-scale images from the€
KDEF (Lundqvist, Flykt & Ohman,1998) comprising
36 distinct identities (18 male) each displaying six facial
expressions (fear, anger, disgust, happy, sad, surprise)
and a neutral expression. Images were cropped aroundthe face to remove distinctive hairstyles using Adobe
Photoshop, and were aligned along the eyes and mouthusing Psychomorph software (Tiddeman, Burt & Perrett,2001). The images (256 9 256 pixels) were similarly
normalized for contrast and luminance using the SHINE
toolbox (Willenbockel, Sadr, Fiset, Horne, Gosselin
et al., 2010) in MATLAB 7.10.0 and displayed on an© 2015 John Wiley & Sons Ltd

800 9 600 grey background at a distance of 50 cm
subtending 10o 9 14o to simulate a natural viewing
distance during social interaction (Hall, 1966). The
stimuli were presented on an Acer Aspire 5742 laptop
using the Psychophysics toolbox (PTB-3) with MATLAB 7.10.0 and QUEST (Watson & Pelli, 1983), a
Bayesian adaptive psychometric method (described
below) to produce the level of stimulus intensity foreach trial. An external USB keyboard was attached to
the laptop so the experimenter could key the responses
on behalf of the child participants.
Procedure
Before participating, to familiarize the children with thecomputerized emotion recognition task, each child wasshown seven faces expressing the six basic emotions anda neutral expression on individually printed sheets ofpaper and asked to respond to the question, ‘How doyou think this person is feeling?’ To facilitate thefamiliarization task for the younger children in particular, the first image presented was always a happy face. Ifchildren were unsure of an emotional expression in thefamiliarization task they were told what the emotion was.
The children were then asked if they could repeat thistask by looking at images on a computer; however, thistime the faces would be slightly hidden or blurred so it
might be more difficult to see what the person was
feeling, but to please respond as well as they could.
Children aged 12 and under responded verbally and theexperimenter keyed the response. Children were also toldthat if they were unsure of an expression, or could not
sufficiently see the expression to make a judgement, they
could say ‘next’ and a new face would be presented. Such
responses were then coded as ‘don’t know’ by theexperimenter. Adolescent and adult participants weretold that they would see a series of faces expressing an
emotion and were asked to respond as accurately as they
could about which emotional expression they saw bypressing the corresponding key on the keyboard. Labels
were placed on the bottom row of keys for each of theseven expressions, and on the space bar for ‘don’t know’
responses. Adolescent and adult participants were given
as much time as they needed to familiarize themselveswith the response keys before beginning the experimentand were told that accuracy not response time wasimportant so to take as much time as needed and to lookat the keys if necessary before giving their response.
The experiment began with 14 practice trials to allow
participants to become familiar with viewing faces
covered with random noise. The transition from practice
trials to experiment proper was seamless so theparticipant was not aware that the initial trials were forMapping the development of facial expression recognition

practice only. At the beginning of each trial a fixation
cross was presented for 500 ms to attract the participant’s visual attention, followed by a 500 ms presentation of the face stimulus displayed at the estimated levelof signal strength from the QUEST psychometric
procedure (described below), directly followed by amask of random noise (see Figure 1 for an illustrated
example of a trial). The emotional expression stimuli
were displayed randomly and when the recognition
threshold for an expression was obtained (see section
below on the QUEST procedure for details), thatparticular expression was no longer displayed and only
images of the remaining expressions were sampled.
Keying a response triggered the subsequent trial, so
care was required with children to ensure that they wereready for the next stimulus presentation before theresponse was entered. The number of trials for eachparticipant varied as a function of the QUEST procedure (again described below), so for the youngest
children the experiment was paused at roughly midway and continued after a break.
The QUEST Bayesian adaptive psychometric procedure
QUEST is a psychometric function that uses an adaptive
staircase procedure to establish an observer’s threshold

Figure 1 Example trial. Each trial began with a fixation crosspresented for 500 milliseconds, followed by 500 millisecond
presentation of a randomly selected face expressing one of six
emotional expressions (happiness, surprise, fear, anger,
disgust, sadness) or a neutral expression (randomly sampledfrom the 252 available images) at a signal strength estimatedby the QUEST procedure, followed by a random noise mask
which remained on the screen until the user provided a
response and the next trial was initiated. All images werenormalized for contrast and luminance.

© 2015 John Wiley & Sons Ltd

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sensitivity to some physical measure of a stimulus, mostcommonly stimulus strength (Watson & Pelli, 1983). The
threshold obtained by the procedure therefore provides ameasure of how effectively an observer can discriminate
a stimulus. Adaptive staircase procedures obtain thethreshold by adapting the sequence of stimulus presentations according to the observer’s previous responses.
Adaptive staircase methods can therefore be seen as
more efficient in determining the observer’s perceptual
threshold for stimulus detection since the range ofstimuli presented is reduced by staying close to theobserver’s threshold by accounting for their previous
responses.
We adopted QUEST for this efficiency as it allowed us
to implement a paradigm including all seven expressions
at once for the first time in a developmental study. The
QUEST threshold-seeking algorithm was implemented
in MATLAB 7.10.0 with the Psychophysics Toolbox(PTB-3) to parametrically determine an observer’s perceptual threshold for discriminating each of the six
emotional expressions and a neutral expression. Adopting a signal-detection approach, QUEST was used toparametrically adapt the signal strength of the grey-scalefacial expression images presented to the participant byadding a mask of random noise to the image according
to the current signal strength parameter determined by
the function, based on the participant’s previous
performance. If the expression was accurately or inaccurately discriminated on a given trial, then thesubsequent signal strength estimate was decreased orincreased. The final threshold estimate is determined as
the signal strength where the expression is predicted tobe discriminated on 75% of trials. In this way equal
performance is maintained across observers. Three
QUEST procedures were implemented each with different initial stimulus strengths (60%, 40%, and 20%) toprevent possible bias in the final estimate towards the
direction of the initial value. The threshold for detecting
an expression was therefore the mean of the final
estimates from each of the three procedures. The QUEST
procedure terminates for an expression after threeconsecutive correct or incorrect trials in which the signal
strength standard deviations are less than 0.025. The
threshold is then calculated as the mean stimulus
strength of these trials.

Data analyses
Two-way mixed model ANOVA
To investigate the effect of age on emotion recognition
thresholds, we performed a mixed model repeated930 Helen Rodger et al.

measures ANOVA with emotional expression (7) as thewithin-subjects factor, and age group (8) as the betweensubjects factor.
The threshold estimated by QUEST is the bestindicator of performance using an adaptive staircase
procedure. The number of trials might not be indicativeof performance with this type of procedure, as a shortnumber of trials can be indicative of either a wellrecognized expression or a poorly recognized expressionthat terminated quickly due to consistent inaccurate
categorization. Conversely, a long sequence of trials canbe indicative of a mixed performance, which hasalternated between correct and incorrect responses.
For this reason we will not examine the number oftrials here.
Generalized Linear Model Regression analyses withbootstrap procedure
In order to characterize the decrease or increase in theamount of information required to accurately categorize
an expression across development, we fitted General
Linear Models (GLMs) across age groups independently
for each emotional expression. For each expression, we
sampled with replacement the participants’ mean recognition thresholds independently per group. We thencomputed the trimmed mean (30%) across 20 randomly
chosen participants (with replacement), and repeatedthis procedure 1000 times, leading to 1000 (samples) 9 7(emotions) 9 8 (age groups) threshold scores. We thenused GLM to fit a line across the 8 age groups
independently per emotion and sample, thus obtaining
7 9 1000 fitted linear models. We took the first derivative of each fitted line (which is equivalent to the betaobtained by fitting a GLM with an intercept), resulting
in 1000 derivative values. Each derivative thus indicatesthe rate of decrease/increase in the amount of information required to categorize an emotional expression
across age groups. To test whether the increase/decrease
across age groups in the amount of information required
to categorize an expression was significantly different
between emotional expressions we computed 95% confidence intervals (btCI) on the differences (across all
pairs of emotions) for our 1000 bootstrapped derivatives.
For a given comparison, btCIs non-overlapping withzero indicate that the rate of decrease across development is significantly different.
Similarity matrix and multidimensional scaling analyses
To further characterize the relationship between age andexpression recognition we computed a similarity matrix
by correlating the average recognition threshold for all© 2015 John Wiley & Sons Ltd

expressions across groups. We computed the mean acrossparticipants independently for each age group andemotional expression, leading to eight vectors (one pergroup) of seven expression thresholds. We then iteratively
Pearson correlated these vectors across all groups to
obtain our similarity matrix. Each value within thismatrix thus indicates the similarity of response profiles
between two age groups. To clarify which age groups
showed closest similarity in response profiles (i.e. calculated by correlating the vector of the mean recognition
thresholds for all expressions across two age groups)
during development we conducted multidimensional
scaling analysis with a metric stress criterion. This
produced an unsupervised arrangement (i.e. without
presupposing categorical structure) of the age groups
according to their response similarity (Torgerson, 1958;
Shepard, 1980; Edelman, 1998). Thus age groups placed
close together elicited similar response patterns.

Results
Mean expression recognition thresholds acrossdevelopment
The mean age group recognition thresholds for each
expression category are shown in Figure 2, which
provides a visual re-representation of the thresholds
required for expression categorization across age groups.
Overall, the mean recognition thresholds improve withage between the youngest and oldest age groups. As
predicted, the happy expression had the lowest perceptual threshold across all age groups. This expression
could be discriminated at very low signal levels from theyoungest age group. Conversely, all age groups showed
the highest perceptual threshold for fear. Almost a fullstrength signal was required by all age groups tocategorize this expression. Between the highest andlowest thresholds there was variation across age groups
in the ranking of the thresholds for the remaining
expressions. Figure 3 also illustrates the mean age groupthresholds, line-plotted for each expression.
ANOVA recognition thresholds by age group
The mixed model repeated measures ANOVA examining
the effects of age (8 age groups) and emotional expression revealed significant main effects for both age, F(7,152) = 11.4, p = .000, and emotional expression, F(4.46,
678.09) = 303.34, p = .006. The interaction between age
group and emotional expression was also significant,F(42, 152) = 3.76, p = .000, with Greenhouse-Geisser
corrections applied to the within-subjects factor.

Mapping the development of facial expression recognition

931

Figure 2 Mean recognition thresholds across development. Mean recognition thresholds for each expression category per age
group. Numbers in parenthesis report the  standard errors of the mean.

Generalized Linear Model regression analyses withbootstrap procedure
The General Linear Model regression analyses (Figure 4) revealed a general improvement across development in facial expression recognition for all expressions
except fear and happiness. The slope declines from thefitted models illustrate the level of decrease in recognition thresholds with age, and thus the decrease in theamount of information required to categorize an expression. Each emotional expression showed a uniquetrajectory across development and these trajectories
could be more broadly categorized into three groups:
expressions that showed a steep improvement in recognition with age up to adulthood – disgust, neutral, andanger; expressions with a more gradual improvement
across development – sadness, surprise; and expressions
that remained stable from age 5 up to adulthood –happiness and fear. The disgust expression showed thesteepest improvement in recognition with age, closely
followed by neutral. Alternatively, happiness and fear
showed no significant improvement across age with slope
derivatives remaining close to zero.

© 2015 John Wiley & Sons Ltd

Figure 5a illustrates the boxplots of the means of
the 1000 bootstrapped derivatives for each expression.
Mean derivatives closest to zero indicate no rate ofchange in recognition thresholds across age groups.
Recognition thresholds for fear and happiness withmean derivatives close to zero therefore did not
improve across development. The disgust, neutral andanger expressions with mean derivatives furthestfrom zero showed the steepest rate of improvement
in recognition thresholds across development. The
mean derivatives for sadness and surprise fall between
the no rate of change fear and happiness expressions
and the expressions showing the steepest rate of
improvement across development: disgust, neutral andanger.
Figure 5b shows the 95% confidence intervals (btCI)
on the differences across all pairs of emotions for the1000 bootstrapped derivatives. High and low CIs nonoverlapping with zero indicate significant differences
between the mean slopes of a given pair of derivatives;the 95% btCIs show that the rate of decrease acrossdevelopment for both fear and happiness differedsignificantly from all other expressions.

932 Helen Rodger et al.

Figure 3 Age group mean recognition thresholds plotted per facial expression of emotion. Error bars report the  standard errors ofthe means.

Similarity of emotion recognition thresholds acrossdevelopment
The correlation of group means for all seven expressions
between age groups is illustrated with a similarity matrix
in Figure 6a. Recognition thresholds were most similar
between age groups closest in proximity. More broadly,
the youngest age groups up to age 12 correlated well, as
did the older age groups from age 13 up to adulthood.
The multidimensional scaling analysis (Figure 6b) verified which age groups across development showed themost similar response profiles in overall mean recognition scores; the mean squared distances of age groups 5
to 12 clustered together showing similar overall response© 2015 John Wiley & Sons Ltd

patterns, as did the age groups from 13 up to adulthood,suggesting that there are two main phases during
development in the recognition of facial expressions ofemotion.
Response biases for emotion categories
Finally, to examine response biases we calculated confusion matrices for each expression and age group(Figure 7). For all age groups, fear was the mostcommonly confounded expression, as shown in the top
right-hand corner of the confusion matrices for each age
group. Fear had the highest confusion rate with surprise,reaching up to 40% in the 11–12 age group. The

Mapping the development of facial expression recognition

933

Figure 4 Linear Models: recognition thresholds across development. Slope decline of the fitted General Linear Model indicates thelevel of decrease in recognition thresholds with age. The derivatives were centred on the 11–12 age group for visualization purposes.
Disgust, neutral, and anger expressions show the greatest level of improvement in recognition with age. Recognition for sadness andsurprise improves more gradually with age whereas happiness and fear remain stable.

confusion rate for fear with disgust also increased
between the ages of 15 and 18 but remained lower thanthat of fear and surprise. The second most commonly
confounded expressions were disgust and anger, reaching
30% for 7–8-year-olds and to a lesser extent disgust withsadness, reaching 21% for 9–10-year-olds. Lastly, sadness
was most frequently confounded with the neutral
expression across all age groups, with rates resting
between 15 and 20%.

Discussion
Our results provide a fine-grained mapping of thedevelopment of facial expression recognition for all six
basic emotions and a neutral expression throughout
childhood into adulthood. Using a novel psychophysicalapproach that maintains equal performance acrossobservers and facial expressions of emotions, we parametrically manipulated the quantity of face signals
available to determine an observer’s perceptual thresholdfor each of the six basic emotions and a neutral
expression. By controlling for low-level properties suchas contrast and luminance, we precisely estimated thequantity of signal necessary to achieve effective recognition for all basic facial expressions of emotion for the
first time with young children up to adulthood. The
precision and novelty of this approach therefore offer
new insight into the understanding of how the development of facial expression recognition unfolds acrossdevelopment.

© 2015 John Wiley & Sons Ltd

Overall, recognition accuracy improved with age forall expressions except fear and happiness, for which all
age groups including the youngest remained within theadult range. Across development, happiness was theeasiest expression to recognize as it was correctly
categorized with minimum signals, whereas fear was
the most difficult requiring maximum signals. This result
confirms the particular status of both facial expressionsof emotion, suggesting that the coding for these expressions is already mature at 5 years old. While fear andhappiness were the most difficult and easiest expressions
to recognize, the developmental profile of each expression was unique. Unique developmental trajectories forthe recognition of individual facial expressions of emotion have been reported in the literature previously andour results provide further evidence of this uniqueness(Boyatzis, Chazan & Ting, 1993; Vicari et al., 2000;
Herba & Phillips, 2004). In particular, our findings
characterize the unique trajectories in recognition of thesix basic emotions into three distinct groupings: expressions that show a steep improvement in accuracy withage up to adulthood – disgust, neutral, and anger;
expressions with a more gradual improvement acrossdevelopment – sadness, surprise; and expressions thatremain stable from age 5 up to adulthood – happinessand fear. Two main stages in the development of facial
expression recognition were also identified. In the firststage, between the ages of 5 and 12 years, recognition
thresholds across expressions followed a similar response
profile and developed progressively. The second stage ofdevelopment began with the onset of adolescence and934 Helen Rodger et al.

(a)

(b)

Figure 5 (a) Boxplot of the derivatives from the bootstrap populations. Boxplots of the derivatives from the bootstrap populations of
the lines fitted across age groups independently per expression recognition threshold. The central mark reports the median of the
distribution, the edges of the box are the 25th and 75th percentiles (i.e. interquartile range – iqr), the whiskers extend to a maximumof 1.5 times the length of the iqrs. Values falling 1.5 times outside the iqr are considered outliers and plotted as red crosses (‘+’).
Smaller values indicate greater slope decline and therefore greater improvement in recognition with age. (b) 95% bootstrapped
confidence intervals of the mean slope derivatives for all possible expression comparisons. The upper and lower red lines depict thehigh and low 95% bootstrap confidence intervals (CIs), respectively. High and low CIs that both fall on either side of zero,
represented by the dashed line, indicate significant differences between mean slope derivatives across two conditions. Both fear andhappiness differ significantly from all the other facial expressions of emotion.

continued up to adulthood as recognition thresholds
across expressions during this stage also showed similar
response profiles. It is worth noting that such response
patterns were related to similar mean recognition
thresholds for all of the expressions (see Figure 2),
ruling out the possibility that the high correlation values
were a result of significantly lower overall thresholds
between two age groups. Within the second stage
described here, our data do not clearly distinguishwhether there is an additional period during earlyadolescence where the overall threshold diverges fromthe oldest three age groups. Further studies including© 2015 John Wiley & Sons Ltd

more measures are necessary to clarify this patternduring early adolescence. Altogether, our data showthat the recognition of facial expressions of emotion does
not follow a unique monotonic dynamic throughout
development.
Emotional expressions with a steep improvement inrecognition across development: disgust, neutral, anger
Within the first grouping of expressions showing a steep
improvement in performance with age, anger has similarly been found to show a sharp increase duringMapping the development of facial expression recognition

(a)

935

(b)

Figure 6 Similarity matrix of recognition thresholds by age group. (a) Similarity matrix (i.e. matrix of correlated pair values) of themean group threshold profiles, for all six expressions plus neutral. Dark red indicates high similarity, and the values on the diagonal
are of 1. (b) Multidimensional Scaling Analysis: Euclidean distances of overall mean group thresholds.

Figure 7 Facial expression of emotion categorization errors – confusion matrices. Response biases for expression categorization
across age groups (%).

development in several other studies (Montirosso et al.,2010; Gao & Maurer, 2010; Thomas et al., 2007; Vicari
et al., 2000), although its comparative trajectory with allseven expressions at once has not been identified
previously as we show here. In studies examining
recognition performance as a function of expression
intensity, somewhat closer to the psychophysical methodology employed here, all have shown a sharperincrease in recognition of anger but not disgust withthe exception of Herba et al. (2006) who report steeper
improvements for both disgust and fear but not anger.
Thomas et al. showed a marked increase in sensitivity toanger from adolescence to adulthood, but examined only
fear and anger expressions. While the study did notinvestigate the neural underpinnings of this result© 2015 John Wiley & Sons Ltd

directly, Thomas et al. suggest that later developmentin the recognition of anger fits with neurologicalevidence as the PFC continues to develop throughout
adolescence, with the orbitofrontal cortex in particularbeing implicated in anger recognition (Murphy, NimmoSmith & Lawrence, 2003). In addition to neurobiologicalaccounts for later maturation of anger recognition, theeffect of experience has also been evidenced as children
growing up in hostile environments show higher accuracy for anger than typically developing children (Pollak& Sinha, 2002). More broadly, cultural differences havebeen shown in face recognition (Blais, Jack, Scheepers,
Fiset & Caldara, 2008) and the expectations of facial
expression signals and how these signals are decoded in
adult populations, so socio-cultural experience also

936 Helen Rodger et al.

impacts recognition of expressions (Jack, Caldara &Schyns, 2012a; Jack, Blais, Scheepers, Schyns & Caldara,
2009; Jack, Garrod, Yu, Caldara & Schyns, 2012b). Here,younger children showed comparatively more difficulty
in recognizing anger than the expressions showing more
gradual trajectories, and were more likely to confoundanger with a neutral expression as opposed to disgust as
the four oldest age groups did. Anger recognition did notreach adult-like performance until the oldest adolescentage group which, coupled with low miscategorization
rates across age, suggests that the late maturation ofanger recognition could reflect accumulated exposure to
this expression which is frequently masked in various
social contexts (Underwood, Coie & Herbsman, 1992).
Previously reported trajectories for disgust recognition
have been mixed. Most recently, an emotional intensity
study found that accuracy for disgust remained at asimilar level in children between the ages of 5 and10 years; however, disgust was tested alongside surpriseand fear expressions only, so it is possible that its
distinctness from these other expressions could havecontributed to the stable performance (Gao &Maurer,2010). An emotion intensity study using dynamic stimuli
similarly did not find an age-related improvement for
disgust between preschool and adolescence, but foundthat anger improved consistently from school age to lateadolescence as we report (Montirosso et al., 2010).
Conversely, an earlier study reports a steep developmental improvement on a labeling task for disgust in children
aged 5 to 10, but a ceiling effect for disgust on amatching task using the same stimuli (Vicari et al.,2000). Since the visuo-spatial configuration of disgust isvery distinctive, the authors suggest that the steeper
developmental improvement in the labeling task occurs
because of greater lexico-semantic abilities in older
children. However, when tested directly a more recent
study found that verbal ability does not significantly
impact FER whereas labeling ability does (Herba,
Benson, Landau, Russell, Goodwin et al., 2008). We do
not attribute the steep improvement in disgust foundhere to greater labeling ability in older children since we
accepted responses from younger children such as ‘hedoesn’t like it’ or more simply a ‘yuck’ noise as accurate
labels of disgust. Considering the more stable performance in disgust recognition found in the emotion
intensity studies described above, methodological differences possibly account for this. Notably, the thresholdobtained by this paradigm was a measure that wasadapted from the observer’s previous response accuracy.
Previous studies have established intensity measurement
increments a priori rather than on a trial-by-trial basis,and these increments have tended to be large so can lackthe sufficient sensitivity to identify developmental© 2015 John Wiley & Sons Ltd

differences in emotion processing where an adaptive
measure permits greater sensitivity. Moreover, it isimportant to distinguish that while both paradigms use
techniques to parametrically reduce the strength of theoriginal expression to establish an observer’s recognition
sensitivity, one provides a measure of the intensity at
which an expression can be recognized, while the otheridentifies the quantity of information required for
accurate expression recognition.
Lastly, neutral, the final expression of the steep
increase with age category, was not included in any ofthe emotional intensity studies previously discussed here
as a distinct emotion category since intensity increments
are defined by morphing neutral and emotional expressions together. In general, neutral expressions have been
under-investigated in behavioral studies so very little is
known about how neutral expressions are perceived
during childhood. An early review states that children
have difficulty recognizing neutral expressions, and to
our knowledge no recent behavioral studies haveaddressed the development of this expression specifically(Gross & Ballif, 1991). Our finding of a steep increase inimprovement between the youngest and oldest age
groups accords with this reported early difficulty andcould be explained by a general bias to attend more toemotive faces throughout our social experiences(Leppanen & Nelson, 2009).
Emotional expressions with a gradual improvement inrecognition across development: sadness and surprise
Sadness showed a gradual improvement in recognition
across development and followed a similar trajectory tosurprise. Generally, corresponding with our findings,
children have been shown to perform well in recognizing
sadness (Herba & Phillips, 2004; Widen, 2013). Several
studies have shown that children aged 5 to 6 years do not
perform as well as older children or adults, which accords
with the slower developmental trajectory reported here
(Vicari et al., 2000; Gao & Maurer, 2010; Montirosso
et al., 2010), and this trend has been more frequently
shown in studies of emotion intensity, with the exceptionof Gao and Maurer (2009) who found that children as
young as 5 can recognize expressions of sadness as
accurately as adults. Previous studies have shown thatsadness is frequently confounded with fear, disgust, or
neutral expressions, but have not tested all seven expressions together at once so confusion rates varied according
to which expressions sadness was categorized alongside.
We show that sadness was most frequently confoundedwith neutral expressions across all age groups. To establish
whether this miscategorization is simply due to closer
similarity in the facial configurations of these twoMapping the development of facial expression recognition

expressions, further studies are needed to determine
information use that leads to both accurate and inaccurate
categorization across development, as a reduction in
miscategorization with age is also shown here.
Surprise similarly showed a more gradual improvement in recognition accuracy across development. Of theemotion intensity studies that we have focused on
because of their closer similarity to the psychophysicalmethod adopted here, similarly to the neutral expression,development in the recognition of surprise is not well
documented. Inconsistency across the range of expressions tested at this stage of development was one of the
motivations for conducting this study with all six basic
emotions and a neutral expression. More generally, othermethodologies have shown that surprise is recognized at
a later stage of development than other expressions(Herba & Phillips, 2004; Widen, 2013); however, wefound that when all expressions are compared together,
the developmental trajectory for surprise is more gradual, with younger children performing well in recognitionof surprise but at the same time showing higher
confusion of surprise with fear than older age groups.
Emotional expressions that remained stable from earlychildhood: happiness and fear
Robust recognition of happiness from an early age wasdemonstrated by this paradigm as even with a very rapid
presentation time of 500 milliseconds in comparison toprevious developmental studies, and distortion of theemotional expression with a random noise mask,
performance for happiness was highest and remained
similar across age groups. Other studies have similarly
found that children as young as 5 can recognize a happy
expression as well as adults, and that happiness is thefirst expression to be accurately recognized (Gao &Maurer, 2009; Herba & Phillips, 2004; Gross & Ballif,
1991). As mentioned above, of the emotional intensity
studies cited here, Herba et al. (2006) found a steeper
developmental trajectory in the recognition of fear than
we report, but the majority of studies showed findings
consistent with a more gradual improvement (Thomas
et al., 2007; Gao & Maurer, 2009, 2010). However,previous emotional intensity studies have shown higher
performance in recognition of fear at lower levels ofintensity than we report, where almost maximum signals
were required across development to categorize fear.
While intensity and signal strength provide distinct
measures, the high signal strength required here can be
explained by the high variance in miscategorization rates
for fear across development which prevented lower
threshold rates from being achieved. Such variancecould not be achieved in previous studies as they have© 2015 John Wiley & Sons Ltd

937

not included all seven expressions simultaneously. Fear
was most consistently miscategorized as surprise at thehighest rate across development of between 22 to 37%,
and variability in miscategorizations was much greatercompared to other expressions as confusions were foundwith all other expressions except happiness across age
groups. The high confusion rate for fear and highvariability of these confusions indicates that below a fullsignal level information was insufficient to categorize
fear.
Fear as the most difficult or one of the most difficult
expressions to accurately recognize with static images is
consistently reported in both the developmental behavioral literature (Gross & Ballif, 1991; Herba & Phillips,
2004; Widen, 2013) and the literature on adults (Rapcsak, Galper, Comer, Reminger, Nielsen et al., 2000;
Calder, Keane, Manly, Sprengelmeyer, Scott et al.,2003); however, this difficulty is frequently juxtaposedwith the evolutionary argument that accurate recognition of fear is critical to our survival in comprehending
environmental threats. Fear is perhaps the strongest
multisensory expression; for instance, people may shout
when expressing fear. Consequently, our results andprevious results showing difficulty in the categorizationof fear suggest that this expression requires additional
information to be effectively recognized, when presented
as a static image in conjunction with several expressions.
Additional cues from other modalities, and body posture
or context, may enable more consistent recognition(Aviezer, Hassin, Ryan, Grady, Susskind et al., 2008).
Overall, our data show that fear and happiness share a
special status in the framework of facial expression
recognition as the coding for these expressions is alreadymature by 5 years of age.
Lastly, whether the perceptual mechanisms governing
facial expression recognition are holistic or feature
based, or whether each type of processing plays adifferential role according to particular facial expressions
is still debated in the adult literature (Beaudry,
Roy-Charland, Perron, Cormier & Tapp, 2014).
Although this question is outside the scope of our study,
it should be acknowledged that if holistic processing wasaffected by the use of noise to control for the quantity ofsignal, then all expressions were equally affected by thismanipulation. Therefore, this potential impairment toholistic processing does not straightforwardly accountfor the differences in recognition thresholds acrossexpressions and age groups. Our results and previous
work (Jack et al., 2009) would instead favor a featurebased processing account for facial expression recognition. However future developmental studies are necessary to directly address this issue, with paradigms
controlling for facial feature information and metrics.

938 Helen Rodger et al.

Conclusion

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Acknowledgements
We would like to thank all of the children who
participated in our study from the following schools in
Switzerland and Scotland: Ecole Primaire de Marly Cite,
Swinton Primary School, Larbert High School, Ecole
Enfantine et Primaire de Marly Grand Pre, Cycle
d’Orientation de Perolles and Ecole de Culture Generale
Fribourg. We would also like to thank the teachers fortheir patience with classroom interruptions, and Head
Teachers, Claude Meuwly, Michelle Wright, Dominique
Brulhart; Principal Teachers, Paul Rodger, Claude
Fragniere; and Technician Christophe Pochon, for organizing the logistics of our visits, which ensured thateverything ran smoothly. We would also like to thank
Stephanie Cauvin, Berangere Prongue, Elena Bayboerek,
Sonia Pupillo, and Sarah Degosciu for their help withdata collection. This study was supported by the
National Center of Competence in Research (NCCR)
Affective Sciences financed by the Swiss National
Science Foundation (no. 51NF40-104897).

© 2015 John Wiley & Sons Ltd

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Received: 21 March 2014
Accepted: 16 October 2014

Supporting Information
Additional Supporting Information may be found in the online
version of this article:
Figure S1: Raw Scores