Journal of Experimental Child Psychology 174 (2018) 41–59

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Journal of Experimental Child
Psychology
journal homepage: www.elsevier.com/locate/jecp

Quantifying facial expression signal and intensity
use during development
Helen Rodger ⇑, Junpeng Lao, Roberto Caldara ⇑Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg, 1700 Fribourg, Switzerlanda r t i c l e

i n f oArticle history:
Received 19 October 2017
Revised 10 May 2018

Keywords:
Facial expressions
Emotion recognition
EmotionDevelopment
Psychophysic
Bayesian modellinga b s t r a c t
Behavioral studies investigating facial expression recognition during
development have applied various methods to establish by which
age emotional expressions can be recognized. Most commonly,
these methods employ static images of expressions at their highest
intensity (apex) or morphed expressions of different intensities, butthey have not previously been compared. Our aim was to (a) quantify the intensity and signal use for recognition of six emotional
expressions from early childhood to adulthood and (b) compare
both measures and assess their functional relationship to better
understand the use of different measures across development.
Using a psychophysical approach, we isolated the quantity of signal
necessary to recognize an emotional expression at full intensity andthe quantity of expression intensity (using neutral expression image
morphs of varying intensities) necessary for each observer to recognize the six basic emotions while maintaining performance at 75%.
Both measures revealed that fear and happiness were the mostdifficult and easiest expressions to recognize across age groups,
respectively, a pattern already stable during early childhood. The
quantity of signal and intensity needed to recognize sad, angry, disgust, and surprise expressions decreased with age. Using a Bayesian
update procedure, we then reconstructed the response profiles forboth measures. This analysis revealed that intensity and signal
processing are similar only during adulthood and, therefore, cannot
be straightforwardly compared during development. Altogether, ourfindings offer novel methodological and theoretical insights andtools for the investigation of the developing affective system.
Ó 2018 Elsevier Inc. All rights reserved.

⇑ Corresponding authors.
E-mail addresses: helen.rodger@unifr.ch (H. Rodger), roberto.caldara@unifr.ch (R. Caldara).
https://doi.org/10.1016/j.jecp.2018.05.005
0022-0965/Ó 2018 Elsevier Inc. All rights reserved.

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H. Rodger et al. / Journal of Experimental Child Psychology 174 (2018) 41–59

Introduction
Perceiving the emotions of others is fundamental to our daily interactions from birth, and by adulthood most have developed the capacity to read the emotional cues of others effortlessly. How we
become proficient in reading the emotional cues of others is a critical developmental question because
it is well recognized that impaired emotion processing has negative consequences on social functioning and well-being at all stages of development (Carton, Kessler, & Pape, 1999; Feldman, Philippot, &Custrini, 1991; Izard et al., 2001; Nowicki & Duke, 1992). Much research has focused on how we recognize emotion from facial expressions because they are one of the most prevalent cues that communicate our internal affective states.
Different behavioral paradigms have been adopted to understand how our ability to process facial
expressions of emotion develops by measuring changes in recognition performance across the lifespan. The broad aim of developmental studies of facial expression recognition, therefore, is to chart
at which age specific emotions can be accurately recognized. In doing so, trajectories representing typical development can be identified and, consequently, early identification of impairments in emotion
processing is possible. However, the application of different methods, varied developmental age
groups, and subsets of facial expressions tested do not provide a uniform picture of how this ability
unfolds during childhood and makes comparisons across studies and age groups difficult (Herba &Phillips, 2004). For example, to date, much of the developmental research on facial expression recognition has targeted infancy and preschoolers (Mancini, Agnoli, Baldaro, Ricci Bitti, & Surcinelli, 2013;
Thomas, De Bellis, Graham, & LaBar, 2007), and few studies address the continued development offacial expression recognition throughout childhood and adolescence up to adulthood (Herba &Phillips, 2004; Rodger, Vizioli, Ouyang, & Caldara, 2015). No study has directly compared facial expression recognition tasks that use emotional expressions of full intensity with those that use morphed
expressions of varying emotional intensities.
To address these discrepancies in the literature, our study focused on the continued developmentof facial expression recognition from school-aged children of 5 years of age up to adulthood. In aprevious study, we mapped the development of recognition of six facial expressions of emotion
and a neutral expression using a novel psychophysical approach (Rodger et al., 2015). Here, we compared two distinct measures of facial expression recognition using a psychophysical approach to
study the continued development of emotion recognition throughout childhood and adolescence.
This approach gives a precise measure of recognition performance across development as the quantity of signal (random image noise blended with emotional facial expression images) or intensity(neutral to facial expression image morphs) is parametrically manipulated. The signal condition iscomparable to conventional facial expression recognition categorization tasks that use expressions
with 100% phase signals, whereas the intensity condition is similar to tasks using parametric morph
designs with expressions of different intensities (however, the intensity increments have been predetermined in studies up until now). The methodological novelty of our psychophysical approachconsists in increasing the sensitivity for both tasks by determining an unbiased fine-grained threshold for the effective categorization of facial expressions with a response-driven approach. The theoretical novelty lies in our investigation of whether such commonly used paradigms in the literature
relate to the same categorization processes across development or not given that up until now they
have been considered interchangeable. To the best of our knowledge, a straightforward relationshipbetween these two measures across development has always been assumed but has never beentested empirically.

Common behavioral methods in the study of the development of facial expression recognition
The most common behavioral methods to investigate facial expression recognition during childhood include matching and labeling tasks and studies of expression intensity. Each method has its
strengths and aims to uncover specific features of emotion processing at a given stage of development.
Comparison of these common methods can reveal what is consistently found for facial expression

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43

recognition during a developmental stage and where methodological gaps or inconsistencies exist.
After reviewing these common methods, we describe the novel psychophysical approach we applied
to investigate the development of facial expression recognition.
Matching and labeling tasks
Facial expression matching tasks have been employed most frequently in developmental studiesof the previous decade. Matching tasks require the child to match one image of an expression toanother image of an expression or to one image among several images. Studies using matching tasks
with two-, three-, or four-alternative forced choices to the target expression have found that recognition performance progressively improves between 4 and 10 years of age (Bruce et al., 2000;
Mondloch, Geldart, Maurer, & Le Grand, 2003; Vicari, Snitzer-Reilly, Pasqualetti, Vizzotto, &Caltagirone, 2000). Such tasks also show converging high-level performance by 10 years. Using a
two-alternative forced-choice matching task, accuracy had reached nearly 100% by 10 years in astudy that consequently classified this type of task as ‘‘easy” among the face-processing tasks investigated (Bruce et al., 2000). Similarly, with a three-alternative forced-choice matching task, by
10 years performance was equivalent to that of adults (Mondloch et al., 2003). Therefore, whereasa high level of performance in matching tasks is possible by 10 years, slight modification of this typeof task to a simple pointing exercise between pairs of expressions for the target expression led toceiling-level performance by 6 years (Bruce et al., 2000). An increase in the number of expression
choices, with a four-alternative forced-choice matching task, similarly showed that by 10 years
performance was high across the expression categories tested (Vicari et al., 2000). Again, modification of this task showed different performance outcomes. When the target expression was covered
after 5 s, thereby placing greater demands on memory, performance consequently dipped in thisage group (Vicari et al., 2000). Therefore, even for relatively simple tasks, varying task demands alter
recognition performance during development, as is acknowledged in the literature (Johnston et al.,2011; Montirosso, Peverelli, Frigerio, Crespi, & Borgatti, 2010; Vicari et al., 2000). The more recent
challenge, therefore, has been to find appropriate tasks with sufficient sensitivity for use acrossdevelopment.
Conventionally, matching paradigms have been used to attempt to minimize verbal ability andmemory confounds. However, as illustrated above, across the variety of matching paradigms that have
employed tasks of increasing or decreasing complexity, high levels of performance have been shownby middle to late childhood, indicating that this type of task does not challenge the maximum capabilities of children at this stage of development. Addressing studies that show little change in accuracy
between 7 and 10 years of age, De Sonneville et al. (2002) proposed that speed of responding can provide a more sensitive measure to reveal age-related changes in facial expression processing. They
found that the speed of responding greatly improved during this age range, whereas accuracy
improvements were small. However, the task used to obtain a speed of response measure was a simple
yes/no response to whether a face shows the target expression. Although this paradigm can reveal
developmental changes where accuracy measures cannot, there are also several constraints. Because
only yes/no response options are possible, accuracy must be significantly greater than the 50% chancelevel and the number of emotions that can be presented is limited because a speed of response versus
length of task trade-off is expected. Information about miscategorizations across emotions similarly
cannot be determined.
Alternatively, labeling tasks, another commonly employed method, allow an unrestricted numberof response options and analysis of miscategorizations. In a labeling task, the child must select thecorrect emotion label for the expression presented from several label options. These tasks havetraditionally used either forced-choice response categories or free labeling that allows for anunrestricted number of response options and the analysis of miscategorizations. Here, we focusedon school-aged children and the use of labeling in intensity studies similar to the method appliedhere, but we note the body of work using labeling tasks with younger children by Widen and Russell
because it comprises one of the few developmental perspectives of how emotion understanding
evolves.
In a series of studies using the labeling of both images of facial expressions and emotion stories,
Widen and Russell (2003) developed the differentiation model of emotion understanding. The model

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describes how children initially understand emotions in terms of the broad dimensions of valence(pleasure–displeasure) and arousal (high–low) rather than as the discrete categories that adults cometo understand. Gradual understanding of these initial broad dimensions is slowly narrowed conceptually to discrete categories such as anger and disgust. Because this model is developed from the use ofdifferent types of labeling tasks including conceptual stories, it is possible that by restricting the stimuli to perceptual images uniquely, the order in which these labels are acquired may change. Moreover,
the type of miscategorizations in a perceptual study may also inform how emotion labels are acquired.
Basic visual stimuli are used in the current study and similarly in other studies of emotion intensitythat have mainly employed labeling tasks. We now focus on intensity studies because intensity comprises one of the measures here. The verbal ability required for labeling tasks limits their use toschool-aged and some preschool-aged groups of children. In comparing matching and labeling tasks,
it is evident that the balance between sensitivity in the measure and complexity of the task is challenging to assimilate for the accurate assessment of recognition performance across different developmental age groups.
Expression intensity studies
To obtain a more nuanced understanding of the development of facial expression recognition, one
approach in more recent behavioral studies has been to vary the intensity of expressions to establish
whether older children can recognize more subtle expressions of emotion in comparison with younger
children. Such results are anticipated because in daily life we more frequently perceive subtle expressions of emotion. Expression intensity is modified by creating parametric linear blends of emotions
called morphs. Typically, morphs are created by blending a percentage of an emotional expression
with a percentage of a neutral expression or another emotional expression. Whereas studies employing this method remain few in the developmental literature, the percentage increments to index
intensities across studies vary, with 5% or 10% increments most commonly used. Similarly, the morph
stimuli used can be static or dynamic.
The results obtained from studies using static morphs have varied, most likely as a consequenceof the various levels of intensity increments used and the various age groups and emotions studied.
Our study focused on typically developing children, but previous original findings have effectively
illustrated the usefulness of the morphing technique by showing the effect of emotional experienceon emotional recognition (Pollak & Kistler, 2002), with physically abused children showing greater
sensitivity to anger. An early study to use morphs with typically developing children investigatedthe correspondence between recognition performance and emotion intensity in three age groups(between 4 and 15 years) and four levels of emotional intensity (25%, 50%, 75%, and 100%) but foundno association between age and level of intensity, as predicted (Herba, Landau, Russell, Ecker, &Phillips, 2006). Moreover, comparison between intensity levels and emotion categories in the explicit
emotion matching task that was used revealed significant differences only between the lowest andhighest intensities, suggesting that the increments were too broad to capture differences in the middle range.
In a study of sensitivity to emotion intensity for fear and anger expressions, again across three
distinct age groups (children, adolescents, and adults) but with finer increments of intensity (at11%), participants needed to judge whether the face stimuli expressed a neutral versus angry expression or a neutral versus fearful expression (Thomas et al., 2007). Sensitivity to emotion intensity wasmeasured by comparing the d0 average and d0 slope across the three age groups and revealed significant differences only between adults and children for fear and between adults and both children andadolescents for anger. Interpreting the results, the authors suggested that there was a marked
increase in sensitivity to anger from adolescence to adulthood, whereas sensitivity to fear showeda more gradual incline with age. However, the relatively broad age categories and intensity increments used in this study may have prevented differences from being revealed across the child andadolescent groups.
Finally, two more recent studies investigated sensitivity to emotion intensity using three child age
groups from 5 to 10 years of 2- or 3-year intervals with the finest intensity measures to date, 20 levels
of 5% increments for each expression studied (Gao & Maurer, 2009, 2010). The studies also included abroader range of expressions and analyzed miscategorizations. Applying the same methodology, both

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studies investigated children’s responsiveness to emotional intensity by calculating a threshold for
accurate discrimination of each emotion—happiness, sadness, and fear in the first study, followedby all six basic emotions in the second study. Thresholds were defined as the intensity level at which
50% of the time the expressive face was recognized as a neutral expression and 50% of the time it wasrecognized as an expressive face. Importantly, this could mean any expression from the emotional
expression categories available and not necessarily the correct one because a second measure formisidentification of expression was also recorded. Unlike the previous studies discussed here, the task
was not computerized. Instead, children were asked to physically categorize photographs of emotional
expressions of varying intensities.
Gao and Maurer (2009) found different developmental patterns for each of the three emotions
investigated. The youngest children matched adult sensitivity for both measures of threshold intensity
and misidentification of happiness. For sadness, even the oldest children, aged 10 years, were prone to
confusing this emotion with fear, and for the fear expression children did not reach adult-like thresholds until 10 years. Gao and Maurer (2010) expanded the number of emotion categories to include all
six basic emotions, which were then subdivided into two groupings based on previous findings of theconfusability of emotion categories. Therefore, participants completed the recognition task in two
blocks, each with distinct emotion categories and not with all six emotions at once. For all age groups,
recognition accuracy for happiness reached ceiling-level performance. Between 5 and 10 years, sensitivity to surprise, disgust, and fear improved, and sensitivity to sadness and anger continued to
improve into adulthood.
Although this was the first study to include a broader range of emotion categories, several featuresof this paradigm make it difficult to draw definitive conclusions on sensitivity to emotion intensity inthe age groups studied. Primarily, the true threshold applied here was a composite measure; amisidentification measure was calculated separately to the initial threshold measure. Furthermore,
emotions could be misidentified only with emotions belonging in the same subgroup; thus, potential
misidentifications across all six emotion categories were not possible. A single measure that accounts
for miscategorizations could provide greater precision in the understating of facial expression recognition. Finally, here, as with previous studies investigating sensitivity to intensity, the increments
were established a priori, so the granularity of the measure can only be as fine as the predefined
increments.

A psychophysical approach
Here, we investigated the continued development of facial expression recognition for all six of thebasic emotions from early childhood (5 years of age) up to adulthood. We introduced a novel psychophysical method using the QUEST threshold-seeking algorithm (Watson & Pelli, 1983) to obtaina sensitive measure of facial expression recognition performance across the age groups studied. Thisalgorithm identifies an individual’s recognition threshold for an expression with a sensitivity of 1% forintensity measures or less for signal measures. The threshold is adapted online during the execution of
the experiment. The algorithm, therefore, permits greater sensitivity in the measure of recognition
performance, and intensity increments do not need to be defined a priori as with other methods.
Our aim was to (a) obtain a precise measure of the quantity of visual information needed to recognize
an expression across development and (b) compare two measures of visual information use, signal versus intensity thresholds, using an experimental design in which each participant is tested under all
experimental conditions to better understand the use of different measures in assessing recognition
performance across development. We predicted that recognition performance would improve withage for both measures and that this improvement would be distinct for each expression. Based on previous findings, we predicted that happiness would be the easiest expression to recognize across age
groups and that fear would be among the most difficult (Herba & Phillips, 2004; Rodger et al.,2015). The QUEST algorithm was used to identify the recognition thresholds for both the signal andintensity measures; however, we had no prediction as to whether one measure would yield higher
or lower thresholds, or whether those measures would be significantly related to one another, because
no study had previously compared these distinct measures.

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MethodParticipants
In total, 159 individuals participated in both the signal and intensity conditions. As described
below, participants were analyzed on a continuum of age in years. For simplicity, we list the participants by age group. The adult group consisted of 19 participants (M = 24.2 years, SD = 1.8; 10 female).
The adolescent group consisted of 60 participants in total: 20 17- and 18-year-olds (M = 17.9 years, SD= 0.65; 17 female), 20 15- and 16-year-olds (M = 16.0 years, SD = 0.73; 12 female), and 20 13- and 14year-olds (M = 14.0 years, SD = 0.5; 12 female). The child group consisted of 80 participants: 20 11and 12-year-olds (M = 11.9 years, SD = 0.5; 8 female), 20 9- and 10-year-olds (M = 9.9 years, SD =0.59; 9 female), 20 7- and 8-year-olds (M = 7.9 years, SD = 0.61; 13 female), and 20 5- and 6-yearolds (M = 5.9 years, SD = 0.56; 13 female). Children were recruited from local schools in the Fribourg
area of Switzerland, and parental consent was obtained for all children under 16 years of age. The
study was approved by the Department of Psychology ethics committee at the University of Fribourg.
Materials
For the signal condition, the stimuli consisted of 252 grayscale images (256  256 pixels) from the
Karolinska Directed Emotional Faces (KDEF) database (Lundqvist, Flykt, & Öhman, 1998) comprising
36 distinct identities (18 female), each displaying six facial expressions (fear, anger, disgust, happy,
sad, and surprise) and a neutral expression. For the intensity condition, we used eight identities (fourfemale) expressing each of the six basic emotions from the KDEF (Lundqvist et al., 1998) image database. Abrosoft FantaMorph software was used to create morphs of 100 increments for each identityand emotional expression, ranging from a 1% morph of a neutral face and an expressive face up to a
100% expressive face. The total number of images used, therefore, was 4800 (8 identities  6
expressions  100 increments). Example stimuli of different expression intensities and signal
strengths are shown in Fig. 1. Participants viewed images only at the intensities calculated by the
QUEST procedure. All images were cropped around the face to remove distinctive hairstyles using
Adobe Photoshop and were aligned along the eyes and mouth using Psychomorph software(Tiddeman, Burt, & Perrett, 2001). Images were also normalized for contrast and luminance using
the SHINE toolbox (Willenbockel et al., 2010) in MATLAB 7.10.0 and displayed on an 800  600 Gy
background at a distance of 50 cm subtending 10°  14° to simulate a natural viewing distance duringsocial interaction (Hall, 1966). The stimuli were presented on an Acer Aspire 5742 laptop using thePsychophysics toolbox with MATLAB 7.10.0 and QUEST (Watson & Pelli, 1983), a Bayesian adaptive
psychometric method (described below) to estimate the level of stimuli strength (signal or intensity)for each trial. An external USB keyboard was attached to the laptop so that the experimenter could key
the responses on behalf of the child participants.
Procedure
To familiarize the children with the computerized emotion recognition task, each child was shown
six faces expressing the six basic emotions on individually printed sheets of paper and were asked torespond to the question, ‘‘How do you think this person is feeling?” To facilitate the familiarization

Fig. 1. Example stimuli from the signal and intensity conditions. In this image, the stimuli are shown in increasing 5%
increments, starting at 20% signal or intensity.

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task for the younger children in particular, the first image presented was always a happy face. If children were unsure of an emotional expression in the familiarization task, they were told what the emotion was. Children were then asked whether they could repeat this task by looking at similar images
on a computer screen. For the signal condition, children were told that this time the faces would beslightly hidden or blurred, so that it might be more difficult to see what the person is feeling, but
to please respond as well as they could. Because there were six expressions to choose from labeledon six computer keys, children aged 12 years and under responded verbally and the experimenter
keyed the responses on their behalf. Children were also told that if they were unsure of an expression
or could not sufficiently see the expression to make a judgment, they could say ‘‘next” and a new facewould be presented. Such responses were coded as ‘‘don’t know” by the experimenter. Adolescent andadult participants were similarly asked to respond as accurately as they could to how the person in the
picture was feeling by pressing the corresponding emotion key labeled on the keyboard. Labels wereplaced on the bottom row of keys for each of the six expressions and on the space bar for ‘‘don’t know”
or ‘‘uncertain” responses. Adolescent and adult participants were given as much time as they needed
to familiarize themselves with the response keys before beginning the experiment and were told that
accuracy—not response time—was important, so they could take as much time as needed and could
look at the keys if necessary before giving their responses.
The experiment began with 6 practice trials to allow participants to become familiar with the computerized task. The transition from practice trials to experiment proper was seamless, so participants
were not aware that the initial trials were for practice only. At the beginning of each trial, a fixation
cross was presented for 500 ms to locate the participant’s visual attention, followed by a 500-ms presentation of the face stimulus displayed at the signal strength or intensity estimate from the QUEST
psychometric procedure (described below), directly followed by a mask of random noise (see Fig. 2
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 ‘‘QUEST Bayesian adaptive
psychometric procedure” section below for details), that particular 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 were ready for the next
stimulus presentation before the response was entered. The number of trials for each participant varied as a function of the QUEST procedure (again described below), so for the youngest children theexperiment was paused at roughly midway and continued after a short break.
QUEST Bayesian adaptive psychometric procedure
The QUEST procedure as implemented by Rodger et al. (2015) was used. QUEST is a psychometricfunction that uses an adaptive staircase procedure to establish an observer’s threshold sensitivity tosome physical measure of a stimulus, most commonly stimulus strength (Watson & Pelli, 1983).
The threshold obtained by the QUEST procedure, therefore, provides a measure of how effectively
an observer can discriminate a stimulus. Here, we investigated threshold sensitivity for signal andintensity of expression in two separate conditions across developmental age groups. Adaptive staircase procedures obtain the threshold by adapting the sequence of stimulus presentations according
to the observer’s previous responses. For example, the stimulus strength becomes weaker or stronger
according to the user’s history of correct and incorrect responses to a particular stimulus category.
Adaptive staircase methods, therefore, can be more efficient in determining the observer’s perceptual
threshold for stimulus detection because the range of stimuli presented is reduced by staying close to
the observer’s threshold by accounting for the observer’s previous responses.
We adopted QUEST for this efficiency because it allowed us to implement a paradigm including all
six expressions at once in a developmental study. The QUEST threshold-seeking algorithm was implemented in MATLAB 7.10.0 with the Psychophysics Toolbox to parametrically determine an observer’s
perceptual threshold for discriminating each of the six emotional expressions. Adopting a signal detection approach, QUEST was used to parametrically adapt the signal strength of the grayscale facial
expression images presented to the participant by adding a mask of random noise to the image corresponding 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

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Fig. 2. Example trial from the signal condition. At the beginning of each trial, a fixation cross was presented for 500 ms to locate
the participant’s visual attention, followed by a 500-ms presentation of the face stimulus displayed at the signal strength orintensity estimate from the QUEST psychometric procedure, directly followed by a mask of random noise until a response wasmade. Depending on accuracy, the next trial was followed by a face containing more signal (in case of an erroneous response) or
less signal (in case of an accurate response).

given trial, then the subsequent signal strength estimate was decreased or increased. Similarly, theintensity of the expression was adapted using neutral expression image morphs from 0% expression(a neutral face) to 100% expression. The final threshold estimate was determined as the intensity or
signal strength where the expression was predicted to be discriminated on 75% of trials. In thisway, equal performance was maintained across observers. The 75% performance threshold was chosenbecause it has been conventionally applied in adult face identity and facial expression recognition
studies (Gosselin & Schyns, 2001; Schyns, Bonnar, & Gosselin, 2002; Smith, Gosselin, Cottrell, &Schyns, 2005). For the signal condition, three QUEST procedures were implemented, each with a different initial stimulus strength (60%, 40%, or 20%), to prevent possible bias in the final estimate toward
the direction of the initial value. For the intensity condition, one QUEST procedure was implemented
with an initial expression intensity of 30%. This intensity was selected because by nature 50% intensity
denotes an image morph of 50% expression and 50% neutral expression, so the initial value should be
below this level of morph. The QUEST procedure terminates for an expression after 3 consecutive correct or incorrect trials in which the intensity or signal strength standard deviations are less than 0.025.
Data analyses
Threshold detection
The participant’s recognition threshold for each task is identified as the level of information(intensity or signal) needed to maintain performance at 75%, as quantified by the QUEST procedure.
For each expression and participant, the QUEST procedure assumes that the response (in terms ofaccuracy rate) and the presented signal or intensity follows a psychometric function. Throughout

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the experiment, this psychometric function is updated and refined for each trial until the end of theexperiment. The final threshold estimate is the level of information at which the participant ispredicted to maintain 75% performance for expression recognition. This estimate is obtained by computing the mean of the QUEST posterior probability density function (pdf) using the QuestMean function from the QUEST toolbox (King-Smith, Grigsby, Vingrys, Benes, & Supowit, 1994; Pelli, 1987),
which uses a Weibull psychometric function. In the signal task where multiple QUEST procedures
were employed, we computed the average of the threshold estimations as our final estimation for eachparticipant. In our previous study, we used the intensity of the last trial from the QUEST procedure as
the threshold estimation (Rodger et al., 2015). However, because some participants cannot achieve
75% accurate identification even when signal or intensity is at the maximum (100%), the previous
calculation returns a ceiling value of 1 (which is equal to 100% signal or intensity). This occurs nearlyexclusively for fear recognition (see the supplementary figure in Rodger et al., 2015). Instead, here, for
the intensity and signal conditions, the threshold estimate now returns values greater than 1 (e.g.,
1.112). Because the expression has failed to be categorized at full strength, by using the precise
estimate instead, the threshold is no longer constrained by its physical limitation, a value of 1, so amore continuous measure is possible, which gives greater sensitivity to detect potential developmental differences even for expressions that are difficult to recognize across age groups.
Signal and intensity recognition thresholds as a linear function of age per emotional expression
To quantify the relationship between age and emotion recognition performance, we fitted generallinear models (GLMs) with age as predictor for each task and expression independently (Rodger et al.,2015). We then compared the regression coefficients between the two tasks for each expression toinfer the effect of age (Fig. 4). GLMs were fitted using the fitlm function in MATLAB with the default
robust option using a bisquare weight function to eliminate the effect of outliers. Hypothesis testingon the model coefficients was corrected for multiple comparisons using a Bonferroni correction.
Response profile analysis
Although the QUEST procedure is efficient in estimating a desired threshold, the returned estimation is a summary statistic that is sufficient only under strong assumptions (e.g., the underlying posterior pdf is parameterized using only the mean). In other words, the uncertainty of the estimation isusually discarded. To fully account for all the information encoded in the response during the QUEST
procedure, we applied a Dirichlet–multinomial model to recover the response profile for each participant. This procedure is conceptually described in Fig. 5.
For each participant, we first extracted the raw response vector and the corresponding intensity/
signal level for one expression in one task (Step 1 in Fig. 5). We then projected each elementin the response vector and its corresponding intensity/signal level into a sparse matrix (Step 2 inFig. 5). To recover the full response profile from the sparse raw response matrix, we applied a
Dirichlet–multinomial model for each intensity/signal level, a probabilistic model widely used tomodel categorical responses. Here, we assumed that at each intensity/signal level the participant’s
response to a random stimulus follows a multinomial distribution: X  Multinomial (response, p).
The response is all six tested expressions plus neutral and the ‘‘I don’t know” response; p is aneight-element vector (summed to 1) coded for the probability of each response. Moreover, p follows
a Dirichlet distribution p  Dirichlet (a), where a is the concentration parameter of the Dirichlet
distribution. To recover the response profile matrix, we started from the lowest intensity/signal
level (0%) with a uniform prior for the Dirichlet distribution: a vector of 1 s as a. We applied theBayes theorem to get the posterior of a and then used the posterior of a as the new prior forthe next intensity/signal level (Step 3 in Fig. 5). The Bayesian update procedure is repeated until
the highest intensity/signal level (100%) is reached. In this way, the dense matrix representationof the response profile (final output in Fig. 5) is recovered. In the resulting response profile, thevalue is the concentration parameter a of the Dirichlet distribution in which a higher value relates
to a higher concentration in the response probability p in the multinomial distribution.
Specifically, we performed the Bayesian update for each intensity/signal step using PyMC3 with
10,000 Metropolis–Hastings sampling. Instead of sampling from a Dirichlet distribution with the concentration parameter a, we sampled from ai  Gamma (aI, 1) and normalized the sum of [a1, . . ., ak] to50

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Fig. 3. Age group mean recognition thresholds plotted per facial expression of emotion. Orange lines indicate the intensity task,
and blue lines indicate the signal task. Error bars report the 95% confidence intervals. (For interpretation of the references tocolor in this figure legend, the reader is referred to the Web version of this article.)

1 to get the parameter p for the multinomial distribution. This formulation allowed us to obtain theposterior of the concentration parameter a for the update at the next intensity/signal level.
It is worth noting that the Bayesian update procedure applied here is conceptually similar to fitting
a psychometric function independently for each row in the raw response matrix in Fig. 5. However,
applying a Dirichlet–multinomial model is more accurate and does not require any collapse of conditions in the computation.
The response profiles for each expression per task across all participants are shown in Fig. 6. To further explore the relationship between the intensity and signal tasks for each participant, we computed
the mutual information between the response profiles (using the algorithm in Kinney & Atwal, 2014).
Importantly, we included the responses only of the six target expressions (i.e., excluding the rows
coded for the neutral expression and ‘‘I don’t know” response) so that the two tasks were consistent.
Robust regression is fitted between the mutual information and age, similarly to as described above, toquantify the effect of age.

Results
Mean expression thresholds across development
SignalThe mean age recognition thresholds and their 95% bootstrapped confidence intervals for each of
the expression categories are plotted in Fig. 3. As predicted, happiness was the easiest expression torecognize across age groups, being recognized with the lowest mean thresholds across age groups.
In contrast, fear was the most difficult expression to recognize across age groups, having the highest
mean thresholds across groups; even at full signal strength, participants generally do not reach thetarget accuracy (75%) for the fear expression, which resulted in threshold estimations of greater than

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51

Fig. 4. Individual recognition thresholds plotted as a function of age (x axis) per facial expression of emotion. Orange dots
indicate the intensity task, and blue dots indicate the signal task. Data from the same participant is linked with a gray line.
Longer lines indicate that the thresholds for signal and intensity are not similar and, therefore, are farther apart. Short lines
indicate that the thresholds for this individual are similar. Line plots show the results of the linear regression between threshold
and age, and dotted lines show the 95% confidence intervals. (For interpretation of the references to color in this figure legend,
the reader is referred to the web version of this article.)

1 by the QUEST algorithm. Across age groups, the rank order of mean expression thresholds between
the highest and lowest mean thresholds varied. The mean number of trials for the signal condition was
216.74 (SD = 49.96).
Intensity
The mean recognition thresholds and their 95% bootstrapped confidence intervals for each expression and age group are plotted in Fig. 3. Similar to the signal condition, happiness and fear were theeasiest and most difficult expressions to categorize across age groups, respectively, having the lowest
mean thresholds for happiness and the highest for fear. Again, as for the signal measure, the majorityof participants do not reach the target accuracy (75%) even at full intensity for the categorization of thefear expression. The ranking of mean thresholds between the highest and lowest intensity thresholds
again varied across age groups, with no set pattern established across age groups for the remaining
expressions. The mean number of trials for the intensity task was 78.06 (SD = 14.15).
Signal and intensity recognition thresholds as a linear function of age per emotional expression
Fig. 4 shows the change in information use across age for each emotional expression. Each individual’s threshold for signal and intensity was plotted, with age along the x axis. The fitted regression
lines for intensity and signal are shown in red and blue, respectively; the dotted line indicates the
95% confidence interval. Overall, a significant decrease in thresholds across age was found for boththe signal and intensity measures for four of the six expressions (square brackets show 95% confidence
intervals, p < .05, Bonferroni corrected): anger (intensity: .02 [.0271, .0135], t(157) = 5.87;

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Fig. 5. Response profile analysis for a single participant for one expression (disgust) during the intensity task. The procedure
starts with the raw response and intensity level (Step 1), projects them into two dimensions (intensity levels by categorized
expressions; Step 2), and applies the Bayesian update to recover the full response profile (Steps 3 and 4). This procedure isrepeated for all expressions in both tasks independently for each participant.

signal: .013 [.0186, .00674], t(157) = 4.21), disgust (intensity: .015 [.0217, .00732], t(157) =3.99; signal: .017 [.0249, .00981], t(157) = 4.55), sadness (intensity: .022 [.0286, .0151],t(157) = 6.36; signal: .014 [.0209, .00614], t(157) = 3.61), and surprise (intensity: .024[.0316, .0164], t(157) = 6.22; signal: .017 [.0238, .0096], t(157) = 4.65). Older participants
were able to recognize an emotional expression with less information, at lower levels of signal or
intensity, than younger participants.

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53

Fig. 6. Average response profiles across groups for the intensity and signal tasks and each facial expression of emotion. Each
subplot shows the average response profile for one expression in one task. The rows represent the different responses fromparticipants (six basic expressions + neutral + don’t know response), whereas the columns represent the signal or intensity level
presented by QUEST. The value of the color maps are the concentration parameter a of the Dirichlet distribution (see the main
text and Fig. 5 for more details). A high value indicates higher probability and greater confidence in choosing that response. (Forinterpretation of the reference to color in this figure legend, the reader is referred to the Web version of this article.)

Response profile analysis
As shown in Fig. 6, there are substantial differences between the response profiles of the two tasks
for most of the expressions. To establish how similar the signal and intensity measures were acrossdevelopment, we performed a mutual information analysis on the response profiles of both measuresfor each participant (Fig. 7). Each plotted point, therefore, represents the similarity in the response
profiles of the two measures for one participant. Overall, there was an upward trend for the response
profiles to become more similar with age, with four of the six emotions showing a significant increasein similarity with age: anger (regression coefficient: b = .014 [.0036, .0243], t(157) = 2.67, p = .0083),
disgust (b = .018 [.0073, .0286], t(157) = 3.33, p = .0011), sadness (b = .012 [.0025, .0219], t(157) =2.48, p = .0143), and surprise (b = .024 [.0013, .0361], t(157) = 4.06, p = 7.73e5). Moreover, a robust
GLM between mean mutual information across expression and age showed a significant positive
correlation, b = .0103 [.006, .0145], t(157) = 4.74, p = 4.75e6. As the response profiles become moresimilar with age, erroneous responses become less random in comparison with younger participants.
Discussion
Using a psychophysical approach and an experimental design in which all participants completed
both experimental conditions, we aimed to (a) isolate the quantities of signal and intensity (using neutral expression image morphs) necessary to recognize six prototypical facial expressions of emotion inchildren from 5 years of age up to adulthood while maintaining performance at 75% and (b) compare
these measures to better understand the use and sensitivity of different measures in assessing
recognition performance across development. To achieve these aims, we used a data-driven methodological approach by analyzing recognition performance on a continuum of age, a novel approach that
overcomes the delimitation and use of arbitrary age boundaries.
The results of the first objective revealed that, as expected, the quantities of signal and intensity
needed to recognize the majority of expressions decreased with age for sad, angry, disgust, and54

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Fig. 7. Mutual information between the intensity and signal tasks for each participant per expression. Higher mutual
information indicates higher similarity in the response profile between the two tasks. Line plots show the linear regression
between mutual information and age, and dotted lines show the 95% confidence region of the regression.

surprise expressions, respectively. Therefore, the processing of both types of visual information
becomes more discriminative during development as less information is needed with age to recognize
these expressions. However, recognition improvement across development was not uniform for these
expressions, as has also been shown in previous studies (Boyatzis, Chazan, & Ting, 1993; Gao &Maurer, 2010; Herba & Phillips, 2004; Lawrence, Campbell, & Skuse, 2015; Mancini et al., 2013;Rodger et al., 2015; Vicari et al., 2000). For fear and happy expressions, age did not have a major
impact on the quantities of signal and intensity use. Therefore, recognition performance for fear
and happy expressions was relatively stable from 5 years of age. For both measures, fear and happiness were the most difficult and easiest expressions to recognize across age groups, respectively. Earlier studies have similarly shown that happy expressions have the highest recognition performanceand that this remains stable from an early age (Gao & Maurer, 2009, 2010; Gross & Ballif, 1991;
Herba & Phillips, 2004; Mancini et al., 2013). However, one recent study showed that despite theyoungest age group tested (6-year-olds) showing 92% recognition accuracy for happy expressions,
there was a small but significant improvement in accuracy with age (Lawrence et al., 2015). Stability
in accuracy levels for fear recognition from an early age was similarly found in an earlier study measuring signal recognition thresholds uniquely (Rodger et al., 2015). Overall, the recognition thresholds
for both measures showed a similar trend in improvement or stability across expressions and in theease and difficulty of happy and fear recognition, respectively.
Although similar developmental trajectories for recognition of these expressions have been
revealed in other studies, the reasons behind such particular patterns of trajectories remain widely
speculated. Studies that have tested different developmental cohorts have shown the effect that experience has on emotion recognition. For example, a cross-cultural eye-tracking study testing Caucasianand Asian infants recently revealed information sampling biases in infants as young as 7 months when
they discriminate facial expressions of emotion (Geangu et al., 2016; Caldara, 2017). Early culturespecific experience, therefore, can affect which visual information we sample from the environment.
The authors speculated that within the cultural environment, it is possible that parental practices

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most prevalently affect young infants. For example, Asian mothers have been found to be less emotionally expressive and to use more non-direct body contact compared with Western mothers(Kisilevsky et al., 1998), which could affect infants’ attentional strategies toward the culturally specific, emotionally salient features of the face and body.
Similarly, by testing different developmental cohorts, children who have been exposed to physicalabuse and those who have not, Pollak and colleagues’ work has notably demonstrated the effect ofemotional experience on emotion recognition (Pollak, Cicchetti, Hornung, & Reed, 2000; Pollak &Kistler, 2002; Pollak, Messner, Kistler, & Cohn, 2009; Pollak & Sinha, 2002). Children who had suffered
physical abuse were consistently found to recognize anger more rapidly, or with fewer physical cues,
than non-abused children. Because the children studied had similar sociodemographic and family
backgrounds except for the experience of physical abuse, the explanation of this heightened sensitivity for anger recognition alone suggests that affective experience can influence perceptual representations of emotions.
As described above, developmental studies of facial expression recognition have repeatedly shownthat happy is the easiest and earliest expression to be recognized. This facility with happiness could bepartially explained in typically developing children by our frequent exposure to smiling faces during
early childhood combined with the visual distinctiveness of happiness from other expressions (Calvo& Marrero, 2009; Kohler et al., 2004). By contrast, expressions of fear, although critical to our survival,
are not commonly experienced frequently during daily life. Although experience alone might notaccount for poor recognition rates of fear in adult and developmental studies (Calder et al., 2003;
Gross & Ballif, 1991; Herba & Phillips, 2004; Rapcsak et al., 2000; Widen, 2013), it is a possible contributory factor. The low rates of fear recognition from both signal and intensity measures in our study
suggest that for optimal recognition additional information is required, perhaps from several modalities. Experiential factors affecting the recognition of sadness have also been shown in studies ofdepressed adults (Arteche et al., 2011; Gollan, McCloskey, Hoxha, & Coccaro, 2010; Gur et al., 1992;
Kluczniok et al., 2016). Precisely because measuring an individual’s prior experience of emotional
expressions empirically is difficult, measuring different cohorts to inform how cultural and social
experiences affect our capacity to recognize emotions is valuable. Future cross-cultural, clinical, anddevelopmental studies with diverse cohorts could adopt the paradigm here to establish possible differences in sensitivity to signal and intensity information and to further determine whether any differences found are related to experiential factors.

Comparison of response profiles for signal and intensity measures
To establish how comparable the signal and intensity measures were across development, and tobetter understand the use and sensitivity of different measures in assessing recognition performanceacross development, we compared the measures using a novel data-driven analysis. We used mutual
information analysis to establish how similar the response profiles of the signal and intensity measures were for each individual on a continuum of age. The analysis of age in years on a continuous scalerather than a categorical scale is a data-driven, non-biased approach that permits a finer level of analysis to provide a more precise picture of how the development of facial expression recognition
unfolds. The mutual information analysis showed that the response profiles of the signal and intensity
measures became more similar with age for the sad, angry, disgust, and surprise expressions. Again,
for fear and happy expressions, no significant change across development was evident. Similarity inthe response profiles of the sad, angry, disgust, and surprise expressions was evident only in the oldest
participants. Therefore, the response profiles for emotional expressions of full intensity in the signal
condition did not correspond with the profiles obtained from expressions of varying intensities in
the morph condition throughout the majority of development.
The mutual information analysis, therefore, established that two types of stimuli commonly used in
facial emotion processing studies (expressions at full intensity vs. expressions of varying intensities)
cannot be straightforwardly compared during development. This critical point is another explanatory
factor, along with differences in age groups, expressions, and tasks, for the differences in recognition
trajectories found throughout the developmental literature described in the Introduction.

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Importantly, as the response profiles for sad, angry, disgust, and surprise expressions became moresimilar with age, erroneous responses become less random. This suggests that representations of emotional expressions are more robust in the oldest participants tested given that they produced systematic confusions, for example, fear for surprise. Therefore, novel analysis of the overall response profiles
for the expression recognition tasks, rather than the more standard practice of analyzing the final
response values per se, revealed subtle but important changes in the sequence of responses along
the continuum of age.
The presence of more robust expression representations in adulthood aligns with Widen and
Russell’s differentiation model of emotion (Widen, 2013; Widen & Russell, 2003, 2008) that emotion
concepts are acquired gradually throughout development, beginning with a broad concept including
any emotion of the same valence, and hence the potential for greater confusion, with concepts gradually narrowing and becoming more discrete with age. Only visual information was available in thecurrent study without any social or contextual information to aid accurate categorization. Thus, it isplausible that the randomness in the categorization errors of younger children might arise from theirlack of sufficiently robust visual representations of an emotion despite already having a concept of
the emotion. A potential mechanism for this refining of emotion categories and greater robustness
in their perceptual representations is provided by Leppänen and Nelson (2009). In their review of
how the developing brain becomes tuned to the social signals of emotional expressions, theydescribed an experience-dependent mechanism that is necessary for the development of a maturesystem. They proposed that our perceptual representations of facial expressions are initially coarsely
specified and develop into a mature system with adult-like specificity only through exposure to
species-typical emotional expressions. The experience-dependent nature of facial expression
processing has been shown by the disruption caused to typical development by species-atypical parenting and social deprivation (e.g., Pollak & Kistler, 2002). In contrast, typical development, as shown
here, results in a mature system with more highly specified categorical representations of expressions that are also prone to more systematic errors. Future studies can apply these methods tofurther investigate sensitivity to specific emotions and discrepancies in response confusions acrossthe lifespan as well as in diverse clinical groups.
Future studies should also investigate some of the limitations of the current study. This crosssectional study has revealed specific developmental trajectories and response profiles for expression
recognition using signal and intensity information. A longitudinal design with neural measures could
further establish how processing of signal and intensity information evolves with age, for example,
whether neural populations processing the two types of information overlap with age as sensitivity
for decoding both types of information becomes more similar. It is also worth noting that there is large
variability for the mutual information estimation between the response profiles from the two tasks.
Although our data show a significant association between age and the estimated mutual information,
it is important to consider the practical significance of the observed effect. Indeed, the coefficient estimation values from the regression model are generally between .01 and .025, which is not a largechange compared with the intercept (with an estimate of roughly 1 bit). However, no studies havecompared the change of multivariate response patterns in a behavioral task using mutual information;
thus, it is difficult to straightforwardly interpret the current changes of information use. Further studies are necessary to quantify the practical significance of the effects observed here. Similarly, adultface stimuli uniquely were studied. The question of whether there is an own-age advantage for emotion recognition is still debated (Griffiths, Penton-Voak, Jarrold, & Munafò, 2015; Hills, 2012; Wiese,
Komes, & Schweinberger, 2013). It is possible that with own-age stimuli, children may recognize emotional expressions alternatively and show different developmental trajectories for recognition than isfound with adult faces. Finally, because faces are not the only signal used in the natural environment
for effective emotional communication, future studies are needed to further evaluate how contextual
and other social cues contribute to the processing of emotional expressions across development todetermine, for example, the effect of non-facial emotional cues on recognition when face signals or
intensity is modified as it is here.

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Conclusions
These findings have important theoretical and methodological implications for developmental,
lifespan, emotion, and face processing research. First, findings from facial expression recognition studies with different age cohorts using emotional expressions of full intensity, as in the signal condition
here, cannot be straightforwardly compared with findings using varied expression intensities.
Throughout development, the response profiles for recognition of expressions at full intensity were
not comparable to those of varied intensities. Second, by examining individual responses along an
age continuum, as opposed to the mean responses of age group categories, a finer level of analysis
is possible that can provide a more precise picture of differences occurring during development. Here,this revealed a gradual reduction in information use for recognition of four of the six expressions
tested and, for the same expressions, a gradual increase in the similarity of response profiles withage. Third, by analyzing the response profiles (i.e., the sequence of responses across trials) rather than
the fixed end-point measure of recognition score, a richer explanation of what is occurring is possible
as we compare the overall distribution of responses. For example, this approach revealed that the
response profiles become more similar with age due to less random erroneous categorizations. This
broader analysis, therefore, can provide insight into the underlying processing of visual information;
because the categorization errors become less random with age, this suggests that the expression representation becomes more robust. Potentially, therefore, the neural populations processing the two
types of information—signal and intensity—overlap with age as sensitivity for decoding both typesof information becomes more similar. Altogether, our data provide novel methodological and theoretical insights into the developing affective system.
Acknowledgments
We especially thank all of the participants from various schools in the canton of Fribourg, Switzerland, for being willing to take part in our study: Ecole Enfantine et Primaire de Marly Grand Pré, Cycle
d’Orientation de Pérolles, and Ecole de Culture Générale Fribourg. We also thank all of the teachers fortheir patience with classroom interruptions as well as head teacher Dominique Brulhart, principal teacher Claude Fragnière, and technician Christophe Pochon for their essential organization of our visits.
Furthermore, we thank Bérangère Prongue, Elena Bayboerek, Sonia Pupillo, Vincent Junod, and Faustine Etter for their help with data collection.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in the online version, at https://doi.
org/10.1016/j.jecp.2018.05.005.
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