*3.1. Signature Data Acquisition*

For this study, we captured online signatures from 74 persons on an iPad tablet with a capacitive touch-screen of 2048 × 1536 pixels. The signatures were sampled at 63 Hz and stored as a sequence of discrete values [*xt*, *yt*], where *xt* and *yt* are the coordinate values and *t* is the time stamp.

Each person signed 25 times with their usual signatures. No instructions were given to participants when they signed, letting them acquire their signatures naturally, freely in terms of posture and position of the device, so that they would feel comfortable with the mobile device when signing. This leads to different acquisition conditions according to persons, exactly like it would be in real mobile usages.

Additionally to their usual signatures, we asked participants to append other types of signatures separately: name-surname, initials, date and place of birth. We considered these signature types because: (i) in terms of usages, they are traditionally reported by persons in legal and administrative documents; (ii) they convey complementary handwritten information on the user's identity. Each type of signature was done by the person 25 times. This dataset thus contains 9250 (74 × 25 × 5) genuine signatures of different types.

Figure 1 displays an example of one person's usual signature and the associated place of birth. We plot below in Figure 2 the velocity temporal function for both handwritten information.

**Figure 1.** Examples of (**a**) a usual signature and (**b**) the associated place information of a user who authorized their publication.

**Figure 2.** Velocity profile of (**a**) the usual signature and (**b**) the associated place information displayed in Figure 1.

In order to assess signature vulnerability to attacks, we acquired 10 skilled forgeries per signature type after displaying on the screen the shape and kinematics of the target signature. This type of forgery is considered in the literature as being the best attacks [3,43,44]. We thus obtain 3700 skilled forgeries (74 × 10 × 5) done by different forgers. Figure 3 shows an example of skilled forgeries of the usual signature and the associated place of birth displayed in Figure 1. We also display in Figure 4 the velocity temporal function for both handwritten information forgeries.

**Figure 3.** Examples of skilled forgeries of (**a**) the usual signature and (**b**) the associated place information displayed in Figure 1.

**Figure 4.** Velocity profile of (**a**) the forged usual signature and (**b**) the forged place information displayed in Figure 3.

#### *3.2. Signature Types*

We considered the five types of signatures separately: the usual signature (S), the initials (I), the name-surname (N), the date of birth (D), and the place of birth (P). From these five simple types, we constructed 7 hybrid signature types by combining:

These instances of hybrid types were constructed by concatenating the sequences of the corresponding simple signature types, resulting in a single time sequence. The identity of the user is thus expressed through several signature types of different length, which convey different complementary information to strengthen the user's identity.

## *3.3. Quantifying Quality of Signature Types*

To assess information enrichment at the enrollment phase, we quantify the information content of other simple signature types than the usual signature (initials, name-surname, date and place), and also of the 7 hybrid types mentioned in Section 3.2.

The concept of entropy is a good alternative for quantifying the information content or the disorder in signatures. In [28,40–43], we proposed the concept of personal entropy (PE), an entropy-based quality measure that quantifies simultaneously both the complexity and variability of a person's signatures. In fact, complexity and variability are related to disorder at two different levels: complexity corresponds to the intrinsic disorder in a signature sample; variability corresponds to the intra-class disorder in a set of signatures belonging to a given user.

A user's PE is measured by exploiting the local probability densities estimated when training the user's HMM on a set of 10 genuine signatures described only by *x* and *y* attributes. Indeed, the HMM automatically generates portions by the Viterbi algorithm and estimates a mixture of Gaussian densities on each portion [28]. Figure 5 illustrates how PE is computed locally, on the segments generated by the user's HMM.

**Figure 5.** Personal entropy computation on portions of a signature.

Therefore, a random variable *Z* can be associated to each stationary portion *i* of the signature, generated by the Viterbi algorithm by the user's HMM. The number of portions *N* is the number of states of the HMM. The entropy *H*(*Zi*) of a portion *i* is computed as follows:

$$H(Z\_i) = -\sum\_{z \in S\_i} p(z) \cdot \log\_2(p(z)),\tag{1}$$

where *z* corresponds to a given point in the signature described by its coordinates (*x,y*), belonging to the current portion *i*, and *p*(*z*) is the probability of observing *z*.

We studied the number of genuine samples necessary for a good HMM estimation and showed that 10 instances lead to stable PE values [28]. The local probability distribution function is estimated using all the sample points belonging to each portion, across the 10 genuine samples. After that, the entropy of each genuine signature *H*∗(*Z*) is the average of entropy values *H*(*Zi*) on all the *N* portions of the signature, divided by the signing time *T*:

$$H^\*(\mathbf{Z}) = \frac{1}{N \ast T} \sum\_{i=1}^N H(Z\_i) \, , \tag{2}$$

Finally, by averaging *H*∗(*Z*) across the 10 user's genuine samples, we obtain a user's PE for each signature type. We demonstrated that PE allows obtaining three categories of signatures, coherent across several databases, spanning from short and highly variable signatures (high PE category) to stable, longer and complex signatures (low PE category). Moreover, we showed that for different classifiers, persons with low PE are the most robust to skilled forgeries. Persons with high PE are considered being "problematic" users in the literature [28,43,45]. These results were obtained considering the usual signature of each person [28,40–43].

## *3.4. Signature Verification System*

As our aim is to assess the impact of our strategy in a mobile scenario, we used a statistical verification system that has already been evaluated on large databases acquired on mobile sensors [9,12, 15,16,27,46], and has shown to maintain good performance on well-known databases in interoperability scenarios [47], as reported in Table 1. Indeed, Table 1 presents our system's performance on several online signature databases, some acquired in an office-like scenario using a Wacom digitizer with an inking pen, and other in a mobile scenario on different touch-screen sensors (PDA, iPad, iPhone). We report the EER values on skilled forgeries only, since it is the most challenging configuration for signature verification. The system has been evaluated in BSEC'2009 and ESRA'2011 competitions on very large databases of 382 persons [15,16] that signed both on a Wacom digitizer and on a PDA device. We observe that in the mobile context, the verification performance of our system is clearly better on recent capacitive touch-screen sensors (iPad and iPhone) compared to the results obtained on the PDA device (DS3-210, PDA-64, DS3-382).

**Table 1.** Performance of our HMM-based ASV system on several online signature databases acquired in office-like (Wacom device) and mobile scenarios (touch-screen sensors), considering skilled forgeries.

Table 2 summarizes the state-of-the-art of online signature verification systems on mobile sensors, when considering skilled forgeries. We observe that in some publications, performance is not reported on skilled forgeries, which is the most challenging case for ASV systems. When comparing the results on mobile sensors in Tables 1 and 2, we note that our system shows good performance compared to the state-of-the art. Indeed, on iPad and iPhone mobile sensors, an EER of 7.04% and 4.95% respectively is reached on signatures of the same 74 users. Compared to e-Biosign database containing real signatures of 65 users acquired with a stylus on two mobile devices, we notice that our HMM-based system shows slightly better performance on the iPad device (7.04% vs. 7.9% in the best case, or vs. 10.7% on the other mobile device) and much better performance on the iPhone device (4.95% vs. 7.9% in the best case, or vs. 10.7% on the other mobile device).

Our system behaves well in mobile conditions because it is based on a statistical model, namely a continuous left-to-right HMM with four Gaussian components per state [47–49]. In other words, each writer's signature is modeled through a double stochastic process, characterized by a given number of states with an associated set of transition probabilities among them, and in each state, a continuous density, a multivariate Gaussian mixture is used to model the emission probability density. This model has the advantage of absorbing the intra-personal variability of signatures [47], which increases significantly in mobile conditions.

**Table 2.** Performance of ASV systems of the literature on several online signature databases acquired in mobile scenarios with touch-screen sensors, considering skilled forgeries.

A personalized number of states is determined according to the total number *Ttotal* of sampled points available in the genuine signatures of the HMM's training set. We consider that in average 30 sampled points are enough to estimate the mean vector and the covariance matrix of each Gaussian [47]. The number of states *N* is computed as:

$$N = \left[\frac{T\_{total}}{M \bullet 30}\right],\tag{3}$$

where *M* = 4 is the number of Gaussian densities per state and brackets denote the integer part.

Nineteen dynamic features are extracted point-wise for all signature types. These features are described in detail in the Appendix A. The usual information extracted from an HMM is the likelihood of the input signature given the user's model. We have noticed that the information coming from the segmentation of the test signature by the target user's model is complementary to that of the likelihood, especially for forgery detection. Indeed, we have shown in [47] that the segmentations made by the target model on forgeries differ from those obtained on genuine signatures. For this reason, in the verification phase, the classifier performs a score fusion combining two levels of signature analysis: one based on a local point-wise analysis of each signature by the HMM (log-Likelihood score), the other on the analysis of the signature at the level of portions, automatically segmented by the same HMM (Viterbi score) [47–49]. At the first level (log-Likelihood score), on a particular test signature, a distance is computed between its log-Likelihood and the average log-Likelihood obtained on the training signatures; then it is shifted to a similarity value—called "Log-Likelihood score"—between 0 and 1, by the use of an exponential function [47]. At the second level of analysis (Viterbi score), the user's HMM automatically performs by the Viterbi algorithm, a segmentation of each training signature into portions, according to the most likely path displayed in Figure 6. A "segmentation vector" can then be associated to each signature: the *N*-components segmentation vector, *N* being the number of states in the claimed identity's HMM has in the *i*-th position the number of points (observations) associated to state *i* by the Viterbi path, as illustrated in Figure 6. Each training signature is then characterized by a *Reference segmentation vector*. In the verification phase, on a particular test signature, a distance between its corresponding segmentation vector and each *Reference segmentation vector* is computed, and such distances are averaged to compute the final distance. It is then shifted to a similarity measure between 0 and 1 (Viterbi score) by an exponential function [47].

**Figure 6.** Computation of a signature's segmentation vector generated by the user's HMM. Feature vectors describing the signature are on the *x*-axis and the left-to-right HMM on the *y*-axis.

Finally, the similarity score for a given test signature is thus the fusion by a simple arithmetic mean of the log-Likelihood score and the Viterbi score. If the final score is higher than the value of the decision threshold the claimed identity is accepted, otherwise it is rejected.

In this work, for simple signature types, we train an HMM per person and per signature type. For hybrid types, we train an HMM for each person considering the whole time sequence constructed by concatenating the time sequences of the concerned simple types. As example, for the SDP type, we train an HMM on the complete sequence composed of the usual signature, the date and the place. Note that according to signature types, the length of the complete signature sequence will vary: for simple signature types, it will tend to increase when considering name-surname and to decrease when considering initials. For hybrid types, the length of the signature will be even higher. This will impact the number of states of the user's HMM.

#### **4. Results**

## *4.1. Quality Measure of Usual Signatures*

In a first step, we quantify the quality of usual signatures of the 74 persons available in our dataset. To this end, we trained for each person, an HMM on 10 genuine signatures to measure the user's PE. Then, a hierarchical clustering was performed on the obtained PE values, resulting in three user categories displayed in Figure 7.

**Figure 7.** Examples of signatures captured in uncontrolled mobile conditions with (**a**) high, (**b**) medium and (**c**) low PE.

In Figure 7a, we observe three examples of signatures with high PE: they are the shortest and the simplest signatures, having the aspect of a flourish, and are the most variable (see Figure 8). Such signatures are considered as being "problematic" in the literature [28,43,45]. On the other hand, Figure 7c shows three examples of signatures with low PE: they are longer, the most complex and the most stable (see Figure 8). In between, there is a transition category in terms of complexity and stability, the category of medium PE (see Figures 7b and 8).

**Figure 8.** Boxplots of PE values for all 74 persons clustered into high, medium and low PE.

#### *4.2. Quality Measure of All Signature Types*

Figure 9 displays two examples of each simple signature type, captured separately on the iPad: usual signature, initials, name-surname, date and place of birth.

**Figure 9.** Examples of the simple signature types: (**a**) usual signature; (**b**) initials; (**c**) name-surname, (**d**) date of birth, and (**e**) place of birth. These signatures belong to persons who have authorized their publication.

For each person, we compute PE values for the five simple signature types separately, and for the seven hybrid signature types: SI, SD, SP, SN, SDP, NDP and SIDP. Figure 10 presents the boxplots of the obtained PE values for the 12 signature types.

**Figure 10.** Boxplots of PE values for the 74 persons per signature type.

We first notice that initials have the highest PE values. This result is coherent since initials are the most simple and variable type of signature. We also notice a strong spread out of their boxplot in Figure 10. Moreover, we observe that some initials have a comparable PE to that of usual signatures: indeed, for initials, some entropy values are below the first quartile. This can be explained by the fact that some persons appended their initials into two, three and even four letters, sometimes linking them as usually done when producing a short signature. In this case, the initials show a higher complexity and stability.

Furthermore, we notice that the more the signature is enriched (name-surname, SI, SD, SP, SN, SDP, NDP, SIDP), the lower PE becomes: the complexity of signatures is higher and variability is lower. The hybrid types SDP, NDP and SIDP are those showing the lowest PE values and the lowest variance between persons in the boxplots displayed in Figure 10.

In the sequel, we study the relationship between information content quantified by PE and verification performance. Our objective is to identify which types of signatures are more resistant to attacks in uncontrolled mobile conditions.


**7. Diagnosis**

In the presence of hair loss, the key step is taking a thorough history of the patient. In addition, the affected areas of the scalp should be examined to determine the presence or absence of follicular orifices. It helps to distinguish the scarring and nonscarring alopecia [23]. In the presence of follicular orifices, nonscarring alopecia can be suspected, although further history findings are useful to unveil the main cause [14].

The diagnosis of TE is usually based on the patient's history, physical examination findings, and hair pull test results (**Table 2**) [10]. The recognition of diffuse, noninflammatory, nonscarring hair loss should raise the clinical suspicion for TE, when it occurs acutely and is preceded by a physiological or psychological stressor, in particular. In the majority of cases, scalp biopsy is not required [10].

**Table 2.** Diagnosis of telogen effluvium [10].


**Figure 2.** Composition of oil and distillate fractions.

**Figure 3.** Energy dispersive X-ray fluorescence (EDXRF) results and water concentration analysis of oil and distillate fractions.

**Figure 4.** Fractions obtained by fractional distillation.

### **4. Discussion**

The pyrolysis of WEEE or flame-retarded polymers contained in WEEE with and without catalyst has been conducted in a number of recent works [2,6,9,10,15,20–23,30,32,76,77]. Furthermore, mixed WEEE residues can vary widely in terms of composition, strongly depending on their origin [1] (pp. 209–212) [2,3,7,9].

On average, the thermal conversion of WEEE at different temperatures yields 36 wt.% condensate, 39 wt.% gas, and 25 wt.% solid residue [6]. Recent works yielded 71–91 wt.% condensate, 3–21 wt.% solid residue and 2–8 wt.% gas from thermal pyrolysis of real mixed WEEE at 600 ◦C in a laboratory scale [76,78]. The iCycle® process applied for this work yielded 29 wt.% condensate, 32 wt.% solid residue, and 39 wt.% gas on a demonstration scale plant. The converted WEEE contained around 40% metals and inorganics, which explains the comparatively high amount of solids. Due to the operating temperature of 650 ◦C, the production of 39 wt.% gas is in an expectable range. However, the distribution of the products is, aside from operational parameters and scale, also highly dependent on the composition of the input material. Similar results (40 wt.% condensate, 30 wt.% solid residue, 12.5 wt.% tar, and 13.5 wt.% gas) were found by Vasile et al. [77] throughout the pyrolysis of mixed WEEE in a temperature range of 430–470 ◦C in a smaller demonstration scale plant. The produced oil (condensate) comprised a comparable content of 62.75 vol% aromatics as BTEX, styrene, and phenol derivatives. The condensate produced throughout the present work was also very rich in monocyclic aromatic compounds (including BTEX aromatics, styrene, phenol, and cresols), representing 76.81 area% in the pretreated pyrolysis oil. Condensates by Santella et al. [76] contained less than 20 wt.% aromatic compounds and mostly styrene. The pyrolysis oil produced from mixed WEEE by Hall et al. [78] consisted mainly of phenol, isopropyl phenol (30.2 wt.%), and styrene (5.9 wt.%), indicating major differences in the composition of the investigated feedstock. However, Hall et al. [78] found a similar chemical composition of the liquid pyrolysis product from waste refrigerators; bromine and chlorine contents of the condensate were 0.3 % and 0.1 %, respectively.

The current investigation showed that the pretreatment of the condensate in terms of filtration and phase separation is capable of removing roughly 10 wt.% of substances as solids and water that are undesired for further upgrading of the oil, as they are likely to disturb the refinement processes to produce other materials, e.g., bulk chemicals or fuels. Hence, the pretreatment technologies are crucial in terms of condensate processing, and they need to be conducted before further upgrading.

The chlorine and bromine concentrations of the investigated pyrolysis oil amount to roughly 5000 ppm chlorine and 2000 ppm bromine. Other related works found less than 600 ppm chlorine and less than 900 ppm bromine in the pyrolysis oil from mixed WEEE [78]. This confirms the enormous differences in WEEE composition [7,72] and manifests the need of the dehalogenation of pyrolysis oil in order to upgrade them to fuels or chemicals.

By means of fractional distillation, pure BTEX fractions and concentrated monocyclic aromatic fractions were produced:

The results evidenced that the pyrolysis of WEEE shredder residues and subsequent distillation of the condensate might be applied to produce high purity chemical fractions which can be used as a feedstock in the chemical industry and in polymer synthesis to produce virgin grade chemical products and polymers and also to substitute crude oil consumption.

The chlorine and bromine concentrations were significantly reduced by up to 99% in the distillate fractions. The EDXRF analysis showed that bromine accumulates in the distillation residue where chlorine concentration is decreased in all fractions, including the residue. The decrease in chlorine concentration in all fractions promote the assumption that chlorine (in the form of HCl) was dissolved in the oil and released throughout distillation [77].

In the first three fractions (RT–140 ◦C), bromine concentrations do not exceed 20 ppm, which represents a reduction of 99% in relation to the crude condensate and can already be sufficient debromination for the industrial application of the produced chemicals. Fractions 4–7 comprise bromine concentrations roughly between 200 ppm and 400 ppm, representing a significant reduction from the initial oil (2151 ppm).

Fractions 1, 2, 4, and 6 chlorine concentrations were reduced to roughly 250–470 ppm from 4843 ppm in the initial oil. Fraction 3 and 5 still comprise 913 ppm and 1914 ppm chlorine, respectively. Further dehalogenation is needed in order to reuse such fractions for industrial applications [77].

Beside the chemicals, the solid fraction is also suitable for recycling. It can be supplied to copper smelters, where metals are recovered. The gas can be used for energy recovery after treatment as described in Figure 1. The water phase, however, needs to be treated as hazardous waste as it contains metal compounds, organic, and halogen residues.

Several studies in the field of chemical recycling of WEEE have investigated the production of fuels, which is less valuable as a product compared to high-purity monocyclic aromatic fractions. Furthermore, the chemical recycling to valuable basic chemicals and monomers has a positive environmental impact in terms of climate change and fossil resource depletion compared to the productions of fuels. Thus, the conversion of WEEE to valuable chemicals has great potential to close the loop of the nonmetal fraction of WEEE as it will be mainly kept in the material cycle where the production of fuels entails the release of the nonmetal fraction (especially in the form of carbon dioxide) to the atmosphere [79,80].

### **5. Conclusions**

A significant amount of more than 200 kg real WEEE shredder residues from a German recycling company were treated in a demonstration scale pyrolysis plant and the subsequent fraction was distillated, in order to produce basic chemicals with high purity. Such treatment of liquid pyrolysis products from WEEE shredder residues to produce pure chemical fractions have not been reported yet. Thus, a new approach for recovering valuable chemicals like aromatics from pyrolysis oil was investigated and was successfully proven in a demonstration scale plant.

From the conducted experiments, it can be concluded that:

Current results revealed that a combination of filtration followed by fractional distillation is suitable for the reduction of halogen content. The halogen content was reduced up to more than 99% in the obtained fractions. Therefore, the recovered aromatics have great potential to be used as feedstock in chemical industry.

Estimations made by Fraunhofer UMSICHT suggest that the pyrolysis of WEEE for the recovery of metals is already economically viable in a scale >250 kg/h. The recovery of aromatics from WEEE nonmetal fraction instead of energetic utilization, however, enables significant improvement of both economic and ecologic aspects of WEEE recycling.

Further research works need to be conducted on higher halogen reduction and are part of our current projects. The development and scale-up of the presented technologies are the next steps to implement the recovery of aromatic compounds from different plastic waste streams in industry.

**Author Contributions:** Conceptualization, A.H. (Alexander Hofmann); methodology, T.R. and V.P.; software, T.R. and V.P.; formal analysis, V.P.; data curation, J.C.O. and T.R.; writing—original draft preparation, T.R.; writing—review and editing, A.H. (Alexander Hofmann), M.F. and V.P.; visualization, T.R.; supervision, A.H. (Andreas Hornung), A.H. (Alexander Hofmann), V.P. and T.R.; project administration, A.H. (Alexander Hofmann), V.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors are grateful to Jan Grunwald and Martin Nieberl for technical support and discussion.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


**7. Prognosis**

MIS-C can be potentially life threatening. The need for admission to ICU was described, 40% of patients needs inotropic drugs, 15% is connected to mechanical ventilation, and even needs for extracorporeal oxygenation membrane (ECMO) have been reported. Despite what has been described, MIS-C is a disease with low mortality, less than 2%. Recovery from acute organ dysfunctions occurs quickly with a median ICU length of stay of 5 days. Recovery from sequelae is close to 6 months; however, there are reports of poor exercise tolerance. The disease was found less than 3 years ago, so large follow-up studies of these patients are needed to determine the long-term outcomes of these children [61].


First, we performed univariate analyses of the variables of interest, which included the mean, median, standard deviation, standard error, and sample size for each age group. Then, normality and homoscedasticity of the variables were evaluated. When the parametric requirement were not met, trigonometric transformations were applied to the variables, and assumptions were re-tested with the transformed variables. Non-parametric statistics were used, because some assumptions were indicators of UBN not met. The Mann–Whitney U-test was used for independent samples that considered the age (4 or 5 years), gender (girls, boys), and living conditions (favorable, unfavorable), and emotional appraisal (children appraisal, task appraisal) as grouping variables. The Kruskal–Wallis test was used when the grouping variable was language skills (low, moderate, high). Descriptive and inferential analyses were conducted using SPSS software (version 15.0).

## RESULTS

### Analysis of Variations of the Emotional Appraisal Responses

Because it was found that the children appraisal did not coincide with the stimuli valences (task appraisal), an analysis was made considering the accuracy appraisal as a variable of interest. We

TABLE 1 | Mean and standard deviation of education, occupation, housing, and overcrowding scores of living conditions homes.

Scores obtained from NES. The sample size is lower than the total sample (N = 116), however, no significant differences were found in the cognitive performance (emotional appraisal and emotional memory) of the children who have socio-environmental information, and children who do not (p ≥ 0.05).

did not found that this variable was previously explored in the literature. In this way, the valences that children attributed to images were compared (children appraisal compared with task appraisal) and an accuracy appraisal was obtained. Accuracy appraisal varied from 0 to 5 in the negative (Md = 2), neutral (Md = 2), and positive valences (Md = 3).

#### Age Differences

An analysis using the Mann–Whitney U-test that compared the accuracy appraisal between ages, showed significant differences between 4- and 5 years olds in the positive (Z = −3.14, p = 0.002), but not in the negative (Z = −1.35, p = 0.18) and neutral valences (Z = −0.610, p = 0.54). The 5 years old children (M = 1.74, SD = 0.85) were more accurate in the positive valence, compared with the 4 years old children (M = 2.27, SD = 0.89).

#### Differences by Gender, Language Ability, and Living Conditions at Home

However, the accuracy appraisal did not vary between children with respect to gender (p ≥ 0.05), living conditions at home (p ≥ 0.05), or language ability (p ≥ 0.05) as variables groups.

According to these results, appraisal accuracy should be incorporated into the analyses of expression memory.

### Analysis of Emotional Memory Taking Into Account the Children Appraisal or the Task Appraisal Separately

According to the results described above, children appraisal and task appraisal were compared in the performance of the emotional memory. A global analysis with the Mann–Whitney U-Test indicated that the performance memory was significantly different for the negative images (Z = −2.03, p = 0.04), but it did not differ for neutral images (Z = −1.04, p = 0.3) and positive images (Z = −0.42, p = 0.67). In general, children remember more negative images compared to positive and neutral ones.

#### Age Differences

Separated analysis were run for each age group to test differences between children appraisal and task appraisal. Results indicated significant differences only in 5 years olds for negative images (Z = −2.00, p = 0.04), but not for positive or neutral images

(p ≥ 0.05). Children who were 5 years old recalled more negative images when they were evaluated taking into account their own assessment than when considering the valence of the task. **Figure 1** shows the emotional accuracy of the age groups (4 and 5 years old) to the emotional valence (positive, neutral and negative). Children evoke of negative images was different when comparing children appraisal with task appraisal. Those differences were not evidenced for positive or neutral images.

### Differences by Gender, Language Skills, and Living Conditions at Home

When we examined the children's appraisal condition compared with the task appraisal condition as variable group, the Mann– Whitney U-Test showed no significant differences in the free recall of negative, neutral, and positive images (p ≥ 0.05) for each category of the considered factors (gender, language skills, and living conditions). That is, for boys or girls, from favorable or unfavorable living conditions, and with low, moderate or high language skills, children had similar memories of the three valences regardless of whether we used children appraisal or task appraisal.

### CONCLUSION AND GENERAL DISCUSSION

The objective of the present study was to analyze emotional and memory associations during the preschool period. We explored differences in those associations considering individual (i.e., age, gender, language skills) and socio-environmental (i.e., living conditions at homes) factors. We found that appraisal in 4 years-old did not coincide with task appraisal, and that children who were 5-years-old showed greater accuracy appraisal. This differential accuracy is presented in the appraisal of positive visual images. However, the accuracy appraisal did not vary with respect to gender, living conditions at home, or language ability.

With respect to the memory test performance (7–10 days after the session 1), results suggest differences in the number of free recall images between 4 and 5 years old children. In particular, older children evoked more images, regardless their valence. When individual and environmental factors were considered in the performance analysis, no differences were identified. Nevertheless, when the accuracy appraisal (children appraisal or task appraisal) was taken into account, differences in performance were identified for negative stimulus. Five-yearsold children evoke more images which they had appraised as negative. This pattern of results provides preliminary evidence regarding the need of including appraisal as a specific aspect of processing in the emotional memory study at these ages. It also suggests that different aspects of emotional memory are possible to be evaluated in young children from different socioenvironmental contexts.

Several studies have indicated that the emotional processing of images can generate variations in the consolidation of memory (Channell and Barth, 2013; Kuppens et al., 2013; Leventon and Bauer, 2016). In this sense, those images or events with emotional valences (positive or negative) were remembered more clearly than those with neutral valences (Christianson, 1992; Quas and Lench, 2007; Brainerd et al., 2010; Justel et al., 2013). Our study presents a different pattern of results in relation to other studies (Leventon and Bauer, 2016), in which children evoked images with negative and positive valences in a similar way. One of the differences of the present study is that it analyzes the emotional memory in children with different living conditions, even though the comparisons between the groups of children with favorable and unfavorable conditions were not statistically significant.

Regarding language development and its relation to memory expression, it would be relevant for future studies to analyze vocabulary with and without emotional content in children of these ages, in order to explore whether and how the valences of words are associated with their subsequent.

Higher memory performances of negative visual images when evaluated according to children appraisal may be due to the fact that people usually attribute emotions based on their own emotional states rather than those corresponding to task stimuli (Lerner and Keltner, 2000, 2001). It is also possible that due to this information would be better consolidated and evoked. These differences tend to be less pronounced when children are younger and when memory performance is evaluated according to task appraisal. On the other hand, these differences may be because at this stage the emotional categories are not yet consolidated (Nelson and Russell, 2016), so that children may exhibit variations in their initial appraisal of the stimuli. There are no studies of children that relate these variables, however, Kuppens et al. (2013) studied emotional appraisal of visual stimuli and its relationship with memory processes in adults. They examined how these prominent cognitive and affective components of emotional experience related dynamically to each other in daily life. Their findings revealed insightful patterns of the continuous interplay between core affect and appraisal. That is, appraisals and core affect were not independent properties of emotional experience, but were intricately related in a dynamic interplay that was characterized by congruency between appraisals and valence that suggested a central role in acting and responding to the environment for arousal (Kuppens et al., 2013). Therefore, emotions or moods should not be understood as static phases, but as a dynamic phenomenon that involves components that change and follow each other across time continuously (Kuppens et al., 2009; Boden and Berenbaum, 2010). Based on this reasoning, our study was limited by not including the arousal of visual stimuli. Future studies are proposed to incorporate the evaluation of arousal when analyzing the interaction between emotional appraisal and emotional memory.

On the other hand, appraisal accuracy was surprisingly low for the emotional appraisal task, especially for negative and neutral conditions (performance was around chance level). This brings into question the validity and reliability of this task in assessing emotional appraisal accuracy in 4–5 years olds. This task was used in different studies which reported similar results. However, these findings do not rule out the possibility that these responses are specific to the population that participated in these studies (in all cases, girls and boys residing in Buenos Aires City), and it is possible that cultural factors associated with the expression of

these responses could modulate their performance. All of these aspects should be explored in future studies.

The main contribution of our work is the inclusion of the appraisal as an important component to consider in a specific way in the expression of emotional memory of children at this stage of development. The modulation of appraisal on emotional memory opens questions about previous findings in which this component has not been taken in consideration and the implications on the consolidation and retrieval of the memories, especially the negative ones. In this sense, it is important to consider that several studies indicate the prevalence of negative memories during childhood. For example, negative events elicited more false memories than neutral event in a sample of 7-year-old children (Otgaar et al., 2008). In another study, children who developed negative pain memories were more likely to have experienced greater pain intensity and state anxiety during previous painful experiences (Noel et al., 2009, 2012). In a more recent study, emotion influenced the recognition memory of negative scenes in children (Leventon et al., 2014). Other studies provide evidence for negative emotions effects on memory in the school years (Davidson et al., 2006; Cordon et al., 2013). In this sense, the results of the current study also indicate a prevalence of negative memories, however, the performance is greater when evaluating the memories taking into account children's appraisal, compared to task appraisal. Thus, our results emphasize the need to include individual differences in emotional appraisal when analyzing the subsequent performance of children.

### ETHICS STATEMENT

Informed consents were obtained from parents/caregivers, and ethical approval was obtained from the Ethical Review Committee (Protocol N◦ 961). This study was conducted in accordance with the American Psychological Association ethical standards, and international and national children rights laws.

### REFERENCES

Once authorization was obtained from the schools where the activities were to be conducted, informative meetings were organized for parents in which they had an opportunity to interact with the research group. The researchers informed them about the objectives and activities of the study, and provided them with an information sheet. Consequently, they were invited to participate and to authorize the participation of their children, which required them to sign an informed consent for each of the evaluations. Likewise, prior to any evaluation, the permission of the children was requested.

### AUTHOR CONTRIBUTIONS

ER participated as designer, operator, and supervisor, performed the tabulation and prepared the datasets, designed and executed the statistical analysis, and wrote the manuscript. MS and SL participated as designer and coordinator, designed and supervised the statistical analysis, and wrote the manuscript. VR collaborated in preparation of the datasets, statistical analysis design, and manuscript review.

### FUNDING

This research was supported by CONICET, Ministry of Education (Buenos Aires City Government), and the National Fund for Scientific and Technical Research (Grant PICT 2014-3134).

### ACKNOWLEDGMENTS

We would like to thank the following people for their contribution to the studies: supervisors, schools directors, teachers, families and children who participated in the study. We would also like to thank Thomas A. Gavin, Professor Emeritus, Cornell University, for the help with editing of the manuscript.

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of Argentinean preschoolers from diverse socioeconomic backgrounds. Front. Dev. Psychol. 13:A205.

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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