Thirty preschool children participated in the study. The data from two children were excluded because they did not participate in all sessions. This left 28 children (11 female, 17 male) aged 4.00–5.83 years (mean age = 4.98, SD = 0.48) for the final analysis. The children and their parents came from relatively high socioeconomic status backgrounds and were recruited from the wider Paderborn region (North Rhine-Westphalia, Germany). Recruitment was conducted in local kindergartens and libraries or through newspapers and our database of families willing to participate in our research studies. In addition, we assessed the level of parental education. None of the children or the parents had ever seen the robot before the experiment. Prior to their children’s participation, parents provided written consent and filled out a questionnaire on their child’s general and language development. Criteria for inclusion in the sample were: a) age between 4 and 6 years; b) normal general development such as normal sensory and cognitive skills; and 3) no developmental language delays. Additionally, detailed information regarding children’s language skills was obtained by measuring their receptive language abilities with a subtest of the standardized SETK 3–5 (Grimm et al., 2001) and their expressive vocabulary with the AWST-R (Kiese-Himmel, 2005). Parents were present during all interactions, but did not participate actively in the interaction. Children also provided verbal assent prior to taking part in the interaction, and the interaction could be discontinued at any time at no disadvantage to the child. Moreover, each child received stickers and a toy to thank them for their participation.

The children and their parents were invited to visit our laboratory at Paderborn University for four sessions within a period of two weeks. Each session lasted around 20–35 min, and all sessions were recorded on video. Each participating child was accompanied by one parent. Figure 1 displays the seating arrangement. The experimenter operated the robot, avoiding any interaction with either parent or child. In addition, parents were instructed to avoid talking to their child during the experimental part of the child’s interaction with the social robot.

Based on previous work and ethical considerations (Vogt et al., 2019; Tolksdorf et al., 2020a; Tolksdorf and Mertens, 2020), we conducted a warm-up phase with each child and her or his parent before the learning situation with the robot. This introduced the robot to the children in a comfortable way with their caregiver as an available resource (Manner et al., 2018; Tolksdorf et al., 2020a) and reduced the novelty effect (Kanero et al., 2018). During the warm-up, the experimenter first introduced the robot in a powered-off state to the child and parent and explained its functions. For example, it was explained that the robot can talk and move with the help of small motors, because a pilot study had shown that some children were surprised when they heard that the robot’s movements were loud. In a second step, children and parents were further familiarized with the capabilities of the robot: The robot introduced itself and performed a short game by imitating animal movements and asking the child, the parent, and the experimenter to repeat the movements. Although the experimenter structured the situation and was the main interaction partner for the child, parental involvement was considered as an important element during the warm-up phase, because prior work has demonstrated that young children may rely on the emotions with which their familiar caregiver interprets the ongoing situation, especially during first encounters with a social robot (Rohlfing et al., 2020; Tolksdorf et al., 2021). After the game was completed, the robot said goodbye for the moment and announced that it had prepared a story that it wanted to share with the child. Subsequently, the experiment started and the script for the first learning situation was launched.

When designing the learning situation, we were guided by theoretical concepts of learning postulating that interaction partners jointly co-construct the communicative situation in a goal-oriented way (Rohlfing et al., 2016; Rohlfing et al., 2019). Therefore, we chose a setting that included activities with which preschoolers are familiar. Specifically, the robot told the child a story that had been created to frame the learning situation. The story contained the plot of the robot’s trip to Paderborn University and the things it had seen on its journey. This narrative served as the context in which the children encountered six novel words (color adjectives) during the interaction. The referents of the novel words were presented as pictures hanging on the wall. They were covered by a small cloth, and the robot asked the child to uncover them one by one over the course of the interaction (see Figure 2A). This context was also chosen because past work has shown that the context of a story is particularly conducive to children’s learning of new words (Horst, 2013; Nachtigäller et al., 2013).

Furthermore, in order to render the robot’s interaction behavior child-oriented and to fulfill the important role of multimodal joint activities (Rohlfing et al., 2019; Tolksdorf and Mertens, 2020), the robot also performed a number of actions such as accompanying the novel words with pointing gestures to coordinate the child’s attention and establish a shared reference. In the same way, the robot coordinated its gaze between the child and the referents of the target words. Additionally, after naming the first four target words, the robot also walked with the child to the two remaining target referents in order to make the situation more natural and to take advantage of the physical presence of the robot (van den Berghe et al., 2019). Once the robot had finished the story, it thanked the child and said goodbye.

Subsequently, in the second and third sessions, a similar learning situation with the robot took place again in which the robot shared the story, and the children were exposed to the novel words.

Following the third learning situation in the third session and a 5 min break, children’s learning achievements were assessed by testing their ability to generalize the acquired knowledge. In this generalization task, comprehension was defined as the child’s ability to extend or transfer the target words to new objects, and whether they were able to transfer the learned pattern of word formation to new colors when presented with separated units of a compound. This method ensured assessment of whether the children were able to transfer their knowledge to other objects and whether their knowledge was stable. We used a routinized activity for children and embedded the test procedure within a shared picture book reading situation (Grimminger and Rohlfing, 2017). In this test, the child was asked to turn the pages while the robot talked about the pictures with the child and elicited the trained words (see Figure 2B).

Two to three days later, the last, fourth session was conducted, in which a delayed test using the same procedure was administered to assess the children’s knowledge again.

Our study used the Nao robot from Softbank Robotics. This is a small, toy-like, humanoid robot used widely in child–robot interaction studies (Belpaeme et al., 2018). It is 58 cm high with 25 degrees of freedom. Teleoperation was employed to enable the robot to act contingently (Kennedy et al., 2017). We implemented the behaviors in the NAO robot by using Choregraphe and used the integrated text-to-speech production of the robot with German language enabled and speech reduced to 85% speed to achieve a more natural pronunciation. The target words imparted by the robot were spoken at a speed of 75% in order to emphasize them verbally. The target items consisted of six morphologically complex words (noun–adjective compounds such as “quince yellow [quittengelb]”) that represented different colors as features of different objects. Each item was presented on a picture measuring 14.8 × 21.0 cm.

Because we were interested in children’s behavior during all the learning and testing situations with the social robot, we followed Colonnesi et al. (2013) and Colonnesi et al. (2014) and coded positive as well as negative reactions with and without signs of aversion such as body or head aversion. Positive behaviors of the children were measured by, for example, smiling identified by raising the corners of the lips, constriction of the eyes, raising of the cheeks, or opening of the mouth. Negative behaviors were frowning or sad facial expressions. When these behaviors were accompanied by an aversion (of gaze, head, and body), they fell into the category of positive or negative expressions of shyness. We measured this behavior across the time period of the interaction during which the robot a) shared the story and taught the new words (learning situation T 1–3) and 2) tested the child within the shared picture book situation (Test 1 and Test 2), (see Figure 3).

We decided to analyze this sequence because, at this stage, all children had already achieved a certain familiarity with the new interaction partner, and our focus was on investigating development across different sessions. Additionally, the analyzed sequence represented the main part of the interaction, whereas a welcome or farewell situation would reflect a different social situation with its own contextually appropriate social behaviors (Vaughn and La Greca, 1992). Examining this sequence is particularly relevant, because it provides an opportunity to understand how shy children interact during a learning situation with a social robot. Because the duration of the interactions varied slightly between children, children’s behavior was expressed in proportion per minute. To evaluate coding reliability, two coders independently coded a random subset of 15% of the data. We used Cohen’s Kappa to measure the agreement between the coders for positive and negative reactions, expressions of shyness, and children’s aversion. The mean Kappa values were between 0.88 and 0.94, indicating a high level of internal consistency.

Following recent methodological accounts (Rohlfing and Grimminger, 2019), we chose to assess children’s word-learning performance in detail on different linguistic layers rather than in a binary way (e.g., only correct or incorrect). To provide a measure of word learning, we created a composite score by averaging each of the children’s naming performances in percentages on a phonological, morphosyntactic, and pragmatic–semantic level. Thus, on a phonological level, we calculated the proportion of correctly produced syllables of a target word. On a morphosyntactic level, and independent of the semantic meaning, we assessed the sophistication with which the children constructed a noun–adjective compound and distinguished between no compounding, partial compounding, and fully correct compounding. Finally, we evaluated each child’s response on a pragmatic–semantic level, differentiating between no response, a semantically adequate response, a partial retrieval of the target word, and a fully correct retrieval. The maximum composite score that could be achieved in each test session was 3, which would reflect a fully accurate performance on each of the three linguistic levels.

To assess the children’s degree of shyness, we used the Inventory on Integrative Assessment of Child Temperament (German: IKT—Inventar zur integrativen Erfassung des Kind-Temperaments, Zentner, 2011). This is a standardized questionnaire that is widely used in clinical practice and is specifically designed for the age group addressed. The IKT has been validated with a normative sample of over 4,400 children, possesses convergent validity with equivalent English-language temperament diagnostics (e.g., the CBQ by Rothbart et al., 2001), and measures the temperament of 2- to 8-year-olds on five levels based on the integrative approach of Zentner and Bates (2008). With their approach, the authors pursue the goal of overcoming the manifold conceptions of child temperament research and providing a questionnaire that is valid across theories (Zentner and Bates, 2008). The levels comprise shyness (behavioral inhibition), susceptibility to frustration, activity level, attention span/task persistence and perceptual sensitivity. In our study, caregivers were asked during the first session (T1) to fill out the questionnaire and to estimate how often their child shows a described behavior using a 6-point Likert scale ranging from 1 (never) to 6 (always). This included behavioral aspects such as “hides behind her mother when she meets strangers.” Based on the raw scores obtained from the responses to the questions, the evaluation procedure of the test requires a conversion into percentile ranks to allow an adequate interpretation of the child’s temperament according to age and gender in relation to the normative sample of the test. The higher the percentile rank value, the shyer the child, with the minimum and maximum value being 0 and 100 respectively. In this vein, the IKT allows children to be considered as notably shy if they have a clearly above-average score of over 75. Additionally, the shyness scale used in the test procedure provides a good internal consistency (α = 0.81). In the normative sample, the agreement of both parents as a measure of interrater reliability was clearly above average (r = 0.73) and thus highest on the shyness level compared to the other levels (Zentner, 2011). Finally, in accordance with the evaluation procedure and based on the percentile ranks obtained, we grouped our sample into two levels: nonshy (n = 18) and shy (n = 10).

A statistical analysis of shyness markers was not possible, because values tended toward zero in almost all training and testing periods. Therefore, we focused on analyzing the behavioral markers indicating pleasure and distress described in the following. In the Limitations section, we shall discuss some issues that may have led to the very rare occurrence of typical shyness reactions.

First, we wanted to know how far positive reactions were more likely in the nonshy group. We also assumed that due to the familiarity of the situation, positive reactions would increase over time in both groups, but especially in the shy group that would start off being more reserved but become more uninhibited over the course of the sessions.

Parametric statistical tests could not be used to analyze this dependent variable due to nonnormally distributed data and the small sample size. Therefore, we used the ANOVA type statistic (ATS)—a nonparametric equivalent of a mixed ANOVA (Akritas et al., 1997)—performed with the software R (package: nparLD, Noguchi et al., 2012). The ATS is regarded as a distribution-free test, but is mathematically more appropriate than classical rank-sum statistics such as Wilcoxon’s rank-sum test. The test statistic is quite similar to ANOVA’s F tests and exactly meets the α level while being conservative. It has been applied in developmental studies (Viertel, 2019; Tolksdorf et al., 2021). In addition, the ATS can tolerate unequal group sizes in the sample and is robust when studying longitudinal data because it considers their progression over time rather than comparisons between groups at each timepoint that may inflate type I error. The relative treatment effect (RTE) is a measure of the effect size and is estimated based on the actual sample. It can be determined for main effects as well as for interaction effects—that is, even for multifactorial designs with repeated measurements (Noguchi et al., 2012) as in the present study. The value of the relative effect RTE ranges between 0 and 1, whereby the occurrence of 0 and 1 means completely different conditions (e.g., for the shy and nonshy group); 0.5 indicates that the conditions do not differ at all (Brunner and Munzel, 2002; Noguchi et al., 2012).

A significant main effect demonstrated that nonshy children (Mdn = 1.14, IQR = 1.67, RTE = 0.57) used positive behaviors significantly more often than their shy peers (Mdn = 0.50, IQR = 1.37, RTE = 0.37), F(1.00, 17.33) = 6.51, p < 0.05, regardless of the situation (learning and testing). This supported Hypothesis H1a.

Additionally, the ATS revealed a highly significant main effect of time, F(3.00, ∞) = 7.77, p < 0.001. Positive reactions were highest at the beginning of the training of novel words (T1: Mdn = 1.71, IQR = 1.54, RTE = 0.61), decreased steadily over the course of training (T2: Mdn = 1.27, IQR = 1.06, RTE = 0.56; T3: Mdn = 0.63, IQR = 0.97, RTE = 0.40), and then remained relatively stable in both tests (Test 1: Mdn = 0.55, IQR = 1.15, RTE = 0.39; Test 2: Mdn = 0.60, IQR = 1.03, RTE = 0.40). Post hoc tests were applied with Bonferroni corrections. A significant decrease of positive reactions was detected from training T1 to Test 1 (difference = 34.0) and Test 2 (difference = 38.0) respectively, χ ²(4) = 15.86, p < 0.01. In all cases, the critical difference was 33.21. This observed development of positive reactions contradicted Hypotheses H2a and H2b that positive reactions would increase with the repetition of a learning or test situation.

Moreover, contrary to our hypotheses, there was no interaction between time and shyness level, F(3.00, ∞) = 0.65, p = 0.58. Hence, shy children remained reserved over time by demonstrating fewer positive reactions such as smiles over the course of both the learning and the testing situations.

In a further step, we asked how far the frequency of negative reactions differed between shyness groups and training conditions depending on the time of training and testing situation. We hypothesized that the shy group would show negative reactions more frequently (H1b), but that these would decline more rapidly among the shy group than among the nonshy group in both the training situation (H2a) and the testing situation (H2b). Again, we refrained from using parametric statistics and used the ATS.

The ATS revealed a highly significant effect of time, F(3.34, ∞) = 14.22, p < 0.001. It was evident that negative reactions were stable across the first two training sessions (T1: Mdn = 0.45, IQR = 1.04, RTE = 0.58; T2: Mdn = 0.51, IQR = 0.75, RTE = 0.56), decreased during last training (T3: Mdn = 0.00, IQR = 0.32, RTE = 0.29), briefly increased during the first test (Test 1: Mdn = 0.57, IQR = 1.00, RTE = 0.62), and finally, flattened in the second test (Test 2: Mdn = 0.19, IQR = 0.20, RTE = 0.35).

There was also a trend toward a significant difference between shyness groups, with shy children (Mdn = 0.16, IQR = 0.50, RTE = 0.42) demonstrating negative reactions less frequently than nonshy children (Mdn = 0.33, IQR = 0.64, RTE = 0.55) in all training and test situations, F(1.00, 18.21) = 3.33, p = 0.07. Therefore, we rejected Hypothesis H1b.

However, both factors need to be interpreted in relation to each other, because there was a significant interaction effect between time and shyness group, F(3.34, ∞) = 2.58, p < 0.05. Post hoc tests showed that shy children expressed negative reactions less frequently, especially during the second training (Mdn = 0.24, IQR = 0.46, RTE = 0.41) as well as during the second test (Mdn = 0.12, IQR = 0.17, RTE = 0.26) compared to nonshy children (T2: Mdn = 0.66, IQR = 1.43, RTE = 0.71; Test 2: Mdn = 0.26, IQR = 0.19, RTE = 0.45). Differences during training (W = 144.5, p < 0.01, r = −0.49) and testing (W = 146.5, p < 0.01, r = −0.51) were both very significant. Thus, the result that only shy children showed negative reactions significantly less often when a situation was repeated supported Hypotheses H2a and H2b.

Additionally, in the shy group, multiple comparisons across time revealed that negative reactions decreased significantly from first to third training (T1 vs. T3: χ ²(4) = 16.17, p < 0.01), with the observed difference of 20.5 exceeding the critical difference of 19.85. In the nonshy group, a significant reduction of negative reactions from T2 to T3 could be identified in the training situations, χ ²(4) = 19.34, p < 0.001. The observed difference was 30.5 and thus higher than the critical difference of 26.63. Surprisingly, in the group of nonshy children, there was a significant increase in negative reactions from the last training session (T3) to the first testing session (Test 1) with an observed difference of 29.5. In the shy group, a tendency toward an increase was identified based on the observed difference of 19.5 (critical difference: 19.85). In conclusion, over the course of the training phase for novel words (T1–T3), negative reactions decreased in both groups, but this familiarization effect occurred more rapidly in the group of shy children.

Summarizing the frequency of both positive and negative reactions in the learning and test situations, it can be concluded that the group of shy children was generally less expressive compared to the group of less shy peers.

Finally, we focused on shy children’s word learning, which we assumed would be less successful than that of nonshy children––but only during the first test session (H3). Children’s word learning was measured based on the calculated linguistic composite score (0–3) reflecting their performance during the test tasks (cf. section Assessment of Naming Performance). As hypothesized, a strong trend could be observed during the first test, and shy children (Mdn = 0.51, IQR = 0.36, range = 0–1.52) tended to be less successful in retrieving the taught words than their nonshy peers (Mdn = 0.64, IQR = 0.27, range = 0–2.54), W = 122, p = 0.06, r = −0.35. Furthermore, also as hypothesized, both groups (shy: Mdn = 0.48, IQR = 0.27, range = 0–2.54; nonshy: Mdn = 0.72, IQR = 0.72, range = 0–2.06) did not differ significantly in their success at retrieving the words during the second test session (W = 119.5, p = 0.16, r = −0.26), which was a repetition of the first test a few days later.

Last of all, we wanted to go beyond the dichotomous group comparison and ask how the entire range of the shyness spectrum as an influencing factor predicted a gain in word learning when the children were already familiarized with the experienced testing situation during Test 1. As described, the testing situation took place in two different sessions (Test 1 and Test 2). Thus, we calculated the gain in word learning by measuring the difference scores between the first and the second test as a metric of learning gain. As a predictor variable, we did not use the categories nonshy and shy, but the calculated percentile ranks taken from the IKT described above (cf. section Assessment of Shyness and Shyness Questionnaire) that represented the full shyness spectrum and would allow us to take a closer look at the link between temperamental characteristics and word-learning processes.

Therefore, our hierarchical regression model included the shyness score as a predictor of a gain in word learning (first step). Moreover, when measuring children’s word learning, it is known from the literature that existing linguistic knowledge should be taken into account because it contributes to word learning success (Stelmachowicz et al., 2004; McMurray et al., 2012). Therefore, in the second step, we integrated a language measure of children’s receptive linguistic abilities (SETK 3–5 subtest sentence comprehension, cf. Table 1).

In Step 1, the model did not differ significantly from zero, F(1, 26) = 2.72, p = 0.11, with shyness level accounting for 9.48% of the variance in learning gain. In Step 2, the model approached statistical significance and accounted for an increased portion of gain in word learning, F(2, 25) = 3.18, p = 0.06, and could explain 20.28% of the variance. Shyness scores related significantly to a gain in word learning (B = 0.005, t = 2.18, p = 0.04) and uniquely contributed 14.63% to the total variance of our dependent variable (see Figure 4). The shyer the children were, the higher their growth of learning as suggested by the significant positive semipartial correlation (r = 0.38, p = 0.05). In terms of receptive linguistic abilities, we detected a nonsignificant trend (B = 0.01, t = 1.84, p = 0.08) toward a positive association between children’s receptive language and word learning gain (r = 0.33, p = 0.09), whereas language abilities independently accounted for 10.96% of the variance. A comparison of both models confirmed that in Step 2, R ² tended to change, F(1, 25) = 3.38, p = 0.08.

Finally, we checked whether other factors, which had not been considered in the multiple regression model before, were associated with word learning. Neither age (r = 0.12, p = 0.27, one-sided) nor expressive vocabulary collected by the AWST-R (r = −0.02, p = 0.55, one-sided) related significantly to a gain in word learning. In summary, this means that shy children showed greater gains in word learning than children who were less to moderately shy, but only when their receptive skills were considered in the model as well.

As expected, and in accordance with previous literature (Reddy, 2000; Putnam et al., 2006; Feinberg et al., 2012; Abe et al., 2014; Vogt et al., 2019; Tolksdorf et al., 2020b), we found that shy children were more reserved in their positive reactions toward the robot compared to their nonshy peers in all learning and testing situations. This lower level of positive reactions could be explained by the typical expressive pattern of shyness in novel social situations (Reddy, 2000; Colonnesi et al., 2014)—that is, shy children tend to display reduced emotional reactions and be more inhibited in unfamiliar social interactions (Poole and Schmidt, 2019). Surprisingly, we did not find support for our hypothesis that the number of positive reactions would increase with the repetition of a situation and increasing familiarity. Instead, our results revealed an opposite trend. Overall, the frequency of occurrences of positive reactions was most pronounced in the first two learning situations and then decreased steadily, reaching its lowest level in the final two test situations. With regard to the nonshy children, the results might be explained by the fact that precisely the novelty of the situation (e.g., the novel interaction partner and storytelling setting as suggested in Kanero et al., 2018) led to higher levels of engagement that were reflected in more positive reactions. In this vein, our results corroborate existing research in the area of child–robot interaction demonstrating that with the increasing duration of an interaction and repetitive behavior of a robot, children’s engagement in terms of enjoyment could drop (de Wit et al., 2020). Thus, the repeated sessions with the robot might have led to a habituation effect and resulted in decreasing positive reactions. However, we could not observe a significant change in the expressiveness of the shy children’s positive reactions over the course of the sessions, suggesting that their display of enjoyment of the situation remained constant on a low level, even during the learning situations.

Regarding the negative reactions as an expression of discomfort in a new situation, results show that behaviors such as frowning or narrowing the eyes reduced significantly more quickly over time in the group of shy children, probably due to the fact that they had become accustomed to the learning and testing situation. In particular, when the shy children were already familiarized with a specific setting (first learning situation or first test), reactions of distress were significantly lower in comparison to nonshy children. A decrease in reactions of distress also occurred in the nonshy children–but at a slower rate. In addition, in the latter group, the significant increase in negative reactions stood out when the setting (from learning to testing) and the associated demands on the children changed.

This finding has strong implications not only for studies evaluating social robots as interaction partners in educational settings, but also for studies examining word learning processes or other cognitive abilities in young children in general. Negative reactions (of distress) relate positively to general and social anxiety (Colonnesi et al., 2014), and negative shy reactions are associated with physiological changes that can activate the fight-or-flight system (Colonnesi et al., 2020) that consequently inhibit adaptive behavior and cognitive processes. During learning with others, they are often inhibited, which is reflected in deviant attentional processes (Hilton et al., 2019) as well as infrequent eye gaze to the other (Putnam et al., 2006). Thus, in our view, a warm-up that usually takes place a few minutes before the training or testing may not be sufficient for a specific population such as very shy children. Considering the fact that especially nonshy children expressed the highest proportion of reactions of distress during the first two learning sessions (T1 and T2), our conclusion is not exclusively of relevance for shy children, but also for children with different temperament traits.

Our third hypothesis addressed how word learning in shy children differs from that in their nonshy peers at different time points. Motivated by the literature (Hilton and Westermann, 2017), we assumed that shy children would be less successful at retrieving the learned words than nonshy children, especially in the first test situation. We further hypothesized that the difference between groups would decline during the second test due to familiarization with the test procedure. At first impression, the performance of both shy and nonshy children in the word learning tests seems to be in line with our hypothesis, because in both tests, shy children were less successful in retrieving the trained words. Although the difference was marginally significant in the first test session, it disappeared in the second session. However, we considered that a further, more nuanced approach was needed, because there could be legitimate objections to our categorization into shy and nonshy. In particular, because children who were not at all shy fell into the same category as children who were on the threshold of being very shy (according to the questionnaire), we regarded our dichotomization as not being precise enough. Therefore, we conducted a regression analysis to determine the relation between the degree of shyness and word learning. Moreover, our multilevel model also took into account the children’s linguistic knowledge, and we were able to show that, in addition to a shy temperament, the children’s receptive abilities also tend to contribute to a success in word learning. Surprisingly, past studies on learning words with shy children (Hilton and Westermann, 2017; Hilton et al., 2019) or language learning studies with robots (Vogt et al., 2019) have not considered these linguistic abilities; therefore, our study marks another novelty in this field.

Interestingly, the children who were the shyest according to their parents made the largest gains in word learning, whereas those with the lowest shyness scores remained stable or even scored lower on the second test. This contrasts with results obtained in studies concluding that shy children are a) less likely to learn and retain new words (Hilton and Westermann, 2017) or b) have poorer productive vocabularies (e.g., Crozier and Hostettler, 2003). Additionally, these prior studies drew their findings from a single test—that is, word learning or vocabulary of shy children is typically assessed in a new environment with unknown people without any prior familiarization with the situation. This raises the question whether the shy children’s test performance would be similar to that of nonshy children if they were already familiar with the situation (as realized in our study). As described above, shy children are afraid of being evaluated in unfamiliar situations, which consequently inhibits their performance, as evidenced by studies that a) examine vocabulary less invasively, for example by parents or in familiar school settings (Spere and Evans, 2009); or 2) in more anonymous group settings (Crozier and Hostettler, 2003); but also by studies that 3) determine the receptive vocabulary (Evans, 1996). These studies conclude that the linguistic performance of shy children does not differ from that of nonshy children. Therefore, based on our data, we assume that the shy children were more confident during the second testing and verbally expressed themselves more often once they were familiarized with the exact procedure and the demands of the test situation.

As well as the repetition of the testing situation, another possible reason could contribute to a short familiarization: the pragmatic frame of the situation of joint book reading that is structurally anchored in the test situation (Rohlfing et al., 2016). Accordingly, we can assume that the test situation recedes into the background or is not perceived as such by the children, so that their cognitive abilities can unfold (Rohlfing et al., 2016). Additionally, because our robot was introduced as a coequal peer (Kory Westlund et al., 2016; Vogt et al., 2017), it might not be perceived as an authoritative character, as other examiners are often perceived to be by shy children, but rather as an interaction partner who elicits learned words or, in general, verbal responses in a familiar situation. Therefore, shy children may feel less evaluated during an interaction with a robot, especially in terms of their performance, and this could lead them to be less cognitively inhibited and more confident when attempting to guess an answer.