Imitation is defined as the faithful, deliberate copying of an observed action in the sense that an observer achieves the goal of an action by using the same method and the same or similar topographical body movements as the model (Heyes, 2001, 2021; Hoehl et al., 2019; Horner & Whiten, 2005). Imitation has a significant function for learning in child development (e.g., Meltzoff, 1999; Meltzoff et al., 2009; Užgiris, 1981). By imitating others, children do not need to acquire instrumental skills through trial-and-error learning only, but may profit from what other people have already learnt and accelerate their own learning (Meltzoff et al., 2009). Furthermore, cultural practices and inventions (e.g., writing and numerical system) are being passed on to children through (verbal) instruction, interaction with and imitation of other people (Harris, 2012; Heyes, 2021; Nielsen, 2012). This makes imitation an important social learning tool. Imitation enables children to live, communicate, and form relationships with other people in a cultural world full of norms, different values, and accumulated knowledge. Social Imitation From 12 months of age, children imitate selectively: They show differences in the extent of their imitation depending on situational constraints (Gergely et al., 2002; Schwier et al., 2006; Zmyj et al., 2009) and the age (Zmyj et al., 2012), competence (Zmyj et al., 2010), and group membership of the model (Buttelmann et al., 2013; Gruber et al., 2019; Wilks et al., 2018). This suggests that children do not imitate blindly but take the context into account for more efficient learning (e.g., Langeloh et al., 2020). In contrast, other work shows that children imitate task irrelevant actions (i.e., overimitation; Nielsen & Blank, 2011; Nielsen et al., 2008; Over & Carpenter, 2009) suggesting that, in some cases, children do not follow an epistemic, but rather a social goal (Jaswal & Kondrad, 2016; Over & Carpenter, 2009, 2013; Užgiris, 1981). This social function of imitation has been studied intensively: Children use imitative behavior to communicate nonverbally (Nadel, 2002), affiliate with others (Nielsen & Blank, 2011; Over & Carpenter, 2009; Užgiris, 1981), conform to cultural and social norms (Clay et al., 2018; Oláh & Király, 2019), and comply with group membership (Buttelmann et al., 2013; Gruber et al., 2019; Wilks et al., 2018). Being imitated by others increases children’s prosocial behavior (Carpenter et al., 2013; Sauciuc et al., 2020) and trust towards the imitator (Over et al., 2013), and higher perceived similarity to the model increases imitation in children (McGuigan & Robertson, 2015; Rosekrans, 1967; for a review on the social function of imitation see Over, 2020). Social imitation, that is imitation that is directed towards social purposes (Over, 2020), is usually investigated by measuring overimitation (e.g., Gruber et al., 2019; McGuigan & Robertson, 2015; Nielsen & Blank, 2011), nonconscious mimicry (e.g., Lakin & Chartrand, 2003; Stel et al., 2010), and automatic imitation (e.g., Genschow et al., 2021; Leighton et al., 2010). Overimitation describes the imitation of not only relevant but also irrelevant action components within a sequence of actions (Keupp et al., 2018; Lyons et al., 2007). Nonconscious mimicry is defined as the unconscious copying of someone’s behavior in social settings (Chartrand & Bargh, 1999; Over, 2020). For example, participants show an unconscious increase of face-touching when they are with a confederate who touches his face often (Chartrand & Bargh, 1999). Automatic imitation refers to the phenomenon that movement execution is being facilitated when observing congruent movements and interfered when observing incongruent movements (Cracco et al., 2018). Notably, the different measures of social imitation have mostly been assessed in different age groups. While overimitation is often assessed in childhood, nonconscious mimicry and automatic imitation are mostly studied in adulthood (Over, 2020). This makes it difficult to compare findings across the lifespan and a task allowing for the assessment of social imitation across age groups is needed. Therefore, one goal of the current study is to explore whether children’s social imitation may be measured with an automatic imitation paradigm, a task typically used with adults. Automatic Imitation in Adults There is strong support for the validity of automatic imitation paradigms to measure imitation in adults (Cracco & Brass, 2019). Furthermore, automatic imitation paradigms create more reliable and bigger effects than mimicry tasks (Genschow et al., 2017). A typical paradigm to investigate automatic imitation is the imitation-inhibition task by Brass et al. (2000; 2001). In this task participants are asked to execute finger movements in response to either congruent or incongruent finger, object movements, or symbolic cues. Participants typically show shorter reaction times and less errors to congruent than incongruent movements indicating automatic imitation (Brass et al., 2000; Cracco et al., 2018; Genschow et al., 2021). Automatic imitation is explained by the associative sequence learning theory (Brass & Heyes, 2005; Heyes, 2005) and the ideomotor theory (Greenwald, 1970; Prinz, 1990, 1997). Both theories assume that not only the execution but also the observation of an action leads to the activation of the associated motor representations (perception-action-link; Brass & Heyes, 2005; Greenwald, 1970; Prinz, 1990, 1997). This proposal finds support in research on the mirror neuron system, a cortical network involving areas which respond similarly to the execution as well as the observation of an action (Gallese et al., 1996; Iacoboni, 2009). In automatic imitation, the observation of an action (i.e., index finger movement) activates the corresponding motor plan. This makes it easier to respond with the same action (i.e., index finger movement), while the response with a different action (i.e., middle finger movement) needs inhibition and is more difficult (Cross et al. 2013). Therefore, reaction times and error rates are lower in congruent trials (facilitation effect) and higher in incongruent trials (interference effect) (e.g., Brass et al. 2000; Brass et al. 2001). Both reaction times and error rates have shown to be influenced by contextual and individual factors. For example, in adults, automatic imitation is stronger for non-goal-directed than goal-directed actions (Cracco et al., 2018), if the participant and the model are the same gender (Butler et al., 2015; Cracco et al., 2018), and for human agents as opposed to nonhuman agents (Cracco et al., 2018). In a recent study with students by Genschow et al. (2021), automatic imitation was increased when participants were asked to focus on the similarities instead of the differences between their own and another person’s hand shown in the stimuli. The authors suggest that this effect is driven by the focus on differences that is decreasing automatic imitation, and not by the focus on similarities that is increasing automatic imitation. In sum, the reviewed findings suggest that automatic imitation (i.e., reaction times, error rates) is influenced by perceived similarity between the participant and the model. This influence may be explained by the above-mentioned perception-action-link (Brass & Heyes, 2005; Greenwald, 1970; Prinz, 1990, 1997). That is, observing an action of someone similar may support the activation of the corresponding motor representations in the participant. Observing actions of a dissimilar model may interfere with the activation of motor representations and imitation. In contrast to this assumption, another study by Genschow and colleagues (Genschow et al., 2022) found that group membership did not modulate automatic imitation in the imitation-inhibition task (Brass et al., 2000, 2001). Furthermore, perceived similarity and feelings of affiliation did not moderate the effect of group membership on automatic imitation (Genschow et al., 2022). In this online study, group membership was manipulated by using either nationality or ethnicity, indicated by the color of gloves or the skin color of an animated hand shown in the pictures. This manipulation seems rather artificial. Furthermore, the study was conducted online, which may have biased the results (e.g., decreased attention to the stimuli because of distractions in the home environment). Therefore, to date, it is not clear whether perceived similarity influences automatic imitation in adults. Automatic Imitation in Children Up until now there are only few studies which assessed automatic imitation in children. O’Sullivan and colleagues showed that automatic imitation in children was higher for actions thought to be performed in synchrony (e.g., clapping) compared to other actions like pointing (O’Sullivan et al., 2018). Another study found that automatic imitation in children was higher when they responded to the hand movements of in-group models instead of out-group models (Essa et al., 2019). In a more recent study, automatic imitation in 3-year-olds was assessed with the imitation-inhibition task by Brass et al. (2009) but the findings could not be replicated (Brezack et al., 2021). However, in contrast to Brass et al. (2009), Brezack and colleagues only assessed error rates, because children’s reaction times were extremely variable. In sum, similar to research on adults, work on children indicates that automatic imitation is influenced by contextual factors such as the synchrony of actions or the group membership of the model. In contrast to adult literature, there is no research directly testing the influence of similarity on automatic imitation in children. Studies employing different paradigms show that imitation is stronger if perceived similarity is higher. For example, preadolescent boys showed more spontaneous imitation if their perceived similarity to the model was high (Rosekrans, 1967) and children over-imitated peers more than puppets (McGuigan & Robertson, 2015). Other studies suggest that children imitate in order to communicate the similarity between them and the model (Carpenter, 2006; Over & Carpenter, 2009). While these studies suggest an influence of perceived similarity on children’s automatic imitation, this influence is likely to be different in children and adults because the above-mentioned perception-action-links develop through self-observation and interaction with others (Casile et al., 2011; Heyes, 2010; Ray & Heyes, 2011). Therefore, the second aim of the current study is to explore a possible influence of perceived similarity on children’s automatic imitation. The Present Study In the present study, we ask whether the perceived similarity of the participating children with another child (the model) shown in the stimulus influences children’s automatic imitation measured with the imitation-inhibition task by Brass et al. (2000). The findings of this study add to the existing literature in the following ways: First, the present study explores the possibility to assess automatic imitation in children by means of the imitation-inhibition task by Brass et al. (2000). Second, the study will be the first to test whether similarity influences automatic imitation in middle childhood and when the perception-action-link is developing. Third, the study contributes to the findings of the study by Genschow et al. (2021), by employing a different manipulation of similarity, using not only external (appearance) but also internal (preferences) attributes. To answer our research question, we will first assess school children’s automatic imitation (neutral phase) with an adapted version of the imitation-inhibition task by Brass et al. (2000). Subsequently, we will manipulate the perceived similarity of the participating child with the model in the imitation-inhibition task. We will show the children a picture of another child (the model) and either describe the model as similar or dissimilar to the participating child regarding gender, age, and shirt color (external similarity) and food, toy, and leisure activity preferences (internal similarity). After this manipulation of perceived similarity, we will measure automatic imitation again using the same imitation-inhibition task (test phase) telling the children that the hand they see in the task belongs to the model we just presented to them as similar or dissimilar. We will measure and compare children’s reaction times in both imitation-inhibition tasks (neutral and test phase). We decided to focus on reaction times and not error rates because most studies investigating automatic imitation using a stimulus-response compatibility paradigm (e.g., Leighton et al., 2010; O’Sullivan et al., 2018; Press et al., 2008; Wermelinger et al., 2018) as well as the recent meta-analysis conducted by Cracco et al. (2018) have reported reaction times.