Describing interactions between objects often requires the speaker to choose a perspective, and mention one object first and the other second. Speakers have a tendency to make the most animate object in a scene the subject of a sentence, and to mention this object first (Prat-Sala & Branigan, 2000). For example, it is more common to say the man ran away from the dog rather than the dog chased the man, therefore taking the perspective of the man. This bias is considered to occur because of the way speakers perceive animacy: on a hierarchy, with humans at the top and concepts or inanimate objects near the bottom (Harris, 1978; Aissen, 2003). An animacy hierarchy is thought to be the product of conceptual accessibility; the more conceptually accessible something is, the easier it is to retrieve and the more likely it is to be uttered earlier on in a sentence. Animate entities are considered to be more conceptually accessible than less animate entities (McDonald, Bock & Kelly, 1993; Bock & Warren 1985), and this results in an animacy bias whereby speakers utter them earlier on in their sentences. Participants will view scenes of two figures interacting, who will differ in animacy (one human and one robot). Participants will describe these scenes using so-called symmetrical predicates (Gleitman, Gleitman, Miller, & Ostrin, 1996). These are verbs that imply a symmetry of action between the two figures (e.g. arguing, kissing, playing), but when used in a sentence they require the speaker to make a decision as to which figure should be mentioned first, which may be potentially conditioned on a factor such as animacy. Participants will form transitive sentences (e.g. “Kate is arguing with Botz”) to describe the events, which they will type into a text box, and we will measure if they mention the human or the robot first. The events depicted in this experiment constitute three “valences”; intimate (e.g. kissing), negative (e.g. arguing) and casual (e.g. playing). We will investigate if the animacy bias is stronger on trials depicting emotional (intimate/negative) events. In terms of animacy, robots constitute a special control category for human stimuli. Contemporary humanoid robots are capable of self-propelled motion, speech (in some cases), and can exhibit what appears to be intentional behaviour (for example through eye movements or gestures), hence, they provide some cues to animacy to human observers (Bartneck, Kulić, Croft & Zoghbi, 2009). However, as previous studies on the perception of mind have shown, humans would be hesitant to call any robot “alive” (Gray, Gray & Wegner, 2007). When rated on their capacity of mind, robots rank below human adults and infants, and receive somewhat comparable ratings to pets, with little means to sense or feel the world, but with some capacity of agency. A study that asked participants to rate robots’ ability to plan and act (their agency) and their ability to sense and feel (their experience of the world) confirmed that participants assigned high rankings to pictures of human faces for both agency and experience, whereas robots where ascribed some agency but no experience, and objects ranking lowest on both criteria (Henschel, Bargel, & Cross, 2020). We therefore propose that robots should rank lower than humans on the animacy hierarchy, while controlling for humanlike features (i.e. having a comparable body shape and similar set of facial features). Our previous research suggests that closeness to the speaker influences the conceptual accessibility of an entity. When describing an interaction between two people, speakers are more likely to take the perspective of the figure most similar to themselves in social group (e.g. the same race or gender as themselves). These studies are in progress, but the pre-registrations can be found here (https://osf.io/bfhrn) and here (https://osf.io/t928m). The results of these studies suggest that speakers often demonstrate a ‘Like Me’ bias to name the figure from their own social group first and the figure from the out-group second. Thus, in addition to our main analysis of the animacy bias, we will further consider the ‘Like Me’ bias in exploratory analyses by varying the social group of the human in these pairs (a black woman, a black man, a white woman and a white man) and conducting this experiment with participants from these four demographic groups. In doing so, we will measure if participants demonstrate an animacy bias by routinely taking the perspective of the human figure, and if this bias is stronger on trials where the human figure is in the participant’s ingroup than their outgroup. Prior to this main experiment, we conducted a norming study with a separate group of 80 participants (of the same four demographic groups as in this experiment) to validate our novel set of robot stimuli. We investigated how much mind is ascribed to the four robotic agents to be used in the main study, and found that two of our robots (‘Pepper’ and ‘NAO’) were rated as having higher (and similar levels of) perceived mind than the other two (‘Gresh’ and ‘Princess Firecracker’ – see Files for portraits of all four robots used in this experiment). According to this norming study, people perceive the humanoid robots (Pepper and NAO) as having greater agency and experience (Henschel et al., 2020) than the Lego robots (Gresh and Princess Firecracker). We expect the animacy bias to favour the human figure even more on trials where the human is paired with an object with low perceived mind. We will test this hypothesis by entering the continuous variables of perceived robot agency and perceived robot experience (ratings provided in the norming study) into our model as predictors of mentioning the human figure first. As gender and race are of importance in the current experiment, we also included rating items about the robots’ perceived race, sex and gender. In order to establish gender-neutral names for the robots that are not too distinct from the set of human names, participants further rated a set of six names on perceived gender and sex. The four robot names used in this study were those that were rated as being most gender-neutral on 9-point Likert scales (very female – very male, very feminine – very masculine). Descriptions of how we intend to use these items of perceived robot race and combined gender/sex from the norming study are explained in the Exploratory Analyses section.