# Social Responses to Virtual Humans: The Effect of Human-Like Characteristics
## 1 Introduction
### Framework of Social Responses and Hypotheses  
Four characteristics are identified through prior research as human-like characteristics: personality, emotion, facial embodiment, and voice. While facial embodiment and voice implementation can each directly elicit social responses [47], personality and emotion presumably work indirectly through facial expression, voice, or specific word choices. There are other variables such as intelligence or autonomy that need further investigation. [PERSON] and [PERSON] [70] found that human forms presented on a computer screenare assessed as more intelligent than simple geometric forms. [PERSON] et al. [71] suggested that autonomy is one of the key features for the achievement of believability. Nevertheless, none of the studies have provided empirical evidence that virtual humans embodied with such constructs elicit social responses. Figure 1 presents the complete framework. Human-like characteristics of virtual humans act as a cue that leads a person to place the agent into the category \"human\" and thus, elicits social responses. Social responding is so ingrained through practice that a human-like cue will set off social responses (i.e., automatic over-reliance), which are automatic and therefore generally unconscious [25]. Specifically, a single cue might activate the human category and block other cues (i.e., asocial cues) that would activate the virtual human category. However, many factors such as knowledge of virtual humans, motivation, accessibility, and the length of interaction can play a contributing role in ascribing the cue to the virtual human category rather than the human category. The more time spent with a virtual human, the greater chance there is to identify the asocial nature of a virtual human. That is, one will deliberately map the stimulus (virtual human) and the responses one would apply to a virtual human and not to a human (Virtual Human Categorization in Figure 1). For example, experience with virtual humans might lead a person to realize how the technology is still far from perfectly mimicking a human. This would form an exclusive virtual human category, where its disposition is non-human and non-social. A negative emotional experience with a virtual human due to its asocial characteristic might expedite this process. For example, one may feel frustrated at an improper emotional expression by a virtual human. If one is an expert on virtual humans, then one would build up a separate set of inference rules that apply to virtual humans exclusively and independently of the rules one applies to humans. For example, a lag in human conversation might mean that the person is simply pondering or choosing their words carefully, but a lag in a virtual human conversation might mean to an expert that the system fell in an infinite loop, or the lag is a result of lack of processing power. We hypothesize that as long as a clearly observable human-like characteristic of a virtual human exists, the number of human-like characteristics might affect the strength of social response. The majority of psychological studies of categorization have used cues that vary on a few (2-4) dimensions (e.g., shapes, colors; [72]) with most target items ranging from living entities (e.g., cats, dogs) to nonliving entities (e.g., chair, sofa). Given that all prominent categorization models (exemplar, decision bound, and neural network models) assume that the cues may be presented as points in a multidimensional space, the number of cues matters, especially when classifying an entity that has a competing entity (e.g., distinguishing an alligator from a crocodile). Figure 1: The framework of how humans respond socially to virtual humans. One underlying psychological construct related, believability (i.e., behavioral realism), is relevant here. Although realism seems to strengthen social responses, there are caveats.