The term “media equation” means that media equal real life. It implies that people process technology-mediated experiences in the same way as they would do nonmediated experiences, because an “individual’s interactions with computers, television, and new media are fundamentally social and natural, just like interaction in real life” (Reeves & Nass 1996, 5). In 1996, Reeves and Nass first introduced this theory, and carefully summarized their previous media equation studies in the seminal book The media equation. Since the publication of this book, Nass, Reeves, and their colleagues have expanded the domain of the theory from traditional communication settings to radically different areas, from e-commerce to human–robot interaction.
In general, media equation studies can be categorized as having one of two themes: (1) audience responses to physical features of traditional media, and (2) user responses to social characteristics of computers and software agents. Reeves and his colleagues have usually worked on the first issue, whereas Nass and his lab members have focused on the latter issue under the research paradigm of “Computers Are Social Actors (CASA).”
Audience Responses To Physical Features Of Traditional Media
Media equation studies on audience responses to physical features of traditional media can be categorized into two parts: media and emotion; and media and form. With regard to media and emotion, Reeves and his colleagues provide convincing results that the emotional valence (good vs bad) of media stimuli has the same effect on the brain as real-life stimuli in terms of electroencephalogram (EEG) activities. In addition, they found that negative events on the screen are disliked yet remembered more, just as negative real-life events are processed in human brains. In terms of arousal, they found that arousing media events are remembered more on both short-term and long-term bases in the same way that arousing events in real life are remembered better.
Studies on media and form have focused on audience responses to five physical characteristics of media forms: size; fidelity; synchrony; motion; and scene changes. With regard to audience responses to image size, the studies found that big objects on the screen yield more arousal, better memory (descriptive, not image recognition), and more positive social responses (e.g., social attraction, credibility) than smaller ones even when the content is identical. These results confirm general human attention to and preference for big objects in real life. In contrast to audience responses to image sizes, the visual fidelity of a scene does not bring significant differences in arousal, attitudes, and memory. These results indicate that both virtual and real-life objects are visually processed in the same way in which objects and environments are mainly processed through the peripheral vision field, rather than the foveal vision field. With regard to the synchrony factor, Reeves and his colleagues found that asynchrony between audio and video signals generates negative evaluation of speakers on screen, in the same way people distrust speakers with inconsistent verbal and nonverbal cues. Finally, motion and scene changes on screen cause strong visual orienting responses from audience just as moving objects in real life do (Reeves et al. 1985). As a result, objects shown after the movement on screen are remembered better.
User Responses To Social Characteristics Of Computers
In the CASA research paradigm, Nass and his colleagues have studied user responses to social characteristics of computers and software agents. Contrary to the commonsense view that people’s interaction with computers or machines is not social, Nass and colleagues theorize that individuals consistently apply social interaction and categorization rules to computers (although users acknowledge that it is absurd to do so). As a result, the paradigm proposes, computers become social actors to their users. This research paradigm is based on the idea that when confronted with a machine that has anthropomorphic cues related to fundamental human characteristics, individuals automatically respond socially, are swayed by the fake human characteristics, and do not process the fact that the machine is not a human. Primary cues that appear to be important are words for output, interactivity, filling of roles traditionally held by humans, and voice (Nass & Moon 2000). These kinds of cues automatically invoke schemas associated with human–human interaction, without the need to psychologically construct a relevant human. The first-generation CASA studies reported in The media equation (Reeves & Nass 1996) focused on user responses to three major social characteristics of computers: manners; personality; and social roles. With regard to user responses to the social manners of computers, Nass and colleagues found that users evaluate computers positively when the computers behave politely, flatter them, and criticize themselves (as opposed to blaming others), in the same way that people like other people who are polite, flattering, and/or self-criticizing. With regard to the personality of computers, the researchers found that users can successfully identify the personality of computers or software agents. Moreover, users apply complicated personality-based social rules, such as the similarity-attraction and the consistency-attraction rules, when they interact with computers (or agents) manifesting a particular personality. Plus, users like a computer adjusting its personality to imitate the personality of the users. With regard to the social roles of computers, they found that users easily identify the gender, voice identity, and ingroup-ness of computers that they are interacting with. On the basis of the identified social roles of computers, users apply various social rules (e.g., gender stereotyping; ingroup favoritism) in their interaction with the computers.
The second-generation CASA studies expanded the domain of research to e-commerce, voice user interfaces, and human–robot interaction. In the area of e-commerce, Moon at Harvard Business School found that customizing the message style of a computer according to user personality can increase the computer’s social influences on users (Moon 2002). She also reports that consumer self-disclosure in e-commerce sites can be enhanced by having a computer disclose itself first, because people unconsciously reciprocate computers (Moon 2000). Since The media equation, Nass and his colleagues have focused on the issue of voice user interfaces. The impressive line of evidence on social responses to computers in the context of voice user interfaces is well documented in Wired for speech (Nass & Brave 2005). Most notably, the researchers found that even with conscious knowledge of the nature of synthetic voice, humans keep responding to the synthetic voice as if it were a real human voice and apply various social rules (e.g., multiple source effect, personality identification, racial and gender stereotypes, consistency preference) to the synthetic voice. Finally, Lee and his students at USC Annenberg School for Communication combined two research traditions – social presence and CASA – in the study of human interaction with artifacts, and investigated human interaction with robots. They found that people feel high social presence and respond socially to a robot when it manifests compelling personality (Lee et al. 2006) and long-term artificial cognitive development (Lee et al. 2005).
Why The Media Equation Occurs
The impressive amount of empirical findings on media equation phenomena calls for a fundamental question: “What makes human minds not notice the virtuality of mediated and/or artificial stimuli?” (See Lee 2004 for a detailed explication of the concept of virtuality.)
The main reason is that human brains evolved in a world in which all perceived objects were real physical objects and only humans possessed human-like shapes and human-like characteristics such as language, rapid interaction, emotion, personality, and so on. Therefore, to human minds, anything that seemed to be real was real and any object that seemed to possess human characteristics such as language was a real human. When people use media such as television and computer, people usually do not overcome the evolutionary limitation of accepting everything on the media at its face value. As a result, people respond to simulations of social actors and natural objects as if they were in fact social and natural. Another fundamental reason for the media equation phenomenon is that humans have a strong tendency to promptly accept any incoming stimuli as if true, unless there is strong counterevidence (Gerrig 1993). Throughout human evolution, prompt reactions to situations have been much more critical for survival than accurate yet delayed judgments; inaccurate and unnecessary reactions to situations result in the waste of energy, whereas accurate yet delayed reactions could cause death or serious harm (Mantovani 1995). Consequently, mediated and/or artificial objects are processed as if real first, before coming under careful scrutiny. Put together, the media equation theory has successfully demonstrated how hardwired information-processing mechanisms in our Stone-Age brains continuously influence our responses to modern media and computer technologies.
- Gerrig, R. J. (1993). Experiencing narrative worlds. New Haven, CT: Yale University Press.
- Lee, K. M. (2004). Presence, explicated. Communication Theory, 14, 27–50.
- Lee, K. M., Park, N., & Song, H. (2005). Can a robot be perceived as a developing creature? Effects of a robot’s long-term cognitive developments on its social presence and people’s social responses toward it. Human Communication Research, 31, 538 –563.
- Lee, K. M., Peng, W., Yan, C., & Jin, S. (2006). Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human–robot interaction. Journal of Communication, 56, 754 –772.
- Mantovani, C. (1995). Virtual reality as a communication environment: Consensual hallucination, fiction, and possible selves. Human Relations, 48, 669 – 683.
- Moon, Y. (2000). Intimate exchanges: Using computers to elicit self-disclosure from consumers. Journal of Consumer Research, 26, 324 –340.
- Moon, Y. (2002). Personalization and personality: Some effects of customizing message style based on consumer personality. Journal of Consumer Psychology, 12, 313 –326.
- Nass, C., & Brave, S. (2005). Wired for speech: How voice activates and advances the human–computer relationship. Cambridge, MA: MIT Press.
- Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56, 81–103.
- Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. New York: Cambridge University Press.
- Reeves, B., Thorson, E., Rothschild, M. L., McDonald, D., Goldstein, R., & Hirsch, J. (1985). Attention to television: Intra stimulus effects of movement and scene changes on alpha variation over time. International Journal of Neuroscience, 27, 241–255.