The Media Equation

The Media Equation is a general communication theory that claims that people tend to treat computers and other media as if they were either real people or real places. The effects of this phenomenon on people experiencing these media are often profound, leading them to behave and to respond to these experiences in unexpected ways, most of which they are completely unaware of.[1]

Originally based on the research of Clifford Nass and Byron Reeves at Stanford University, the theory explains that people tend to respond to media as they would either to another person (by being polite, cooperative, attributing personality characteristics such as aggressiveness, humor, expertise, and even gender) – or to places and phenomena in the physical world – depending on the cues they receive from the media. Numerous studies that have evolved from the research in psychology, social science and other fields indicate that this type of reaction is automatic, unavoidable, and happens more often than people realize. Reeves and Nass (1996) argue that, “Individuals’ interactions with computers, television, and new media are fundamentally social and natural, just like interactions in real life,” (p. 5).

The Media Equation Test (1996)

Reeves and Nass established two rules before the test- when a computer asks a user about itself, the user will give more positive responses than when a different computer asks the same questions. They expected people to be less variable with their responses when they took a test and then answered a questionnaire on the same computer. They wanted to see that computers, although not human, can implement social responses. The independent variable was the computer (there are 2 in the test), and the dependent variable was the evaluation responses. The control was a pen-and-paper questionnaire.

Reeves and Nass designed an experiment in which 22 people come to a laboratory and told them they would be working with a computer to learn about random facts of American pop culture. At the end of the session they would ask them to evaluate the computer that they used. They would have to tell Reeves and Nass how they felt about that computer and how well it performed. 20 facts were presented in each session, and participants would answer if they knew “a great deal, somewhat, or very little” about the statement. After the session, participants were tested on the material and told which questions they had answered correctly or incorrectly. This computer, computer #1, then made a statement of its own performance, always stating that it “did a good job”.

Participants were then divided into 2 groups to evaluate the computer's performance and participants were asked to describe this performance from the choice of about 20 adjectives. Half of the participants were assigned to evaluate on computer #1, the computer that praised its own work. The other half were sent to another computer across the room to evaluate computer#1's performance.

The conclusion resulted in evaluations done on computer #1 after testing on computer #1 yielded much more positive responses about the session. Evaluations completed on the other computer after testing on computer #1 resulted in much more varied and more negative responses about the session. For the control, the pen-and-paper questionnaire, the evaluations had similar results to that of evaluations done on computer #2. Respondents felt more comfortable being honest when a different computer or paper asked about the sessions completed on computer #1. It is as if participants were talking behind the computer 1's back- not being honest to it, but then expressing more honesty to a third party evaluator. Reeves and Nass found that participants had automatic social reactions during the test.

Reeves and Nass ran the test again but added a voice speaker to both computers that would verbally communicate information to make the human-social theme more explicit. The test resulted in almost exactly the same results. They concluded that people are polite to computers in both verbal and textual scenarios. The respondents did not need much of a cue to respond socially to the computers. The experiment supports the hypothesis that social rules can apply to media and computers can be social initiators. Participants denied being intentionally polite to the computer, but the results suggest differently.

Propositions

The media equation relies on eight propositions derived from the research:[2]

  1. Everyone responds socially and naturally to media – The media equation applies to everyone regardless of their experience, education level, age, technology proficiency, or cultures.
  2. Media are more similar than different – Psychologically speaking, a computer is not much different from a television and a sophisticated version of a technology is remarkably similar to a simpler version of the technology. As Reeves and Nass (1996) say, “social and natural responses come from people, not from media themselves,” (p. 252). In other words, the media does not make people react the way they do.
  3. The media equation is automatic – Since the media equation assumes that responses are “social and natural” then these reactions occur automatically without conscious effort. This can occur with minimal prompting.
  4. Many different responses characterize the media equation – The media equation occurs even with the most passive uses of media. When using any type of media, a person is likely to assign it a personality, pay extra attention to it, or even assess its personality.
  5. What seems true is more important than what is true – Perception of reality is far more influential than the actual objective reality. A person can know that a computer is a box made of wires and processors but can still assign a personality to it. The important point to remember is that these responses are just part of being human and participating in a communication event.
  6. People respond to what is present – Despite knowing that the media merely provide a symbolic version of the world, people still tend to respond to what the media appears to be as if it were real and immediately present. For the most part, people are more concerned with the interpretation of cues or messages they receive, rather than trying to determine the original intention of the message's creators.
  7. People like simplicity – The need for simplicity and to reduce complexity is an innate human need. People are comfortable with simple. Simplicity indicates a level of predictability that makes people more comfortable.
  8. Social and natural is easy – When interacting with media, Reeves and Nass (1996) argue, “people should be able to use what comes naturally – rules for social relationships and rules for navigating the physical world,” (p. 255). People already know how to function in the natural world (be polite, how to handle difficult personalities) so designers should take these reactions and phenomena into consideration when designing new media.

Research-based findings

The assumptions and conclusions of the media equation are based on a rigorous research agenda that relies on objective empirical data using reliable social science research methods. As Reeves and Nass (1996) explain, “Our strategy for learning about media was to go to the social science section of the library, find theories and experiments about human-human interaction – and then borrow…Take out a pen, cross out ‘human’ or ‘environment’ and substitute media. When we did this, all of the predictions and experiments led to the media equation: People's responses to media are fundamentally social and natural,” (p. 251). The empirical data to support the media equation is thorough and expansive. Studies have tested a wide variety of communication characteristics with the media – manners, personality, emotion, social roles and form. Below are explanations of some of the more interesting findings that support the media equation.

Politeness

Politeness is one measure that researchers have used to study human-computer interaction. Being polite is an automatic response in most interpersonal interactions. When a person asks a question about themselves, most people will give a positive response, even if it may be a dishonest answer, to avoid hurting the other person's feelings. To test this idea with human-computer interaction, researchers designed an experiment in which participants would work with a computer on a tutoring exercise.[3] The computer would provide them with a fact about American culture and then provide supplemental information. The computer then prompted participants to take a test to evaluate what they have learned. After completing the tests participants were asked to evaluate the computer's performance. The participants were assigned to one of three conditions – a pencil and paper evaluation, an evaluation on a different computer, or an evaluation on the same computer. The results indicate that participants who were asked to evaluate the same computer gave the computer more positive feedback than the other two conditions. To learn more about this experiment, see Nass, Moon, & Carney, 1999.[4]

Negativity

In psychology there is a law of hedonic asymmetry that says evaluations of good and bad are important but not the same; negative experiences tend to dominate. In other words, people tend to dwell on the negative more than the positive.[5] Responses to negative situations are automatic and require more attention to process than positive experiences. Allocating more resources to process negative information takes away from resources available to process positive information, thus impeding one's ability to remember events preceding the negative event. The media equation suggests that people have a similar experience when they encounter a negative experience with media. A study was developed to examine the idea that “negative images retroactively inhibit memory for material that precedes them, while they proactively enhance memory for material that follows them,” (Newhagen & Reeves, 1992, p. 25). In other words, will watching negative images on the news prevent someone from remembering information that they learned just prior to viewing the negative material? And conversely, will they better remember information they received just after viewing the negative material?

In the study,[6] researchers created two versions of the same news story – one with compelling negative images and one without. Participants were asked to watch a 20-minute news video (half of the participants saw the negative images and the other half did not) and an additional ten-minute video. They were instructed to pay attention because they would be tested afterwards. A follow up survey was sent 6 to 7 weeks later to measure memory and recall from the news video. The results support the idea that people better remember information that comes after a negative event. Respondents who viewed the negative images better remembered the second half of the newscast than the part preceding the negative images. The findings of this study further support the media equation assumption that mediated experiences are the same as natural experiences.

For a more in depth look at this study, see Newhagen & Reeves, 1992.[7]

Teammates

Psychology has demonstrated that being a part of a team has a direct influence on attitude and behavior of team members. Members of a team think they are more similar to each other than people on the outside. There are two main characteristics that define team interactions – identity and interdependence. For a group to become a team the members must identify with each other and exhibit some degree of interdependence on each other. These two characteristics were tested to determine if a computer can be a teammate.

In this study,[8] participants were assigned to one of two conditions. In the first condition they would be paired with a computer and would become the blue team. The computer had a blue sticker and the human wore a blue wristband to signify that they were in fact a team. The second condition was blue individual, in which a person would use a computer but they were not considered teammates, rather the computer was just a resource. The task was to complete a “Desert Survival Guide” activity in which participants rank items they deem most important if they were left on a deserted island. Human participants initially completed the activity on their own and then completed it using a computer (either as a teammate where both the computer and human were evaluated or just using the computer as a resource). Finally, the participants were allowed to revise their rankings, if they wished to do so. The results of this study indicated that participants who worked with the computer as a teammate viewed the computer as more like them, worked in a similar style to their own, was more cooperative and friendlier than people who worked individually. Another finding of this study showed that participants who worked with the computer as a teammate were more likely to change their behavior and conform to the group ideal even when the teammate was a computer. This study supports the notion that developing a sense of interdependency is the key to establishing team affiliation. For a more detailed account of this study, see Nass, Fogg, & Moon, 1996.[9]

These are just a few of the many studies that support the media equation. For more in depth reading on this subject and past studies, see the “Further Reading” section at the end of this article.

Alternative explanations

Some alternative explanations for the media equation have been proposed. But, as Nass and Moon (2000)[10] argue, these explanations do not add up to the body of empirical evidence that supports media equation. One explanation is that people attribute human characteristics to computers, also known as anthropomorphism. Nass and Moon (2000) refute this claim, saying, “Participants in our experiment were adult, experienced computer users. When debriefed, they insisted that they would never respond socially to a computer, and vehemently denied the specific behavior they had in fact exhibited during the experiments,” (p. 93). A second argument against media equation is that participants are actually responding to the programmers behind the computer. Nass and Moon (2000) refute this argument by citing that studies involving multiple computers generally found differences in interactions from computer to computer. If a person was interacting with the programmer behind the computer, then there would be no difference in interaction between computers. Critics have also argued that the way the experiments and questionnaires were designed in the Stanford research may have predisposed their subjects to interact socially with technology. Nass and Moon (2000) counter-argued by saying that the experiments were not misleading. None of the computers used in the experiments were personalized; the computer never referred to itself as “I” and participants interacted with simple text on a screen.

Grice's Maxims

Reeves and Nass explain that H. Paul Grice's maxims for politeness are perhaps the most generally accepted rules on politeness communication and that Grice's rules are a vital basis to explaining the media equation. The four principles consist of Quality, Quantity, Clarity, and Relevance. Reeves and Nass used these principles to help explain how they believed computers could be social actors. Quality refers to how information presented in a conversation should have value, truth, and importance. Quantity refers to how speakers in interaction should present just the right amount of information to make the conversation as useful as possible. Too much or too little information may damage value of information. Reeves and Nass argue that quantity is not something social media executed very well; they feel it causes frustration because computers display too much or too little information to humans when trying to communicate. Relevance refers to the content of information being translated into an interaction- this information should be both relevant and on-topic. Reeves and Nass argue that computers should be customizable so the user has control over relevance, and they observed how computers struggle to respond to wishes or goals of the user.

Reeves and Nass argue that Grice's maxims are vital guidelines to the media equation because violations of these rules have a social significance. If one side of social interaction violates a rule, it may come off to the other party as a lack of attention being paid, or a diminishing of the importance of the conversation; in other words, they get offended. This leads to a negative consequence for both the party that violated a rule and to the value of conversation.

Further reading
  • Byron Reeves & Clifford Nass – The Media Equation: How People Treat Computers, Television, and New Media like Real People and Places, Cambridge University Press: 1996.[11]
  • Clifford Nass & Corina Yen – The Man Who Lied to His Laptop: What Machines Teach Us About Human Relationships, Current/Penguin: 2010.

References

Notes
  1. Reeves and Nass, 1996
  2. Reeves and Nass, 1996
  3. Nass, Moon, and Carney, 1999
  4. Nass, Moon, and Carney, 1999
  5. Reeves and Nass, 1996
  6. Newhagen and Reeves, 1992
  7. Newhagen and Reeves, 1992
  8. Nass, Fogg, Moon, 1996
  9. Nass, Fogg, Moon, 1996
  10. Nass and Moon, 2000
  11. Reeves and Nass, 1996

Bibliography
  • Nass, C., & Yen, C. (2010). The Man Who Lied to His Laptop: What Machines Teach Us About Human Relationships. Current/Penguin.
  • Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103.
  • Nass, C., Fogg, B., & Moon, Y. (1996). Can computers be teammates? International Journal Human-Computer Studies, 45, 669–678.
  • Nass, C., Moon, Y., & Carney, P. (1999). Are people polite to computers? Responses to computer-based interviewing systems. Journal of Applied Psychology, 29(5), 1093–1110.
  • Newhagen, J. E., & Reeves, B. (1992). The evening's bad news: Effects of compelling negative television news imagery on memory. Journal of Communication, 2, 25–41.
  • Reeves, B., & Nass, C. (1996). The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press.
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