![The Media Equation: How People Treat Computers, Television, and New Media like Real People and Places (CSLI Lecture Notes)]({"https://m.media-amazon.com/images/I/511P5KJKRML._SY342_.jpg":[342,222],"https://m.media-amazon.com/images/I/511P5KJKRML.jpg":[475,308],"https://m.media-amazon.com/images/I/511P5KJKRML._SY385_.jpg":[385,250],"https://m.media-amazon.com/images/I/511P5KJKRML._SY466_.jpg":[466,302],"https://m.media-amazon.com/images/I/511P5KJKRML._SY425_.jpg":[425,276]})
The media equation, as introduced by Nass and Reeves, is that "media equals real life" and that our interactions with media are "fundamentally social and natural" (p. 5). This book is a popularization of established, replicated research on how people interact with television advertising, tutoring systems, error messages, loud noises, sudden movement, etc. For instance, one widely replicated result is that computer tutoring systems get better evaluations if the evaluation program is run on the same computer. Moving the reviewer to a new computer (with the same program), significantly lowers the score. The social science literature shows that teachers who collect their own evaluations score much more highly than those whose evaluations are collected by others. This is the kind of evidence Nass and Reeves bring to bear in support of the media equation. They don't claim that we are consciously thinking about the computer's feelings and don't want to hurt them. Rather, to the contrary, subjects claim they were doing no such thing. Yet the evidence of our behavior seems incontrovertible.
The media equation is a good enough predictor of user behavior, at least for telephone-based spoken dialog systems of the form my company builds, that it has informed our designs from top to bottom. Our applications apologize if they make a mistake. Callers respond well to this. Sure, the callers know they're talking to a machine, but this doesn't stop them from saying "thank you" when it's done or "please" before a query or feeling bad (or angry) if the computer can't understand them. Another strategy recommended by Nass and Reeves that we follow is trying to draw the caller in to work as a team with the computer; again, Nass and Reeves support this with several clever experiments. There is also a useful section on flattery, looking at the result of the computer flattering itself and its users; it turns out that we rate computers that flatter themselves more highly than ones that are neutral.
Among other interesting explanations you get in this book are why we're more tolerant of bad pictures than bad sound, why we focus on moving objects, speaking rate equilibrium, what we can do to make someone remember an event in a video, and the role of gender.
This book is very quick and easy to read. I read it in two days while on vacation it was so fascinating. In contrast to the classical yet dry social science format of hypothesis, experimental methodology, results, and essentially a summary of the results as a conclusion, Nass and Reeves only vaguely summarize their experimental methodology and take a no-holds-barred approach to drawing conclusions. This may annoy social scientists, most of whom expect their own kind to be far more circumspect.
This book is an absolute must-read for anyone designing mediated interfaces. For those who don't believe the results, I'd suggest running some experiments; our company did, and it made us believers.
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