People believe many things about themselves. Having an accurate view of oneself is valuable because it can be used to generate both expectations that will be fulfilled and plans that can be successfully executed. But in being cognitively limited agents, there is pressure for us humans to not only have accurate self-views, but to have efficient ones.
In his new book, How We Get Along, philosopher David Velleman puts it this way:
At one extreme, I have a way of interpreting myself, a way that I want you to interpret me, a way that I think you do interpret me, a way that I think you suspect me of wanting you to interpret me, a way that I think you suspect me of thinking you do interpret me, and so on, each of these interpretations being distinct from all the others, and all of them being somehow crammed into my self-conception. At the other extreme, there is just one interpretation of me, which is common property between us, in that we not only hold it but interpret one another as holding it, and so on. If my goal is understanding, then the latter interpretation is clearly preferable, because it is so much simpler while being equally adequate, fruitful, and so on. (Lecture 3)
That is, one way my self-views can be efficient representations is if they serve double duty as others’ views of me — if my self-views borrow from others’ views of me and if my models of others’ views of me likewise borrow from my self-views.
Sometimes this back and forth between my self-view and my understanding of how others’ view me can seem counter to self-interest. People behave in ways that confirm others’ expectations of them, even when these expectations are negative (Snyder & Swann, 1978, for a review see Snyder & Stukas, 1999). And people interact with other people in ways such that their self-views are not challenged by others’ views of them and their self-views can double as representations of the others’ views of them, even when this means taking other people as having negative views of them (Swann, 1981).
Self-verification and behavioral confirmation strategies
People use multiple different strategies for achieving a match between their self-views and others’ view of them. These strategies come in at different stages of social interaction.
Prior to and in anticipation of interaction, people seek and more thoroughly engage with information and people with self-views expected to be consistent with their self-views. For example, they spend more time reading statements about themselves that they expect to be consistent with their self-views — even if those particular self-views are negative.
During interaction, people behave in ways that elicit views of them from others that are consistent with their self-views. This is especially true when their self-views are being challenged, say because someone expresses a positive view of an aspect of a person who sees that aspect of themselves negatively. People can “go out of their way” to behave in ways that elicit negative self-views. On the other hand, people can change their self-views and their behavior to match the expectations of others; this primarily happens when a person’s view of a particular aspect of themselves is one they do not regard as certain.
After interaction, people better remember expressions of others’ views of them that are consistent with their own. They also can think about others’ views that were inconsistent in ways that construe them as non-conflicting. On the long term, people gravitate to others’ — including friends and spouses — who view them as they view themselves. Likewise, people seem to push away others who have different views of them.
Do people self-verify in interacting with computers?
Given that people engage in this array of self-verification strategies in interactions with other people, we might expect that they would do the same in interacting with computers, including mobile phones, on-screen agents, voices, and services.
One reason to think that people do self-verify in human–computer interaction is that people respond to computers in a myriad of social ways: people reciprocate with computers, take on computers as teammates, treat computer personalities like human personalities, etc. (for a review see Nass & Moon, 2000). So I expect that people use these same strategies when using interactive technologies — including personal computers, mobile phones, robots, cars, online services, etc.
While empirical research should be carried out to test this basic, well-motivated hypothesis, there is further excitement and importance to the broader implications of this idea and its connections to how people understand new technological systems.
When systems models users
Since the 1980s, it has been quite common for system designers to think about the mental models people have of systems — and how these models are shaped by factors both in and out of the designer’s control (Gentner & Stevens, 1983). A familiar goal has been to lead people to a mental model that “matches” a conceptual model developed by the designer and is approximately equivalent to a true system model as far as common inputs and outputs go.
Many interactive systems develop a representation of their users. So in order to have a good mental model of these systems, people must represent how the system views them. This involves many of the same trade-offs considered above.
These considerations point out some potential problems for such systems. Technologists sometimes talk about the ability to provide serendipitous discovery. Quantifying aspects of one’s own life — including social behavior (e.g., Kass, 2007) and health — is a current trend in research, product development, and DIY and self-experimentation. While sometimes this collected data is then analyzed by its subject (e.g., because the subject is a researcher or hacker who just wants to dig into the data), to the extend that this trend will go mainstream, it will require simplification by building and presenting readily understandable models and views of these systems’ users.
The use of self-verification strategies and behavioral confirmation when interacting with computer systems — not only with people — thus presents a challenge to the ability of such systems to find users who are truly open to self-discovery. I think many of these same ideas apply equally to context-aware services on mobile phones and services that models one’s social network (even if they don’t present that model outright).
Social responses or more general confirmation bias
That people may self-verify with computers as well as people raises a further question about both self-verification theory and social responses to communication technologies theory (aka the “Media Equation”). We may wonder just how general these strategies and responses are: are these strategies and responses distinctively social?
Prior work on self-verification has left open the degree to which self-verification strategies are particular to self-views, rather than general to all relatively important and confident beliefs and attitudes. Likewise, it is unclear to what extent all experiences, rather than just social interaction (including reading statements written or selected by another person), that might challenge or confirm a self-view are subject to these self-verification strategies.
Inspired by Velleman’s description above, we can think that it is just that other’s views of us have an dangerous potential to result in an explosion of the complexity of the world we need to model (“I have a way of interpreting myself, a way that I want you to interpret me, a way that I think you do interpret me, a way that I think you suspect me of wanting you to interpret me, a way that I think you suspect me of thinking you do interpret me, and so on”). Thus, if other systems can prompt this same regress, then the same frugality with our cognitions should lead to self-verification and behavioral confirmation. This is a reminder that treating media like real life, including treating computers like people, is not clearly non-adaptive (contra Reeves & Nass, 1996) or maladaptive (contra Lee, 2004).
Kass, A. (2007). Transforming the Mobile Phone into a Personal Performance Coach. In B. J. Fogg & D. Eckles (Eds.), Mobile Persuasion: 20 Perspectives on the Future of Behavior Change. Stanford Captology Media.
Lee, K. M. (2004). Why Presence Occurs: Evolutionary Psychology, Media Equation, and Presence. Presence: Teleoperators & Virtual Environments, 13(4), 494-505. doi: 10.1162/1054746041944830.
Nass, C., & Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56(1), 81-103.
Reeves, B., & Nass, C. (1996). The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press.
Snyder, M., & Stukas, A. A. (1999). Interpersonal processes: The interplay of cognitive, motivational, and behavioral activities in social interaction. Annual Review of Psychology, 50(1), 273-303.
Snyder, M., & Swann, W. B. (1978). Behavioral confirmation in social interaction: From social perception to social reality. Journal of Experimental Social Psychology, 14(2), 148-62.
Swann, W. B., & Read, S. J. (1981). Self-verification processes: How we sustain our self-conceptions. Journal of Experimental Social Psychology, 17(4), 351-372. doi: 10.1016/0022-1031(81)90043-3
Velleman, J.D. (2009). How We Get Along. Cambridge University Press. The draft I quote is available from http://ssrn.com/abstract=1008501
Mobile phones are gateways to our most important and enduring relationships with other people. But, like other communication technologies, the mobile phone is psychologically not only a medium: we also form enduring relationships with devices themselves and their associated software and services (Sundar 2004). While different than relationships with other people, these human–technology relationships are also importantly social relationships. People exhibit a host of automatic, social responses to interactive technologies by applying familiar social rules, categories, and norms that are otherwise used in interacting with people (Reeves and Nass 1996; Nass and Moon 2000).
These human–technology relationships develop and endure over time and through radical changes in the situation. In particular, mobile phones are near-constant companions. They take on roles of both medium for communication with other people and independent interaction partner through dynamic physical, social, and cultural environments and tasks. The global phenomenon of mobile phone use highlights both that relationships with people and technologies are inﬂuenced by variable context and that these devices are, in some ways, a constant in amidst these everyday changes.
Situational variation and attribution
Situational variation is important for how people understand and interact with mobile technology. This variation is an input to the processes by which people disentangle the internal (personal or device) and external (situational) causes of an social entity’s behavior (Fiedler et al. 1999; Forsterling 1992; Kelley 1967), so this situational variation contributes to the traits and states attributed to human and technological entities. Furthermore, situational variation inﬂuences the relationship and interaction in other ways. For example, we have recently carried out an experiment providing evidence that this situational variation itself (rather than the characteristics of the situations) inﬂuences memory, creativity, and self-disclosure to a mobile service; in particular, people disclose more in places they have previously disclosed to the service, than in new places (Sukumaran et al. 2009).
Not only does the situation vary, but mobile technologies are increasingly responsive to the environments they share with their human interactants. A system’s systematic and purposive responsiveness to the environment means means that explaining its behavior is about more than distinguishing internal and external causes: people explain behavior by attributing reasons to the entity, which may trivially either refer to internal or external causes. For example, contrast “Jack bought the house because it was secluded” (external) with “Jack bought the house because he wanted privacy” (internal) (Ross 1977, p. 176). Much research in the social cognition and attribution theory traditions of psychology has failed to address this richness of people’s everyday explanations of other ’s behavior (Malle 2004; McClure 2002), but contemporary, interdisciplinary work is elaborating on theories and methods from philosophy and developmental psychology to this end (e.g., the contributions to Malle et al. 2001).
These two developments — the increasing role of situational variation in human-technology relationships and a new appreciation of the richness of everyday explanations of behavior — are important to consider together in designing new research in human-computer interaction, psychology, and communication. Here are three suggestions about directions to pursue in light of this:
Design systems that provide constancy and support through radical situational changes in both the social and physical environment. For example, we have created a system that uses the voices of participants in an upcoming event as audio primes during transition periods (Sohn et al. 2009). This can help ease the transition from a long corporate meeting to a chat with fellow parents at a child’s soccer game.
Design experimental manipulations and measure based on features of folk psychology — the implicit theory or capabilities by which we attribute, e.g., beliefs, thoughts, and desires (propositional attitudes) to others (Dennett 1987) — identified by philosophers. For example, attributions propositional attitudes (e.g., beliefs) to an entity have the linguistic feature that one cannot substitute different terms that refer to the same object while maintaining the truth or appropriateness of the statement. This opacity in attributions of propositional attitudes is the subject of a large literature (e.g., following Quine 1953), but this has not been used as a lens for much empirical work, except for some developmental psychology (e.g., Apperly and Robinson 2003). Human-computer interaction research should use this opacity (and other underused features of folk psychology) in studies of how people think about systems.
Connect work on mental models of systems (e.g., Kempton 1986; Norman 1988) to theories of social cognition and folk psychology. I think we can expect much larger overlap in the process involved than in the current research literature: people use folk psychology to understand, predict, and explain technological systems — not just other people.
Apperly, I. A., & Robinson, E. J. (2003). When can children handle referential opacity? Evidence for systematic variation in 5- and 6-year-old children’s reasoning about beliefs and belief reports. Journal of Experimental Child Psychology, 85(4), 297-311. doi: 10.1016/S0022-0965(03)00099-7.
Dennett, D. C. (1987). The Intentional Stance (p. 388). MIT Press.
Fiedler, K., Walther, E., & Nickel, S. (1999). Covariation-based attribution: On the ability to assess multiple covariates of an effect. Personality and Social Psychology Bulletin, 25(5), 609.
Försterling, F. (1992). The Kelley model as an analysis of variance analogy: How far can it be taken? Journal of Experimental Social Psychology, 28(5), 475-490. doi: 10.1016/0022-1031(92)90042-I.
Kelley, H. H. (1967). Attribution theory in social psychology. In Nebraska Symposium on Motivation (Vol. 15).
Malle, B. F. (2004). How the Mind Explains Behavior: Folk Explanations, Meaning, and Social Interaction. Bradford Books.
Malle, B. F., Moses, L. J., & Baldwin, D. A. (2001). Intentions and Intentionality: Foundations of Social Cognition. MIT Press.
McClure, J. (2002). Goal-Based Explanations of Actions and Outcomes. In M. H. Wolfgang Stroebe (Ed.), European Review of Social Psychology (pp. 201-235). John Wiley & Sons, Inc. Retrieved from http://dx.doi.org/10.1002/0470013478.ch7.
Nass, C., & Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56(1), 81-103.
Norman, D. A. (1988). The Psychology of Everyday Things. New York: Basic Books.
Quine, W. V. O. (1953). From a Logical Point of View: Nine Logico-Philosophical Essays. Harvard University Press.
Reeves, B., & Nass, C. (1996). The media equation: how people treat computers, television, and new media like real people and places (p. 305). Cambridge University Press.
Ross, L. (1977). The intuitive psychologist and his shortcomings: Distortions in the attribution process. In L. Berkowitz (Ed.), Advances in Experimental Social Psychology (Vol. 10, pp. 174-221). New York: Academic Press.
Sohn, T., Takayama, L., Eckles, D., & Ballagas, R. (2009). Auditory Priming for Upcoming Events. Forthcoming in CHI ’09 extended abstracts on Human factors in computing systems. Boston, Massachusetts, United States: ACM Press.
Sukumaran, A., Ophir, E., Eckles, D., & Nass, C. I. (2009). Variable Environments in Mobile Interaction Aid Creativity but Impair Learning and Self-disclosure. To be presented at the Association for Psychological Science Convention, San Francisco, California.
Sundar, S. S. (2004). Loyalty to computer terminals: is it anthropomorphism or consistency? Behaviour & Information Technology, 23(2), 107-118.
Over at Captology Notebook, the blog of the Stanford Persuasive Technology Lab, Enrique Allen considers features of Facebook that influence users to update their status. Among other things, he highlights how Facebook lowers barriers to updating by giving users a clear sense of something they can right (“What are you doing right now?”).
I’d like to add another part of the interface for consideration: the box in the left box of the home page that shows your current status update with the most recent updates of your friends.
This visual association of my status and the most recent status updates of my friends seems to do at least a couple things:
Influencing the frequency of updates. In this example, my status was updated a few days ago. On the other hand, the status updates from my friends were each updated under an hour ago. This juxtaposes my stale status with the fresh updates of my peers. This can prompt comparison between their frequency of updates and mine, encouraging me to update.
The choice of the most recent updates by my Facebook friends amplifies this effect. Through automatic application of the availability heuristic, this can make me overestimate how recently my friends have updated their status (and thus the frequency of status updates). For example, the Facebook friend who updated their status three minutes ago might have not updated to three weeks prior. Or many of my Facebook friends may not frequently update their status messages, but I only see (and thus have most available to mind) the most recent. This is social influence through enabling and encouraging biased social comparison with — in a sense — an imagined group of peers modeled on those with the most recent performances of the target behavior (i.e., updating status).
Influencing the content of updates. In his original post, Enrique mentions how Facebook ensures that users have the ability to update their status by giving them a question that they can answer. Similarly, this box also gives users examples from their peers to draw on.
Of course, this can all run up against trouble. If I have few Facebook friends, none of them update their status much, or those who do update their status are not well liked by me, this comparison may fail to achieve increased updates.
Consider this interface in comparison to one that either
- showed recent status updates by your closest Facebook friends, or
- showed recent status updates and the associated average period for updates of your Facebook friends that most frequently update their status.
[Update: While the screenshot above is from the “new version” of Facebook, since I captured it they have apparently removed other people’s updates from this box on the home page, as Sasha pointed out in the comments. I’m not sure why they would do this, but here are couple ideas:
- make lower items in this sidebar (right column) more visable on the home page — including the ad there
- emphasize the filter buttons at the top of the news feed (left column) as the means to seeing status updates.
Given the analysis in the original post, we can consider whether this change is worth it: does this decrease status updates? I wonder if Facebook did a A-B test of this: my money would be on this significantly reducing status updates from the home page, especially from users with friends who do update their status.]