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Creating Understanding

How Communicating Aligns Minds

by Jessica Gasiorek (Author) R. Kelly Aune (Author)
Textbook XII, 172 Pages

Table Of Content


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Acknowledgments

There are many important people that have contributed to this book, directly and indirectly. We thank the Series Editor, Howie Giles, for his helpful comments and suggestions for improving and refining this text, as well as Marko Dragojevic for his questions and comments on earlier versions of core chapters. We also thank Richard Huskey for his invaluable feedback and time discussing content related to cognitive science and communication neuroscience, and for directing us to resources on these topics. Earlier versions of some of the material in this book were also presented as conference papers (Aune & Gasiorek, 2019; Gasiorek & Aune, 2019), and we appreciate the feedback from peer reviewers we received on those iterations of our work.

We are grateful to our colleagues in the Department of Communicology at the University of Hawaiʻi at Mānoa for supporting the creation of undergraduate and graduate classes on creating understanding. They embraced the arguments that creating understanding should be central to the educational experiences we provide our majors and graduate students. We also thank the hundreds of students in our classes over the past decade that worked with us as we developed the material for this book. Material in this book incorporates and further expands text from an Open Educational Resource we developed for one of these courses (Gasiorek & Aune, 2017), the creation of which was supported by a grant from the University of Hawaiʻi at Mānoa’s Outreach College.

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JG would like to express her appreciation to Howie Giles, Linda Putnam, Scott Reid, and Rene Weber for teaching her how to see the world through the lens of scientific theory, and how to ask and answer questions rigorously and systematically. She also thanks Karen Nylund-Gibson for introducing her to Bayesian statistics.

Finally, we thank our families and friends for their support and encouragement. RKA thanks his sons Brian, Alex, Nathaniel, and Kenny for their patience with his tendency to answer questions with lectures. They never complained—eye rolls perhaps, but no complaints. RKA would also like to acknowledge his spouse and colleague, Krystyna, and remind her—once again—that “all good things come from you.”

JG thanks her spouse Jack for his giving her time on weekends and early mornings to write and edit, particularly during a challenging stretch of pandemic stay-at-home orders without childcare (a few months before this manuscript was due). She is also grateful to her mother Joan, for her patience with a lifetime of questions and for fostering a love of learning. Finally, JG would like to thank her daughter Kira—who was born partway through the process of writing this book—for giving her a new appreciation for how humans communicate, entrain, and share the world with one another.

References

Aune, R. K., & Gasiorek, J. (2019, November). Five thousand years of studying communication: In search of square one. Paper presented at the National Communication Association Annual Convention, Baltimore, MD.

Gasiorek, J., & Aune, R. K. (2017). Message processing: The science of creating understanding. Honolulu, HI: University of Hawaiʻi at Mānoa Outreach College. Retrieved from: http://pressbooks.oer.hawaii.edu/messageprocessing/

Gasiorek, J., & Aune, R. K. (2019, May). Toward an integrative model of communication as creating understanding. Paper presented at the International Communication Association Annual Conference, Washington D.C.

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Introduction

In this brief introduction, we describe the origins of this book and its primary goals: to offer an explicit conceptualization of understanding, and to offer insight into the process of creating understanding in human communication. We outline why this is an important topic for communication researchers, and offer a brief sketch of contemporary interdisciplinary scholarship on understanding. We conclude with an outline of this book.

We (the authors) are both communication scholars by training; not surprisingly, we have pursued this path because we are interested how communication works. As a field, communication has great breadth, spanning from the fine arts to neuroscience. We are quantitative social scientists; as such, we represent, and work in a narrow slice of the field’s wide span, which we will refer to as the discipline of communication1. Looking across the considerable body of theoretical and empirical work in communication, we were both struck, and surprised, by the lack of research in our discipline on understanding, and how people come to understand each other.

We came to this question from different backgrounds. One of us (JG) pursued an undergraduate degree in foreign languages (French and Italian), and spent time living and working in France and Belgium before pursuing graduate work in communication. In this, she spent countless hours trying to master new communicative systems, and struggling to express herself—and have others ←1 | 2→recognize what she intended to express—using those systems. Living abroad, where multilingualism (with varying degrees of proficiency) was the norm, she regularly watched people negotiate what language they would agree to use for an interaction. She also watched people employ a range of creative strategies when their language proficiency presented an obstacle to expressing their ideas. (She also used her own share of these strategies herself). All of these experiences put the process of creating understanding front and center in her everyday life, and rendered it something that could not be taken for granted. When she pursued graduate studies in communication—where she ultimately focused on studying communication accommodation—she was surprised (and honestly, a bit confused) to find that the discipline had relatively little insight into how people create understanding, and related issues.

The other (RKA) found his way to the question of creating understanding without the challenges and benefits of traveling abroad. In graduate school, as a student of human communication, RKA was drawn to studying how people learned implicitly, and how they processed information in non-analytical ways. He was particularly interested in how people communicated successfully using incomplete utterances. In graduate coursework outside his home department, RKA was introduced to scholarship in linguistics and philosophy, where he found scholars addressing these issues. As a faculty member, his research eventually narrowed to what he came to think of as creating understanding, including developing a theory addressing how people assess responsibility for creating understanding in communicative interaction (Aune et al., 2005). Over time, he developed a class that focused solely on how people create understanding in interaction. When JG arrived in the department, RKA happily discovered a colleague that was interested in questions of understanding, and the discussions that ultimately led to this book began in earnest.

Wanting to learn more about understanding, but finding little in our disciplinary home, we looked to our social scientific neighbors and beyond, and found serious inquiries, and insights, into this topic in research from cognitive sciences, psychology, linguistics and philosophy. However, and not surprisingly, scholars in each of these fields addressed these questions through their own disciplinary lens. They focused on different aspects of the process, and approached the question at different levels of abstraction.

Reading this work was illuminating, but no single source captured the process of creating understanding in a way that we, as communication scholars, were seeking. We also wanted to bring the insights that these other sources provided back to our colleagues in the discipline. As scholars and teachers of communication, we felt that our discipline’s lack of engagement with the topic of ←2 | 3→understanding was a significant—indeed, critical—omission. These sentiments, and the path they ultimately took us down, were the impetus for this book.

Why Understanding Matters

So, one might ask, does our discipline’s lack of engagement with understanding matter? An omission is not in and of itself problematic; sometimes a topic or concept receives minimal attention because it is not important or interesting (Davis, 1971). We believe that the question of how people create understanding matters very much, and is both important and interesting.

From a theoretical perspective, this topic is foundational: the creation of understanding underlies many of the other outcomes that communication scholars study. For example, when interpersonal communication researchers examine the role of disclosure in developing and maintaining relationships (e.g. Taylor & Altman, 1987), there is an implicit assumption that people understand the content of each other’s disclosures (if not, simply making sounds and/or being in the presence of another person should be sufficient to build a relationship). Similarly, persuasion researchers assume that people process and comprehend the content in persuasive messages that researchers craft and test, and that this processing and comprehension of message content underlies message effects. There are also many areas of communication research that address understanding implicitly or indirectly (see Chapter 1 for a more extended discussion of this point). This collective body of work would likely benefit from having a consistent theoretical foundation.

For empirical social scientists, the topic of creating understanding also has important methodological implications. At present, most empirical work in the discipline of communication (and indeed, we would argue, in our neighboring social scientific disciplines as well) takes for granted that research participants understand the instructions they are given, and that they understand the communicative stimuli researchers employ. When studies involve interaction, researchers assume that participants (and sometimes, confederates) understand each other. It is standard practice to include memory checks or manipulation checks to ensure that participants recall what they were exposed to, and that a stimulus had the effect that was intended. However, this approach does not necessarily confirm that participants understood the message they encountered as the researcher intended—it simply shows that the message was recalled, or it had the desired effect. (For a discussion of related issues with manipulation checks in persuasion research, see O’Keefe, 2003). As a discipline, communication has not clearly theorized how people come to understand each other, or how people comprehend the ←3 | 4→messages they encounter. Without a theoretical compass that orients researchers to this topic, it becomes easier to overlook in the practices that guide study design and execution.

Finally, from a practical perspective, knowing how people (effectively) create understanding is useful and valuable. There are a range of contexts where ensuring comprehension of specific content is important. Communicating information about risks, explaining health-related diagnoses and treatment (e.g. how often to take medication, at what dosage; Burgers et al., 2015), or providing warnings relating to personal safety are just a few of many possible examples (e.g. Gasiorek & Aune, 2017). Closely related, teaching and learning—activities that are central to people’s growth, development, and daily lives—essentially consist of creating understanding via communication. Knowing how the process of creating understanding works should allow people to troubleshoot and fix problems more efficiently and effectively across these contexts (or at the very least, have the satisfaction of knowing why something is happening, even if they cannot change it).

Contemporary Scholarship on Creating Understanding

There is a significant body of contemporary scholarship related to how people create understanding; however, it comes from scholars, theorists, and researchers outside the discipline of communication. In what follows, we provide just a few examples.

In the mid-20th century, philosophers Grice (1975, 1989) and Searle (1969) advanced theories that began to look at how communicators found more meaning in utterances than could be explained by a surface analysis of the utterances. Speech acts theory (Searle, 1969) offered insight into how utterances that appear to be of one functional form—for example, a statement or an assertion—are intended and (successfully) interpreted as another functional form—for example, a question. Grice’s (1975, 1989) ideas about conversational implicature and his cooperative principle explained how communicators are able to infer more than is literally stated in many utterances.

Researchers in pragmatics (a sub-field of linguistics addressing language use in context) and linguistic anthropology have also made important contributions to this topic. Sperber and Wilson’s (1995) relevance theory provided an alternative explanation for how communicators construct and make sense of utterances that appear superficially incomplete or irrelevant. Sperber and Wilson’s work is situated at the intersection of pragmatics and cognitive psychology. Additionally, ←4 | 5→Levinson and colleagues (e.g. Enfield & Levinson, 2006; Levinson, 2006) have written extensively on language, cognition, and human social interaction, and argued for fundamental similarities in human interaction across a range of languages and cultures.

Much of the work done on understanding comes from the disciplines of psychology and/or cognitive science. Clark, Fussell, and Krauss (e.g. Clark, 1996; Clark & Brennan, 1991; Fussell & Krauss, 1989, 1991) have all contributed to theorizing about the knowledge, beliefs, and assumptions that communicators share in interaction (i.e. common ground) and its role in creating understanding. Pickering and Garrod (2004, 2006, 2013) have theorized about the nature of dialogue and comprehension as processes of coordination and alignment at multiple levels. Finally, Scott-Phillips (2015) and colleagues have incorporated theory and research from evolutionary theory and evolutionary psychology into the study of human communication.

More recently, neuroscientific research has provided insight into the brain activity that underlies social interaction (e.g. Lieberman, 2013) and communication (e.g. Hasson et al., 2012; Stephens et al., 2010). This work provides a window into communication, and understanding, at a different level—that is, that of observable biological activity—than previous empirical and theoretical work.

Not surprisingly, scholars from these different areas approach the topics of communication and understanding in different ways, and from different angles. As a result, they provide insights into different aspects of these topics, and do so at different levels of abstraction and granularity. For instance, the conceptual work of Searle and Grice provides a starting point for conceptualizing understanding, as well as insights into the range of ways that communicative behavior can function pragmatically (e.g. as different speech acts). Relevance theory offers a potential explanation for how people arrive at particular inferences, or mental states, based on their interlocutors’ communicative behavior, and how people select what communicative behavior to exhibit for a given interlocutor. Pickering and Garrod’s (2004, 2013) models emphasize alignment as a key outcome of successful communication, and offer explanations (some of which contrast with relevance theory) for how communicators achieve a state of alignment in interaction. Research on grounding provides insights into how people interactively monitor and address what is or is not perceived to be understood in conversations. Finally, neuroscientific research offers a window into the physiological substrates (i.e. brain activity) that underlie human communication (including many of the interactive activities referenced in other models).

However, no single source offers a satisfying, physiologically grounded, end-to-end explanation of both what understanding is and how people manage to ←5 | 6→achieve it across a variety of different kinds of interactions. The literature that comes the closest (e.g. Pickering & Garrod, 2013; Sperber & Wilson, 1995) focuses heavily on conversation—and thus, language—which we believe is an important part of the story, but not the whole story. Additionally, much of this work does not actually address creating understanding explicitly or directly, despite offering important observations and insights into the process.

This Book

In this book, our aim is to provide an integrative discussion of how people create understanding in interaction, through the process of communication. As communication scholars, we focus this discussion at the level of the dyad—that is, concentrating on a system of two people—though we address other possible interactional configurations. In contrast to much (although not all) of the extant scholarship we draw on, we aim to place creating understanding at the theoretical center of this discussion, focusing on the connections between the process of communication and understanding as a state experienced by communicators.

We begin the book with a more detailed discussion of our perspective, approach and aims. We also provide a summary of current interdisciplinary scholarship on the topics of communication and understanding (Chapter 1). We then introduce and explain concepts we will draw on, and offer a conceptualization of understanding as entrainment, or alignment, of communicators’ mental models of an interaction (Chapter 2). Next, we present a set of foundational observations about human cognition in which we ground subsequent explanations (Chapter 3). In the following chapter, we outline key components of human social interaction, including how people initiate interaction, how people orient and attend to each other’s contributions, and how the stimuli people present activate meme states (mental representations) (Chapter 4). We then articulate a theoretical model of how people create understanding. In this, we propose that when people communicate, they construct, test, and refine mental models of a joint experience on the basis of the meme states activated by social stimuli. We explain how this can result in the alignment of mental models—that is, understanding—when all parties in an interaction engage in this process in good faith (Chapter 5). In the next chapter, we discuss contextual factors that can influence how people create understanding, including features of the interactional context, the communication medium, communicators’ goals, and social and cultural norms (Chapter 6). We then address how codification relates to understanding, and how communicative systems emerge in interaction as a result of efforts to create understanding ←6 | 7→(Chapter 7). In the book’s penultimate chapter, we discuss how the perspective and model we introduce complement and connect to other, extant concepts and theories in the discipline of communication, including theories of interpersonal and intergroup communication and deception. We also discuss the theoretical and methodological implications of our framework (Chapter 8). Finally, we conclude with a summary of the contributions and limitations of the framework we propose, with an eye to future directions for research (Chapter 9).

Note

1. Although the terms “field” and “discipline” are often used interchangeably, we use “field” to refer to the broader collective of arts, humanities, and sciences that have communication as their common area of interest, and “discipline” to refer to the subgroups of artists, scholars, and scientists who identify themselves with specific, narrow, and cohesive ontological and epistemological approaches to studying communication. In this book, we will use the term “discipline” to refer to social and natural science approaches to studying communication.

References

Aune, R. K., Levine, T. R., Park, H. S., Asada, K. J. K., & Banas, J. A. (2005). Tests of a theory of communicative responsibility. Journal of Language and Social Psychology, 24(4), 358–381. https://doi.org/10.1177/0261927X05281425

Burgers, C., Beukeboom, C. J., Sparks, L., & Diepeveen, V. (2015). How (not) to inform patients about drug use: Use and effects of negations in Dutch patient information leaflets. Pharmacoepidemiology and Drug Safety, 24(2), 137–143. https://doi.org/10.1002/pds.3679

Clark, H. H. (1996). Using language. Cambridge University Press. https://doi.org/10.1017/CBO9780511620539

Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition, (pp. 127–149). American Psychological Association. https://doi.org/10.1037/10096-006

Davis, M. S. (1971). That’s interesting! Towards a phenomenology of sociology and a sociology of phenomenology. Philosophy of the Social Sciences, 1(2), 309–344. https://doi.org/10.1177/004839317100100211

Enfield, N. J., & Levinson, S. C. (Eds.). (2006). Roots of human sociality: Culture, cognition and interaction. Berg.

Fussell, S. R., & Krauss, R. M. (1989). The effects of intended audience on message production and comprehension: Reference in a common ground framework. Journal of Experimental Social Psychology, 25, 203–219. https://doi.org/10.1016/0022-1031(89)90019-X

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Fussell, S. R., & Krauss, R. M. (1991). Accuracy and bias in estimates of others’ knowledge. European Journal of Social Psychology, 21, 445–454. https://doi.org/10.1002/ejsp.2420210507

Gasiorek, J., & Aune, R. K. (2017). Text features related to message comprehension. In R. Parrott (Ed.), Oxford encyclopedia of health and risk message design and processing. Oxford University Press. https://doi.org/10.1093/acrefore/9780190228613.013.303

Grice, H. P. (1975). Logic and conversation. In P. Cole & J. Morgan (Eds.), Syntax and semantics (Vol. 3, pp. 41–58). Academic Press.

Grice, H. P. (1989). Studies in the way of words. Harvard University Press.

Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S., & Keysers, C. (2012). Brain-to-brain coupling: A mechanism for creating and sharing a social world. Trends in Cognitive Science, 16(2), 114–121. https://doi.org/10.1016/j.tics.2011.12.007

Levinson, S. C. (2006). On the human “interaction engine”. In N. J. Enfield & S. C. Levinson (Eds.), Roots of human sociality: Culture, cognition, and interaction (pp. 39–69). Berg.

Lieberman, M. D. (2013). Social: Why our brains are wired to connect. Broadway Books.

O’Keefe, D. J. (2003). Message properties, mediating states, and manipulation checks: Claims, evidence, and data analysis in experimental persuasive message effects research. Communication Theory, 13(3), 251–274. https://doi.org/10.1111/j.1468-2885.2003.tb00292.x

Pickering, M. J., & Garrod, S. (2004). Toward a mechanistic psychology of dialogue. Behavioral and Brain Sciences, 27(2), 169–190. https://doi.org/10.1017/S0140525X04000056

Pickering, M. J., & Garrod, S. (2006). Alignment as the basis for successful communication. Research on Language and Computation, 4(2–3), 203–228. https://doi.org/10.1007/s11168-006-9004-0

Pickering, M. J., & Garrod, S. (2013). An integrated theory of language production and comprehension. Behavioral and Brain Sciences, 36(4), 329–347. https://doi.org/10.1017/S0140525X12001495

Scott-Phillips, T. C. (2015). Speaking our minds: Why human communication is different, and how language evolved to make it special. Palgrave Macmillan.

Searle, J. R. (1969). Speech acts: An essay in the philosophy of language. Cambridge University Press. https://doi.org/10.1017/CBO9781139173438

Sperber, D., & Wilson, D. (1995). Relevance: Communication and cognition (2nd Ed.). Blackwell.

Stephens, G. J., Silbert, L. J., & Hasson, U. (2010). Speaker-listener neural coupling underlies successful communication. Proceedings of the National Academy of Sciences, 107, 14425–14430. https://doi.org/10.1073/pnas.1008662107

Taylor, D. A., & Altman, I. (1987). Communication in interpersonal relationships: Social penetration processes. In M. E. Roloff & G. R. Miller (Eds.), Interpersonal processes: New directions in communication research (pp. 257–277). SAGE.

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CHAPTER ONE

Communication and Understanding

This chapter addresses the relationship between communication and understanding, in the context of contemporary communication scholarship. Understanding is a topic that communication scholars have largely ignored; we suggest this is a likely a product of the discipline’s history, which we briefly summarize. We then review the key features of “code models” of communication, which have dominated disciplinary theoretical and empirical research in communication, and explain their shortcomings. Finally, we argue that our discipline needs a theoretical framework that addresses understanding as its primary focus, and outline how we believe this should be undertaken.

This book is about how people create understanding through communication. Creating understanding is only one of many things that people do when they communicate, we readily acknowledge. However, we contend that it is one of the most fundamental. Most of the other things people do with communication—for example, influence or persuade; make people happy or sad; build or extinguish relationships; define and maintain group boundaries—follow understanding, or are in some way built on understanding as a foundation. However, in the discipline of communication, the construct of understanding has received surprisingly little direct attention from scholars.

There are many topics, constructs and theories in contemporary social scientific communication research that address understanding indirectly. For example, media effects research offers theories describing how people process and arrive at ←9 | 10→conclusions about the messages they encounter in mass media (e.g. Geise & Baden, 2014; Lang, 2000, 2017), which can include comprehending the content of those messages. In health communication research, patients’ variable understanding of medical terminology and information has received considerable attention (e.g. Desme et al., 2013; Majerovitz et al., 1997). Health communication research also addresses how different features and types of messages relate to people’s understanding of health-related issues (Mazor et al., 2010) and risks (e.g. Stone et al., 2015). Scholarship on sarcasm, irony, and metaphor examines how these forms of language are interpreted, and by extension (mis)understood, by recipients (e.g. Thompson & Filik, 2016). Miscommunication and misunderstanding—which are clearly related to understanding—have received considerable attention from various angles in interpersonal and intercultural communication research (e.g. Coupland et al., 1991; Sillars, 1998; Sillars et al., 2005), and appear in research on humor (e.g. Buijzen & Valkenberg, 2004).

However, across these bodies of work, communication researchers generally treat “understanding” as a primitive term; it is rarely defined or otherwise probed. Likewise, these bodies of work do not draw on any kind of shared or unified theoretical foundation that explicitly addresses what understanding is, and how it is created. This is likely because the discipline of communication lacks theories that focus on understanding. Most widely used introductory theory textbooks in the field (e.g. Griffin et al., 2019; West & Turner, 2018) do not offer any dedicated scientific theories focusing on how people create understanding in social interaction. Instead, the majority of contemporary scientific theorizing and research in the discipline of communication addresses other outcomes, such as social influence, persuasion, interpersonal relationships, group and organizational dynamics, and media effects.

Origins of an Omission

We believe this state of affairs is likely a result of the discipline’s history. The discipline’s core areas of research, and the graduate and undergraduate courses reflecting that research, were not developed through a mindful deliberation of what a discipline of communication ought to be. Rather, they emerged somewhat organically, with roots in a variety of disciplinary traditions (Berger, 1991). The task of comprehensively delineating the discipline’s history has been undertaken elsewhere (e.g. Delia, 1987), and we do not seek to repeat or reinvent it here. Rather, we wish to briefly highlight some key elements of this history to help shed light on why it has not seriously engaged with the topic of understanding.

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Modern social scientific communication research dates to the early part of the 20th century (Delia, 1987). Through the middle of that century, research on communication was generated by scholars from a variety of disciplines, including psychology (e.g. Bateman & Remmers, 1941; Droba, 1931; Festinger, 1957; Murphy, Murphy & Newcomb, 1937; Osborn, 1939), sociology (e.g. Lazersfeld & Stanton, 1949; Mead, 1934; Park, 1940), and journalism (e.g. Lippman, 1922; Nafziger, 1937). The emergence of communication technologies in the early to mid-20th century (e.g. telegraph, telephone, film, radio, television) propelled an interest in media and mass communication that subsequently shaped the emerging field.

With these new technologies came questions about their effects; answering these questions, using the tools of social science, became a major area of focus for communication researchers. A key feature of this work, Delia (1987) highlights, was an interest in how media, messages, and features of the recipient affected responses to messages (our emphasis). Researchers generally did not examine whether or how these messages were comprehended. Rather, they were interested in the effect of these messages on people’s attitudes and behaviors. Thus, influence, rather than understanding, became a dominant focus of social scientific communication research.

As part of this work on media and media effects, models of communication were developed. Most of these models had their origins in Shannon and Weaver’s (1949) model of digital communication (which we discuss further below). Across the decades that followed, communication scholars amended and expanded Shannon and Weaver’s model to address additional variables they observed influencing human communication. Examples of such derivative models include those by Schramm (1954) and Berlo (1960).

In the latter half of the 20th century, interpersonal communication emerged as a research area. With this initially came increased interest in how human communication worked, and an interest in further developing and improving extant models of communication (e.g. Miller & Steinberg, 1975; Watzlawick et al., 1967/2011). However, these derivative models generally did not explicitly or directly address understanding. Instead, their focus was on communication as a process of sending and receiving messages, a depiction of communication that retained the essence of a digital communication (i.e. signal-processing) model.

From the 1980s onward, researchers addressing interpersonal communication gravitated toward studying topics addressing the outcomes and effects of communicating (e.g. relationship development and dissolution, love, intimacy, social support, self-disclosure, rules and norms, conflict, and deception), rather than the process of communication itself. Today, textbooks and introductory courses in interpersonal communication do address the nature of communication as a ←11 | 12→process (e.g. Knapp et al., 2014); however, it is generally approached as a springboard to discuss other topics, rather than a topic of interest in and of itself. In the limited time and space that the nature of communication is given in courses and textbooks, the concept of understanding receives minimal attention.

In short, due to a confluence of different factors and dynamics, the contemporary discipline of communication never came to see understanding, and the process by which people create it, as focal topics for scholarly investigation. For decades, there was a background hum in the primary journals of the discipline that reflected some scholars’ concerns that the discipline should be defined more mindfully. If this had been undertaken, someone might have pointed out that scholarship on understanding was conspicuously absent. However, to date, communication researchers have generally allowed the discipline to evolve in a grounded manner (e.g. Angus & Lannamann, 1988; Berger & Chaffee, 1987; Wiemann et al., 1988). This grounded approach has led to context-based lines of research and courses such as interpersonal, small group, organizational, and health communication (Berger & Chaffee, 1987; Rains et al., 2020), rather than lines of research and courses addressing shared, underlying processes in communication.

We propose that as a result of this history, communication researchers have been overlooking a fundamental process—if not the fundamental process—in our alleged domain of expertise. We also believe that the process through which people create understanding—which is taken for granted in so much of contemporary communication scholarship—is something communication researchers should investigate. As a starting point for this, the discipline needs a clearly articulated conceptualization of understanding, and a framework that explains how people create it. In this book, we seek to offer some initial steps toward these goals.

Classic “Code Models” of Communication: The Problems

With these goals in mind, we now turn our attention to how researchers have historically modeled communication. Most of the discipline’s contemporary models of communication depict communication as a process of “sending” and “receiving” messages using coded signals or symbols. Sperber and Wilson (1995) have termed this category of models “code models”, because the mechanism than enables communication is shared “codes” (or symbol systems) such as language.

The basic logic of a code model is that thoughts or ideas (i.e. mental representations) cannot actually travel across time and space, because they are conceptual abstractions, and do not have a physical form. However, if they are converted into ←12 | 13→something that has a physical form, then this signal (i.e. set of physical stimuli which stand for the mental representation of interest) can travel. If an entity can convert the physical signal back into a conceptual abstraction at its destination, then this allows thoughts or ideas to “travel.” To be able to reliably convert or translate mental representations into signals, and signals into mental representations, codes—that is, systems that reliably pair signals with mental representations—are required.

According to the code model, communication occurs via encoding and decoding of messages. In this process, senders encode their thoughts into signals. This signal is then transmitted through some kind of medium from Point A to Point B, across space and time. During the transmission process, the signal can be distorted, disrupted, or otherwise affected, meaning that the signals that are received at a destination may not be identical to what was sent from a source. Assuming some kind of signal arrives, a receiver decodes the message back into thoughts, using the same code that the sender initially used. If this process is successful, then the receiver will end up with the same thought, or mental representation that the sender had at the start of the process. In other words, one person’s mental representation will have effectively “traveled” from one point to another. These models essentially position communication as a form of signal processing, a depiction that can be traced back to their origin, Shannon and Weaver’s (1949) model of digital communication. In a code model, understanding is (implicitly) seen as the result of successful encoding, transmission, and decoding.

Several important assumptions are implicit in the code model. First, as its name suggests, this model treats codes as essential to communication. In the code model, codes are the means by which mental representations (as conceptual abstractions) can be converted into and out of messages. Second, and following from this, this model relies on the application of systematic associations as the primary mechanism by which communication occurs (i.e. using “entries” in a shared code book). This leads to a third assumption: the key skill or ability required to communicate is representing and applying associations. Any entity that can reliably associate signals with corresponding conceptual abstractions, following a set of clearly defined rules (i.e. a code, which pairs them together), should be able to communicate effectively. Accordingly, the “meaning” of a message—that is the ideas or thoughts encoded in a physical signal—is relatively stable and fixed. Thus, a message’s form and meaning should be clearly and reliably linked, through the code used to create the message. As such, “meaning” can be seen as a property of a message.

A final, implicit assumption of the code model is that senders and receivers perform their respective operations—encoding and decoding—independently of ←13 | 14→each other. Because codes are established systems that provide reliable associations between signals and their “meaning”, senders and receivers do not need each other to figure out what a signal “means”, as long as they both know the code being used. As a practical consequence of this, there is no theoretical problem with researchers focusing on one person, or role (i.e. sender or receiver), at a time when studying communication processes. Thus, this model allows, and to an extent encourages, treating the individual as the primary unit of analysis in research.

Intuitively, code models are appealing; at first glance, they seem to capture what people do when they communicate. However, a closer examination reveals a number of flaws, which scholars have pointed out over the last several decades (e.g. Scott-Phillips, 2015; Sperber & Wilson, 1995). Most importantly, its critics argue that the code model cannot fully explain much of everyday communication, particularly face-to-face interpersonal interactions. Although the model easily describes and explains how a person would interpret a literal statement (e.g. “It is cold in here” to mean, “The temperature is low in this location”), it does not do as well explaining how people successfully create understanding using non-literal or indirect statements (e.g. “It’s cold in here” to mean, “Please close the window”). If the primary means by which people share meaning is through a code, it is difficult to explain how people manage to successfully decode non-literal or indirect messages, as their intended meaning does not directly correspond to what is “coded” into the words that speakers use.

Many scholars have sought to address this issue within the paradigm of the code model. For example, some have suggested that we comprehend metaphors, which are one type of non-literal statement, by first processing the literal meaning and then searching for an alternative when the literal meaning does not fit the context (e.g. Clark & Lucy, 1975). However, empirical work has challenged this model of metaphor comprehension, suggesting that people can and do access the meaning of a metaphor directly, often aided by contextual information (e.g. Gildea & Glucksberg, 1982; Wilson & Sperber, 2012). Some have argued that this kind of processing explanation still could, conceivably, be seen as consistent with the code model, if particular code “entries” are accessed differentially in different contexts. However, a mechanism for determining or enabling differential access is then needed. The need for such extra steps to create a viable explanation for non-literal meaning suggests that these situations do not fit cleanly within a code model’s framework.

Similar problems arise when one attempts to explain situations where people successfully communicate using ambiguous signals, or stimuli—that is, stimuli that do not necessarily have one (or more) clearly delineated mental representation(s) ←14 | 15→associated with them. Behaviors such as shared glances, smiles, sighs, or gestures frequently fall in this category: the same facial expression or bodily action can have a wide variety of different “meanings” in different situations. Indeed, a signal such as a pointing finger can “mean” so many different things (e.g. “look at that”, “watch out for that”, “I think you would find this interesting”, “the object you are seeking is here”, “this is the problem”, “this is the solution”) that it would be difficult to argue that the stimulus can be clearly linked to a manageably finite set of concepts or ideas. People use such behaviors quite frequently in their daily lives, however, and a vast majority of the time, they are unremarkable and unproblematic in interaction. However, the code model struggles to explain how people manage to understand each other in these circumstances.

Another situation that the code model struggles to explain is how people interact when they do not share a common code. Consider, for example, a situation in which two people who do not speak the same language try to communicate. (Anyone who has ever travelled to a country or region where they did not speak the local language has likely had this experience). Although they do not initially have a code to rely on—which, according to the code model, is required for successful communication—they are often able to create understanding well enough for their purposes. How do people manage this? In some cases, interactants may be able to switch from their “default” code (e.g. native language) to another code that is shared with their interlocutor (e.g. second or foreign language; use of conventional gestures). For example, someone who speaks Japanese (but not Tagalog) and someone who speaks Tagalog (but not Japanese) might be able to have a conversation in English if they both know English as a second language. Through this adjustment, they are able to create a situation in which a common or shared code becomes available. However, this kind of adjustment is not always an option. When it is not, people often use ambiguous nonverbal stimuli (e.g. gestures, facial expressions, pointing at objects) to try to express and share their thoughts with others. This then returns us to the scenario we considered in the previous paragraph—communicating using ambiguous stimuli—which is sometimes possible, but usually not easy, to explain in terms of the code model.

A final, related criticism of the code model is that it cannot adequately explain situations where people use instantaneous conventions, or improvise, to communicate. Instantaneous conventions are communicative practices (established by usage) that are generated “on the spot” in an interaction (Misyak et al., 2016). Because they are not formalized or set before an interaction, the associations between mental representations and stimuli in such conventions are generally flexible: the same stimulus can be used to indicate one or more different mental representations (e.g. thoughts, ideas), both within and between conversations. For ←15 | 16→example, waving one’s hand in a particular way might be used to indicate, “open the window” in one instance; later in the conversation, the same motion might be used to indicate, “close the window.” In a study by Misyak and colleagues (2016), the researchers set up a computer game in which players—who could not see or talk with each other—had to work together to open boxes containing rewards, and avoid opening boxes that contained punishments. One player knew what was in each box but could not open them; the other player had a digital tool to open boxes, but did not know what was in each box. Depending on the resources available for communication and the configuration of rewards and penalties in the boxes in different rounds, the players were observed using the same signal (e.g. placing a digital token on a box) to indicate (a) “open this box” and (b) “do not open this box.”

This kind of communicative behavior is very difficult to explain with the code model, which relies on stable associations between mental representations and stimuli to explain how meaning is shared via signals. Indeed, a code in which the same signal (e.g. a hand wave) could indicate two opposite meanings (e.g. both “open” and “close”) is not very helpful or useful for communicating, if that code is the only means people have to create understanding. That people use instantaneous conventions (as well as use more established conventions in novel and flexible ways) in trying to create understanding, and that they do so successfully, suggests that there must be more to human communication than the code model depicts.

In short, we can see that a code model does not offer a satisfactory picture of how human communication works; indeed, it fails to adequately explain many everyday communicative experiences. However, we contend that this does not necessarily make the model wrong or inaccurate. It is just incomplete as a model of human communication; it is only able to explain communication in a subset of situations. First, as its name would suggest, a code model works when there is an established, shared code used by all communicators. Second, this model works for direct and/or literal statements, because they can be encoded and decoded with minimal ambiguity. These qualities generally characterize “well-posed problems”: tasks or situations that have a clear “right” answer that one can arrive at by systematically applying sets of rules. However, the code model does not work as well for situations where there is not a shared, established code, stimuli are ambiguous, or signals are being improvised. These qualities generally characterize “ill-posed problems”: problems that do not have a clear “right” answer that one can determine or calculate using sets of pre-defined rules. In short, the code model appears to work reasonably well for well-posed (communicative) problems, but not for ill-posed problems. For better or for worse, much of human ←16 | 17→communication behavior involves negotiating ill-posed problems. Thus, the processes outlined in the code model need to be either augmented or reconsidered to be able to describe and explain the wide range of situations and experiences that constitute human communication.

We propose that conceptualizing human communication in terms of the code model, alongside other elements of the discipline’s history (see above), has influenced the way researchers study communication. To date, a majority of research studies looking at human communication have been designed to examine the thoughts and behaviors of one person at a time. Researchers tend to study communicative situations characterized by communicators in clearly defined and relatively static roles as “senders” and “receivers”, often with limited feedback between them. If researchers are interested in message construction, they focus on the thoughts and actions of the “sender” or message source. If researchers are interested in message effects, they focus on the thoughts and actions of “receiver”, or message target/audience. In either case, the researchers’ unit of analysis is the individual. This way of studying communication has been termed a monologicapproach, because it focuses on what one person at a time is doing or thinking (Pickering & Garrod, 2006).

This is the discipline’s dominant approach to studying communication, at present. In addition to the discipline’s history and code model conceptualizations of communication, there are also other factors that have perpetuated this state of methodological affairs. First and perhaps foremost, this approach is convenient: focusing on just one individual at a time is much easier than two or more. Second, and related, many of the methods and tools that communication researchers use are oriented toward analyzing static quantities, with the individual as the unit of analysis. Conventional psychological and communication research methods produce outputs such as Likert-type scales assessing attitudes or beliefs, or behaviors that are quantified via coding procedures. (The situations in which these data are collected may be experimental or observational, but the outputs are the same: individual-level, static quantities). Basic inferential statistics (e.g. general linear models), which is what most quantitative social science students are trained to use, allow for comparisons between static quantities, and assume independence of observations, rather than interdependence.

Examining more than one interactant at a time, and looking at how interactants affect each other over time, requires dyadic (or more generally, clustered) data, and/or data collected across multiple time points. To be able to analyze such data appropriately involves more advanced statistical techniques such as multilevel modeling, time series models, or autoregressive models. These are not currently “standard” training for scholars in the discipline of communication, so ←17 | 18→many researchers are not well versed in these techniques, unless they have gone out of their way to pursue additional statistical training.

If communication is conceptualized in terms of a code model, studying communication using a monologic approach is not intrinsically problematic, and could even be seen as logical. As described above, a code model assumes that communicators can and do engage in encoding and decoding independently, so examining each of these processes independently is reasonable. And indeed, in domains such as oratory or mass communication—which were major areas of focus in the discipline’s formative decades—communicative events involve considerable separation in space and/or time between the sending and receiving of a message. In these cases, focusing exclusively on the activities of a “sender” or “receiver” in a given study is reasonable.

However, just as a code model only adequately describes a subset of communicative experiences, a monologic approach to studying human communication is only adequate for investigating a subset of communicative experiences. Specifically, it is a passable approach for situations where communicators are actually processing communicative stimuli independently from each other—for example, when separated in time and space, without the opportunity for any kind of interaction. But in many communicative situations, this is not the case. Interpersonal communication is often face-to-face, dynamic, synchronous, and fluid with respect to sending and receiving; people are generally “sending” and “receiving” simultaneously. Here, it is neither logical nor unproblematic to examine communicators independently: their behavior and cognitions are interdependent.

An alternative to a monologic approach to studying communication is a dialogic approach, which examines what happens to two (or more) people interacting together (Pickering & Garrod, 2006). A dialogic approach directs attention to the ways in which people’s actions and cognitions affect each other as they interact. In this work, the researchers’ unit of analysis is the dyad or group (in the case of interactions involving more than two people). By studying what happens to all individuals involved in a communicative exchange together, researchers can get a picture of how interactants influence each other in the process of communicating, and creating understanding (e.g. Hari & Kujala, 2009; Pickering & Garrod, 2004). In this book, we take a dialogic approach to examining the process of communication and how people create understanding—while acknowledging that there may be situations where a more monologic focus may be reasonable (according to contextual circumstances and researchers’ interests in empirical investigations).

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Communication as Creating Understanding

Having discussed the pitfalls with how researchers have historically modeled communication, we would now like to articulate our perspective on conceptualizing communication, and the relationship between communication and understanding. Etymologically, the word “communicate” comes from the Latin word “communicare”, which means to “share” or “make common”. (This is the same word root as “communal”, “community” and “common”). Thus, etymologically, when people communicate, they are sharing, or making something common. More specifically, we will argue, they are making thoughts “common”, or shared, between communicators. Whenever people seek to communicate—for example, when one person gives directions to another, when a professor is explaining a complex phenomenon to her students, when a parent is explaining the ramifications of a child’s behavior to the child—there is a situation characterized by the need to bring one person’s thought processes in line with another.

Certainly, there are many situations in which people’s primary (or conscious) goal in an interaction is something other than just sharing their thoughts. Social interaction is a goal-directed activity, and people can and frequently do have a wide range of goals beyond “communicating” in the narrow sense we have defined here. For example, people may wish to persuade, influence, express respect, provide social support, or deceive; indeed, in some cases, people may wish to actively prevent others from knowing, or thinking, what they are thinking. However, we contend that even in situations like these examples—in which other goals predominate—there is a fundamental, underlying process that entails communicators’ aligning (or subverting alignment of) their thoughts with one another. (The role of communicators’ goals in both communication and creating understanding is discussed in greater depth in Chapter 6).

Viewed this way, communication is an inherently dyadic process. It takes two (or more) to communicate, and the defining feature of the process—assuming it is successful—is creating a state in which thoughts or ideas are shared in common. This conceptualization distinguishes communication from related constructs such as interpretation or meaning-making. We conceptualize interpretation and meaning-making as individual-level processes: they address how people process incoming stimuli, and what conclusions they arrive at as a result. Communication is dyadic: it entails the alignment of two (or more) people’s thoughts (though these thoughts may include interpretations of the stimuli presented by the other communicator; see Chapter 2).

In this text, we will focus our discussions and theorizing on ostensive human communication—that is, communication in which interlocutors make manifest ←19 | 20→their intentions to communicate something specific (Sperber & Wilson, 1995). Focusing on ostensive communication, and making the distinction between interpretation as an individual-level process and communication as a dyadic process, we reject the popular perspective that “one cannot not communicate” (Watzlawick et al., 1967/2011). We would argue that, given humans’ inherently social nature (see Chapter 3), it might be reasonable to say that “one cannot not process social stimuli and make inferences from it” (which is much less catchy, we admit). However, people can—and frequently do—not ostensively communicate.

Our conceptualization of understanding—which we sketch in broad strokes here, and define more precisely in the next chapter—follows directly from defining communication as a process of “ostensively making common.” We conceptualize understanding as a state in which two (or more) people experience shared, or aligned, mental representations as a result of communication. As such, understanding can be seen as a first-order outcome of communication. Communication can also result in any number of other outcomes—for example, changing attitudes or behavior; changing the nature of the relationship between people; delineating group boundaries—but we argue that these outcomes are secondary (or tertiary), because they are built on a foundation of understanding, or shared mental representations.

We recognize (and wish to emphasize) that these conceptualizations of “communication” and “understanding” are narrower than that of many contemporary scholars. However, this is necessary for our purposes: when terms are used so liberally that they can refer to anything and everything, it is problematic for theorizing. Narrow definitions allow us to distinguish these constructs from others that are related (e.g. interpretation, meaning-making), and thus to increase the precision of our discussions of—and ultimately, we hope, explanations for—how people create understanding.

Viewing creating understanding as the first-order outcome of communication, we could reframe the question, “How do people create understanding?” to become, “How does the process of communication work?” Superficially, the discipline of communication would seem to have answers to this: nearly every introductory communication textbook features a series of models of communication (e.g. those of Miller & Steinberg, 1975; Schramm, 1954; Shannon & Weaver, 1949; Watzlawick, et al., 1967/2011). However, as the preceding critique of code models illustrates, these models do not provide an accurate depiction of what actually happens when people communicate.

We believe our discipline needs a theoretical framework that addresses understanding as its primary focus, and accurately depicts what happens when people communicate. Specifically, such a framework should both conceptualize ←20 | 21→understanding, and describe how understanding is created through the process of communicating. Ideally, such a framework should encompass both objective and subjective dimensions of this experience—that is, what happens physically (i.e. in people’s brains and bodies, in the material world) and what people consciously experience in the process of creating understanding. Many current models or explanations of communication offer loose or metaphorical descriptions of how communication works (e.g. “sending and receiving messages”); we argue that the discipline needs a framework that offers a physically plausible description of what actually occurs when people communicate, at multiple levels.

A theoretical framework addressing communication as a process of creating understanding should also be able to address the entire range of communicative situations in which people successfully create understanding, including those that involve indirect statements, ambiguous stimuli, or flexible conventions. To do so, this framework needs to move beyond code models and their signal processing perspective on communication, and provide an alternative explanation for how people’s communication behavior allows them to achieve states of understanding. As noted in the introduction, there has been considerable theoretical and empirical research conducted outside the discipline of communication that addresses the process of communication, and by extension, understanding. This research and theorizing offer substantial guidance on what a feasible alternative to code models looks like.

To address the level(s) of abstraction and analysis that interest communication researchers, we believe an ostensive-inferential model of communication (Sperber & Wilson, 1995) provides the most viable alternative to a code model. Briefly summarized, an ostensive-inferential model views communication as a process in which communicators makeinferences about what each other is thinking or intending based on communicative behavior in context. (We describe this model of communication, including its key claims, in greater detail in subsequent chapters). In this model, inferences about others’ thoughts—rather than encoding and decoding using a shared code—are the mechanism through which communication occurs. As such, this model explains how it is possible to communicate successfully when communicators do not share knowledge of a common code, stimuli are ambiguous, or signals are being improvised.

More generally, a theoretical framework addressing communication as a process of creating understanding should also be consistent with theorizing about communication and human psychology from outside the discipline of communication. This includes ideas about the social and cooperative nature of communication and social interaction (e.g. Grice, 1975, 1989; Scott-Phillips, 2015), interactive grounding processes (Clark, 1996; Clark & Brennan, 1991), and ←21 | 22→conceptualizing successful communication as a form of alignment or entrainment (Hasson et al., 2012; Pickering & Garrod, 2004). It also encompasses current thinking about theory of mind, a capacity required for making inferences about others’ thoughts (e.g. Frith & Frith, 2005), mentalizing (e.g. Lieberman, 2013; Scott-Phillips, 2015), and current theorizing about predictive processes in human cognition (e.g. Clark, 2013; Friston & Frith, 2015; Hutchinson & Barrett, 2019).

A theoretical framework addressing the process of creating understanding should also be consistent with the empirical findings from research conducted outside the discipline of communication that addresses these topics directly and indirectly. This includes experimental findings on inference-making (e.g. Wilson & Sperber, 2012), how people engage in grounding (e.g. Clark & Krych, 2004), and how verbal references change across time in interaction (e.g. Holtgraves, 2002). Further, such a theoretical framework should cohere with the recent and growing body of neuroscientific research that provides insight into what happens in people’s brains when they communicate and, more generally, when they think about other people (e.g. Hasson et al., 2012; Lieberman, 2013; Stephens et al., 2010).

Finally, we believe the content of a theoretical framework focusing on creating understanding should offer explanations that are consistent with the principles of natural selection and evolutionary theory (e.g. Buss, 1995). Like that of any species, humans’ cognition and behavior has evolved over millennia in response to various selection pressures. Thus, any model or framework that seeks to describe or explain human communication should be consistent with theorizing in evolutionary psychology.

In the chapters that follow, we propose a framework that aims to take up this charge. Specifically, the framework we outline conceptualizes communication as a process of social inference-making. Our proposals for mechanisms by which people make inferences are informed by current interdisciplinary research on cognitive science and communication. To date, most models of communication have focused on behavior and mental representations, as people experience them. While these dimensions or levels are certainly important, there is also a neural substrate to them—that is, there is brain activity that corresponds to these forms of physical and mental action. We are not, and do not claim to be, neuroscientists, so brain function is not the focus of our framework or writings. However, to the best of our ability, we aim to propose a framework that is consistent with contemporary neuroscientific research, and to connect our work with findings in this domain. In this, we hope to present a framework that can speak to both the subjective and objective reality of what occurs when we create understanding. In the following chapters, we introduce foundational constructs (Chapter 2), ←22 | 23→premises (Chapter 3), and key components (Chapter 4) for a process model of creating understanding.

Summary

In this chapter, we have offered a brief summary of the history and current state of the discipline’s scholarship on communication and understanding. We have also presented our own perspective on the relationship between these two constructs. We have critiqued the historically dominant approach to conceptualizing communication, code models, and shown that it cannot fully explain human communication. Following from this, we argued that the discipline needs a theoretical framework that explicitly addresses how people create understanding through communication, and outlined what we believe such a framework should include. In the next chapter, we begin to lay the foundation for a theoretical model that focuses on understanding and offers a viable alternative to code models in its depiction of the process of human communication.

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CHAPTER TWO

Conceptualizing Understanding

In this chapter, we introduce foundational constructs for studying understanding, including social stimuli (i.e. sensory input that produces a cognitive, affective, or behavioral reaction), meme states (i.e. mental representations of concepts, ideas, or experiences) and situation models (i.e. multifaceted mental representations of a communicative episode). We then use these concepts to articulate our conceptualization of understanding as two (or more) people experiencing entrainment of their situation models.

In this chapter, we focus on the constructs that serve as a foundation for modeling how people create understanding. In this, we also introduce the associated terminology—some of which will likely be new to readers—that we will use to discuss understanding, and how people create understanding in interaction.

Before proceeding, we would like to offer a brief remark about terminology and language use in this book. One of the biggest challenges we have had discussing understanding—between ourselves as colleagues and co-authors; with our students in classes; and in writing this book—has been that of language. The vocabulary that communication scholars have to discuss communication is an indirect product of decades of theorizing (or in some cases, lack of theorizing) about this topic. In many cases, this means that current ways of talking about communication implicitly reinforce a code model or signal processing perspective. For instance, terms like “sender” and “receiver”, and describing communication ←27 | 28→as a process of “sending messages”, are ubiquitous, and difficult to work around. However, these terms have problematic implications: they suggest that people play distinct and independent roles in the process of communicating, and that ideas are somehow packaged and transported from one place to another when people communicate.

This vocabulary is not well-suited for the way that we wish to conceptualize communication and understanding (e.g. as intrinsically dyadic endeavors). In the chapters that follow, we have done our best to avoid these traditional ways of describing communicative phenomena because we do not wish to implicitly reinforce a monologic, code model perspective. When we do invoke more traditional terminology, we primarily do so in order to draw and clarify connections to existing scholarship. In these cases, we use our preferred terminology in the main text of a sentence, followed by the traditional term in parentheses. We also follow this convention when summarizing theories from other areas that use similar concepts our own, but label them differently. Like all scholars, we are standing on the shoulders of giants, and we want to acknowledge the intellectual debt we owe to those who have come before us. However, we are also aware of the power of language to implicitly reinforce and reify perspectives (e.g. Beukeboom, 2013; Sutton, 2010), and have made choices about our language use accordingly.

Foundational Terms and Assumptions

Distinguishing Stimuli and Memes

We now turn our attention to introducing key concepts and related assumptions that serve as a starting point for conceptualizing understanding. To begin, we define the basic components of communication as a process. As outlined in Chapter 1, we conceptualize communication as a process in which people share, or make common, their thoughts. However, concepts or ideas do not have any material form, and as such cannot (physically) travel across time and space (e.g. Shannon & Weaver, 1949). In classic “code models” of communication, this issue is addressed through the use of codes and signals. Concepts or ideas are encoded into physical signals that travel across time and space; these signals are subsequently decoded back into concepts or ideas at their destination (Shannon & Weaver, 1949). As discussed in the previous chapter, this view of communication is not wholly inaccurate, but it has considerable limitations, and does not adequately describe or explain much of everyday human communication.

As a basis for a broader, more comprehensive description of human communication (following Sperber & Wilson, 1995, among others), we propose it is ←28 | 29→more useful to describe the process of communication—and thus explain how concepts or ideas “travel”—in terms of stimuli and memes (or meme states). We define stimuli as any kind of sensory input (i.e. something in people’s environment that is accessible to them via hearing, sight, touch, taste, or smell) that produces a cognitive, affective, or behavioral reaction. Any environment people are in contains energy in many different forms (e.g. various frequencies of sound, light with various wavelengths). However, people cannot perceive all of it: human senses can only sample from a narrow set of non-overlapping bandwidths of that energy. In any given instance, we only attend to some portions of those bandwidths, while other portions of those bandwidths are processed at very low levels of awareness, or not processed at all. We use stimulito refer to the portions of those bandwidths that (a) are accessible to our senses, and (b) evoke, or bring about, some kind of response, as opposed to just being present in an environment. Stimuli have a physical or material form, and are directly accessible to multiple people.

Stimuli can manifest in many ways: a stimulus could be the sound of a voice, the smell of someone’s perfume, a gesture, markings on a page, a facial movement or expression, or the act of taking someone’s hand, among many other things. Most of the stimuli we will focus on in our discussions of creating understanding are social to varying degrees. We consider stimuli to be social stimuli to the extent that they come from and are intended for other people, or are used for purposes that relate to other people.

Memes, in turn, are people’s mental renderings of concepts, ideas, and experiences. Memes exist in the mind of an individual and are not directly accessible to other people. Drawing on Dawkins’ (1976) use of the term meme as a bounded unit of cultural transmission, we conceptualize memes as discrete mental units that are capable of being shared and modified via social interaction (see also Dennett, 2017). Dawkins (1976) thought of a meme as comparable to a gene. Genes are replicators whose likelihood of being replicated was directly tied to the value it had for the host. Dawkins thought certain ideas—memes—would be more likely to be replicated, shared, or communicated if they proved valuable to the communicators that “host” them. (The word “meme” is modeled on “gene”, using the Greek word “mimēa”, meaning “that which is imitated”). These properties of memes offer useful dimensions for thinking about the content of our minds in ways not shared by related terms such as concepts, thoughts, cognitions, and ideas. Conceptualizing the contents of our minds as memes highlights features like cultural transmission, host value, and replicability.

As we conceptualize them, memes can vary in their complexity. When people think of a single object (e.g. a dog) or concept (e.g. love), the corresponding meme is relatively simple and straightforward. However, memes can also be considerably ←29 | 30→more complicated, involving multiple lower-level memes and specified relationships between them. Just as the human body is a unit that comprises many individual sub-units (e.g. limbs, organs), which are in turn comprised of smaller units (e.g. cells), more complex memes can be comprised of multiple elements, each of which could be considered a meme on its own. However, at a given level of abstraction, a meme is a bounded unit that can be represented and labeled as such.

Stimuli and memes are conceptually distinct, and this distinction is well-established. Ancient Greek philosophers differentiated these two concepts in their discussions of the nature of signs, and the signed and signifier1 are foundational concepts, distinguished from one another, in the study of semiotics (e.g. de Saussure, 1911/2004). Similarly, a cursory comparison of any two languages or dialects clearly demonstrates that different stimuli (in this case, words) can be used to call to mind the same meme state (i.e. concept), indicating these two are distinct (Zinszer et al., 2016). Although few contemporary communication researchers would dispute this distinction, it is often glossed over in the way communicative processes are described. “Message”, in particular, is a term that is used widely but ambiguously in communication scholarship: while some researchers use the term to indicate a physical manifestation or set of objective features (stimuli), others use it to refer to the mental representation or experience evoked (meme) (e.g. O’Keefe, 2003). We believe that making a clear distinction between these concepts, and using terminology that consistently reflects this distinction, is important for describing and explaining the process of creating understanding in a precise manner.

Thus, stated formally, one assumption of our framework is that memes (mental representations) are distinct from stimuli (manifest, material expressions), and they have different properties: memes are internal and accessible only to a given individual via his or her mind; stimuli are external and are accessible to multiple individuals via their senses.

Activating Meme States

Having made this distinction, we now turn to the how stimuli and memes relate to each other. A second assumption of our framework is that when people perceive and attend to stimuli, those stimuli activate particular memes in context (e.g. Pickering & Garrod, 2006, p. 221). This notion is grounded in models of human cognition and memory, which tell us that we can access memes stored in our memory when we are prompted to by some kind of cue, or stimulus, that is associated with that meme (Henke, 2010; Rudmann, 2018). When we do encounter such a stimulus, the associated meme is brought to mind: the meme is activated. ←30 | 31→Generally, memes that have been recently activated are easier to activate in the present and (near) future (Pickering & Garrod, 2004; 2006); that is, they have a higher resting level of activation (Sharwood Smith, 2019).

According to most widely accepted models of human memory and cognition, memes do not exist in isolation; rather, they are connected to one another (e.g. Collins & Loftus, 1975). These connections are created when something is initially learned, or encoded into memory, which involves physiological changes in the brain (referred to as memory traces). Connections may also be made in the processes of recall and active reflection on items in memory. The number and nature of connections between memes can and do vary from meme to meme (potentially, although not exclusively, as a function of the type of processing involved; for a model of memory organized by processing modes, see Henke, 2010). An important consequence of associations between memes is that activating one meme can affect or activate other memes that are connected to it. More specifically, activation of one meme can lead to spreading activation across other, related memes (e.g. Collins & Loftus, 1975; Pickering & Garrod, 2004; Scherer & Wentura, 2018).

We now return to our earlier assertion that when people perceive and attend to stimuli, those stimuli activate memes (more specifically, meme states; see below) in context. We use the term activate to highlight the rapid and often unconscious nature of how stimuli bring a meme to people’s conscious awareness, or working memory. In most cases, attending to a sound, image, smell, taste, or texture immediately brings to mind some form of mental representation. People generally do not have to try or extend any conscious effort to have these mental experiences; they are so immediate that people may not distinguish them from “objective” reality. (This point has been the object of much discussion in scholarship on perception as well as philosophy of science; see for example, Hanson’s [1969/2002] discussion of seeing versus seeing as). In practical terms, the process of rapid meme activation means that people experience the world in terms of objects, actions, and intentions brought to mind by stimuli, rather than in terms of the actual stimuli. For example, people subjectively experience (i.e. “see”, “hear”) a person laughing rather than a series of changes in color and lines in their visual field and compressions and rarefications of air hitting their eardrums.

In practice, memes are always activated in context (e.g. having a conversation, reading a text, listening to a speech or piece of music, etc.). As we discuss further in Chapter 4, the context in which the meme is activated provides stimuli (that may or may not be social in nature) that also activate memes. As just described, activation of a given meme in context results in a spreading activation pattern across other memes that are associated with the focal meme. Thus, when people encounter a stimulus in context, it activates more than a single meme; it activates ←31 | 32→a complex of memes that are associated through previous experience, and influenced by current context. We refer to this complex as a meme state. For example, we can talk about the meme “dog” in the abstract, but the visual stimuli “d-o-g” or the corresponding oral utterance of “dog” will activate a meme state that reflects—to varying degrees—other memes that have become associated with the meme “dog”, as well as memes associated with the present context in which the utterance takes place. The “dog” meme state that is activated via the utterance, “That dog is good with kids” may thus be different than the “dog” meme state activated via the utterance, “That dog is the one that bit me.” The meme states activated through interaction are dynamic; as new stimuli are presented and perceived, the meme states that are activated are updated and changed.

Situation Models

As part of the process of communicating, people also construct higher-order mental representations of communicative episodes: these are situation models. A situation model is a “multi-dimensional representation of the situation under discussion” (Pickering & Garrod, 2004, p. 172; see also Kintsch & van Dijk, 1978). Essentially, situation models capture the content and nature of a communicative episode at a given point in time. Described in our terminology, situation models consist of activated meme states and their structural relationships to each other. They also include pragmatic and functional information related to a communicative episode (such as whether an utterance is a statement or a request; e.g. Searle, 1969). For instance, a situation model could represent an interaction as “greeting colleagues”, “exchanging observations about changes in daily life during a pandemic”, or “seeking social support for the loss of a loved one.” In each case, the corresponding situation model would include meme states addressing the people involved and various components of the content being addressed (e.g. the nature of the greeting; the content of observations; the nature of the problem and type of support being sought or provided). The situation model would also include the relationships or connections between those components (e.g. in the final example, how the components of the problem relate to each other and comprise a larger whole, how the problem causes the need for a particular type of support, and how the support provided attempts to meet that need).

We conceptualize situation models as both descriptive and predictive: to the extent that they model what is happening in a given communicative situation, they can be used to generate informed hypotheses about what will occur next. As we conceptualize it, the function of a situation model is to accurately reflect the state and nature of the communicative episode (Friston, 2010). Because communication ←32 | 33→is a joint endeavor, this includes accurately representing what other interactants are thinking and doing. Situation models are dynamic; as new meme states are activated (through communicators’ presentation of stimuli), communicators’ situation models are updated and change accordingly. (A more detailed discussion of this proposed process is provided in Chapter 4 and Chapter 5). Like meme states, situation models are internal and accessible only to their owner.

In sum, both meme states and situation models are internal mental renderings of concepts, ideas and experiences that are accessible only to their owners. As such they cannot be shared directly. However, stimuli have a physical form and are accessible to multiple individuals, and can activate memes states, and consequently contribute to situation models. Thus, when people want to share a meme state and/or situation model with another person, they use (i.e. select, present, attend to, and interpret) stimuli as a means to accomplish this goal. This, we argue, is the essence of what happens when people communicate with the goal of creating understanding: communicators use stimuli to activate meme states in their interlocutors’ minds. This, in turn, drives the construction, development, and refinement of their interlocutors’ situation models (see Chapter 5).

Conceptualizing Understanding

Having articulated and established that stimuli and memes are distinct, and that attending to stimuli activates meme states, we can now offer a working definition of understanding, the central focus of this book. Specifically, we conceptualize understanding as two (or more) people experiencing entrainment of situation models as a result of at least one person’s use of social stimuli. In this definition, entrainment refers to experiencing congruent situation models, and related meme states (i.e. mental representations).

For two situation models to be congruent, they must share essential features or qualities relevant to the current context, but they need not be identical in all respects or dimensions. The degree and nature of these shared features or qualities depends on the demands of the task at hand, as well as communicators’ desires (see below). In face-to-face interaction, entrainment of situation models is typically coordinated in time and across time. In other communicative contexts (e.g. reading a book), people’s experiences of congruent situation models may occur at different points in time. However, we generally expect them to be temporally coordinated relative to the content of the communication. For example, following our conceptualization, someone reading page 120 of a book would experience a ←33 | 34→situation model that is congruent with the situation model the author had when writing the text on page 120.

Generally, we consider communicators to have successfully created understanding when their situation models are sufficiently congruent and coordinated for their present purposes. These purposes can and will vary from situation to situation, such that different degrees of entrainment may be required for understanding in different contexts. In some cases, such as discussing plans for a rocket launch, a high degree of precision is required. In these situations, we would expect communicators’ situation models (and their component meme states) to be highly congruent and tightly entrained, down to the smallest detail.

In other cases, communicators may wish to be strategically ambiguous, or may not have an unambiguous or fully formed meme state to communicate. For instance, if two people are discussing how they feel about unfolding current events, and they are still forming opinions about those events, neither individual may have a well-defined mental representation of their opinions. In these situations, we would not expect two peoples’ situation models (and component meme states) to be highly similar or entrained in their details. However, we would expect them to be similar or entrained in broad strokes, at a higher level of abstraction, if some degree of understanding has been successfully created. (Indeed, their entrained situation models might include their uncertainty about their own, and each other’s, opinions). Similarly, if two people are talking about “dogs” generically, it may be fine for one to mentally represent a prototypical “dog” as a Golden Retriever, and the other as an Australian Shephard. However, if two people are talking about a specific dog—for example, “Sally’s dog”—then their mental representations must match at a higher level of detail and precision for understanding to occur.

In short, when people understand each other, their mental representations of an experience, event, or concept converge in a given moment; if understanding is sustained, these mental representations are coordinated across time. This entrainment, or convergence and coordination of situation models, is a transitory state that communicators move in and out of across, and through, interaction. When communicators realize they are no longer entraining, this can be an impetus to initiate repair strategies, a topic that we will address in greater detail in Chapter 6 (see also Pickering & Garrod, 2004, 2006). To the extent that communicators ultimately experience functionally similar, or aligned, situation models as an interaction progresses, we can describe them as successfully creating understanding. Thus, creating understanding results in entraining and aligning mental representations between people, or coordinating minds so they are—in lay terms—thinking the same thing (Pickering & Garrod, 2004, 2006, 2013).

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The conceptualization of understanding as a form of entrainment or alignment of mental content draws on both theory and empirical research related to this topic across disciplines. Sperber and Wilson (1995) describe the process of ostensive-inferential communication as “a process involving two information-processing devices. One device modifies the physical environment of the other. As a result, the second device constructs representations similar to representations already stored in the first device” (p. 1; our italics). Similarly, Pickering and Garrod’s (2004, 2006) interactive alignment model describes successful communication as a process in which interactants’ situation models become aligned. As we view communication as a process in which the fundamental goal is creating understanding (see Chapter 1), the definition we propose is consistent with this work (even if these scholars do not explicitly reference “understanding”).

The definition we propose is also broadly consistent with Clark and colleagues’ work on grounding, which describes how people construct and manage shared mental representations in interaction (e.g. Clark, 1996; Clark & Brennan, 1991). Research on grounding typically focuses on participants’ use of stimuli to negotiate common ground (defined as mutual or shared beliefs, assumptions, and knowledge). Clark and colleagues have argued that people generally assume that they have successfully established common ground in interaction unless they encounter evidence to the contrary. Scholarship on common ground and grounding generally emphasizes how people arrive at beliefs about understanding, rather than understanding itself. However, Clark and colleagues’ treatment of communication as a process of negotiating shared or common mental representations coheres with our conceptualizations of both communication and understanding.

Our conceptualization of understanding is also consistent with the conceptual and operational definitions used by Sillars and colleagues in their empirical work on cognition in communication. Specifically, these researchers studied cognition during conflict in married couples (Sillars et al., 2000) and communication between parents and adolescents (Sillars et al., 2005). In these studies, interactions between participants were recorded, and then a video-assisted recall procedure was used to ask each participant what they were thinking, and what they thought their partner was thinking, at regular intervals during the recorded interaction. In these studies, understanding is conceptualized and operationalized as individuals (i.e. each member of a couple, or a parent and adolescent) having aligned responses at approximately the same time in an interaction.

Finally, the conceptualization of understanding as experiencing congruent, or functionally similar, mental states is also consistent with recent neuroscientific research on communication. This work has demonstrated that when people communicate successfully—that is, when they understand each other—patterns ←35 | 36→of neural activation become synchronized, or entrained, across brains (Hasson et al., 2012; Leong et al., 2017; Nguyen et al., 2019; Stephens et al., 2010). In other words, people’s brains enter into transient states of coupled neural activity when people communicate successfully. Thus, conceptualizing understanding as entraining situation models is not just a description of subjectively accessible mental experiences; it also has a physical and physiological basis.

Summary

Details

Pages
XII, 172
ISBN (PDF)
9781433183768
ISBN (ePUB)
9781433183775
ISBN (MOBI)
9781433183782
ISBN (Book)
9781433168154
Language
English
Publication date
2021 (January)
Published
New York, Bern, Berlin, Bruxelles, Oxford, Wien, 2021. XII, 172 pp.

Biographical notes

Jessica Gasiorek (Author) R. Kelly Aune (Author)

Jessica Gasiorek (Ph.D., University of California – Santa Barbara) is an associate professor in the Department of Communicology at the University of Hawai`i at Mānoa. Her research addresses communication accommodation, social cognition, intergroup dynamics, and communication about age and aging. R. Kelly Aune (Ph.D., University of Arizona) is a professor in the Department of Communicology at the University of Hawai`i at Mānoa. His research and teaching address message processing, inference-making and implicature, and creating understanding.

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Title: Creating Understanding