Information Processing

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I. Introduction

II. Intellectual Foundations

III. Intentional, Design, and Physical Stances

IV. The Intentional Stance and “Ordinary Mental Concepts”

V. Design And Hybrid Intentional/Design Stance Approaches

A. Method, Data, and Domain

B. Systems and Mechanisms

C. Hybrid Intentional/Design Approaches

VI. The Physical Stance

I. Introduction

Information processing is an approach to the study of behavior which seeks to explain what people think, say, and do by describing the mental systems that give rise to those phenomena. At the heart of the information-processing perspective is the conception of the mind as a representational system. That is, the mind is viewed as a system that (1) holds information in some form, and (2) processes (i.e., utilizes, transforms, manipulates) that information in some way in carrying out its input-processing and behavioral production activities. To be more precise, the mind is viewed not as a single representational system, but as a collection of subsystems, each coding information in its own way, and each carrying out its own particular operations on that information. The basic idea, then, is to describe how information is held in one or more subsystems, and how that information is processed in those subsystems, in order to explain the perceptual, mental, and behavioral phenomena of interest. The goal of the information-processing approach is to explain the link between what a person hears, sees, tastes, feels, and smells and what he or she thinks, says, and does.

The information-processing perspective has proven to be enormously important in advancing our understanding of a wide range of phenomena of interest to communication scholars. In part this is because so much of what is involved in fundamental processes of meaning-giving and message-making transpires in the mind. As the entries in the “Information Processing and Cognitions” area of this encyclopedia illustrate, this approach has touched virtually every corner of the field. The list of the phenomena and processes encompassed by information processing is a long one, indeed, but, just as examples, it includes the following: attention, listening, comprehension, memory, learning, planning, decision-making, emotion, language acquisition, skill acquisition, message production, and creativity.

II. Intellectual Foundations

Philosophers have concerned themselves with the nature of knowledge and thought since ancient times – a tradition of inquiry that reached full flower during the Enlightenment of the seventeenth and eighteenth centuries in the work of philosophers such as Descartes, Hume, and Kant. Systematic, empirical investigations of mental processes date as far back as the late 1800s, most notably with the seminal studies of Wilhelm Wundt, who founded the first experimental psychology laboratory in 1879. In the United States, William James’s two-volume Principles of psychology, published in 1890, addressed numerous topics that remain of keen interest today, including localization of specific functions in various brain systems, consciousness, and the nature of the self.

Beginning in the period 1915–1925, experimental psychology came to be dominated by behaviorism, a stance most closely identified with John Watson. The desire to pursue a purely objective science of behavior, based solely on observable stimulus–response relationships, led Watson to argue that appeals to mentalistic concepts had no place in psychology. A successive generation of scholars, the radical behaviorists, including most notably B. F. Skinner, were willing to admit mental states and processes, but deemed such internal events tangential and ultimately unnecessary to understanding human behavior. Even during the heyday of behaviorism (c. 1920–1950), however, there were other prominent voices that were not so willing to dismiss appeals to internal states and processes. Among their ranks were the Gestalt psychologists like Wolfgang Kohler, and the eminent developmental psychologist Jean Piaget. Similarly, social psychologists from the 1930s on accorded a central role in their theorizing to the individual’s construal of the environment and conceptions of attitudes and attitudechange processes. And, as a final example, during the 1940s, scholars who came to be identified with the nascent discipline of mass communication made ready use of cognitive conceptions such as attention, perception, and message memory (Dennis & Wartella 1996; Rogers & Chaffee 1997).

The period between 1950 and 1970 witnessed the emergence of a new experimental psychology, one that, in contrast to behaviorism, accorded central prominence to mentalistic structures and processes. This cognitive psychology drew from an array of disciplines, including linguistics, information theory, and artificial intelligence, in advancing a view of the mind as an information-processing system, i.e., one in which stimulus inputs were subjected to a series of processing stages, culminating in overt responses. Thus, a rudimentary information-processing model might propose that inputs pass through stages of “attention,” “pattern recognition,” “working memory,” “long-term memory,” and “response formulation” (see below). Central to the approach were issues of representation and processing – at each stage, information represented in some way was assumed to be manipulated or transformed via processes that both made that information available for subsequent stages and left a “footprint” of their presence and operation which was discernible in overt responses. Measures of reaction time, memory recognition and recall, response errors, verbal reports, and so on were examined, then, in order to shed light on the nature of these intervening processing steps.

The notion of the mind as an information processing system came to exert considerable influence on thinking in communication and cognate disciplines such as organizational behavior, anthropology, and educational and social psychology. But the disciplinary influences have not been unidirectional. Two trends are particularly noteworthy. Rather than a lone subject responding to laboratory stimuli, scholars have increasingly come to embrace conceptions of the socially embedded information processor – one whose cognitive processing reflects interpersonal and cultural influences. Second, advances in real-time neurophysiological assessment have opened new avenues for understanding what transpires between inputs and responses. These and other influences have given rise on one hand to a cognitive science that draws from numerous traditional disciplines, and on the other to a broad intellectual landscape, extending well beyond the boundaries of cognitive science, that is permeated by concerns with information processing and cognition.

III. Intentional, Design, and Physical Stances

The domain of information processing and cognition is enormously heterogeneous, reflecting a wide array of both general conceptual approaches and specific characterizations of theoretical mechanisms, aimed at explicating a near limitless list of behavioral and mental phenomena, and drawing upon a variety of sources and types of data. No single overarching conceptual framework is likely to encompass the breadth of the domain, but, at least with respect to conceptual approaches and theoretical mechanisms, it may be possible to develop a rudimentary organizational framework that can accommodate many of the most important contributions by drawing upon the work of Daniel Dennett (e.g., 1987).

Dennett distinguishes three approaches for apprehending and predicting the behavior of complex systems like chess-playing computers, or human beings. The “physical stance” seeks to understand the behavior of such systems by recourse to their physical constitution, i.e., the solid-state electronics of the computer or, for people, their neurophysiology. A second approach, the “design stance,” seeks to apprehend a system’s behavior in terms of functional (rather than physical) mechanisms. That is, in order to behave as it does, the system must carry out various functions, and the task is one of characterizing the nature of the mechanisms by which those functions are executed. And, importantly, the same function can be carried out in two systems with very different physical constitution, a fact that makes the physical character of a system of secondary concern to theorists pursuing design stance accounts. Finally, the “intentional stance” seeks to understand and predict a system’s behavior by ascribing to that system various beliefs, goals, expectations, and so on. Thus, the system’s behavior is seen to reflect choices made in order to accomplish goals in light of information at the system’s disposal.

These three approaches, then, suggest a framework for organizing a survey of key conceptual and theoretical developments in the domain of information processing and cognitions, but with the important caveat that it is probably best to think of Dennett’s three stances as “pure types,” and in practice it is possible to identify numerous hybrid approaches that combine elements of two, or even all three, of these perspectives.

IV. The Intentional Stance and “Ordinary Mental Concepts”

As noted above, the intentional stance seeks to apprehend a system’s behavior in terms of its goals and beliefs. Fundamental to this approach is an assumption of rationality, i.e., goals and beliefs are useful in explaining and predicting behavior only as long as the system does what it should, given its objectives and knowledge. Intentional stance accounts of human behavior typically reflect a corollary assumption of self-awareness: the individual is assumed to be aware of his or her own mental states and activities. So, people know what they are trying to accomplish; they are aware of the information that they are able to bring to bear on those goals; they are conscious of their expectations, deliberations, and decision-making. Following from this assumption, the terms of intentional accounts tend to be the sort of mental states and contents that are (presumably) consciously available to the individual. Intentional accounts, then, make ready use of ordinary mental concepts, or “folk psychology” – concepts and terms afforded by everyday language and central to people’s lay understanding of their own behavior and that of others (Churchland 1988; Flanagan 1991). To illustrate, consider just three examples of intentional stance constructs: attitudes, goals, and plans.

Among the most prominent of ordinary mental concepts is that of attitudes. As an element of folk psychology, people commonly rely on inferred attitudes to explain and predict the behavior of others and to give accounts of their own behavior. But beyond this, the attitude construct has held a central place in social scientific study of human behavior for the better part of a century (Eagly & Chaiken 1993). Precise definitions of “attitude” are numerous and varied, but in general they have in common the notion of an evaluation of a person, thing, etc. that typically carries with it behavioral implications.

A second example of an intentional stance construct with both widespread folk and social scientific application is that of goals (along with attendant notions of desire, objectives, etc.). On one hand, as a motivational construct, goals are seen to play a key role in output processes such as decision-making, behavioral regulation, and emotion (Austin & Vancouver 1996). Moreover, imputing goals to others is central to input processes such as attribution, interpersonal uncertainty reduction, and comprehension (Schank & Abelson 1977).

Finally, closely related to conceptions of goals is that of plans (and planning) – a series of projected steps leading to accomplishment of some end. As with other ordinary mental concepts, lay persons have phenomenal experience of their own plans and also readily ascribe plans to others. And, in advancing intentional stance accounts of human behavior, social scientists often resort to conceptions of plans and planning (Berger 1997). One particularly prominent application in the field of communication has been the goals– plan–action (GPA) framework wherein goals are held to give rise to plans, which in turn guide a person’s behavior (Wilson 2002).

Because the constructs of intentional stance theories are generally assumed to be phenomenally available to the individual (i.e., the person presumably knows his or her attitude toward some object, is aware of his or her goals, can articulate his or her plans), the most common method of intentional stance approaches is simply to ask people to report on their thoughts, using paper-and-pencil assessment instruments (e.g., attitude scales), think-aloud protocols, etc. The reliance on such self-report techniques has prompted scrutiny of the conditions under which such data are likely to be more or less accurate (Ericsson & Simon 1984).

V. Design And Hybrid Intentional/Design Stance Approaches

A. Method, Data, and Domain

As noted above, while intentional stance accounts seek to explain a system’s behavior in terms of beliefs, goals, etc. ascribed to that system, the aim of design stance approaches is to explain behavior by recourse to the functional design of systems assumed to give rise to the behavioral phenomena of interest. So, for example, if people exhibit the ability to remember, many years later, events from their college days, then there must be some mental system that preserves these episodes over extended periods of time; if people can recall the general plot of the last novel they read, but not the book’s exact sentences and their order, there must be some memory system that preserves the gist of a narrative, but not its surface form; if people’s ability to monitor the road and other traffic is diminished while talking on a cell phone, then there must be some system that limits the number of activities a person can execute at one time. And importantly, on the design stance, explanation and prediction do not depend upon the assumption of rationality, nor are the mental processes of interest necessarily consciously available to the individual.

The fundamental method of design stance approaches has been characterized as that of “transcendental deduction” (Flanagan 1991). This simply means that the theorist observes some input–output regularity and posits the nature of the system that would produce such a regularity. For example, to observe that people get faster in executing a skill as they continue to practice that activity might lead the theorist to posit some sort of memory structures that are “strengthened” with repeated use. As noted above, the theoretical terms of pure design stance approaches are cast at the level of mind rather than brain. Thus, rather than talking about neuroanatomical structures and neurochemical control systems, the theorist pursuing design stance accounts invokes conceptions of working memory, associative networks, scripts, and so on – structures and processes that have some instantiation in the hardware of the brain, but that are not identified with specific neural structures.

The data from which design stance theorizing proceeds include self-reports like those employed in intentional stance approaches, but because the contents and operations of functional systems may not be consciously available to the individual, other types of data are critical (Greene 1988). Among these are measures of memory recognition and recall, which in the field of communication have proven particularly important in studies of mass media processing and interpersonal communication. Because cognitive processes unfold over time, measures of reaction time (i.e., how long it takes a person to produce a response) are held to yield essential cues about the nature of those processes. And, by extension, assessments of speech-onset latency, speech rate, and so forth provide insights about the temporal characteristics of message production processes. Most (though not all) cognitive models assume a finite pool of processing resources that can be flexibly allocated to various processing tasks, but that limits performance when demands on resources exceed what is available. Assessments of slowed responses and performance decrements, then, can be used to shed light on the processing demands of various tasks. A final example of data plumbed in design stance approaches is performance errors (Fromkin 1980). The mechanisms at work in human perception, memory, and behavioral production tend to produce errors that are recurrent, even predictable (as in familiar visual illusions); conversely, there are other sorts of errors that, though conceivable, virtually never occur. As a result, phenomena such as memory intrusions and speech errors provide important clues to understanding underlying mental processes.

One implication of the method of transcendental deduction is that virtually any sort of mental or overt behavioral phenomenon is seen to be amenable to study from the design stance. In other words, because the essential task is that of positing a model of the mechanisms linking inputs to outputs, almost anything people can think and do falls within the purview of this approach (although, in the main, work conducted within this framework has been concerned with the behavior of so-called “normal” populations and has not focused on groups with various neurological and mental disorders). So, for example, theorists study mental operations like attention, listening, manipulation of mental images, imagining interactions, reasoning, and creating novel thoughts. And, in the realm of overt behavior, they examine phenomena such as language acquisition, speech production, skill development, and problem-solving. Moreover, as is developed in greater detail below, the terms of folk psychology (e.g., attitudes, beliefs, goals, plans, etc.) common to the intentional stance can be explicated by recourse to design stance mechanisms.

Two additional points concerning the method of transcendental deduction merit mention. First, because the theorist’s objective is to posit some mental mechanism that gives rise to particular input–output regularities, it is possible to develop multiple, alternative models, each equally capable of accounting for the phenomena of interest. Second, in seeking to understand a design stance model, it is important to distinguish the “data” from the “domain” – that is, to distinguish the measures made to test a theory from the mental and behavioral phenomena that the theory purports to explain (Greene & Graves 2007). To illustrate, a theorist might posit a model of the processes involved in recognizing facial expressions of emotion, and in order to test the model, assess reaction times to make judgments about the emotional expressions of strangers presented via computer display. In this case, the domain of the theory (i.e., what the theory seeks to explain) is emotion recognition processes, and the data are the reaction time measures collected to test the theory.

B. Systems and Mechanisms

Central to the information-processing perspective are the interrelated conceptions of the mind as a series of stages linking inputs to outputs and the mind as a representational system in which information, held in some form, is processed at each of these stages. Within this perspective, models can be developed at varying levels of detail and sophistication. At the most rudimentary level are those models which simply specify a series of stages or systems that information passes through between input and output with little attempt to specify in detail the nature of the mechanisms operating at each stage. Such an approach, sometimes termed “white boxology” or a “box and arrow model,” provides a general framework of the mind, and there is some consensus about the major systems comprising such models, as the following:

  • Sensory registers (including echoic and iconic memory systems): buffers that hold sensory information for very brief periods of time before it is passed on to subsequent processing stages.
  • Pattern recognition system: integrates sensory information with previously acquired knowledge to permit stimulus identification, as, for example, in the case of letter recognition.
  • Working memory (or short-term memory): a limited-capacity system in which a relatively small amount of information can be held and manipulated (Miyake & Shah 1999). Working memory is the site of planning, decision-making, message editing, and so on, and the contents of working memory are generally thought to be available to conscious awareness.
  • Long-term memory: a very large repository where information is held virtually indefinitely. Long-term memory is typically partitioned into various subsystems, including declarative versus procedural memory. Declarative memory is knowledge that – i.e., the factual information one has acquired. In contrast, procedural memory is knowledge how – i.e., information used in executing skills such as riding a bicycle, swimming, or tying one’s shoes. The declarative memory system can be further partitioned into episodic and semantic memory stores. As the name suggests, the episodic memory system is memory for the episodes or events of one’s life; it is essentially autobiographical – the store of information about what one has experienced (although these memories need not be veridical). The semantic memory system, in contrast, contains information abstracted from specific experiences – such as one’s knowledge of the capital cities of the world, the boiling point of water, and, especially, words and their meanings.
  • Effector system: the system responsible for the preparation and execution of motor programs. On balance, design stance theorists have given less attention to this component of the mind, but it has been the subject of considerable scrutiny from those interested in mechanisms of speech production (Rosenbaum 2005).

Again, there are insights to be gleaned from such general characterizations of the information-processing system, but the real strength of the design stance comes from specification of the mechanisms by which the operations at each stage are carried out. Describing these mechanisms involves addressing issues of structure, content, and process. Following from the basic conception of the mind as a representational system, concerns with structure and content involve how and what information, respectively, is represented in the mind. And, on the assumption that the mind is an information-processing system, issues of process concern how information is transformed and used. To illustrate, consider the familiar conception of “scripts.” A script is a long-term memory structure that consists of a series of scenes representing familiar events. Moreover, a particular script (e.g., eating in a restaurant, going through airport security) represents specific content relevant to that type of event. Processes pertaining to memory structures like scripts would include operations such as activation (i.e., retrieving the script from memory), implementation (e.g., using the script to generate expectations for what will happen next), and alteration (e.g., learning new scenes for the script).

As noted above, the methods and data of the design stance permit (at least hypothetically) a nearly limitless range of specifications of mechanisms to account for mental and behavioral phenomena, and theorists have, indeed, proven enormously imaginative in developing structure–process–content descriptions. Nevertheless, there are certain conceptions of underlying cognitive mechanisms that have found widespread acceptance and application, and, partly for this reason, they provide useful examples of design stance theorizing.

Productions are elements of the long-term procedural memory system that preserve knowledge of situation–action relationships in an IF–THEN format. For example, a production for properly addressing another person might be:

IF: You are greeting a person of higher status

THEN: Use his/her title plus last name.

Production systems have found widespread application in psychology (Klahr et al. 1987), but have been less prominent in the field of communication. At the same time, various communication theories have relied upon production-like structures, as in the conception of procedural records in Greene’s original formulation of action assembly theory, and in Wilson’s (1995) and Meyer’s (1997) models of communication goal generation.

Schemas: a number of different specific conceptions of schemas have been developed, but in general the term refers to knowledge structures, typically of declarative memory, but in some cases in procedural memory, that have been abstracted from past experiences. Schemas are held to be general representations in the sense that they apply to entire categories of similar persons, objects, etc. As such, schemas are similar to the aforementioned conception of scripts, and, indeed, in some treatments scripts are viewed simply as a special case of schemas, i.e., those that pertain to familiar sequences of events.

Associative networks: as with schemas, a number of distinct conceptions of associative networks have been advanced, but they have in common the idea that long-term memory (either declarative or procedural) consists of nodes, corresponding to some sort of conceptual entities, and links, or associations, between those nodes. In some theories, in addition to relatively abstract symbolic representations, nodes may also correspond to low-level perceptual or motoric features. Associative links between nodes are established when the individual processes some relationship between nodes, as, for example, when the concept “dog” becomes linked to “barks.” In most models, links between nodes are held to vary in strength, where strength is a function of the recency and frequency with which that link has been accessed. Activation, a kind of energizing force, can spread along associative links such that activating one node will cause other linked nodes to become activated as well.

As previously noted, mechanisms described in design stance models can be unpacked at successively more fine-grained levels of specification. Thus, for example, the IF–THEN relations represented in productions can be recast in associative network terms, and, indeed, both the Meyer (1997) and Wilson (1995) models mentioned above are formulated in this way. Similarly, macro-structures like schemas and scripts can be conceived as networks of nodes and links.

Connectionist models (or neural networks, or parallel distributed processing models): an alternative (or still deeper) explication of long-term memory structures proposes that memory consists of elemental units and connections, but unlike associative network models, the units of connectionist models do not correspond to symbolic entities. Instead, rather than being represented in a single node, a symbolic entity (e.g., “dog”) is represented as a pattern of activation over a large set of units. Learning, or concept acquisition, in such models involves adjusting the weights of the connections on the basis of feedback. Connectionist models have found a wide range of applications in psychology (Rumelhart & McClelland 1986a, b), including analyses of language perception and production, but to date, their use in the field of communication has been somewhat limited (but see, for example, O’Keefe & Lambert 1995).

C. Hybrid Intentional/Design Approaches

Distinguishing “intentional,” “design,” and “physical” stance approaches is a useful convenience, but, as was noted above, these categories represent “pure types,” and, in practice, it is common to encounter hybrids that combine elements of multiple approaches. This is particularly true in studies of social cognition and communication, where hybrid intentional/design models are numerous. Typically, the thrust of these models is to explicate the nature and dynamics of intentional stance terms by recourse to “deeper” design stance mechanisms (in much the same way that, as discussed above, design stance formulations may be unpacked in successively more detailed ways). The example of approaching “goals” from the design stance has already been mentioned, and examples such as “self-concept” and “attitudes” can be used to further illustrate the point.

As a typical term of ordinary folk psychology, people possess a commonsense conception of “self-concept” as one’s collection of beliefs about one’s personal attributes, social roles, and so on. And we commonly perceive that elements of self-concept play a role both in input processing of information and in guiding behavioral responses. An important conceptual advance, however, came with the development of design stance models of the representation and processing of self-relevant information in long-term memory (Baumeister 1998). In this vein, Markus and her associates proposed that the self is essentially schema-like, organizing an individual’s knowledge of him- or herself in various domains (Markus 1977). Such design stance formulations gave rise to numerous experimental investigations that shed light on phenomena such as people’s memory for self-relevant events, their ability to maintain seemingly inconsistent perceptions of themselves, and conditions under which people are more likely to rely upon their beliefs and attitudes in making behavioral decisions.

In much the same way as commonsense conceptions of self-concept, theorists have explicated “attitude” in design stance terms. One prominent example is found in the work of Fazio and his associates (Fazio & Roskos-Ewoldsen 2005), who proposed that attitudes can be understood as associative networks linking an attitude object (e.g., “roller coasters”) with an evaluation (“fun”). And again, pursuing an explication of attitude structure and processing from a design stance perspective, which in this case emphasizes strengthening of associative relations with repetition, has given rise to examinations of the role of attitudes in guiding perception, interpretation, and behavior that were not suggested by lay conceptions of the attitude construct.

VI. The Physical Stance

As previously noted, design stance approaches seek to explicate the functional architecture of the mind, and such models typically accord little consideration to the physical architecture of the brain. In contrast, physical stance theories and models pursue explanations cast at the level of neuroanatomical systems and processes. The physical stance, then, is characteristic of fields such as neurophysiology and neuropsychology, and especially, cognitive neuroscience (Gazzaniga 1995). Cognitive neuroscience maintains the essential conception of the mind as an information-processing system, but seeks to explicate the functional architecture of that system in physical terms. In essence, cognitive neuroscience is an attempt to bridge the gap between the physical and the mental; that is, to understand how the brain gives rise to the mind.

The data that theorists in this tradition draw upon include studies of individuals who have suffered damage to specific regions of the brain – on the assumption that examination of corresponding functional deficits will shed light on the plausibility of possible functional systems and on functional/physical interfaces (Rapp 2001). Similarly, in light of neurophysiological changes occurring during development and old age, investigations of perceptual, memory, and behavioral performance by infants, children, and the elderly are employed (Nelson & Luciana 2001; Hedden & Gabrieli 2004). Most prominently, functional neuroimaging, by use of positron emission tomography (PET), and, more recently, functional magnetic resonance imagining (fMRI) are used to examine brain metabolism under various sorts of experimental tasks and conditions.

At this point, studies of phenomena traditionally of interest to scholars in the field of communication from the perspective of cognitive neuroscience are only beginning to emerge. For example, researchers have begun to examine brain activity while viewing media violence (Anderson & Murray 2006). Other avenues of research have applied neuroimaging techniques in studies of processing facial expressions of emotion (Phelps 2006) and in investigations of language production and processing (Gernsbacher & Kaschak 2003; Beatty & Heisel 2007).


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