Search Results

This chapter presents a critical analysis of claims that perceivers seek social information in ways that constrain targets’ reactions in such a manner as to almost ensure expectancy confirmation. First, it shows that the early research never actually demonstrated anything remotely supporting such a strong conclusion. Second, it reviews the subsequent research, which generally shows that people do not seek social information in ways that constrain targets’ behavior or responses; people generally seek social information in ways maximally conducive to fairly diagnosing or testing their expectations, rather than ways highly biased toward confirming those expectations; people do have a very slight tendency to ask questions to which a “yes” answer is more likely to confirm than disconfirm their expectations; and this tendency combines with acquiescence (a response bias whereby people are more likely to respond “yes” to any question) to create a small bias in social interaction toward targets giving responses that confirm perceivers’ expectations. However, the extent to which even such responses lead to behavioral confirmation of self-fulfilling prophecies is likely to be modest.

Expectations can be self-confirming, not only because they create self-fulfilling prophecies, but also because they may influence, bias, and distort how people interpret, evaluate, judge, remember, and explain others’ behaviors and characteristics. This chapter discusses some of the early research that most dramatically demonstrated this phenomenon. It reviews classic research demonstrating how race, gender, social class, occupational, and sexuality stereotypes bias person perception and how psychopathological labels (e.g., “schizophrenic”) so distort professionals’ judgments as to render the sane indistinguishable from the insane. It reviews two studies showing that beliefs about personality characteristics bias judgment and memory, and another showing that teachers’ expectations bias the grades they give to students. Last, this chapter reviews research demonstrating that expectations even bias how people gather information in expectancy-confirming ways. Taken together, this chapter, especially when combined with Chapter 4, conveys why the great enthusiasm social psychology once had for expectancy-confirming phenomena often led scholars to conclude that the biases and self-fulfilling prophecies created by interpersonal expectations constituted major ways in which people created and constructed social reality.

This chapter reviews research revealing important aspects of how children learn from conversations. Like adults, when children are trying to find out how a novel mechanism works (for example, how a clock functions), they appear to start by asking descriptive questions such as about the names of the salient parts of the mechanisms. They then move on to ask questions that are more focused on causal processes that concern the functional relations among different parts. The chapter provides evidence on how children extract information from adults by relying on their perceptions of the expertise and the familiarity and motivations of potential informants. It shows that children are just as likely to ask information-seeking questions to a stranger and a sibling as to their parents, and that strangers are actually slightly more likely to answer those questions. It discusses classic findings on the relation between learning and depth of processing, and extends this literature by presenting new findings showing that children are significantly more likely to remember information when they have had to ask for it than when they are given the information unrequested.

Over the last decade, studies on metacognition have been playing an increasingly prominent role in the field of animal cognition. Although a growing number of studies have documented responses consistent with metacognition, currently there is some debate about their proper interpretation. This chapter reviews the evidence that has accumulated in the last decade in the so-called information-seeking paradigm, which involves confronting subjects with two or more containers where food can be hidden. Researchers have manipulated several variables including the visibility of the baiting, the food type, and the time since the baiting took place. To get the food, subjects have to select the baited container but before they do so, they can, if they wish, look inside the container to verify its contents. Although the initial results could be explained as a result of random search, response competition, or perceiving anxiety rather than monitoring memories, recent findings have in turn challenged each of these alternative explanations. Information seeking in the great apes can be characterized as targeted (i.e. individuals do not search randomly), integrated (i.e. individuals can incorporate multiple types of information into their decision, including information derived by inference), and facultative (i.e. subjects can increase or decrease their searches depending on the information that they possess and the cost of searching and/or choosing wrongly). These findings, together with those from other metacognition paradigms, suggest that the great apes have some access to the causes of their uncertainty, and they can flexibly deploy means to remedy this situation.

This chapter discusses various theoretical constructs, such as laws, models, and conceptual frameworks, in information-seeking behavior. It first looks at theories in library and information science (LIS), including “in-house” theories. In particular, it examines the work of Elfreda Chatman, the most notable LIS theorist in human information interaction, like her theory of life in the round and living life in the round. Another theory developed by Chatman is the theory of normative behavior, which consists of four concepts: social norms, worldview, social types, and human information behavior. The chapter then turns to theories borrowed from other fields such as psychology and phenomenography.

A neuroethological approach to decision making posits that neural circuits mediating choice evolved through natural selection to link sensory systems flexibly to motor output in a way that enhances the fit between behavior and the local environment. This chapter discusses basic prerequisites for a variety of decision systems from this viewpoint, with a focus on two of the best studied and most widely represented decision problems. The first is patch leaving, a prototype of environmentally based switching between action patterns. The second is social information seeking, a behavior that, while functionally distinct from foraging, can be addressed in a similar framework. It is argued that while the specific neural solutions to these problems sometimes differ across species, both the problems themselves and the algorithms instantiated by biological hardware are repeated widely throughout nature. The behavioral and mathematical study of ubiquitous decision processes like patch leaving and information seeking thus provides a powerful new approach to uncovering the fundamental design structure of nervous systems.

Research on human information interaction (HII) focuses on people’ s relationship with information, rather than with technology (as in human-computer interaction) or with the information agency (as in librarianship). HII is inherently a multidisciplinary field, encompassing areas ranging from human-computer interaction to computer-supported cooperative work, human factors, social informatics, management, and library and information science (LIS). LIS is the only field that has given HII a significant position among its areas as the field of human information behavior (HIB). This chapter provides an overview of HII and some of its research disciplines. It discusses relevance judgment in information seeking and presents examples of current and potential areas of research related to HII, including knowledge organization, information retrieval, and information and communication technologies.

This chapter describes a rational-ecological approach to derive the processes underlying the balance between exploration and exploitation of actions as an organism adapts to a new environment. The approach uses a two-step procedure: the general environment is first analyzed to identify its invariant properties; then a set of adaptive mechanisms are proposed that exploit these invariant properties. The underlying assumption of the approach is that cognitive algorithms are adapted to the invariant properties of the general environment. The current proposal is that suboptimal performance can be often explained by the interaction of the cognitive algorithms, information samples, and the specific properties of the new environment so that the obtained samples of the environment may provide a biased representational input to the cognitive algorithms. The current approach is applied to analyze behavior in two information-seeking tasks. It is shown that suboptimal performance is often an emergent property of the dynamic interactions between cognition, information samples, and the characteristics of the environment.

Information need is a foundational concept related to both information seeking and human information interaction. Robert S. Taylor, a scholar of human information behavior, made significant contributions to research on information need. This chapter discusses the definitional and operational challenges associated with the study of information need. It also examines the decision ladder, a model of the decision making process introduced by Jens Rasmussen (1986) and his colleagues as part of a conceptual framework which they developed for cognitive work analysis. The decision ladder describes decision making as a process involving three phases: situation analysis, evaluation, and planning.

This chapter examines how people behave as they seek and use information in different contexts. It discusses three influential models developed by Kuhlthau, Dervin, and Wilson, highlighting their roots in epistemology. Carol Kuhlthau’s information search process model analyzes information seeking as a process of knowledge construction that progresses through different stages, each characterized by different cognitive, affective, and behavioral dimensions. Brenda Dervin investigates information seeking through the metaphor of sensemaking. Her metaphor depicts a person traveling through time and space encountering a gap where the sense has run out, thus requiring the person to seek information in order to continue the journey. Tom Wilson (1997) proposes a general model of human information behavior that encompasses information needs, information seeking, and information processing and use.

This chapter reflects on implications of previous chapters and identifies key challenges. Public health professionals may fail to recognize their own epistemology, i.e., their assumptions that scientific methods and data are appropriate types of evidence is not shared by all. For many people, personal experiences or ideology can override scientific consensus. Health communication continues to be both art and science with no “best way” of communicating. Many types of public health data are susceptible to misuse and misinterpretation, hence the importance of ethics and integrity. The growing availability of data on many subjects increases opportunities to selective choose or locate findings conforming to existing beliefs and biases. As long mathematical literacy and numeracy levels remain low, lay audiences will continue to rely on communication intermediaries whose motives must be scrutinized. More research is needed on, among other things, how the type and format of data influence information seeking strategies by lay audiences.

Human information behavior emerged as a scholarly area in the early 1960s and has since undergone several transformations, particularly the transition from the first research generation to the second. Projects in the first generation often concentrated on a defined group of users searching a context-specific system, whereas many projects in the second generation focused on patterns of user behavior. The two generations can be differentiated based on the level of generalizability to which the field aspired and the nature of the expected results. Research initially focused on scientists and engineers and their information-seeking behavior, followed by information needs and uses of the public and catalog use studies. The shift from the first to the second generation of HIB is widely considered a shift from a system-centered to a user-centered view.

This chapter examines the nature of and the relationship between organizational knowledge, knowledge acquisition, organizational learning, and organizational information behavior. It suggests that organizations behave as information-seeking and belief-forming social systems simultaneously, so that information is constructed through epistemic practices as much as beliefs are the outcomes of information seeking and use. The chapter makes the case for a normative view of organizational knowledge and organizational knowing that sets out the epistemic norms an organization needs to pursue in order to acquire valid knowledge and true belief that is valuable to the organization. Chapter 1 also describes the objective and scope of the book and provides overviews of the book’s chapters.

Models are used to guide the design of all artifacts, whether a chair, a bridge, an airplane, or an information system. The models can be presented in various forms, from pictures to blueprints and narrative descriptions. Aside from their form of presentation, models can be classified by other categories that can help identify models that are most beneficial for design. This chapter examines models that contribute to design, including descriptive models, normative models, and formative models. Descriptive models are very useful in research on information-seeking behavior (ISB), normative models are ideal for informing design because they prescribe the “right” behavior, and formative models represent the possible ways in which ISB could take place and thus offer the foresight necessary for future design. The chapter also considers constraints-based formative models.

This chapter brings together the epistemic and information norms and practices identified earlier in the book to describe an inquiring organization. An inquiring organization is one that is motivated to acquire knowledge, where this motivation for knowledge includes not only the pursuit of truth but also understanding, creativity, and curiosity. Moreover, an inquiring organization has developed epistemic norms and practices of information seeking and knowledge acquisition that are truth-conducive, granting it reliable success in acquiring knowledge and true belief that are advantageous to the organization. The idea of “inquiring” here embraces the tenets of pragmatist inquiry discussed in Chapter 3. Thus, knowledge is an ongoing process of inquiry in which knowledge is provisional and is always being improved upon, beliefs should be linked to experience, and the seeking of knowledge is an inclusive, collective endeavor.

Defined as the invariance of system structure or function following a nontrivial perturbation to one or more important system components, robustness is a characteristic property of all adaptive systems. This chapter reviews the theory of robustness in biology, the design of experiments used to assay robustness (including the functional behavior or outputs of a system), and the adaptive response of those parts or components which are compromised by a perturbation. Emphasis is given to a rigorous logic of measurement that carefully factors apart the many causal contributions to robust function. Insights from the study of robustness in biology are applied to the social and decision-making domains, and modifications of experimental design and theory are proposed to account for challenges unique to human agents.

This chapter provides a tutorial on how economics can be used to model the interaction between users and systems. Economic theory provides an intuitive and natural way to model Human-Computer Interaction which enables the prediction and explanation of user behaviour. A central tenet of the approach is the utility maximisation paradigm where it is assumed that users seek to maximise their profit/benefit subject to budget and other constraints when interacting with a system. By using such models it is possible to reason about user behaviour and make predictions about how changes to the interface or the users interactions will affect performance and behaviour. In this chapter, we describe and develop several economic models relating to how users search for information. While the examples are specific to Information Seeking and Retrieval, the techniques employed can be applied more generally to other human-computer interaction scenarios. Therefore, the goal of this chapter is to provide an introduction and overview of how to build economic models of human-computer interaction that generate testable hypotheses regarding user behaviour which can be used to guide design and inform experimentation.