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Information Foraging in Information Access Environments.
Abstract
lnfonnation foraging theory is an approach to the analysis of buman activities involving information access technologies. The theory derives from optimal foraging theory in biology and anthropology, which analyzes the adaptive value of food-foraging strategies. Information foraging theory analyzes trade-offs in the value of information gained against tbe costs of performing activity in human-computer interaction tasks. The theory is illustrated by application to information-seeking tasks involving a Scatter/Gather interface, which presents users with a navigable, automatically computed, overview of the contents of a document collection arranged as a cluster hierarchy.
... Prior work has focused on designing tools help with quickly moving information from the information foraging loop to the sensemaking loop (refer to Figure 2) [81,82,89,90]. For example, there are several research and industry tools to support active reading while searching using highlighting and note-taking [30,87,88], collecting information by bookmarking and clipping web content [12,49]), curating and organizing collected web content in a way that helps make sense of information [27,32,66,105], re-fnding information or resuming search sessions [39,75,104]. ...
... This might be afected by the availability heuristic, which is a mental shortcut where people often form connections, here of usefulness, between things that co-occur or seen in the same place together [28,69,99]. Previous work has explored the role of query suggestions in creating information scent (i.e. the proximal cues from which searchers perceive the value of distal information sources) [53,58,59,81]. As InterWeave suggestions present the user with gaps in their knowledge directly next to the parts of what they already know, it is creating a more contextualized trail of information which in turn helps with assessing usefulness and relevance of suggestions and information found on SERPs and websites. ...
... It consists of two interlinked interfaces: a powerful text-based search and a visual analytics exploration tool ( Figure 1). In the spirit of Pirolli and Card's (Pirolli and Card, 2005) sensemaking process, we aim to enable an improved information foraging (Pirolli and Card, 1995) process by enriching search with attribute-based exploration that visualizes the context of data sets and provides a means of semantic top-down exploration, which is a common approach for exploring unknown data or for analyzing collections (Patterson et al., 2001). ...
The ever-increasing number of biomedical data sets provides tremendous opportunities for re-use but current data repositories provide limited means of exploration apart from text-based search. Ontological metadata annotations provide context by semantically relating data sets. Visualizing this rich network of relationships can improve the explorability of large data repositories and help researchers find data sets of interest. We developed SATORI—an integrative search and visual exploration interface for the exploration of biomedical data repositories. The design is informed by a requirements analysis through a series of semi-structured interviews. We evaluated the implementation of SATORI in a field study on a real-world data collection.SATORI enables researchers to seamlessly search, browse, and semantically query data repositories via two visualizations that are highly interconnected with a powerful search interface. SATORI is an open-source web application,which is freely available at http://satori.refinery-platform.org and integrated into the Refinery Platform.
... Information foraging theory (IFT) is a theory that seeks to explain how people seek information (Pirolli and Card 1995). In IFT, developers forage for information in patches which, in this study, correspond to the resources displayed in application windows. ...
Given a task description, a developer’s job is to alter the software system in a way that accomplishes the task, usually by fixing a bug or adding a new feature. Completing these tasks typically requires developers to use multiple tools, spanning multiple applications, within their environment. In this paper, we investigate how existing desktop environments align with and facilitate developers’ needs as they tackle their tasks. We examine how developers use their tools to perform their tasks and the ways in which these tools inhibit development velocity. Through a controlled user study with 17 subjects and a field study with 10 industrial engineers, we found that developers frequently formulate specific objectives, or goals, on-demand as they encounter new information when progressing through their tasks. These goals are often not achievable directly in the environment, forcing developers to translate their task into goals and their goals into the low-level actions provided by the environment. When carrying out these low-level actions, developers routinely perform extra work such as locating and integrating resources and adapting their needs to align with the capabilities of the environment. This extra work acts as a form of friction, limiting how quickly and directly developers can complete their tasks. Much of this extra work exists due to mismatches between current tools and environments and how developers actually work in practice. This work identifies seven types of development friction and provides design recommendations that future tools and environments could use to more effectively help developers complete their tasks.
Reaching even one additional person with your research can have an impact. This chapter maps a path from reaching one new person with your science to reaching one hundred thousand people, teaching you increasingly advanced design principles at each level. At minimum, boost your impact by posting the scientific products you are already creating on public, Open Access repositories (your papers on preprint websites, your posters on Figshare.com, and your talks/lectures on YouTube). Learn how to check your views to keep yourself motivated. Building an audience is easiest with social media, but also doable by attracting people organically to your articles and videos. Boost your engagement further by creating scientific products that are high value and low effort. Inspire people to share your science by adding emotion (i.e., do not neglect affective processing). Finally, you can maximize your impact by finding and communicating what you personally find most meaningful about your research, because meaning is attractive.KeywordsImpactResearchSocial mediaScience disseminationKnowledge translationScience communication
The purpose of this article is to review where we stand with regard to modeling the kind of cognition involved in human-computer interaction. Card, Moran, and Newell's pioneering work on cognitive engineering models and explicit analyses of the knowledge people need to perform a procedure was a significant advance from the kind of modeling cognitive psychology offered at the time. Since then, coordinated bodies of research have both confirmed the basic set of parameters and advanced the number of parameters that account for the time of certain component activities. Formal modeling in grammars and production systems has provided an account for error production in some cases, as well as a basis for calculating how long a system will take to learn and how much saving there is from previous learning. Recently, we were given a new tool for modeling nonsequential component processes, adapting the 'critical path analysis' from engineering to the specification of interacting processes and their consequent durations. Though these advances have helped, there are still significant gaps in our understanding of the whole process of interacting with computers. The cumulative nature of this empirical body and its associated modeling framework has further highlighted important issues central to research in cognitive psychology.
One of Dennett's principal arguments for an instrumentalistic construal of intentional attributions (e.g., attributions of belief, etc.) is that such attributions are environment relative. I argue that one can and should adopt a realist perspective toward such attributions, but accommodate their environmental relativity by treating intentional properties as relational properties. By doing so one acquires a useful perspective on experimental cognitive psychology; in particular, one can overcome the temptation to treat ecological accounts and information processing accounts as incompatible alternatives and come to see them as mutually supportive. Treating intentional properties as relational may be counter‐intuitive, but I provide examples of how other sciences have had to treat what seem to be intrinsic properties as relational.
First, a new model of searching in online and other information systems, called 'berrypicking', is discussed. This model, it is argued, is much closer to the real behavior of information searchers than the traditional model of information retrieval is, and, consequently, will guide our thinking better in the design of effective interfaces. Second, the research literature of manual information seeking behavior is drawn on for suggestions of capabilities that users might like to have in online systems, Third, based on the new model and the research on information seeking, suggestions are made for how new search capabilities could be incorporated into the design of search interfaces. Particular attention is given to the nature and types of browsing that can be facilitated.
Introduction, 99. — I. Some general features of rational choice, 100.— II. The essential simplifications, 103. — III. Existence
and uniqueness of solutions, 111. — IV. Further comments on dynamics, 113. — V. Conclusion, 114. — Appendix, 115.
An introduction for the general audience to the theories and theorists of information-processing concepts and computer simulation, and the psychological implications of this approach. Harvard Book List (edited) 1971 #328 (PsycINFO Database Record (c) 2012 APA, all rights reserved)