rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Cognitive Biases in Visualizations
  name:
    name part: Ellis, Geoffrey.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 184 p. 31 illus., 27 illus. in color. online resource.
  abstract: This book brings together the latest research in this new and exciting area of visualization, looking at classifying and modelling cognitive biases, together with user studies which reveal their undesirable impact on human judgement, and demonstrating how visual analytic techniques can provide effective support for mitigating key biases. A comprehensive coverage of this very relevant topic is provided though this collection of extended papers from the successful DECISIVe workshop at IEEE VIS, together with an introduction to cognitive biases and an invited chapter from a leading expert in intelligence analysis. Cognitive Biases in Visualizations will be of interest to a wide audience from those studying cognitive biases to visualization designers and practitioners. It offers a choice of research frameworks, help with the design of user studies, and proposals for the effective measurement of biases. The impact of human visualization literacy, competence and human cognition on cognitive biases are also examined, as well as the notion of system-induced biases. The well referenced chapters provide an excellent starting point for gaining an awareness of the detrimental effect that some cognitive biases can have on users' decision-making. Human behavior is complex and we are only just starting to unravel the processes involved and investigate ways in which the computer can assist, however the final section supports the prospect that visual analytics, in particular, can counter some of the more common cognitive errors, which have been proven to be so costly.
  table of contents: So, What are Cognitive Biases? -- Part I - Bias Definitions, Perspectives and Modelling -- Studying Biases in Visualization Research: Framework and Methods -- Four Perspectives on Human Bias in Visual Analytics -- Bias by Default? A Means for a Priori Interface Measurement -- Part II - Cognitive Biases in Action -- Methods for Discovering Cognitive Biases in a Visual Analytics Environment -- Experts' Vs Optimality of Visualization Design- Data Visualization Literacy and Visualization Biases: Cases for Merging Parallel Threads -- The Biases of Thinking Fast and Thinking Slow -- Part III - Mitigation strategies -- Experimentally Evaluating Bias-Reducing Visual Analytics Techniques in Intelligence Analysis -- Promoting Representational Fluency for Cognitive Bias Mitigation in Information Visualization -- Designing Breadth-Oriented Data Exploration for Mitigating Cognitive Biases -- A Visualization Approach to Addressing Reviewer Bias in Holistic College Admissions -- Cognitive Biases in Visual Analytics: A Critical Reflection.
  note: edited by Geoffrey Ellis.
  subject:
    topic: User interfaces (Computer systems)
  subject:
    topic: Cognitive psychology
  subject:
    topic: Mathematics
  subject:
    topic: Visualization
  subject:
    topic: User Interfaces and Human Computer Interaction
  subject:
    topic: Cognitive Psychology
  subject:
    topic: Visualization
  classification: QA76.9.U83
  classification: QA76.9.H85
  classification: 005.437
  classification: 4.019
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319958316
  identifier: https://doi.org/10.1007/978-3-319-95831-6
  record info:
    record creation date: 180927
    record change date: 20191028141145.0
    record identifier: 978-3-319-95831-6