Chemnitz dec2014

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  • Visual Analytics as a Cognitive Science

    Brian Fisher

    SFU School of Interactive Arts & Technology and Program in Cognitive Science

    UBC Media & Graphics Interdisciplinary Centre (MAGIC)

  • My Background UG Biology, Medical Biophysics tech at CWRU Med Scientific Programmer, Varian Ph.D Experimental Psychology, UCSC UWO & Rutgers Centres for Cognitive Science Human-Information Interaction AKA Cyberpsychology

    Institute for Robotics and Intelligent Systems Networks of Centres of Excellence a Cognitive Basis for the Design of Intelligent User Interfaces to Complex Systems (SFU-UWO)

    1999- Associate Director, UBC Media And Graphics Interdisciplinary Centre, Computer Science, Psychology, Institute for Computing, Information and Cognitive Systems

    2004- SFU School of Interactive Arts and Technology and Program in Cognitive Science

  • My double life

    Ph.D in Experimental Psych

    Postdoc w Cognitive Science society founder &

    president

    Psychonomics Fellow

    VIS-related symposia at Cogsci, & APS, papers on

    cogsci of interaction

    Fuzzy-logic/Bayes models

    Postdoc funded by Inst. for

    Robotics and AI

    VAST SC, VEC, VACCINE

    Led Dagstuhl Interaction with

    Information for Visual

    Reasoning, Cogsci-based

    papers at VIS, CHI, BELIV.

    Participants

  • Big Data: Volume, Velocity & Variety

    In 2011, data expected to be about 1.8 zettabytes (1021).

    In 2013, Internet traffic to reach 667 exabytes (1018)/yr.

    Comparison: US Library of Congress is ~10 petabytes (1015).

    By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. (McKinsey Global Institute 2011)

  • Challenges for computational approaches

    3 Vs challenge Relevance, validity, reliability of data uncertain Insufficient time to reach solution

    Model challenges Multiple models to chose from Assumptions may or may not hold

    Wicked problems challenge (Rittel) Lack criteria to evaluate solution Each problem is unique (no population) Problem is not understood until solution is found

    The information needed to understand the problem depends upon ones idea for solving it. -- Rittel & Webber 1973

  • The ultimate challengeIn business, government & the professions specific people are responsible for the decisions that are made and how they are executed. These people are derelict in their duties if they only accept what the model tells them. Either we re-engineer society to accept a computer model as the ultimate authority or we find a way that human decision makers can exercise due diligence for computational as well as informational aspects of the problem.

  • An prehistory of visual analytics

  • Visualization history

    NSF meeting: Visualization in Scientific Computing Nov. 1987 Computer

    Graphics First IEEE Visualization (now

    SciVis) conference in 1990

    The purpose of [scientific] computing is insight, not numbers. Richard Hamming

    Visualization is a method of computing. Authors of report

  • Information visualization 1990 Conference

    on diagrammatic reasoning

    1995 InfoVis Conference Information

    Visualization: Wings for the Mind Keynote by Stuart Card

  • Stuart Cards view Increase the memory & processing

    resources available to users Reduce the search for information by using

    visual representations to enhance the detection of patterns

    Engage perceptual inference operations Use perceptual attention mechanisms for

    monitoring Support manipulation of information

  • Infographics represent information (data, knowledge, opinions, etc.) in context to support understanding

    Visual thinking: Infographics

  • Visualization literacy

    Build a language of collections of images that support thinking

    Diagrammatic reasoning science of how we understand complex diagrams

  • iThinking as smart seeing and Projecting

    Actively looking at external representations and projecting onto them makes us more powerful thinkers than thinking in our heads alone.

    DDavid Kirsh example

  • Design Approach: Bertin

    6

    Bertin Semiologie Graphique (1967)

    Cartographer, built description of how data should be represented visually

    Jock Mackinlay, Stanford Ph.D. dissertation

    Tableau software, used for our work with Boeing etc.

  • Design Approach: Edward Tufte

  • Big Data Example (Amaral)Metabolism

    Experts are overwhelmed by sheer volume and complexity of data

  • Cartographic Representation (Amaral)

    Guimera & Amaral, Nature 433, 895 (2005)

  • http://www.visual-literacy.org/periodic_table/periodic_table.html

  • Role of psychology in VIS

    Design based on theories (but no effective eval of those theories)

    Adaptation of methods from cogsci (but original methods not well understood)

    Rarely, ongoing collaboration with cognitive scientists

  • On the Death of Visualization (Lorensen 2004)

    Can It Survive Without Customers?

    Visualization, alone, is not a solution. Visualization is a critical part of many applications. Visualization, the Community, lacks application

    domain knowledge.

    Visualization has become a commodity. Visualization is not having an impact in applications.

  • Visual analytics origins

  • Battelle PNNL R&D Agenda Panel

    In US, Panel meeting in 2004 Brown, GMU, Georgia Tech, OSU, Penn State, Purdue,

    SFU , Stanford, UC, UI, UM, UNC, UU, WPI Boeing, Microsoft, PARC, Sandia Labs CIA, DHS, FBI, NIST, NSA, unspecified

    Gave rise to Industry Consortium DHS Centre of Excellence

    Ccicada (Rutgers DIMACS) VACCINE (Purdue et al)

    In Europe, EU Vismaster Coordination action DFG Scalable Visual Analytics Priority program

  • Visual analytics

    This science must be built on integrated perceptual and cognitive theories that embrace the dynamic interaction between cognition, perception, and action. It must provide insight on fundamental cognitive concepts such as attention and memory. It must build basic knowledge about the psychological foundations of concepts such as meaning, flow, confidence, and abstraction. Illuminating the Path (IEEE Press)

    The science of analytical reasoning facilitated by interactive visual interfaces

  • How are VA Information systems different?

    Development based on understanding of expert cognition in situ Informed by current cognitive & social science Engagement with community of experts Emergent cognitive science of expert reasoning

    Clear support for analytical processes-- reasoning, collaboration & interaction Graphical analog for analytic processes Support Human-information discourse Integrated across roles in the community

  • Visual analytics

    Pg. 4 in Thomas, J., Cook, K., Institute of Electrical and Electronics Engineers, Dept of Homeland Security, & United States. (2008). Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Press. Retrieved from http://www.osti.gov/energycitations/product.biblio.jsp?osti_id=912515

  • Cognitive & Perceptual Sciences

    Visual Information Systems

    Graphic & Interaction Design

    Mathematical & Statistical Methods

    Effective Situated

    R&D

  • http://www.vacommunity.org/HomePage

  • Visual analytics as a translational cognitive science

  • How to bridge informatics & psychology?

    VIS offers: Implementations Funding Awesome

    Research Questions

    Psych offers Methodology Theory Phenomena Cheap talent

    Challenges: defining boundary objects, culture clash, publication venues, academic jobs for Cogs grads

  • My approach: start in the middle! Develop bridging

    Cyberpsychology theory & methods

    Hope is that They are building

    from each shore Somehow we will be

    able to align things

    http://www.magic.ubc.cahttp://www.icics.ubc.ca

    http://interaction-science.iat.sfu.ca

  • Bridging ideas from D-Cog

    Visualization literacy is a form of Smart seeing and projecting w external representation

    We propose 2 additional D-cog perspectives: Agent-machine coupling: coordination of thought and

    action in dynamic artificial environments Cognitive architecture modeling and characterizing

    personal equation of individual differences (e.g. perceptual expertise)

    Socially-distributed cognition Grounded theory in Clarks Joint Activity Theory

    framework analysis of pair/group collaborative decision making

  • Agent-machine coupling in air traffic control

    Cognitive architecture from psychology Extend to expert human performance

    Cognitive expertise Visual expertise Visuomotor expertise Multimodality & modularity

    Test human capabilities in dynamic display environments

  • Controller/display systems in air traffic control

    NextGen ATC fishtank projection

    Change camera position for better view

    How will global motion affect tracking?

    Liu, G. Austen, E. L., Booth, K.S. Fisher, B., Argue, R. Rempel, M.I., & Enns, J. (2005) Multiple Object Tracking Is Based On Scene, Not Retinal, Coordinates. Journal of Experimental Psychology: Human Perception