Developing Cognitive Systems to Support Team Cognition

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Steve Fiore from the University of Central Florida presented “Developing Cognitive Systems to Support Team Cognition” as part of the Cognitive Systems Institute Speaker Series

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Developing Cognitive Systems to Support Team Cognition

  1. 1. Stephen M. Fiore, Ph.D. University of Central Florida Cognitive Sciences, Department of Philosophy and Institute for Simulation &Training Fiore, S. M. (2017). Developing Cognitive Systems to SupportTeam Cognition. Invited (Virtual) Presentation to the IBM Cognitive Systems Institute Group Speaker Series. February 9th. This work by Stephen M. Fiore, PhD is licensed under a Creative Commons Attribution-NonCommercial- NoDerivs 3.0 Unported License 2012. Not for commercial use. Approved for redistribution. Attribution required.
  2. 2. ¡ Part 1. OfTeams andTeam Cognition ¡ Part 2. AugmentingTeam Cognition through Cognitive Computing ¡ Part 3. ConcludingThoughts (for Science and for Society)
  3. 3. n Teamwork andTeam Cognition (Salas & Fiore, 2004; Salas, Fiore, & Letsky, 2012) n Team Cognition is the cognitive processes arising during this complex and dynamic interaction are the focus of team cognition research n Overarching Epistemological Issue for Scientific Collaboration n How does the manifestation of cognition in teams eventually result in a coordinated scientific problem solving entity? n How can technology be leveraged to augment individual and team cognition in service of collaboration Salas, E. & Fiore, S. M. (Editors). (2004). Team Cognition: Understanding the factors that drive process and performance. Washington, DC: American Psychological Association. Salas, E., Fiore, S. M., & Letsky, M. (Editors). (2012). Theories ofTeam Cognition: Cross-Disciplinary Perspectives. NewYork & London: Routledge.
  4. 4. Macrocognition inTeams ¡ Interdisciplinary Integration Drawing from Multiple Fields •Situated Cognition •From Education Research •Distributed Cognition •From Cognitive Science •Communication Processes •From GCT Research •Group Cognition •From CSCW Research
  5. 5. § Cognitive Engineering (Fiore, 2012) § Design of human-technology systems § Examine phenomena emerging at intersection of humans and technology ¡ Macrocognition (Hollnagel, 2002). 1. Across natural and artificial cognitive systems, the process and product of cognition will be distributed. 2. Cognition is not self contained and finite, but a continuance of activity. 3. Cognition is contextually embedded within a social environment. 4. Cognitive activity is not stagnant, but dynamic. 5. Artifacts aid in nearly every cognitive action. ¡ Now Macrocognition inTeams Fiore, S. M. (2012). Cognition and technology: Interdisciplinarity and the impact of cognitive engineering research on organizational productivity. In S. Koslowski (Ed.) Oxford Handbook of Industrial and Organizational Psychology (pp. 1306-1322). Oxford University Press. Hollnagel, E., (2002). Cognition as control: A pragmatic approach to the modeling of joint cognitive systems. Theoretical Issues in Ergonomic Science, 2(3), 309-315.
  6. 6. ¡ Conceptual Representation of Macrocognition inTeams Fiore, S. M., Rosen, M., Salas, E., Burke, S., & Jentsch, F. (2008). Processes in ComplexTeam Problem Solving: Parsing and Defining the Theoretical Problem Space. In M. Letsky, N.Warner, S. M. Fiore, & C. Smith (Eds.). Macrocognition inTeams:Theories and Methodologies. London: Ashgate Publishers. D to I to K § Our theoretical goal is to understand how teams build knowledge in service of problem solving § Illustrates a four person team interacting to build knowledge and solve problem § Represents the parallel, interdependent, and iterative nature of nested processes unfolding in the context of collaboration.
  7. 7. ¡ Next, meta-model integrates three theoretical elements § Multi-level in that it encompasses individual and team level factors § Addresses internalized and externalized cognitive functions § Incorporates temporal characteristics to examine problem solving phases through which group moves D to I to K Fiore, S. M., Rosen, M. A., Smith-Jentsch, K. A., Salas, E., Letsky, M. &Warner, N. (2010).Toward an Understanding of Macrocognition inTeams: Predicting Processes in Complex Collaborative Contexts. Human Factors, 52, 2, 203-224. Fiore, S. M., Smith-Jentsch, K. A., Salas, E.,Warner, N., & Letsky, M. (2010b).Toward an understanding of macrocognition in teams: Developing and defining complex collaborative processes and products. Theoretical Issues in Ergonomic Science, 11(4), 250-271.
  8. 8. ¡ Internalized team knowledge § Refers to the collective knowledge held by team members § This is the unique expertise of the team (shared and complementary). ¡ Individual knowledge building § Actions taken by individuals in order to build their own knowledge. § Inside the head (e.g., reading, mentally visualizing objects) or overt actions (e.g., accessing a something from screen). § This can include processes ranging from literature review to data collection and analyses ¡ Team knowledge building § Actions taken by teammates to disseminate information and to transform that information into actionable knowledge for team members. § This can range from collaborative data collection to deliberation and discussion on theory and methods to report writing. Foundation for Understanding Scientific Problem Solving
  9. 9. ¡ Externalized team knowledge § Refers to facts, concepts and artifacts made explicit and concrete by team. § This can range from analytical output to graphs/charts, and to manuscripts. ¡ Team problem solving outcomes § Form and quality of team’s solutions in relation to objectives ALL Entry Points for Cognitive Computing ¡ Defines dimensions of MITM ¡ Describes potential targets for augmenting processes associated with MITM ¡ Team Cognitive Computing? Fiore, S. M., Rosen, M. A., Smith-Jentsch, K. A., Salas, E., Letsky, M. &Warner, N. (2010).Toward an Understanding of Macrocognition inTeams: Predicting Processes in Complex Collaborative Contexts. Human Factors, 52, 2, 203-224. Foundation for Understanding Scientific Problem Solving
  10. 10. ¡ Part 1. OfTeams andTeam Cognition ¡ Part 2. AugmentingTeam Cognition through Cognitive Computing ¡ Part 3. ConcludingThoughts (for Science and for Society)
  11. 11. ¡ The Way Forward for AugmentingTeam Cognition § Cognitive Computing – How can we use advances in computational intelligence to support problem solving? § Integrate cognitive computing with theories of team cognition ¡ “Technology asTeammate” and the future of complex problem solving (Fiore & Wiltshire, 2016) § Supports team cognitive processes required for 21st century challenges § Extends resources available within the human and across the net § Allows teams to focus on more cognitively complex problem elements (i.e., aspects of problems computers not yet able to manage) Fiore, S.M. &Wiltshire,T.J. (2016).Technology asTeammate: Examining the Role of External Cognition in Support ofTeam Cognitive Processes. Frontiers in Psychology: Cognitive Science. 7:1531. doi: 10.3389/fpsyg.2016.01531.
  12. 12. MITM: Internalized Knowledge and Individual Knowledge Building ¡ MACHINE READING - Overcomes challenges of literature-based compilations of data and information from incomplete and difficult to access databases § Uses machine reading to automatically locate and extract data from heterogeneous text, tables, and figures in publications. § Accommodates data types, such as morphological data in biological illustrations and associated textual descriptions. ¡ Next Steps…? § A way forward for Cognitive Computing to develop “internalized knowledge” and to augment “individual knowledge building” (e.g., data integration and synthesis) Peters, S.E., Zhang C., Livny, M., Ré, C. (2014) A Machine Reading System for Assembling Synthetic Paleontological Databases. PLoS ONE 9(12): e113523.
  13. 13. MITM: Internalized Knowledge and Individual Knowledge Building ¡ NATURAL LANGUAGE PROCESSING – Erasmus System – Developed to overcome challenges of interdisciplinary work and lack of expertise to distill opaque literatures § Relies on IBM’s Cognitive Computing and AlchemyAPI § Helps researchers quickly visualize problem and solution spaces in technical domains § Locates relevant texts and extracts major concepts § Interface uses visual map of concepts based upon relevance § Allows for expanding scope of exploration to pursue intriguing tangents ¡ Next Steps…? § A way forward for Cognitive Computing to develop “internalized knowledge” and to augment “individual knowledge building” (e.g., information gathering and synthesis) Goel, A., Anderson,T., Belknap, J., Creeden, B., Hancock,W., Kumble, M., ... &Wiltgen, B. (2016). Using Cognitive Computing for Constructing Cognitive Assistants. Advances in Cognitive Systems, 4. http://www.cogsys.org/papers/ACS2016/Papers/Goel_et.al-ACS-2016.pdf .
  14. 14. MITM: Externalized Knowledge andTeam Knowledge Building – NetDraw (Balakrishnan, Fussell, & Kiesler, 2008) § Uses information visualization to support collaborative problem solving. § Provides shared access to data to help overcome decision biases § Helps problem solvers ‘connect the dots’ in disparate data § Next Steps…? § A way forward for Cognitive Computing to help “externalize knowledge” and increase “team knowledge building” processes such as information sharing among members with unique (complementary) knowledge Balakrishnan, A. D., Fussell, S. R., & Kiesler, S. (2008). Do visualization improve synchronous remote collaboration? In Proceedings of ACM CHI: Conference on Human Factors in Computing Systems, 1227-1236.
  15. 15. MITM: Externalized Knowledge andTeam Knowledge Building VISUALIZATIONTools for Collaborative Sensemaking ¡ Allow learners to construct representations (Goyal & Fussell, 2016) § Next Steps…? § A way forward for Cognitive Computing to help build “externalized knowledge” (e.g., knowledge object development such as diagrams, maps) to illustrate relations between data and evidence Goyal, N., & Fussell, S. R. (2016, February). Effects of SensemakingTranslucence on Distributed Collaborative Analysis. Proceedings of the 19th ACM Conference on Computer-Supported CooperativeWork & Social Computing (pp. 288-302). ACM.
  16. 16. MITM: Externalized Knowledge,Team Knowledge Building, and Problem Solving Outcomes ARGUMENTATIONTools for Solution Evaluation ¡ Orient team members with respect to subject matter and structure interaction to improve coherence (Lu, Lajoie, &Wiseman, 2010) § Next Steps…? § A way forward for Cognitive Computing to help “team knowledge building” processes such as argumentation and solution evaluation Lu, J., Lajoie, S. P., &Wiseman, J. (2010). Scaffolding problem-based learning with CSCL tools. Computer-Supported Collaborative Learning, 5, 283-298.
  17. 17. ¡ Part 1. OfTeams andTeam Cognition ¡ Part 2. AugmentingTeam Cognition through Cognitive Computing ¡ Part 3. ConcludingThoughts (for Science and for Society)
  18. 18. Cognitive Computing and Intelligent Decision Aiding ¡ Cognitive Computing Systems for MITM § Research needs to understand how to extend and augment individual and team macrocognitive processes. ¡ A Way Forward for Team Cognitive Computing (cf. Fiore & Wiltshire, 2016) § How can new computing models that make use of data coming from video, images, symbols and natural language, become part of the team? § How can systems trained using artificial intelligence (AI) and machine learning algorithms to sense, predict, and infer, contribute to collaborative problem solving processes? http://www.research.ibm.com/cognitive-computing
  19. 19. Team Cognition and Cognitive Computing ¡ Requires a “research roadmap” for augmenting human cognition in service of complex problem solving § We must encourage research collaborations between all areas of scholarship and stakeholders to pursue understanding that serves the solving of complex problems. ¡ Philosopher Andy Clark describes collaboration between humans and technology as a continuous reciprocal causation. § “Much of what matters about human intelligence is hidden not in the brain, nor in the technology, but in the complex and iterated interactions and collaborations between the two. …The study of these interaction spaces is not easy, and depends both on new multidisciplinary alliances and new forms of modeling and analysis.The pay-off, however, could be spectacular: nothing less than a new kind of cognitive collaboration involving neuroscience, physiology, and social, cultural, and technological studies” (Clark, 2001, p. 154). Clark, A. (2001). Mindware. Oxford, England: Oxford University Press.
  20. 20. Collaborate to Solve the Big Problems ¡ “Forget about finding your passion. Instead, focus on finding big problems. Putting problems at the center of our decision-making changes everything. It’s not about the self anymore. It’s about what you can do and how you can be a valuable contributor. People working on the biggest problems are compensated in the biggest ways. I don’t mean this in a strict financial sense, but in a deeply human sense. For one, it shifts your attention from you to others and the wider world.You stop dwelling.You become less self-absorbed. Ironically, we become happier if we worry less about what makes us happy.” Segovia, O. (2012). To Find Happiness, Forget About Passion. Harvard Business Review. Retrieved from http://blogs.hbr.org/2012/01/to-find-happiness-forget-about/.
  21. 21. Stephen M. Fiore, Ph.D. University of Central Florida Cognitive Sciences, Department of Philosophy and Institute for Simulation &Training sfiore@ist.ucf.edu

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