Stephen M. Fiore's presentation discusses the integration of cognitive computing to enhance team cognition and problem-solving processes. By exploring concepts like macrocognition, the document highlights the importance of both internalized and externalized team knowledge in collaborative contexts. It emphasizes a multidisciplinary approach to understanding human-technology interactions and suggests advancements in cognitive computing as a means to support scientific collaboration.

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. ¡ Part 1. OfTeams andTeam Cognition
¡ Part 2. AugmentingTeam Cognition through Cognitive
Computing
¡ Part 3. ConcludingThoughts (for Science and for Society)

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. 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. § 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. ¡ 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. ¡ 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. ¡ 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. ¡ 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. ¡ Part 1. OfTeams andTeam Cognition
¡ Part 2. AugmentingTeam Cognition through Cognitive
Computing
¡ Part 3. ConcludingThoughts (for Science and for Society)

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. 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. 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. 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. 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. 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. ¡ Part 1. OfTeams andTeam Cognition
¡ Part 2. AugmentingTeam Cognition through Cognitive
Computing
¡ Part 3. ConcludingThoughts (for Science and for Society)

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. 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. 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. Stephen M. Fiore, Ph.D.
University of Central Florida
Cognitive Sciences, Department of Philosophy and
Institute for Simulation &Training
sfiore@ist.ucf.edu