MET:Distributed Cognition

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Edwin Hutchins on board an airline flight deck.

Distributed cognition was developed by Edwin Hutchins and his colleagues at the University of California in the 1980s as a new paradigm for rethinking all domains of cognitive phenomena (Rogers, 1997). It is a learning theory or framework, not a methodology. The roots of distributed cognition stem from the work of Vygotsky’s Mind in Society (1978) and Minsky’s Society in Mind (1985) where cognition was thought of as a system with many different actors (Hutchins, 2000). Distributed cognition distinguishes itself from other approaches by viewing human activity “in the wild” (Hutchins, 1995). According to Hutchins, “…distributed cognition looks for a broader class of cognitive events and does not expect all such events to be encompassed by the skin or skull of an individual” (2000, p.1). Distributed cognition broadens the spectrum beyond the boundaries of the individual by including interactions between people and with resources and materials in the environment. The framework also recognizes that cognition cannot be separated from the study of culture because agents live in complex cultural environments (Hollan et al., 2000).

Distributed cognition recognizes the following key aspects of cognition (Hutchins, 2000):

Cognitive processes may be distributed across the members of a social group.
Cognitive processes may be distributed in the sense that operation of the cognitive system involved coordination between internal and external (material or environmental) structure.
Cognitive processes may be distributed through time in such a way that the products of earlier events can transform the nature of later events.

It is important to note that distributed cognition makes no distinction between people and artifacts. Both are treated as ‘media’ that hold and transform representations. Distributed cognition enhances the understanding of interactions between humans, machines (or technologies), and environments. With the underpinning application of technology, distributed cognition shifts the unit of analysis to the system of “person-in-interaction-with-technology” (Hutchins, 1995, p. 155).

Applications

The very notion of distributed cognition arose from the ethnographical work that Hutchins did in the 1980s on ship navigation while he was aboard US Navy Ships (Hollan et al., 2000). He observed that the outcomes that mattered to the ship were not determined by the cognitive properties of any single navigator, but instead were the product of the interactions of several navigators with each other and with a complex suite of tools. It showed how cognitive processes required to manipulate a tool are not the same as the computations performed by manipulating the tool. It also showed how learning happened not only at the individual level, but also at the organizational level. Hutchins performed similar studies in airline cockpit automation and air traffic control.

Hutchins work on distributed cognition has been greatly influential in understanding how humans work with technology. Specifically, it has been most widely applied in the field of human-computer interaction (HCI) and distance learning, with particular emphasis on Computer-Supported Collaborative Learning (CSCL). Because of its inclusion of the role that external materials play in cognition, distributed cognition has been utilized in understanding socio-technological systems.

Examples of its applications include collaborative tagging on the internet now offered on websites like WordPress and Youtube. Tagging allows users to apply a key work to a particular form of media (text, photos, videos, audio, etc.), and allow users to search by these tags to find specific information. Since many tags can be added by many users, media is easily searched and navigated. Distributed cognition has also been leveraged as the key framework of cognitive ethnography, also developed by Hutchins. In addition to understanding ship navigation and airline cockpits, it has also been used to understand the efficiencies or inefficiencies of various workspaces, and to understand why accidents involving humans and technologies occur.

Systems of cognition

File:ClassicalcogVSdistributedcog.jpg

Classical cognition vs. distributed cognition.

Distributed cognition looks for cognitive processes wherever they may occur on the basis of “functional relationships of elements that participate together in the process” (Hollan et al., 2000). According to Preece, Rogers, and Sharp (2002), four elements that are common to all cognitive systems are: sensors that take input into the system, action generators that produce the output, information processing units that transform representations to convert input to output, and memory to store representations. These cognitive systems can evidently be found in socio-technical systems where individuals and technology collaborate to solve problems: the inputs range from the problems themselves to their associated resources; action generators and information processing units include people and systems that transform the input to produce artifacts and ideas; and memory lies within the artifacts in which representations are stored. Hence, distributed cognition is theoretically present in socio-technical systems where groups of people are communicating with each other while working on or with artifacts. The entire socio-technical system can be thought of as a single cognitive entity or system since it is involved in the “creation, transformation, and propagation of representational states” (p. 49, Hutchins, 1995).

Within a socio-technical system, distributed cognition can occur at the individual and group level. Individual distributed cognition occurs when a person is working with external devices or artifacts and using them to support their own work (Perry, 1999). External cognition (Preece et al., 2002) takes place when external devices are used to offload some of the mental activity by sharing the cognitive load with the individual. As Downey (1998) suggests, these tools become an extension of our mind. At the group level, cognition is socially distributed when members use technology or each other as a resource to help them think. The environment becomes a shared external space in which cognitive processes occur. Technology can either facilitate the work or be used as a medium for the work (Carstensen & Schmidt, 1999).

Influence on understanding cognition and technology

As Latour (1996) has noted, Hutchins’ method of distributed cognition offers the most empirically sophisticated account of both learning and technology in action. Traditionally, the majority of work around cognitive science and neurocognition has focused on individual processes (Bransford et al., 2000; DeMiranda, 2004; DeMiranda & Folkestad, 2000). This is problematic because it has since been theorized that cognitive processes occur not only at the individual level, but also amongst social groups, in the environment, and within materials and artifacts (Davis and Sumara, 2002; Hutchins, 1995, 2000, 2005; Hollan et al., 2000; Papert, 1991; Pea and Kurland, 1987). According to Clark (1997, 2003), the further one delves into the internal workings of the head, the more one is focused on the individual as the unit of analysis, and the less one is able to account for the environment. Only a few studies to date utilize theoretical frameworks sophisticated enough to account for technology and cognition (Petrina et al., 2008). The bulk of research relies on types of constructivism, based on the premise that learners are active in the construction of knowledge (Barnes, 2002; Mauch, 2001; Nourbakhsh et al., 2005; Robinson, 2005; Rogers & Portsmore, 2004). One critique of constructivism is the tendency to dismiss technology as mere tools for learning—missing the point that technology is central to cognition rather than merely instrumental to it (Petrina et al., 2008; Schaffer & Clinton, 2006). Davis and Sumara (2002) also note that constructivists cannot adequately account for social interaction and cultural dynamics. In fact, this is one of the reasons that Papert and MIT colleagues shifted from constructivism to constructionism (Beer et al., 1999; Harel & Papert, 1991; Papert, 1992, 1993).

The breakthrough work of Vygotsky, Luria, and Leont’ev recognized technology as being more than material for augmented learning — they observed that activity or learning was nearly always artefact-mediated and that it is always situated within an activity system. Activity theory allows us to rethink the learning-technology relationship and shift theories from mediated to cyborgenic learning (Petrina et al., 2008). In his book The Machine in Me, Downey (1998) demonstrated that learners are heavily integrated into Computer-Aided Design (CAD) systems. He notes that cognition within these systems cannot be understood without fully accounting for technology. Many theories that extend the mind and expand cognition beyond the individual, however, treat technologies as components or processes nested within, or incidental to other systems (Brennan et al., 2007; Petrina et al., 2008; Winograd and Flores, 1986). Distributed cognition plays a special role in understanding the interactions between people and technology. In particular, it recognizes that technology plays a central rather than a peripheral role in understanding cognition – it becomes a significant part of the cognitive system itself. Distributed cognition offers a theoretical framework that shifts the unit-of-analysis to person-in-interaction-with-technology (Hutchins, 1995), thereby recognizing technology as a central component to cognition.

Stop Motion Animation

References

Barnes, D. J. (2002, February-March). Teaching introductory Java through Lego Mindstorms models. Paper presented at SIGCSE 2002 Technical Symposium on Computer Science Education, Covington, Kentucky.
Beer, R. D., Chiel, H. J., & Drushel, R. F. (1999). Using robotics to teach science and engineering. Communications of the ACM, 42(6), 85-92.
Bransford J. D., Brown A. L., Cocking R. (Eds.) (2000). How people learn: Brain, mind, experience and school. Washington DC: National Research Council.
Brennan, K., Feng, F., Hall, L. & Petrina, S. (2007). On the complexity of technology and the technology of complexity. In B. Davis & Sumara (Eds.), Proceedings of the fourth complexity science and educational research conference, 18-20 February, Vancouver, BC.
Carstensen, P. & Schmidt, K. (1999). Computer Supported Cooperative Work: New Challenges to Systems Design. In Kenji Etoh (Ed.), Handbook of Human Factors, Tokyo: Asakura Publishing.
Clark, A. (1997). Being there: Putting brain, body and world together again. Cambridge, MA: MIT Press.
Clark, A. (2003). Natural born cyborgs: Minds, technologies and the future of human intelligence. Oxford: Oxford University Press.
Davis, B., & Sumara, D. (2006). Complexity and education: Inquiries into learning, teaching and research. Mahwah, NJ: Lawrence Erlbaum.
DeMiranda, M. A., & Folkestad, J. E. (2000). Linking cognitive science theory and technology education practice: A powerful connection not fully realized. Journal of Industrial Teacher Education, 37(4), 5–23.
DeMiranda, M. (2004). The grounding of a discipline: Cognition and instruction in technology education. International Journal of Technology and Design Education, 14, 61–77.
Downey, G.L. (1998). The machine in me: An anthropologist sits among computer engineer. New York: Routledge.
Harel, I., & Papert, S. (Eds.) (1991). Constructionism. Norwood, NJ: Ablex.
Hollan J. D., Hutchins E., & Kirsh D (2000). Distributed cognition: A new foundation for human-computer interaction research. ACM To CHI, 7(2), 174-196.
Hutchins, E. (1995). Cognition in the Wild. Cambridge, MA: MIT Press.
Hutchins, E. (2000). Distributed Cognition. Retrieved September 13th, 2009 from http://files.meetup.com/410989/DistributedCognition.pdf.
Hutchins, E. (2005). Material anchors for conceptual blends. Journal of Pragmatics, 37, 1555-1577.
Latour, B. (1996). Cogito Ergo Sumus! Or psychology swept inside out by the fresh air of the upper deck. Mind, Culture, and Activity, 3(1), 54-63.
Mauch, E. (2001). Using technological innovation to improve the problem solving skills of middle participants. The Clearing House, 75(4), 211-213.
Minsky, M. (1988). Society of mind. USA: Simon & Schuster.
Nourbakhsh, I., Crowley, K., Bhave, A., Hamner, E., Hsium, T., Perez-Bergquist, A., Richards, S., & Wilkinson, K. (2005). The robotic autonomy mobile robots course: Robot design, curriculum design, and educational assessment. Autonomous Robots, 18(1), 103-127.
Papert, S. (1992). The Children’s machine. New York: Basic Books.
Papert, S. (1993). Mindstorms: Children, computers and powerful ideas (2nd edition). New York: Basic Books.
Pea, R., & Kurland, D.M. (1987). On the cognitive effects of learning computer programming. In R. Pea, K. Sheingold (Eds.), Mirror of minds: Patterns of experience in educational computing. (pp. 147-177). Norwood, NJ: Ablex.
Perry, M. (1999). The application of individually and socially distributed cognition in workplace studies: two peas in a pod? Proceedings of European Conference on Cognitive Science, Italy, 87-92.
Petrina, S, Feng, F., & Kim, J. (2008). Researching cognition and technology: How we learn across the lifespan. International Journal of Design and Technology Education, 18(4), 375–396.
Preece, J., Sharp, H., & Rogers, Y. (2002). Interaction Design: Beyond Human-Computer Interaction. New Jersey, NY: John Wiley and Sons.
Robinson, M. (2005). Robotics-driven activities: Can they improve middle school science learning? Bulletin of Science, Technology & Society, 25(1), 73-84.
Rogers, C., & Portsmore, M. (2004). Bringing engineering to elementary school. Journal of STEM Education, 5(3&4), 17-28.
Rogers, Y. (1997). “A Brief Introduction to Distributed Cognition,” Discussion paper, Interact Lab, School of Cognitive and Computing Sciences, University of Sussex.
Schaffer, D. W. & Clinton, K. A. (2006). Toolforthoughts: Reexamining thinking in the digital age. Mind, Culture, and Activity, 13(4): 283-300.
Vygotsky, L.S. (1978). Min in society: the development of higher psychological processes. USA: Library of congress cataloging in publication data.
Winograd, T., & Flores, C. F. (1986). Undertaking computers and cognition: A foundation for design. Norwood, NJ: Ablex.

External Links

http://hci.ucsd.edu/lab/index.shtml. The UCSD Distributed Cognition & HCI Laboratory - The laboratory at UCSD, supervised by James Hollan and Ed Hutchins, combines enthnography and experiment to improve the design of digital artifacts for real-world work environments.
http://adrenaline.ucsd.edu/iclsite/home.html. The Interactive Cognition Lab at UCSD - A laboratory at UCSD, run by David Kirsch and Aaron Cicourel, studies the fundamentals of designing interactive environments. Their research includes topics in distributed and collaborative cognition.
http://www.cogs.susx.ac.uk/interact/index.htm. The Interact Lab at Sussex - This Interact Lab is now directed by Geraldine Fitzpatrick and co-directed by founder Yvonne Rogers. The focus of research here is on developing novel user experiences by exploiting the possibilities for interaction between people, technologies, and representations.
http://tecfa.unige.ch/tecfa/research/theme2.html. TECFA Education & Technologies - TECFA at the University of Geneva pursues work on educational technology. One of their research projects focuses on distributed learning systems.
http://staff.bath.ac.uk/ensreh/Distributed_Cognition/index.html. The DME at Bath - This research group investigates the causes of failure in systems of distributed cognition.
http://howdowelearn.com - How do we learn (technology across the lifespan) UBC Research Group Website. Information on a study through UBC's Department of Curriculum and Pedagogy headed by Dr. Stephen Petrina.
http://hci.ucsd.edu/hutchins/ - Edwin Hutchins' official web page