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Distributed cognition

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Distributed cognition is an approach to cognitive science research that was developed by cognitive anthropologist Edwin Hutchins during the 1990s.[1]

From cognitive ethnography, Hutchins argues that mental representations, which classical cognitive science held that are within the individual brain, are actually distributed in sociocultural systems that constitute the tools to think and perceive the world. Thus, a native of the Carolina Islands can perceive the sky and organize his perceptions of the constellations typical of his culture (the groupings of stars are different than in the traditional constellations of the West) and use the position of the stars in the sky as a map to orient himself in space while sailing overnight in a canoe.[1]

According to Hutchins, cognition involves not only the brain but also external artifacts, work teams made up of several people, and cultural systems for interpreting reality (mythical, scientific, or otherwise).

Distributed cognition theory is part of the interdisciplinary field of embodied cognitive science, also called embodied cognition.

Hutchins' distributed cognition theory influenced philosopher Andy Clark, who shortly after proposed his own version of the theory, calling it "extended cognition" (see, for example, the paper The Extended Mind).

Hutchins' distributed cognition theory explains mental processes by taking as the fundamental unit of analysis "a collection of individuals and artifacts and their relations to each other in a particular work practice".[2]

"DCog" is a specific approach to distributed cognition (distinct from other meanings)[3] which takes a computational perspective towards goal-based activity systems.[4]

The distributed cognition approach uses insights from cultural anthropology, sociology, embodied cognitive science, and the psychology of Lev Vygotsky (cf. cultural-historical psychology). It emphasizes the ways that cognition is off-loaded into the environment through social and technological means. It is a framework for studying cognition rather than a type of cognition. This framework involves the coordination between individuals, artifacts and the environment.

According to Zhang & Norman (1994),[5] the distributed cognition approach has three key components:

  1. Embodiment of information that is embedded in representations of interaction
  2. Coordination of enaction among embodied agents
  3. Ecological contributions to a cognitive ecosystem

DCog studies the "propagation of representational states across media".[2] Mental content is considered to be non-reducible to individual cognition and is more properly understood as off-loaded and extended into the environment, where information is also made available to other agents (Heylighen, Heath, & Overwalle, 2003). It is often understood as an approach in specific opposition to earlier and still prevalent "brain in a vat" models which ignore "situatedness, embodiment and enaction" as key to any cognitive act (Ibid.).

These representation-based frameworks consider distributed cognition as "a cognitive system whose structures and processes are distributed between internal and external representations, across a group of individuals, and across space and time" (Zhang and Patel, 2006). In general terms, they consider a distributed cognition system to have two components: internal and external representations. In their description, internal representations are knowledge and structure in individuals' minds while external representations are knowledge and structure in the external environment (Zhang, 1997b; Zhang and Norman, 1994).

DCog studies the ways that memories, facts, or knowledge is embedded in the objects, individuals, and tools in our environment. DCog is a useful approach for designing the technologically mediated social aspects of cognition by putting emphasis on the individual and his/her environment, and the media channels with which people interact, either in order to communicate with each other, or socially coordinate to perform complex tasks. Distributed cognition views a system of cognition as a set of representations propagated through specific media, and models the interchange of information between these representational media. These representations can be either in the mental space of the participants or external representations available in the environment.

These interactions can be categorized into three distinct types of processes:[6]

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

Early research[edit]

John Milton Roberts thought that social organization could be seen as cognition through a community (Roberts 1964). He described the cognitive aspects of a society by looking at the present information and how it moves through the people in the society.

Daniel L. Schwartz (1978) proposed a distribution of cognition through culture and the distribution of beliefs across the members of a society.[citation needed]

In 1998, Mark Perry from Brunel University London explored the problems and the benefits brought by distributed cognition to "understanding the organisation of information within its contexts." He considered that distributed cognition draws from the information processing metaphor of cognitive science where a system is considered in terms of its inputs and outputs and tasks are decomposed into a problem space.[7] He believed that information should be studied through the representation within the media or artifact that represents the information. Cognition is said to be "socially distributed" when it is applied to demonstrate how interpersonal processes can be used to coordinate activity within a social group.

In 1997, Gavriel Salomon stated that there were two classes of distributive cognition: shared cognition and off-loading.[8] Shared cognition is that which is shared among people through common activity such as conversation where there is a constant change of cognition based on the other person's responses. An example of off-loading would be using a calculator to do arithmetic or a creating a grocery list when going shopping. In that sense, the cognitive duties are off-loaded to a material object.

Later, John Sutton (2006)[9] defined five appropriate domains of investigation for research in Dcog:

  1. External cultural tools, artifacts, and symbol systems.
  2. Natural environmental resources.
  3. Interpersonal and social distribution or scaffolding.
  4. Embodied capacities and skills.
  5. Internalized cognitive artifacts.


In ontogenesis, the first act of the mental representation distribution succeeds in the mother-child dyad that constitutes in the child the tools to think and perceive the world. Based on evidence in hyperscanning research[10][11][12][13][14][15] and psychophysiological research studies,[16][17][18][19][20] Research Professor Igor Val Danilov developed the Shared intentionality notion first introduced by Professor of psychology Michael Tomasello. According to the hypothesis, the mother distributes the mental representation to the child to teach the young nervous system how to respond to environmental changes correctly.[21][22] Due to this ecological learning, the child grasps the perception of objects and begins to cognize the environment at the simple reflexes stage of development without communication and abstract thinking. According to Igor Val Danilov, Shared intentionality switches on cognition in the child beginning from the embryonal period.[23]


The application area of DCog is systems design and implementation in specific work environments. Its main method is field research, going into the workplace and making rigorous observations, e.g. through capturing work performances with video, studying and coding the recorded activities using qualitative research methods to codify the various ways in which cognition is distributed in the local environment, through the social and technical systems with which the workers engage.

Distributed cognition as a theory of learning, i.e. one in which the development of knowledge is attributed to the system of thinking agents interacting dynamically with artifacts, has been widely applied in the field of distance learning, especially in relation to computer-supported collaborative learning (CSCL) and other computer-supported learning tools. For example, in the field of teaching English Composition, Kevin LaGrandeur has argued that CSCL provides a source of common memory, collaborative space, and a cognitive artifact (tool to enhance cognition) that allows students to more easily build effective written compositions via explicit and implicit machine-human collaboration. Distributed cognition illustrates the process of interaction between people and technologies in order to determine how to best represent, store and provide access to digital resources and other artifacts.

Collaborative tagging on the World Wide Web is one of the most recent developments in technological support for distributed cognition. Beginning in 2004[24] and quickly becoming a standard on websites, collaborative tagging allows users to upload or select materials (e.g. pictures, music files, texts, websites) and associate tags with these materials. Tags can be chosen freely, and are similar to keywords. Other users can then browse through tags; a click on a tag connects a user to similarly tagged materials. Tags furthermore enable tag clouds, which graphically represent the popularity of tags, demonstrating co-occurrence relations between tags and thus jump from one tag to another.

Dcog has also been used to understand learning and communication in clinical settings and to obtain an integrated view of clinical workplace learning. It has been observed how medical actors use and connect gestural practices, along with visual and haptic structures of their own bodies and of artifacts such as technological instruments and computational devices. In so doing they co-construct complex, multimodal representations that go beyond the mental representations usually studied from a cognitive perspective of learning.[25]

Distributed cognition can also be seen through cultures and communities. Learning certain habits or following certain traditions is seen as cognition distributed over a group of people. Exploring distributed cognition through community and culture is one way to understand how it may work.

With the new research that is emerging in this field, the overarching concept of distributed cognition enhances the understanding of interactions between individual human beings and artifacts such as technologies and machines, and complex external environments.[not specific enough to verify] This concept has been applied to educational research in the areas of distributed leadership and distributed instruction[not specific enough to verify].

Distributed cognition between internal and external processing has also been used to study problem-solving and Bayesian reasoning. For example, it has been observed that the use of external manipulable materials such as cards and tokens can help improve performance and reduce cognitive bias such as the base-rate fallacy, even among adult problem-solvers, as long as they physically interact with these artefacts.[26] It has also been reported that interacting with tokens can reduce the impact of mathematical anxiety on mental calculation performance[27] and supports insight[28][29] although the evidence is mixed with regards to the impact of distributing cognition between internal and external processing with regards to insight.[30]

Metaphors and examples[edit]

Distributed cognition is seen when using paper and pencil to do a complicated arithmetic problem. The person doing the problem may talk with a friend to clarify the problem, and then must write the partial answers on the paper in order to be able to keep track of all the steps in the calculation. In this example, the parts of distributed cognition are seen in:

  • setting up the problem, in collaboration with another person,
  • performing manipulation/arithmetic procedures, both in one's head and by writing down resulting partial answers.

The process of working out the answer requires not only the perception and thought of two people, it also requires the use of a tool (paper) to extend an individual's memory. So the intelligence is distributed, both between people, and a person and an object.

Another well-researched site for analyzing distributed cognition and applying the discovered insights towards the design of more optimal systems is aviation, where both cockpits and air traffic control environments have been studied as scenes that technologically and socially distribute cognition through systems of externalized representational media. It is not the cognitive performance and expertise of any one single person or machine that is important for the continued operation or the landing and takeoff of airplanes. The cognition is distributed over the personnel, sensors, and machinery both in the plane and on the ground, including but not limited to the controllers, pilots and crew as a whole.[31]

Hutchins also examined another scene of distributed cognition within the context of navigating a US navy vessel.[32] In his book on USS Palau,[1] he explains in detail how distributed cognition is manifested through the interaction between crew members as they interpret, process, and transform information into various representational states in order to safely navigate the ship. In this functional unit, crew members (e.g. pelorus operators, bearing takers, plotters, and the ship's captain) play the role of actors who transform information into different representational states (i.e. triangulation, landmark sightings, bearings, and maps). In this context, navigation is embodied through the combined efforts of actors in the functional unit.

In his study on process, representation and task world, Mark Perry[7] demonstrated how distributed cognition analysis can be conducted in a field study. His example was design analysis in Civil engineering. In this work, he showed how an information processing approach can be applied by carrying a detailed analysis of the background of the study - goals and resources, inputs and outputs, representations and processes, and transformational activity, "how information was transformed from the design drawings and site onto tables of measurements (different representations)" and then onto "a graphical representation" which provided a clearer demonstration of the relationship between the two data sets.[7]


On educational psychology:

People think in conjunction and partnership with others and with the help of culturally provided tools and implements.

— Salomon, 1997 p. xiii

On cognitive science:

Nervous systems do not form representations of the world, they can only form representations of interactions with the world.[33]

The emphasis on finding and describing "knowledge structures" that are somewhere "inside" the individual encourages us to overlook the fact that human cognition is always situated in a complex sociocultural world and cannot be unaffected by it.

— Hutchins, 1995 p. xiii

See also[edit]


  1. ^ a b c Hutchins E (1995). Cognition in the wild. Cambridge, Mass.: MIT Press. ISBN 978-0-262-58146-2.
  2. ^ a b Rogers Y, Ellis J (June 1994). "Distributed cognition: an alternative framework for analysing and explaining collaborative working" (PDF). Journal of Information Technology. 9 (2): 119–28. doi:10.1177/026839629400900203. S2CID 219981758.
  3. ^ Michaelian K, Sutton J (2013-02-20). "Distributed Cognition and Memory Research: History and Current Directions". Review of Philosophy and Psychology. 4 (1): 1–24. doi:10.1007/s13164-013-0131-x. hdl:11693/37950. ISSN 1878-5158. S2CID 9818565.
  4. ^ Perry M. "Some simple definitions in Distributed Cognition (DCog)". Retrieved 22 November 2015.
  5. ^ Zhang J, Norman DA (1994). "Representations in Distributed Cognitive Tasks". Cognitive Science. 18: 87–122. doi:10.1207/s15516709cog1801_3.
  6. ^ Hollan J, Hutchins E, Kirsh D (June 2000). "Distributed cognition: toward a new foundation for human-computer interaction research" (PDF). ACM Transactions on Computer-Human Interaction. 7 (2). New York: ACM Press: 174–96. doi:10.1145/353485.353487. S2CID 1490533.
  7. ^ a b c Perry M (13–15 August 1998). Process, representation and taskworld: distributed cognition and the organisation of information. Exploring the contexts of information behaviour. Proceedings of the Second International Conference on Research in Information Needs, Seeking and Use in different contexts. Sheffield, UK. pp. 552–567.
  8. ^ Salomon, Gavriel (1997). Distributed Cognitions: Psychological and Educational Considerations. Cambridge University Press. ISBN 978-0-521-57423-5.
  9. ^ Sutton J (January 2006). "Distributed cognition: Domains and dimensions". Pragmatics & Cognition. 14 (2): 235–247. doi:10.1075/pc.14.2.05sut.
  10. ^ Liu, J., Zhang, R., Xie, E. et al. (2023). "Shared intentionality modulates interpersonal neural synchronization at the establishment of communication system." Commun Biol 6, 832 (2023). https://doi.org/10.1038/s42003-023-05197-z
  11. ^ Painter, D.R., Kim, J.J., Renton, A.I., Mattingley, J.B. (2021). "Joint control of visually guided actions involves concordant increases in behavioural and neural coupling." Commun Biol. 2021; 4: 816.
  12. ^ Hu, Y., Pan, Y., Shi, X., Cai, Q., Li, X., Cheng, X. (2018). "Inter-brain synchrony and cooperation context in interactive decision making." Biol Psychol. 2018; 133: 54-62.
  13. ^ Fishburn, F.A., Murty, V.P., Hlutkowsky, C.O., MacGillivray, C.E., Bemis, L.M., Murphy, M.E., et al. (2018). "Putting our heads together: Interpersonal neural synchronization as a biological mechanism for shared intentionality." Soc Cogn Affect Neurosci. 2018; 13: 841-849.
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  15. ^ Astolfi, L., Toppi, J., De Vico Fallani, F., Vecchiato, G., Salinari, S., Mattia, D., et al. (2010). "Neuroelectrical hyperscanning measures simultaneous brain activity in humans." Brain Topogr. 2010; 23: 243-256.
  16. ^ Val Danilov I. & Mihailova S. (2023). "Empirical Evidence of Shared Intentionality: Towards Bioengineering Systems Development." OBM Neurobiology 2023; 7(2): 167; doi:10.21926/obm.neurobiol.2302167. https://www.lidsen.com/journals/neurobiology/neurobiology-07-02-167
  17. ^ McClung, J. S., Placì, S., Bangerter, A., Clément, F., & Bshary, R. (2017). "The language of cooperation: shared intentionality drives variation in helping as a function of group membership." Proceedings of the Royal Society B: Biological Sciences, 284(1863), 20171682. http://dx.doi.org/10.1098/rspb.2017.1682.
  18. ^ Shteynberg, G., & Galinsky, A. D. (2011). "Implicit coordination: Sharing goals with similar others intensifies goal pursuit." Journal of Experimental Social Psychology, 47(6), 1291-1294., https://doi.org/10.1016/j.jesp.2011.04.012.
  19. ^ Val Danilov, I., Svajyan, A., Mihailova, S. (2023). "A New Computer-Aided Method for Assessing Children's Cognition in Bioengineering Systems for Diagnosing Developmental Delay." OBM Neurobiology 2023; 7(4): 189; doi:10.21926/obm.neurobiol.2304189. https://www.lidsen.com/journals/neurobiology/neurobiology-07-04-189
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  22. ^ Val Danilov, Igor (2023). "Shared Intentionality Modulation at the Cell Level: Low-Frequency Oscillations for Temporal Coordination in Bioengineering Systems". OBM Neurobiology. 7 (4): 1–17. doi:10.21926/obm.neurobiol.2304185.
  23. ^ Val Danilov, I. (2023). "Theoretical Grounds of Shared Intentionality for Neuroscience in Developing Bioengineering Systems." OBM Neurobiology 2023; 7(1): 156; doi:10.21926/obm.neurobiol.2301156
  24. ^ Mika P (November 2005). "Ontologies are us: A unified model of social networks and semantics.". International semantic web conference. Lecture Notes in Computer Science. Vol. 3729. Berlin, Heidelberg.: Springer. pp. 522–536. doi:10.1007/11574620_38. ISBN 978-3-540-29754-3.
  25. ^ Pimmer C, Pachler N, Genewein U (September 2013). "Reframing clinical workplace learning using the theory of distributed cognition". Academic Medicine: Journal of the Association of American Medical Colleges. 88 (9): 1239–45. doi:10.1097/ACM.0b013e31829eec0a. PMID 23887014. S2CID 12371185.
  26. ^ Vallée-Tourangeau G, Abadie M, Vallée-Tourangeau F (June 2015). "Interactivity fosters Bayesian reasoning without instruction" (PDF). Journal of Experimental Psychology. General. 144 (3): 581–603. doi:10.1037/a0039161. PMID 26030173.
  27. ^ Vallée-Tourangeau F, Sirota M, Vallée-Tourangeau G (December 2016). "Interactivity mitigates the impact of working memory depletion on mental arithmetic performance". Cognitive Research: Principles and Implications. 1 (1): 26. doi:10.1186/s41235-016-0027-2. PMC 5256453. PMID 28180177.
  28. ^ Henok N, Vallée-Tourangeau F, Vallée-Tourangeau G (February 2020). "Incubation and interactivity in insight problem solving". Psychological Research. 84 (1): 128–139. doi:10.1007/s00426-018-0992-9. PMC 6994426. PMID 29480412.
  29. ^ Fleck JI, Weisberg RW (2013-06-01). "Insight versus analysis: Evidence for diverse methods in problem solving". Journal of Cognitive Psychology. 25 (4): 436–463. doi:10.1080/20445911.2013.779248. ISSN 2044-5911. S2CID 146689726.
  30. ^ Chuderski A, Jastrzębski J, Kucwaj H (February 2021). "How physical interaction with insight problems affects solution rates, hint use, and cognitive load". British Journal of Psychology. 112 (1): 120–143. doi:10.1111/bjop.12442. PMID 32125690. S2CID 211835401.
  31. ^ Hutchins E (July 1995). "How a Cockpit Remembers Its Speeds". Cognitive Science. 19 (3): 265–88. doi:10.1207/s15516709cog1903_1.
  32. ^ Caroll JM (2003). HCI Models, Theories, and Frameworks: Toward a Multidisciplinary Science. San Francisco, Calif.: Morgan Kaufmann. ISBN 978-0-08-049141-7.
  33. ^ Hutchins E. "Overview of Distributed Cognition Lecture" (PDF). Distributed Cognition and Human-Computer Interaction Laboratory, Department of Cognitive Science. University of California, San Diego.

Further reading[edit]