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Distributed cognition
Distributed cognition
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 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 Caroline 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

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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 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.

Theory

[edit]

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[16] and psychophysiological research studies,[22] 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.[23][24] 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.[25]

Applications

[edit]

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[26] 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.[27]

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.[28] It has also been reported that interacting with tokens can reduce the impact of mathematical anxiety on mental calculation performance[29] and supports insight[30][31] although the evidence is mixed with regards to the impact of distributing cognition between internal and external processing with regards to insight.[32]

Metaphors and examples

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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.[33]

Hutchins also examined another scene of distributed cognition within the context of navigating a US navy vessel.[34] 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]

Quotes

[edit]

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.[35]

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

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References

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Further reading

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Distributed cognition

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Distributed cognition is a theoretical framework in that views cognitive processes—such as , , problem-solving, and —as extending beyond the individual mind to encompass interactions among people, artifacts (like tools and technologies), and the broader environment. This approach emphasizes that emerges from the coordinated functioning of distributed systems rather than isolated mental computations, highlighting how external representations and social structures shape and support cognition. Developed in the mid-to-late 1980s by Edwin Hutchins and colleagues at the , distributed cognition draws from influences including Lev Vygotsky's work on the social origins of higher mental functions, Marvin Minsky's concept of the mind as a society of agencies, and early connectionist models like parallel distributed processing. Unlike traditional , which focuses on individual information processing within the , this perspective integrates insights from , , and human-computer interaction to analyze cognition as a situated, emergent property of socio-technical systems. It posits three primary forms of distribution: social distribution across groups (e.g., teams coordinating knowledge redundantly to achieve collective goals), material distribution involving cognitive artifacts that offload mental effort (e.g., calculators or maps), and temporal distribution where past actions and representations influence ongoing processes. A seminal example is Hutchins' ethnographic study of ship , where a team's use of charts, compasses, and verbal protocols distributes the cognitive workload of plotting a vessel's position, demonstrating how system-level surpasses individual capabilities. Similarly, analyses of operations reveal how pilots and instruments interact to manage flight tasks, underscoring the role of representational media in enabling coordination. These principles have informed fields like human-computer interaction (HCI) and computer-supported cooperative work (CSCW), guiding the design of technologies that align with distributed cognitive practices, such as adaptive interfaces in or collaborative engineering tools. Overall, distributed cognition challenges atomistic views of the mind, advocating for a holistic understanding of as embedded in cultural and material contexts.

Historical Development

Origins and Early Influences

The conceptual roots of distributed cognition trace back to early 20th-century socio-cultural theories, particularly Lev Vygotsky's work in the 1930s, which emphasized the role of social interactions in . Vygotsky introduced the (ZPD), describing it as the difference between what a learner can achieve independently and what they can accomplish with guidance from more knowledgeable others, thereby distributing cognitive processes across social contexts. This framework highlighted how higher mental functions originate in collaborative activities before becoming internalized, laying a foundation for viewing as inherently socio-cultural rather than solely individual. Vygotsky's ideas influenced later distributed cognition by underscoring that is shared through interactions, tools, and cultural artifacts, as seen in his analysis of how children use symbolic mediators like to extend mental capabilities. In the mid-20th century, provided key precursors by framing within interactive systems involving humans and machines. Norbert Wiener's 1948 book Cybernetics: Or Control and Communication in the Animal and the Machine introduced feedback loops as central to control processes, positing that communication and regulation occur across biological and mechanical entities. This work established groundwork for distributed cognition by conceptualizing intelligent behavior as emerging from coupled human-machine interactions, such as in early computing and , where cognitive functions are offloaded to external devices. Wiener's emphasis on in adaptive systems challenged isolated views of the mind, influencing subsequent ecological approaches to . Gregory Bateson's in the 1950s further advanced in , integrating principles with . In works like Naven (revised 1958) and contributions to the on , Bateson argued for viewing mind as an ecological phenomenon distributed across organisms and environments, describing as patterned interactions within larger systems. This line of thinking culminated in his 1972 book Steps to an Ecology of Mind, where he coined the term "ecology of mind" and proposed that cognitive units extend beyond the , as illustrated by the analogy of a blind person using a cane, with the tool becoming part of the perceptual system and emphasizing permeable boundaries in socio-material networks. Bateson's ideas on double binds and highlighted how cognitive processes distribute across social and environmental relations, prefiguring distributed cognition's focus on systemic interdependence. By the 1970s and 1980s, saw a shift from internalist to externalist views, contributing to pre-1990 developments in distributed cognition. Internalism, dominant earlier, confined mental states to intracranial processes, but externalism—pioneered by Hilary Putnam's 1975 arguments on semantic content depending on external factors like linguistic communities—asserted that involves environmental and social elements. This transition, evident in Saul Kripke's 1980 work on reference and Andy Clark's early 1989 book Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing, began extending cognitive boundaries to include distributed representations and external scaffolds. Clark's pre-1990 explorations of connectionist models portrayed as spread across neural and environmental structures, challenging brain-bound models. Edwin Hutchins' initial fieldwork in the 1980s provided empirical inspirations for distributed cognition through observations of naval navigation practices. Beginning in November 1980 aboard a ship navigating the and , Hutchins noted how teams coordinated visual bearings, depth readings, and chart updates to compute ship positions every three minutes during the fix cycle. Formal studies from 1984 at the Personnel Center involved video-recorded sessions on the USS Palau, capturing shipboard routines like pelorus operators reporting "beamiest" bearings (e.g., 359° to Point Loma) and adaptations during failures, where crews shifted to magnetic compasses, reducing computations from nine to five per fix and evolving modular strategies over 66 lines of position. These observations revealed cognition as distributed across crew members, instruments (e.g., alidades, fathometers), and procedures, with overlapping roles enabling error detection, such as in misreported bearings (e.g., 167° mistaken for 107°). Hutchins' encounters with Micronesian navigators, using paths and etak segments for open-ocean , further illustrated cultural distributions of cognitive labor predating modern tools.

Key Contributors and Milestones

Edwin Hutchins, a pioneering figure in distributed cognition, transitioned from training in to under Roy D'Andrade at in the 1970s, emphasizing the role of cultural practices in human cognition. As a professor at the , Hutchins applied anthropological methods to study cognition in everyday settings, earning a MacArthur Fellowship in 1985 for his innovative approach to real-world cognitive processes. Hutchins' seminal 1991 chapter, "The Social Organization of Distributed Cognition," introduced key ideas about how cognitive processes extend across social interactions, drawing on examples from collaborative tasks. His 1995 book, Cognition in the Wild, established distributed cognition as a foundational framework through ethnographic studies of navigation practices aboard U.S. ships, illustrating how cognitive work is shared among individuals, tools, and environments. In this work, Hutchins analyzed the bridge of a naval vessel as a distributed cognitive system, where artifacts like charts and instruments coordinate team actions to achieve complex tasks such as course plotting. David Kirsh emerged as a key contributor in the 1990s, advancing the study of cognitive artifacts—external objects that augment human thought. His 1995 paper, "The Intelligent Use of Space," explored how agents restructure their physical environments to offload cognitive effort, introducing the concept of "intelligent environments" where spatial arrangements facilitate problem-solving and reduce mental workload. Kirsh's research, grounded in observations of human-computer interaction, highlighted how everyday manipulations of space serve as nonrepresentational aids to cognition. Bonnie Nardi contributed significantly by integrating with distributed cognition, bridging sociocultural and cognitive perspectives in human-computer interaction. In her 1996 edited volume Context and Consciousness: Activity Theory and Human-Computer Interaction, Nardi compared distributed cognition with and situated action models, emphasizing how tools and social structures mediate cognitive activity. Her chapter in the book underscored persistent structures in as complementary to distributed cognition's focus on material and social distribution of thought processes. The term "distributed cognition" gained prominence in the 1990s through Hutchins' publications, formalizing it as an approach that views cognitive systems as extending beyond individual minds into social and material realms. Influenced by 1980s developments in , the framework crystallized with Hutchins' aviation studies, including his 1995 analysis of cockpit operations as socio-technical systems. A major milestone was the 1995 TOCHI paper by Hollan, Hutchins, and Kirsh, which proposed distributed cognition as a foundation for human-computer interaction research. By the 2000s, the field evolved toward computational modeling of distributed systems, as seen in Hutchins' 2000 overview emphasizing functional relationships in cognitive organization. The 2006 special issue of Pragmatics & Cognition marked a key event, featuring papers on the autonomy and mechanisms of distributed cognition, including debates on its implications for AI and Turing-test-like evaluations. This period reflected a shift from ethnographic descriptions to modeling how cognition propagates across interactive elements.

Theoretical Foundations

Core Principles

Distributed cognition posits that cognitive processes are not confined to the individual brain but extend across a comprising multiple individuals, artifacts, and environmental elements that interact to accomplish intelligent behavior. This framework views cognition as a distributed process involving coordination among these components, where the unit of analysis is the rather than isolated minds. A central tenet is the functional distribution of cognitive labor, whereby cognitive tasks are divided and shared among participants and material resources, enabling emergent system-level capabilities that surpass those of any single actor. For instance, in a navigation team, roles such as plotters and observers leverage overlapping expertise and tools to compute positions, adapting dynamically to failures like equipment malfunctions. This distribution allows complex computations to arise from simple individual actions coordinated within the . Another key principle is the propagation of representational states across media, where information evolves and transforms as it moves between internal mental states, external artifacts, and social interactions over varying timescales—from seconds in real-time coordination to millennia in cultural evolution. Representations, such as verbal reports or plotted charts, undergo changes in form and content during this propagation, facilitating the continuity of cognitive processes. Coordination occurs through material and social structures that align these states, such as standardized tools that enforce perceptual-motor mappings or team protocols that synchronize actions. Shared cognition within distributed systems often manifests as a symbiotic process, involving reciprocal interactions between individuals and external systems that mutually enhance cognitive capabilities. This symbiosis highlights the interdependent co-constitution of cognitive processes, where social structures, artifacts, and even AI systems contribute to and are shaped by collective intelligence, as discussed in perspectives on symbiotic cognitive systems from a distributed cognition viewpoint. Representational media play a pivotal role by serving as external substrates that offload and transform cognitive demands, allowing individuals to interact with symbols in ways that simplify internal . Artifacts like nautical s or computational devices act as dynamic media where is inscribed, manipulated, and interpreted, often reducing mental effort through perceptual inferences rather than explicit calculations—for example, aligning a plotting tool with a chart grid to derive ship positions visually. These media not only store but also structure cognitive activity, embedding cultural knowledge and enabling transformations that internal alone could not achieve efficiently. The approach emphasizes by insisting that be studied in situated, real-world contexts where it naturally unfolds, rather than abstracted settings, to capture how cultural and material environments shape cognitive systems. This focus reveals as inherently adaptive to practical demands, such as responses in operational environments.

Distinctions from Traditional Cognitive Theories

Distributed cognition fundamentally diverges from classical cognitivism, which posits as an internal, modular confined to the individual , akin to a "brain-in-a-vat" model where mental operations resemble computational symbol manipulation isolated from external contexts. Instead, distributed cognition views cognitive processes as emergent properties arising from dynamic interactions among individuals, artifacts, and environments, rejecting the notion that cognitive accomplishments can be explained solely by intracranial mechanisms. This shift emphasizes situated, real-world systems over abstractions, where is not decomposed into discrete, modular functions but understood as coordinated activity propagating across distributed elements. While sharing some overlap with embodied cognition, which stresses the role of the body in shaping thought through sensorimotor interactions with the environment, distributed cognition extends further by incorporating external artifacts and social distributions as integral cognitive components beyond mere body-environment coupling. Embodied cognition often limits its scope to how bodily states influence internal processes, whereas distributed cognition treats tools and social structures as co-participants in cognitive work, enabling the offloading and transformation of mental tasks. For instance, in navigational practices, artifacts like charts do not merely support embodied actions but actively mediate and distribute representational states across the system. The , as articulated by and Chalmers, aligns with distributed by challenging strict boundaries between mind and world, proposing that reliably coupled external resources can constitute genuine cognitive states for . However, distributed cognition distinguishes itself through a focus on systemic coordination within broader socio-material ensembles, rather than primarily personal externalism where extends via an individual's dedicated tools. This systemic emphasis highlights emerging from interactions among multiple agents and artifacts, as opposed to the parity principle in extended mind that equates external aids with internal on an individual basis. A core critique of internalism in distributed cognition is its portrayal of tools as passive prosthetics that merely augment an otherwise self-contained mind, whereas these elements function as active cognitive participants that structure and propagate information flows. Internalist models overlook how artifacts embed computations and constrain actions, transforming cognitive tasks into distributed processes that rely on external coordination for efficacy. By reconceptualizing tools as integral to the cognitive system, distributed cognition undermines the internal-external dichotomy, arguing that cognition cannot be fully understood without accounting for these material integrations. Philosophically, distributed cognition draws from enactivist ideas of as enacted through organism-environment relations but diverges by prioritizing —such as artifacts and social practices—over enactivism's emphasis on pure sensorimotor loops. views as constitutively tied to embodied, world-involving activity without representational content, whereas distributed cognition accommodates representations that propagate across external media, highlighting the role of cultural artifacts in systemic sense-making. This distinction underscores distributed cognition's broader integration of socio-historical elements into cognitive dynamics.

Methodological Approaches

Observational and Ethnographic Methods

Observational and ethnographic methods form the cornerstone of research on distributed cognition, emphasizing in-depth study of cognitive processes as they unfold in naturalistic environments through interactions among people, artifacts, and social structures. These qualitative approaches prioritize immersion and event-centered analysis to capture how cognition is distributed beyond individual minds, drawing on core principles such as the integration of social and material resources in cognitive systems. Pioneered in , they enable researchers to document the dynamic coordination of activities in real-world settings like workplaces, avoiding decontextualized simulations. Ethnographic fieldwork involves prolonged immersion in target environments to observe participant interactions with tools and each other, often employing protocols for systematic . In Edwin Hutchins' seminal shipboard studies aboard the USS Palau, researchers embedded with navigation teams over multiple days at sea, spanning various watch periods including overnight shifts, to track routines like position fixing during standard steaming and high-intensity maneuvers. Access was facilitated by securing equivalent privileges to mid-level officers, allowing proximity to key areas such as the chart table and bridge while adhering to military hierarchies. Field notes, interviews, and still photographs supplemented recordings to log untaped elements, ensuring comprehensive coverage of cognitive distributions across team roles like plotters, recorders, and pelorus operators. Video analysis techniques capture and code sequential interactions among agents and artifacts, enabling detailed examination of how information propagates through sociotechnical systems. Researchers deploy wide-angle cameras to record focal events, such as chart plotting or instrument readings, followed by transcription and coding of verbal, nonverbal, and material exchanges to identify patterns in cognitive labor division. In distributed cognition studies, multiple audio tracks—often from lavaliere microphones and sound-powered phone circuits—separate ambient noise from targeted communications, facilitating analysis of coordination in noisy environments like ship bridges. Coding focuses on event sequences, such as bearing reports transforming into chart annotations, to reveal how artifacts mediate shared understanding without relying on retrospective self-reports. Contextual inquiry adapts ethnographic principles for mapping cognitive distributions through in-situ interviews and activity logging, blending observation with collaborative interpretation of ongoing tasks. Participants narrate their actions in real time while researchers probe interactions with tools and colleagues, generating logs that highlight how cognitive resources are allocated across individuals and environments. In complex settings like operating theaters, this method involves shadowing teams during procedures to device use and flows, yielding on distributed processes that inform subsequent analyses. The approach emphasizes minimal disruption, with inquiries timed to natural pauses, to preserve authentic behaviors in sociotechnical contexts. Methods exhibit case-specific adaptations to accommodate domain demands, with high-stakes environments like requiring constrained yet intensive protocols compared to everyday tasks. In cockpits, researchers conduct jumpseat observations—positioned as non-participating passengers—supplemented by video recordings of pilot-instrument interactions and participation in simulator to grasp procedural nuances without compromising . These differ from less regulated settings, such as office workflows, where prolonged shadowing allows broader activity logging; in , focus narrows to critical phases like takeoff, using pre-arranged access to mitigate risks from time-pressured operations. Adaptations ensure scalability, prioritizing event sampling in hazardous domains to balance depth with feasibility. Ethical considerations in these methods center on within and protecting in interconnected systems. Researchers obtain permissions from organizational leaders and teams, using pseudonyms for individuals and sites to anonymize data while minimizing researcher intervention to avoid altering natural processes. In distributed contexts, consent extends to collective activities, addressing how recordings of interactions might inadvertently capture non-consenting bystanders or sensitive operational details. Protocols emphasize revocability and transparency, particularly in high-stakes settings where debriefings clarify data use to foster trust without compromising safety or .

Analytical Frameworks and Tools

Interaction analysis frameworks in distributed cognition adapt methods from to examine human-artifact interactions, focusing on how coordination emerges through sequential actions and shared representations. These frameworks emphasize coding schemes that categorize interaction sequences, such as between humans and tools, alignment of gestures with device feedback, and resolution of misalignments in . For instance, in studies of collaborative tasks, coders identify patterns like "initiation-response-evaluation" cycles extended to include artifact-mediated responses, enabling quantification of coordination efficacy. Cognitive ethnography models, particularly Edwin Hutchins' framework, provide a structured approach for tracing representational transformations across media in distributed systems. This involves mapping how information states evolve as they propagate through agents, tools, and environments, such as from verbal announcements to visual displays in a navigation team. The model highlights functional equivalences in representations, where transformations maintain computational integrity despite media shifts, as observed in ethnographic data from complex work settings. Computational tools support modeling distributed cognition by simulating interactions beyond individual minds. Extensions of the ACT-R cognitive architecture enable multi-agent simulations, where individual cognitive modules interact via shared environments to replicate socially distributed problem-solving, such as team decision-making under uncertainty. Network analysis tools, applied to social cognition flows, visualize propagation of information as directed graphs, identifying bottlenecks in coordination among team members and artifacts. These simulations avoid detailed equations, instead using iterative propagation rules to predict system-level behaviors. Mathematical representations, such as , formalize cognitive distributions by modeling systems as networks where nodes represent agents or artifacts and edges denote interactions. This approach captures the of cognitive processes, revealing how connectivity influences and resilience. For example, a simple can represent a basic distributed system:
Human AArtifact XHuman B
Human A010.5
Artifact X0.800
Human B0.310
Here, entries indicate interaction strength (e.g., frequency or reliability of ), with zeros on the diagonal for self-loops; such matrices facilitate of paths and clusters in cognitive distributions. metrics for distributed cognition assess by measuring how distributions affect , including rates that track discrepancies introduced during representational handoffs. Criteria such as coordination latency (time for synchronized actions) and recovery (speed of error correction across components) quantify overall robustness, with low indicating effective distribution. These metrics prioritize systemic outcomes over individual errors, as demonstrated in analyses of high-stakes environments like cockpits.

Applications

In Human-Computer Interaction and Technology

Distributed cognition has significantly influenced the design of human-computer interaction (HCI) systems by emphasizing how cognitive processes extend beyond the individual mind into interactions with computational artifacts, enabling interfaces that offload mental tasks to external resources for more efficient performance. In HCI, this framework guides the creation of interfaces that leverage external cognition, such as tangible user interfaces (TUIs), where physical objects serve as manipulable representations that distribute between the user and the device, facilitating intuitive manipulation of digital information. For instance, TUIs in design environments enhance by allowing users to interact with tangible models that externalize abstract concepts, reducing mental effort and improving task accuracy. A prominent application appears in technology, where displays embody distributed cognition principles to support pilots' through shared representations that coordinate human and system resources. In modern s, instruments like speed bugs and integrated displays distribute the cognitive labor of monitoring and recalling critical speeds, transforming individual memory demands into a system-wide process that minimizes errors by propagating information reliably across tools and members. This approach has contributed to error reduction in automated s by aligning interface designs with cognitive engineering principles, such as verifying control inputs through distributed feedback loops, thereby lowering the incidence of procedural deviations during high-workload phases like . The integration of distributed cognition with , as envisioned by in the 1990s, has shaped the development of smart environments where computational resources seamlessly augment human cognition without drawing explicit attention. Weiser's concept of computers "weaving themselves into the fabric of " aligns with distributed cognition by treating pervasive devices as extensions of cognitive systems, enabling that supports ongoing activities through ambient information flows in intelligent spaces. This synergy informs designs for environments like smart homes or offices, where sensors and displays distribute perceptual and computational tasks to reduce user overload. In recent technologies of the , distributed cognition frameworks have been applied to AI-assisted tools, particularly collaborative robots, where cognition emerges from human-AI networks that share perceptual, , and execution responsibilities through symbiotic shared cognition, involving reciprocal co-constitution of cognitive abilities between humans and AI systems. For example, in human-robot teams, AI components handle repetitive sensing and tasks, while humans oversee adaptive , creating a distributed that enhances overall performance in dynamic settings like or search-and-rescue operations. This symbiotic distribution fosters mutual understanding, with robots providing transparent representations of their internal states to align with human cognitive processes, thereby improving efficiency and reducing coordination errors. Design implications from distributed cognition emphasize guidelines for minimizing cognitive bottlenecks in software, such as developing adaptive dashboards that dynamically reconfigure based on user context to offload routine monitoring and highlight salient . These interfaces prioritize coordination media—shared representations that facilitate —ensuring that systems support fluid transitions between and computational roles without disrupting . By analyzing resource flows in HCI designs, developers can create more resilient systems that enhance and in complex technological ecologies.

In Education and Organizational Settings

In educational contexts, distributed cognition emphasizes how learning emerges from interactions among individuals, artifacts, and social structures rather than isolated mental processes. Collaborative learning tools, such as computer-supported collaborative learning (CSCL) systems, distribute knowledge across group members via groupware that enables shared problem-solving and scaffolding, thereby extending Vygotsky's zone of proximal development to collective levels. This approach fosters "distributed intelligence," where cognitive resources like external representations and peer contributions amplify individual capabilities in knowledge construction. Classroom practices illustrate this through shared artifacts that offload and distribute cognitive tasks. For instance, interactive whiteboards serve as dynamic spaces for co-authoring ideas, allowing teachers to facilitate processes that sustain focused discussions and promote sustained thinking among students. By enabling real-time annotations and group interactions, these tools transform individual into a collective system, enhancing and conceptual understanding in subjects like physics through embodied and collaborative . In organizational settings, distributed cognition underpins team cognition, where knowledge is represented, organized, and propagated across members and external supports to enable coordinated action. Businesses leverage this in , which facilitates distributed by integrating team inputs and artifacts, thereby enhancing collaborative problem-solving in complex environments. Studies highlight how such systems reduce silos and support emergent team processes, particularly in agile contexts where expertise is specialized and interdependent. Workplace analyses reveal how routines and tools like shared databases coordinate cognition in teams, distributing informational loads and enabling seamless integration of diverse expertise. For example, transactive memory systems—where teams track who knows what—combined with digital repositories, allow efficient knowledge retrieval and adaptation during routine operations. This coordination is evident in knowledge-intensive firms, where artifacts propagate understanding across distributed roles, mitigating cognitive overload and boosting performance. In knowledge work organizations, recommendations for implementing as distributed cognition include assigning epistemic tasks to while ensuring human oversight to prevent , offloading mindless tasks, and re-contextualizing distributed for local use. Post-2020, with the rise of , ethnographic studies using distributed cognition frameworks advocate for hybrid designs that bridge physical distances, such as integrated communication artifacts, to maintain collective problem-solving in dispersed teams. As of 2025, distributed cognition has been applied to AI-supported remote operations, investigating challenges in human-AI for tasks like , emphasizing the need for transparent representations to enhance coordination in remote environments.

Examples and Case Studies

The Cockpit Metaphor

The cockpit metaphor exemplifies distributed cognition through Edwin Hutchins' ethnographic study of commercial airline flight crews operating a 727-200 in a high-fidelity NASA-AMES simulator, analyzing audio and video recordings of routines like takeoff, cruise, and landing from Sacramento to . In this setup, the captain manages communications and charts, the first officer flies and monitors instruments, and the second officer handles paperwork and reports, relying on instruments such as altimeters, (EPR) gauges, and checklists, alongside verbal protocols for cross-verification. These elements form a socio-technical system where cognitive processes extend beyond individuals to interactions among crew members, artifacts, and procedures, enabling complex navigation tasks like speed adjustments and altitude management. Cognitive distribution in the cockpit manifests through redundant representations that facilitate , preventing individual limitations from compromising safety. For instance, altimeter readings appear on multiple displays visible to both pilots—the (PFD), altitude alerter windows on the mode control panel (MCP), and navigation displays—supplemented by verbal callouts such as "one thousand to go" during descent, which prompt cross-checks and aural alerts for deviations. This redundancy ensures that discrepancies, like mismatched altitude settings, are caught via shared access and mutual monitoring; if one pilot overlooks a setting, the other's verification or an automated alert activates, transforming potential errors into system-level safeguards. Similarly, airspeed indicators with adjustable "speed bugs" provide visual cues for configuration changes, reducing memory demands by externalizing critical values across instruments and verbal confirmations like "set and cross-checked." Key findings from Hutchins' analysis reveal how system-level intelligence emerges from these interactions, compensating for human cognitive constraints such as limits and attention lapses. The cockpit's design distributes computational load—e.g., EPR tables offload manual calculations for engine performance—allowing the to achieve reliable outcomes that no single individual could sustain alone, as evidenced by coordinated routines that maintain flight parameters despite divided roles. In one navigation error propagation case, a distraction during (FMS) programming led the captain to enter the present position instead of the intended (BUCKS) for a descent to 15,000 feet; this caused an unexpected turn, disconnection, and a brief descent to 14,600 feet before a low-altitude alert and pilot recovery intervened, highlighting how uncaught input errors can cascade through the system but are often contained by redundant checks. The layout functions as a cognitive , with its spatial arrangement optimizing : duplicate instruments (e.g., dual indicators and altimeters) flank the pilots, checklists are posted centrally, and the fuel quantity panel integrates with speed card booklets for quick reference, creating a bounded environment where representations propagate durably across media. This supports parallel processing, such as simultaneous monitoring of displays and verbal exchanges, fostering emergent reliability in high-stakes navigation. This metaphor has profoundly influenced human-computer interaction (HCI) design in , emphasizing interfaces that embed and procedural cues to enhance error tolerance; for example, modern flight decks incorporate automated cross-checks and intuitive displays inspired by Hutchins' observations, reducing altitude deviations and improving crew coordination in automated systems.

Other Illustrative Scenarios

In everyday settings, such as cooking in a home kitchen, distributed cognition manifests through the interplay of recipes, utensils, and social roles that collectively manage and execution. For instance, when members prepare a , one person may consult a book or digital timer to track steps and timings, while another arranges ingredients and tools in the workspace to facilitate sequential actions, reducing on any single individual. Utensils like measuring cups serve as external scaffolds for precise actions, and verbal cues or gestures coordinate division of labor, such as chopping while another stirs a pot, ensuring smooth progression without centralized oversight. In professional environments like air traffic control rooms, radar screens and radios function as shared cognitive resources that distribute workload across controllers, pilots, and artifacts. Radar displays provide real-time visual representations of aircraft positions, speeds, and headings as blips and vectors, enabling controllers to detect potential conflicts collectively rather than relying on individual mental models. Radios facilitate verbal exchanges that reference these displays and flight strips, transforming abstract data into coordinated actions, such as issuing clearance instructions, thereby extending cognition beyond the human participants to the socio-technical system. A cultural example appears in traditional Polynesian and , where , ocean swells, and chants form distributed memory systems for long-distance voyages. Navigators, or wayfinders, observe star paths rising and setting to maintain direction, while wave patterns and cues provide ongoing feedback on position, offloading spatial computation from internal recall to environmental signals. Chants and oral traditions encode collective knowledge of routes and landmarks, passed intergenerationally, allowing the cognitive process to span individuals, artifacts like stick charts, and the natural world. In modern contexts, apps for social coordination exemplify distributed cognition by offloading and planning to digital artifacts during group activities. For example, when organizing an event, participants use shared calendars or messaging apps to delegate tasks and track updates, with notifications serving as external reminders that synchronize awareness without requiring constant mental effort from each person. This system distributes cognitive responsibilities across users and devices, enhancing coordination through real-time sharing of information like locations or deadlines. These scenarios illustrate core principles of distributed cognition, such as the extension of mental processes into the environment, but differ in materiality: physical artifacts like kitchen tools or star observations rely on tangible, sensory interactions for immediacy, whereas digital ones in smartphones enable scalable, asynchronous distribution but introduce dependencies on technology and interfaces.

Criticisms and Future Directions

Major Critiques and Limitations

One major methodological critique of distributed cognition is its heavy reliance on qualitative data from ethnographic observations and video analyses, which can be time-intensive and difficult to scale for broader applications. For instance, analyzing even brief interactions may require extensive fieldwork, limiting the approach's practicality for rapid design or large-scale studies in human-computer interaction. Additionally, quantifying the distribution of cognitive processes across individuals, artifacts, and environments remains challenging, as there are few standardized metrics to measure how or coordination is allocated, leading to debates on the theory's beyond specific case studies. Recent critiques, as of 2024–2025, highlight a " of harmony" in research, where analyses overly emphasize smooth and successful interactions while undervaluing disruptions or failures in socio-technical systems. Philosophically, some argue against the framework's core distinction between interior mental processes and external distributions, contending it rests on conceptually flawed separations that undervalue individual . Conceptually, distributed cognition faces boundary problems in delineating what qualifies as part of a cognitive system, often resulting in "cognitive bloat" where social, institutional, or technological elements are indiscriminately included without clear criteria. Critics argue this vagueness undermines precise analysis, as proposals to limit boundaries—such as requiring brain-like functions or enactive engagement—fail to resolve how far cognition extends into relational practices. Furthermore, attributing cognitive properties to artifacts risks anthropomorphizing non-human elements, blurring distinctions between human agency and material supports in ways that overextend intentionality to inert objects. Empirically, replicability poses significant challenges in situated studies central to distributed cognition, particularly in HCI, where context-dependent observations resist controlled replication. For example, attempts to replicate ethnographic findings from situated action models, such as those in collaborative work environments, have highlighted difficulties in standardizing variables across labs, contributing to broader concerns about in field-based cognitive research. Philosophically, individualist critics contend that distributed cognition dilutes personal agency by dispersing it across systems, reducing explanations of to or environmental factors without sufficient emphasis on internal mental states. Proponents respond that this integration enriches understanding of real-world , countering individualism's isolation of the mind from its ecological embedding, though debates persist on reconciling the two perspectives. A notable gap in distributed cognition's coverage is its underemphasis on power dynamics within social distributions, such as inequalities in cognitive labor where certain group members bear disproportionate burdens due to hierarchical structures. This oversight can obscure how political and relational asymmetries influence cognitive coordination, limiting the theory's applicability to diverse organizational contexts.

Integrations with Emerging Theories and Recent Advances

Distributed has increasingly integrated with 4E frameworks—embodied, embedded, enactive, and extended—emphasizing how cognitive processes extend beyond individual brains into interactions with environments, tools, and social systems. This synergy builds on foundational overlaps, where distributed 's focus on sociotechnical systems aligns with extended 's parity principle, allowing artifacts like notebooks or digital devices to function as cognitive extensions. Recent analyses highlight how these integrations unpack distributed processes through the four Es, particularly in historical and cultural contexts where emerges from embodied interactions with . In , 2020s research applies this fusion to human-robot teams, redistributing tasks in and care settings; for instance, studies illustrate challenges in collaborative robots (cobots) for actions like cup placement in due to technical limitations, while humans handle adaptive elements, as evidenced in empirical studies from 2019–2023. In and , distributed cognition informs multi-agent systems, particularly in human-LLM collaborations that distribute reasoning across agents. Studies from 2023 onward show LLMs enabling theory-of-mind inference in cooperative tasks, where agents attribute intentions to improve text-based collaboration, outperforming single-agent baselines in multi-turn games. By 2025, research demonstrates how LLM-powered multi-agent setups augment human in brainstorming, with teams achieving 16% higher in idea generation due to AI's "social forcefield" influencing communication patterns. These systems reshape cognition by offloading epistemic tasks, though they risk reducing human motivation if not designed for transparency. Post-pandemic research from 2021–2025 has advanced applications of distributed cognition in virtual teams, highlighting how remote collaboration redistributes via digital tools amid disruptions. Frameworks identify communication and trust-building as key for performance, with virtual teams showing improved by 2020–2021. Neuroscientific validations, such as 2022 fMRI studies, reveal distributed brain networks for tool concepts, with convergent representations in the anterior integrating multimodal experiences like tool manipulation, supporting embodied extensions of . A 2024 study further confirms shared frontoparietal connectivity during complex tool tasks, validating distributed processes across neural and external resources. Looking forward, distributed cognition holds potential in through eco-cognitive systems that distribute environmental awareness across human-tool networks. Cultural innovations like standardized checklists and AI offloading reduce in complex technologies, fostering co-evolutionary adaptations for , as seen in industrial transitions. Emerging debates center on digital distributions in metaverses, where platforms act as distributed cognitive ecosystems; for example, 2023–2025 studies integrate the into educational metaverses, using interactable 3D objects to support knowledge construction and via the Unified Theory of Acceptance and Use of Technology.

References

  1. https://arl.human.cornell.edu/linked%20docs/Hutchins_Distributed_Cognition.pdf
  2. https://nschwartz.yourweb.csuchico.edu/dcog-brief-intro.pdf
  3. https://www.simplypsychology.org/zone-of-proximal-development.html
  4. https://files.eric.ed.gov/fulltext/EJ1081990.pdf
  5. https://www.simplypsychology.org/vygotsky.html
  6. https://www.historyofinformation.com/detail.php?id=664
  7. https://maxplanckneuroscience.org/from-cybernetics-to-ai-the-pioneering-work-of-norbert-wiener/
  8. https://www.kybernetika.cz/content/1969/5/363/paper.pdf
  9. https://monoskop.org/images/c/cc/Harries-Jones_Peter_A_Recursive_Vision_Ecological_Understanding_and_Gregory_Bateson_1995.pdf
  10. https://press.uchicago.edu/ucp/books/book/chicago/S/bo3620295.html
  11. https://uberty.org/wp-content/uploads/2015/07/Edwin_Hutchins_Cognition_in_the_Wild.pdf
  12. https://link.springer.com/content/pdf/10.1007/978-1-4020-6706-8.pdf
  13. https://iep.utm.edu/int-ex-ml/
  14. https://www.researchgate.net/publication/315787060_Externalism_and_Internalism_in_the_Philosophy_of_Mind
  15. https://www.sfu.ca/~kathleea/docs/Microcognition.pdf
  16. https://pages.ucsd.edu/~ehutchins/
  17. https://www.macfound.org/fellows/class-of-1985/edwin-hutchins
  18. https://www.semanticscholar.org/paper/The-social-organization-of-distributed-cognition-Hutchins/b5ae3e55ca7e7d4d2ae05a7ec1fa44b2e01e1515
  19. https://mitpress.mit.edu/9780262581462/cognition-in-the-wild/
  20. https://pages.ucsd.edu/~ehutchins/citw.html
  21. https://scholar.google.com/citations?user=p5ylv3sAAAAJ&hl=en
  22. https://www.sciencedirect.com/science/article/pii/000437029400017U
  23. https://adrenaline.ucsd.edu/kirsh/Articles/Space/AIJ1.html
  24. https://direct.mit.edu/books/edited-volume/3274/Context-and-ConsciousnessActivity-Theory-and-Human
  25. https://direct.mit.edu/books/edited-volume/3274/chapter/101166/Studying-Context-A-Comparison-of-Activity-Theory
  26. http://sonify.psych.gatech.edu/~ben/references/nardi_studying_context_a_comparison_of_activity_theory_situated_action_models_and_distributed_cognition.pdf
  27. https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog1903_1
  28. https://dl.acm.org/doi/10.1145/353485.353487
  29. https://www.jbe-platform.com/content/journals/10.1075/pc.14.2.03har
  30. https://www.jbe-platform.com/content/journals/15699943/14/2
  31. https://cdn.aaai.org/ocs/ws/ws0201/12616-57524-1-PB.pdf
  32. https://www.tandfonline.com/doi/full/10.1080/09515089.2019.1679591
  33. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2020.00674/full
  34. https://pages.ucsd.edu/~johnson/COGS102B/Hutchins01.pdf
  35. https://spot.colorado.edu/~rupertr/DistCog_SageEncyc.pdf
  36. https://www.alice.id.tue.nl/references/clark-chalmers-1998.pdf
  37. https://pmc.ncbi.nlm.nih.gov/articles/PMC4174762/
  38. https://www.lri.fr/~mbl/Stanford/CS477/papers/DistributedCognition-TOCHI.pdf
  39. https://dl.acm.org/doi/10.1145/1978942.1978962
  40. https://link.springer.com/article/10.1007/s10111-014-0314-y
  41. https://hci.ucsd.edu/hollan/Pubs/tochi-revised.pdf
  42. https://nschwartz.yourweb.csuchico.edu/cockpit-cog.pdf
  43. https://www.thinkmind.org/articles/cognitive_2014_8_40_40168.pdf
  44. https://faculty.cc.gatech.edu/~stasko/papers/infovis08-dcog.pdf
  45. https://pmc.ncbi.nlm.nih.gov/articles/PMC5035172/
  46. https://adrenaline.ucsd.edu/kirsh/Articles/Journal_pragmatics/Dist_cog_pragmatics.pdf
  47. https://www.sciencedirect.com/science/article/abs/pii/S0142694X0700107X
  48. https://pages.ucsd.edu/~johnson/COGS102B/Hutchins95.pdf
  49. https://www.researchgate.net/publication/274979694_Reducing_Errors_in_Automated_Cockpits_Through_Cognitive_Engineering_Principles
  50. https://www.ics.uci.edu/~djp3/classes/2012_09_INF241/papers/Weiser-Computer21Century-SciAm.pdf
  51. https://amp.ubicomp.net/wp-content/uploads/2017/10/2017_human-augmentation-IEEE-PvC.pdf
  52. https://dl.acm.org/doi/10.1145/3726986.3727024
  53. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.910801/full
  54. https://arxiv.org/html/2407.17489v2
  55. https://l3d.colorado.edu/wp-content/uploads/2011/09/hci2003-dis-cog.pdf
  56. https://www.sciencedirect.com/science/article/pii/S0166361525000338
  57. https://dl.acm.org/doi/pdf/10.1145/353485.353487
  58. https://files.eric.ed.gov/fulltext/ED557306.pdf
  59. https://www.researchgate.net/publication/306231405_Interactive_Whiteboards_as_a_Means_of_Supporting_Students%27_Physical_Engagement_and_Collaborative_Inquiry_in_Physics
  60. https://www.researchgate.net/publication/373596857_Distributed_team_cognition_and_collaborative_problem-solving_in_project_management
  61. https://journals.sagepub.com/doi/abs/10.1177/8756972820960301
  62. https://onlinelibrary.wiley.com/doi/10.1111/cogs.12009
  63. https://www.apa.org/pubs/journals/features/apl-apl0000136.pdf
  64. https://users.aalto.fi/~asalovaa/publications/asatiani2019-Implementation-of-Automation-as-Distributed-Cognition-in-Knowledge-Work-Organizations-Six-Recommendations-for-Managers.pdf
  65. https://pubs.aip.org/aip/acp/article/2685/1/040025/2888786/Remote-work-and-distributed-cognition-Studying
  66. https://www.researchgate.net/publication/390991388_Distributed_Cognition_for_AI-supported_Remote_Operations_Challenges_and_Research_Directions
  67. https://ntrs.nasa.gov/api/citations/19940011077/downloads/19940011077.pdf
  68. https://www.researchgate.net/publication/2332597_Air_Traffic_Control_as_a_Distributed_Cognitive_System_a_study_of_external_representations
  69. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.908261/full
  70. https://philpapers.org/rec/TRIDCS
  71. https://www.researchgate.net/publication/249726183_Against_Distributed_Cognition
  72. https://www.jstor.org/stable/10.1086/518772
  73. https://johnrooksby.org/papers/CSCW2014_rooksby_replicated.pdf
  74. https://www.researchgate.net/publication/262176036_Wild_in_the_Laboratory_A_Discussion_of_Plans_and_Situated_Actions
  75. https://link.springer.com/article/10.1007/s13164-013-0131-x
  76. https://www.jstor.org/stable/23359439
  77. https://pure.au.dk/ws/files/285088549/4E_cognition_and_the_dogma_of_harmony_Accepted_manuscript_2020.pdf
  78. https://philarchive.org/archive/ANDDCA-4
  79. https://link.springer.com/chapter/10.1007/978-3-658-44458-7_9
  80. https://aclanthology.org/2023.emnlp-main.13.pdf
  81. https://dl.acm.org/doi/10.1145/3715928.3737479
  82. https://journals.sagepub.com/doi/10.1177/10464964211008991
  83. https://www.jneurosci.org/content/42/37/7121
  84. https://www.biorxiv.org/content/10.1101/2024.04.22.590618v1.full-text
  85. https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(25)00212-8
  86. https://ieeexplore.ieee.org/document/10225722/
  87. https://ijarcce.com/papers/the-educational-metaverse-as-a-distributed-cognitive-system-integrating-distributed-knowledge-theory-into-the-design-of-virtual-worlds/
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