An artifact ecosystem – new socio-technical regime for eTextbooks

August 17, 2014

I have just presented my ideas in Kai Pata. Mart Laanpere, Maka Eradze eTextbook as an artifact ecosystem in Future eTextbooks – FeT workshop in ICWL 2014.

Full paper: http://link.springer.com/chapter/10.1007/978-3-319-13296-9_25

Kristo Käo and Margus Niitsoo. MatchMySound. Introducing Feedback to Online Music Education.

o Kai Pata, Mart Laanpere and Maka Eradze. E-textbooks: towards the new socio-technical regime. (c-map of etextbooks as artifact ecosystems)

o Mario Mäeots, Leo Siiman and Margus Pedaste. Designing Interactive Scratch Content for Future E-books

o António Pedro Costa, Luis Paulo Reis and Maria João Loureiro. Hybrid User Centered Development Methodology: An Application to Educational Software Development.

o Andrej Flogie, Vladimir Milekšič, Andreja Čuk and Sonja Jelen. Slovenian “E-school bag”.

o Maka Eradze, Terje Väljataga and Mart Laanpere. Observing the use of e-textbooks in the classroom: towards “Offline” Learning Analytics.

o Terje Väljataga and Sebastian Fiedler. Re-conceptualizing E-textbooks: in Search for Descriptive Framework.

o Arman Arakelyan, Ilya Shmorgun and Sonia Sousa. Incorporating Values into the Design Process: The Case of E- Textbook Development for Estonia

Our paper with Maka and Mart discusses the niche technologies that have and possibly will contribute to the future e-textbooks as a new socio-technical regime. We propose the conceptual map of textbook functionalities aiming at opening the conceptual discussion for brainstorming and finding scenarios how the niche technologies that explored novel textbook applications in learning might be best combined into the new “artifact ecosystems” regime. Jointly with workshop participants we aim to come up with metaphors and concepts depicting learning in this regime.

Full papers are published in http://www.springer.com/computer/book/978-3-319-13295-2


Promoting distributed cognition at MOOC ecosystems

July 4, 2014

In June i attended the HCII 2014 conference in Crete where i presented our paper with Emanuele Bardone.



exploring systemic cognition empirically

April 17, 2014

Activity description:

We have in theory camp the need to combine some theoretical aspects for developing a theory framework for social semantic server, action design approach.
We can use wordpress with KB widget for discovering collectively created resources.
Every participant uses in the first stage KB to search and tag suitable resources. Each resource gets the assignment tag (e.g. social semantic server task has tag social semantic server, or action research has task action research) and as many additional tags as one would need to describe the meaning dimensions of added resources. Each resource added to KB is also shared with others.
In Social semantic server we may have to types of knowledge resources emerging:
– personal resources (personal cognitive niches) around the assignment tag (for example person A has collected resources, which have additional tags systemic cognition, knowledge maturing; whereas person B has tagged resources additionally with communities of practice, scaffolding, adaptive learning etc.).
– shared resources around the assignment tag ( this compiles all resources that have tag social semantic server, as well as the associated tags and presents this tag-set to each user) (collective niche).
In the second phase of the activity, wordpress is used as the meaning-making tool. Each participant has in wordpress the widget with the KB tags. This widget enables to see personal tag-cloud with a certain assignment tag, and shared tag-cloud with the certain assignment tag. Each person uses shared tags to search from resources and uses liked tags and resources for elaborating the question, how social semantic server activities can be explained with different theoretical perspectives.
The meaning-making activity ends with tagging the reflections with the assignment tag and other additionally selected tags. Basically, these matured resources become part of the shared pool of knowledge and may be used in the next iterations of the same activity.
Research questions about systemic cognition: How is the shared knowledge explored and made use of by individuals at meaning-making?
Does personal knowledge (personal cognitive space) determine, which tags are perceived as relevant in the shared tagcloud? We can compare personal tags that are related with assignment tag with the activated tags in the shared tag-cloud around the assignment tag to discover if there is dependency.
Do the most frequently utilized tags in the shared tagcloud (accumulated patterns in the collective niche) get more attention for selecting resources? We can compare most frequent tags with the activated tags in the shared tag-cloud around the assignment tag to discover if there is dependency.
How does the personal cognitive space mature as a result of using collective cognitive space? We can compare initial (phase1) and final (phase 2) tagsets of elaborated resource tags of individuals – is there convergence happening between individuals due to pattern-usage? Can the convergence happen after some cycles of the activity?
Explanation: systemic cognition is the distributed cognition in which personal cognitive space is embedded to the collective cognitive space. However we do not know how collective knowledge space becomes activated due to personal cognitive spaces (basically how culture influences what we decide).
Additionally, in KB some resources can be rated by giving stars. How would such rating increase the visibility and usage of certain tags (basically can the collectively validated resources be chosen more frequently) – basically can we enhance pattern-visibility in the culture, and whether such patterns be
We may try to play such an activity among ourselves in HTK, or among the colleagues in Layers.
The same idea in variations in topics may be done with application partners. For example we can choose a problem-related tag as the central assignment tag. We may also try to play the same activity with students in association with some idea, central concept, but this is better doable in autumn term as part of some master or doctoral course.

systemic cognition and support in socio-technical systems

March 19, 2014

Explaining informal learning@work at managed clusters organized as TEL based socio-technical systems requires binding different level explanations: distributed cognitive level, personal – organizational level, cross-organizational network – cluster level.


Systemic (or distributed) cognition level

Benefits: Focusing on the systemic nature of distributed cognition (on the interplay between the epistemic distributed cognition from the agents’ side and the collective distributed cognition of organizational or professional community cultures) allows using the ecosystem principles for describing how learning services emerge and co-exist in this informal workplace learning ecosystem.

Distributed cognition makes use of vector-spaces for describing cognitive niches of individuals, cultural niches and meaning niches of resources.

Person- Organization level

Benefits: to open up the transformative knowledge conversion between individual and organizational knowledge (Nonaka & Takeuchi, 1995); particularly utilizing agents’ informal learning events for the benefit of organization and motivating self-directed learning at work with social and (cross-)organizational factors.

Problems: Implementing new learning cultures in organizations, moving from unintentional towards intentional informal learning practices in organizations

Cluster and cross-organizational network level

Benefits: increased responsiveness for the cluster and for its member organizations is achieved through temporal cross- and inter-organizational informal learning activities at work, and orchestrated bottom-up and top-to-down systemic management of shared knowledge and provision of services based on the knowledge base (see IntelLEO project results for responsiveness).

Problems: competitive edge between members, sharing restrictions for knowledge, the lack of mutual trust or over-conficence in one’s organization’s knowledge

Workplace learning ecosystems

Basically, the systemic cognition approach views socio-technical systems at workplace learning as learning ecosystems.

There is a variety of learning services at present (created by experts and in general by any learner), which are used by other informal learners and that accumulate and interact at organization’s and cluster’s knowledge-bases.

Agents: novices and experts:

Scaffolding in networks requires considering the differences of agents’ problem contexts, knowledge and expertise.

Self-directed agents create and make use of (request for, validate, share, modify etc.) workplace learning service exemplars when they solve problems or provide help.

Each learning service exemplar provided or utilized must be fit to the prototypical learning services niche of his kind. These niches are determined by many exemplars that agents activate. For example, request for help must contain sufficient information about the specific problem and help needed to attract those help-providers that have suitable expertise for tackling this problem, further, the help provided to meet this request must be useful, it should solve this problem as closely as possible.

Knowledge transfer is primarily inter-personal.

Organization: At socio-technical system level certain prototypical learning services are dynamically provided, depending on which learning services the agents activate:

  • increased awareness for, accepting and forwarding help-requests;
  • providing help adaptively in turn-taking actions that ground the problem;
  • fading out the help when competence increases;
  • indicating towards developing helpful resources (artifacts, objects, tools, persons in the network);
  • validating resources;
  • increasing persons’ expertise and trust level in respect of providing help for learning at work.

Each prototypical learning service is directed towards solving some workplace problem or conceptualizing some idea. These prototypes have contextual meaning niches that emerge and change dynamically as a result of many agents’ activation of the exemplars of that kind. These meaning-niches are like communicative signals offloaded to the socio-technical system. They serve as attraction basins indicating to agents, where organizational learning could be most effective.


  • creates incentives and manages motivation for promoting learning cultures at work;
  • explores and incorporates to organizational practices the usage of new learning@work activities;
  • removes the restrictions for cross-organizational knowledge transfer;
  • promotes open innovation cultures – open access to early prototypes,
  • design solutions or process-innovations with open source licenses;
  • promotes temporal alliances between members from different organizations to identify how to cope with challenges;
  • explore the opportunities or develop innovation.

Cluster management: maintains cluster’s organizational networks and knowledge base (ontologies, competences, norms and guidelines, access to human and virtual-real learning resources) and provides services based on this knowledge:

  • distributes information about challenges, practices and opportunities to learn;
  • identifies and nourishes new ideas that arise in the cluster organizations – such as organizing temporal cross-organizational knowledge-building activities for innovation;
  • provides, evolves and matures professional norms and guidelines;
  • initiates service-based value networks between member organizations;
  • detects proximities between cluster members;
  • promotes the learning culture at work that increases social motivation  – the more users are involved, the more likely it is that system becomes effective and is self-organizing;
  • controls organizational learning with incentives and motivation-management (policies for accreditation possibilities, and validation of workplace learning experiences).

Formal and informal cross-organizational networks are important to transfer knowledge.

The learning services the cluster can initiate depend on the abundance of certain learning service exemplars and of the learning service prototypes and niches at present in the socio-technical system.

Ecosystem principles applicable in learning ecosystems

The first principle in ecology is that the flow of energy and the exchange of matter through open ecosystem is regulated by the interactions of species (in our case types of learning services) and the abiotic component (by the web of energy and matter). Reyna conceptualized “teaching and learning” as this energy that empowers digital learning ecosystems to changing “information to knowledge”. The permeability of a digital learning ecosystem to the export and/or import of information and knowledge depend on the nature of the ‘architecture’ of the components of the system (e. g. connectivity, clustering), the characteristics of species, and their diversity and distribution, and interactions between them (such as commensalism).

The second important ecological principle is existence of the feedback loop to and from the environment that enables species to be adaptive to the environment and the environment to change as a result of species. A recent literature in evolutionary theory elaborates the notion of niche construction as an ecological factor that enables organisms to contribute for and benefit from environmental information. If organisms evolve in response to selection pressures modified by themselves and their ancestors, there is feedback in the system. In our approach to digital learning ecosystems, the “service-species” are activated by users with different roles (learner, facilitator) and their learning intentions. The niches for each service-species in the digital ecosystem may be collected from user-behavior, for example by learning analytics (an emerging approach to tracing digital footprints of learners and groups, visualizing the learning-related patterns).

Applications in social semantic systems: 

Niches are vector spaces – see paper From vector spaces to meanings

If we make use of the Connectionism approach to concept-processing (see the paper of Seitlinger et al, 2013) and extend this approach to epistemic and collective distributed cognition that happens in using mobile learning tools together with social semantic server, we may have an approach for socio-semantic recommendations that provide help based on the meaning niches that fit best to the requests (see the examples below).

In biology the figures for niche breadth figures are used, that may be useful in recommendation, also the idea of fitness landscape and attraction basins may be considers in recommendations.

The third important principle that we extend from ecology to technology-enhanced learning domain is associated with the communicative interactions between species. The digital community is a naturally occurring group of “service-species” populations in e-learning ecosystem who inhabit the same habitat (but use different niches) and form temporary coalitions (communities). For example the mutualisms such as parasitism, symbiosis or commensalism may appear between service species are associated with sharing the resources and associate with our first principle (energy and matter exchanges in the network). Other type of interactions, based on communication, which assumes mutual awareness, signaling between agents (or using the accumulated signals left into the environment) may be distinguished as well.

Application cases of informal learning at work

Below, there are three informal learning and supporting behaviours that may potentially appear in socio-technical systems.


To introduce new knowledge to the newcomers the experts make use of their earlier experiences, they also utilize and evolve resources for providing help, as well as the archetypical scaffolding models in their profession, on the other hand, the help-provision increases the trust level of experts in respect of solving certain problems.ACCUMULATING EXPERTISE


Recommending – relating systemic cognition and connectionist approaches

Some issues of recommending when using the systemic (distributed) cognition approach:recommending

Seitlinger. et al., (2013). Recommending Tags with a Model of Human Categorization

Seitlinger et al.(2013) use in their recommendation model Connectionist model of cognitive processing:

Kruschke 1992 alcove model

First layer can have distributed activation. The model is initialized with equal attention strengths to all dimensions, but as the training proceeds, the model learns to allocate more attention to relevant dimensions and less to irrelevant dimensions.

Internal layer functions as agent’s cognitive niche that incorporates cultural niche for weighting. Internal layer gives weights to the nodes, each hidden node corresponds to a position in the multidimensional space. A state of activation (a) at a given time (t): The state of a set of units is usually represented by a vector of real numbers a(t). These may be binary or continuous numbers, bounded or unbounded. A frequent assumption is that the activation level of simple processing units will vary continuously between the values 0 and 1.

In biology, Hutchinson (1957) defined niche as a region (n-dimensional hypervolume) in a multi-dimensional space of environmental factors that affect the welfare of a species (in our case prototypes). Niches have been conceptualized as the collections of environmental gradients with certain ecological amplitude, where the ecological optimum marks the gradient peaks where the organisms (in our case exemplars) are most abundant.

The welfare of species can be determined by meaning-creation and action-taking possibilities in the environment.

In the gradient concept structural ecosystem properties are comprehended as concentration gradients in space and time (Müller, 1998). Any niche gradient is a peak of the fitness landscape of one environmental characteristic (Wright, 1931), which can be visualized in two-dimensional space as a graph with certain skew and width, determining the ecological amplitude. The shape of the fitness graph for certain characteristic can be plotted through the abundance of certain specimen (exemplar in our case) benefitting of this characteristic. All niche gradients are situated and establish a multi-dimensional hyper-room, which axes are different environmental parameters.

This connectionist theory problem was also explained by T. Ley in Innsbruck meeting.

Also see article From vector spaces to meanings


distributed cognition and supporting learning@work

March 14, 2014

There are two main ways in which, distributed cognition may be framed – epistemic distributed cognition and collective distributed cognition, which are mutually interrelated.

I: Person-centred view

  • Epistemic distributed cognition: distributed cognition is eminently epistemic from the persons point of view, because it deals with the very activity of knowing and understanding the world one lives in. Giere (2007) refers to this kind of distribution as locally distributed cognition.
  • Epistemic distributed cognition results in creating personal cognitive niches. Magnani (2008), and Magnani and Bardone (2008) use the term cognitive niche to mark the distributed space that people create by interrelating individual cognition and the environment through the continuous interplay through abductive processes in which they alter and modify the environment.
  • People use external resources as part of the distributed cognitive system to solve problems. They continuously offload some of their cognitive functions onto external objects and artifacts, which can just be found out-there or designed for accomplishing specific goals and tasks (this approach may be important in Captus).
  • Tacit dimension of knowledge that is different for everyone influences what one is finding worthwhile to consider as part of their cognitive system, due to the tacit knowledge persons are creating diversity and variability in their cognitive niches.
  • Some part of the cognitive niche formation may be seen as the purposeful activity – with the problem or goal in hand one tries to develop distributed cognitive structures (for example in Sharing turbine the White folders)
  • Not all what is incorporated into cognitive niche is useful at the time when it is noticed, but it may become useful later on, since much of the informal learning happens retrospectively. Learning lies outside the realm of regular expectations and involves a forward-looking attitude. We cannot entirely rely on what happened in the past, but we must inevitably look forward, projecting ourselves onto the unknown future. (For example in Bits and pieces).

II: Collective (culture, community, system) centred view

  • Collective distributed cognition is cognition where the output of a certain cognitive process cannot be attributed or tracked back to the effort of a single agent, but it is the product of collective effort. (Relate with social semantic server, also networked scaffolding)
  • Social organisation is seen as a form of cognitive architecture that determines the way information flows in the context of activity (Hollan et al., 2000).  (relate with networked scaffolding)
  • External resources – the objects, artifacts, and at-hand materials and software are temporally integrated into goal-based affordance networks (see Barab & Roth, 2006) that support actions. Barab and Roth (2006) have noted that connecting learners to ecological networks, where they can learn through engaged participation, activates the affordance networks. Affordance networks are extended in both time and space and can include sets of perceptual and cognitive affordances that collectively come to form the network for particular goal sets. Affordance networks are not entirely delimited by their material, social, or cultural structure, although one may have elements of all of these; instead, they are functionally bound in terms of the facts, concepts, tools, methods, practices, commitments, and even people that can be enlisted toward the satisfaction of a particular goal. In this way, affordance networks are dynamic socio-cultural configurations that take on particular shape as a result of material, social, political, economic, cultural, historical, and even personal factors but always in relation to particular functions.
  • The result of many self-organised cognitive activities contributes to ecological enculturation. Ecological enculturation brings the traces of previous activities available in some form for future use. It increases the anticipatedness of the environment by formation of cultural patterns as cultural niches for solving certain problems. (for example in Social Semantic Server)
  • Alexander and associates (1977) define design patterns as the visible/explicit part of a solution to a problem in a field of interest. They assume that patterns tend to focus on the interactions between the physical form of the built environment, and the way in which that inhibits or facilitates various sorts of personal and social behaviour within it (Alexander et al., 1977). Patterns are easily recognisable generalisations of solutions for a problem, that emerge as the contingent result of all the occasions there have been to renew or enrich, or to maintain the stock of this problem’s solutions, using the remains of previous constructions or destructions from individuals.
  • Culturally, each pattern exists as an emergent niche in the system. In the pattern niche the environment becomes anticipated and ecologically encultured due to many learners’ activities. There are no defined patterns one can “take” but patterns exist in an abstract way as effective niches in the encultured environment, which are evolving constantly. These niches emerge as abstract spaces and the range for the pattern niches is created as the fitness of many similar individual patterns is tested in the culture’s ecosystem.
  • Emergent enculturation, the formation of cultural niches occurs as a product of self-organised system behavior from the interactions between various types of actors and the environment. Deliberate enculturation may be done incorporating certain guidelines, and instructions for action to the system
  • When we are have a precise intent in mind, we can turn to certain patterns detectable in the collectively encultured system. (For example when we search help) Interacting with the environment having a specific learning goal, the appropriation of patterns would decrease the need to seek chances, since the ecologically encultured environment can lead you with patterns that might do the job effectively. The trivial understanding of pattern usage is, that taking a pattern it can be used as a template for repeating the pattern. However that view of pattern-replication is misleading, since pattern niches are evolving constantly. Alexander et al. (1977) calls such niches the pattern prototypes.
  • The embodiment of pattern prototypes has person-dependent and culture-dependent components and variability. Patterns can be found because they are cognitively afforded partially internal and partially culture defined multi-dimensional spaces (Zhang & Patel, 2008). Only the learner who is part of the culture can perceive the pattern niches encapsuling some problem solutions that this culture that has sorted out. An options for finding dimensions incorporated into patterns is aligning one’s attention in crowded places, looking for the traces left by others, or mimicking and uptaking others’ behaviours in the environment.  It is only known by the learner whether a collectively formed pattern facilitates something for himself, but even he does not know whether any collectively defined pattern helps him in his learning. 

III: Interrelations between personal (epistemic distributed cognition) and cultural (collective distributed cognition) cognitive niches (see figure 1)

  • Bardone (2011) suggests that human beings act as an integral part of their environment while at the same time actively modifying and constructing this environment. Niche construction as an ecological factor that enables organisms to contribute for and benefit from environmental information (Odling-Smee et al., 2003). If organisms evolve in response to selection pressures modified by themselves and their ancestors, there is feedback in the system. The feedback must persist for long enough, and with enough local consistency, to be able to have an evolutionary effect. Ecological inheritance is a modified environment influenced by organisms, their ancestors or other organism communities what has evolutionary effect and selection pressure to organisms. Ecological inheritance depends on the persistence, between generations, of whatever physical changes are caused by ancestral organisms in the local selective environments of their descendants.
  • Traditionally, enculturation refers to the process by which a person becomes acquainted with a given culture (or community of practice) (Wenger, 1998), which may be related with EDC. This is the process of adapting oneself to the cultural niches.
  • The epistemic distributed cognition comprises two loops of cognitive niche formation – the creative loop of chance-seeking uses the ecosystem unanticipatedness for chance-seeking, and chance amplification and results with personal patterns; the accommodating loop of pattern-finding builds on cultural anticipatedness and results with validating and amplifying these patterns as instances of a cultural pattern
  • The collective distributed cognition is fed by the personal patterns: the chance-seekers create personal patterns as distributed cognitive niches which serve as the destabilizers of cultural patterns that extend or shift the pattern niches, whereas pattern-finding activity validates cultural pattern niches and stabilizes the ecosystem incorporating cultural patterns as optimal collectively selected solution paths to the distributed cognitive environment of the individuals. 

Let’s illustrate this:

  • According to Schmidt, Norman and Boshuizen (1990), expertise formation is associated with the qualitative transition from a conceptually rich and traditional knowledge base and analytical approach in diagnosing to one comprised of largely experiential and non-analytical – that is a radical departure from conventional view of clinical competence development.doctorsin community

    Figure 2. Medical reasoning with new cases (differences for novice and experienced doctors)

    Medical students learn mainly from theories, based on books – the result is a propositional pathophysiological network about the disease causes and consequences in terms of pathophysiological processes – the resulting perspective on disease is rather prototypical, with limited understanding of the variability with which disease manifests in the reality. They use many concepts to explain the phenomena. The medical guidelines have similar prototypical nature.

    In practice with patients the knowledge-in-use will reorganize itself to increase accessibility into simplified causal models explaining signs and symptoms that contain only higher-level concepts from original pathophysiological networks and their relationships. The student begins noticing contextual factors under which disease emerges  (enabling factors in script) and instead of causal processes the different features that characterize clinical appearance of the disease become the anchor points. Simultaneously list-like illness-scripts are formed that contain enabling conditions for the disease, faults and consequences. In case of diagnosing, the script is searched and verified. The script elements appear in specific order that matches the way doctors inform other doctors about their patients’ conditions. Enabling conditions in the script develop quite slowly in response to experiences in daily practice. Expert doctors make more use of constraining information than novice doctors. Because of different experiences different doctors may develop quite different scripts for the same disease. Such idiosyncratic scripts bear only superficial relationship with the prototypical disease cases.

    Finally scripts are supplemented with elaborated instances. Experienced doctors use in diagnosing the case similarity – the new case is mapped to previous patient case – this is a pattern recognition process. “The problems have the life of their own” a large part of expertise appears to consist of matching a problem with similar ones seen before.

    The scripts and elaborated instances at the collective level would form the “living guidelines”.

    In case of such dynamic pattern search and recognition, pattern-instance validation and collective pattern formation process the critical issue is whether novice will understand the support that is provided using the way experts way of structuring knowledge, and against which knowledge structure (prototypical, script or elaborated instances) the validation will be processed.


Protected: exploitation-implementation relations

March 4, 2014

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Two Concept definitions and distributed cognition in informal learning

February 14, 2014

At Learning Layers meeting we had the session for theory that guides the project’s technologies and approaches for informal learning at work.

Tobias Ley explained with the figure how the two ways how the concept has been dealt with.


The upper figure relates with the semantic systems that use top-down ontologies for providing support i finding resources for learning.  It explains how real world objects or resources in the web are represented internally and represented by the annotations (tags). Such a model for concepts enables creating ontologies that define the relations of objects. The ontologies that guide learning may be considered as archetypical models – some of them are based on common knowledge and serve as community recommendation structures, others’ have become standards and work as top-down obligational constraints to guide with the recommendations the activities with the related objects.



The bottom figure describes the object representations through concept-vectors.

I think this view is ecology-driven. The description of an object – the concept perception through vectors of certain  properties (tags) – may be considered as one “organism”, whereas all the object conceptualizations in the (learning) community create the object’s description as a “species” –  and this “species” is fit within the niche described by the vector space described by individuals in certain time moment. The most frequent tags create the part of the niche that is community-specific or stabile and may be considered as an archetypical model.  The borders for community’s archetypical model within the whole niche are perceptional, and may be related with the frequency of certain “organisms” in time period, as well as the community members’ validation to certain “organisms”. (acknowledged or core members’ concepts may be considered more credible than newcomers’ ones and determine the community archetypical model)

If someone searches help from the concept niche defined by the (learning) community, his own concept definition may be less or more fit to this concept niche and its most frequently used areas (the archetypical model).  Adaptive learner may try to accommodate to the archetypical models in the niche – it means following the meaning-patterns defined by many. This learning behavior that may be called pattern appropriation is one of the distributed cognitive behaviors when individual learners orient themselves (or may be automatically guided by) the encultured niches in the meaning ecosystem.

Another interesting thought is, if the concept niche is simultaneously provided to the learner through two niches – as the standard archetypical model and the community-defined archetypical model. The standard archetypical model is usually stabile, while the community-defined model depends of the usage contexts of the concept that are changing dynamically. In this situation the learner’s own concept definition has to “decide” in which niche it is more fit. I think it explains why the community niche of concepts is always having the competitive edge over the standards niche – it requires less cognitive effort to stay using the community-defined archetypical model rather than adopting the personal meaning concepts towards fitting to the niche of the standard archeotypical model.

Yet, if one could see the visualization to what extent both niches overlap, the non-fit  (or out of standard niche) parts (tags?) of own  concepts could be consciously detected and abandoned in order to adopt own concepts to the arceotypical models.

In some conditions it is actually not useful to stay in the rigid and timely not updated standard niche, but rather to let oneself be guided by the context-tested niche of the community defined archetypical model.  This model (and the relevant niche) is less mature in this sense that it may contain areas what are not sufficiently proven to be useful. But it may contain also more useful areas than the standard niche.

In our discussions with Emanuele Bardone we defined two distributed cognitive behaviors – pattern appropriation clearly relates with the niches as pattern-spaces. Every person repeats for himself certain  meanings for the concept, and actually therefore creates the personal cognitive niche that is a vector space defined by these trial usages of the concept. The personal cognitive space can be located somewhere within the community and normative niches. It seems cognitively easier to be staying mostly within the range of his own cognitive niche, especially if it is fit to the cultural/standard archetypical niches.

But there is also another behaviour – chance-seeking that is extending one’s cognitive niche with new elements, adapting to be more fit. The chance-seeking can be used for extending the community defined archetypes for the concept.  The chance-seeking may be made conscious by visualizing the chance cases that appear outside the current personal cognitive niche. Chance amplification would then mean consciously repeating such cases, testing their viability empirically to extend one’s cognitive niche.

Chance amplification becomes the collective process of distributed cognition  when one can see the changes in his cognitive niche in the cognitive landscape of the community (the standard- and community niches of the archetypes) and get recommendations from those community niches. Several persons contribute to these community niches dynamically by sharing their concept-instances (“organisms”) and can be simultaneously aware of others’ chance-seeking events, if these are made explicit. The collectively empowered chance amplification increases the possibility that positive chances would be incorporated to the community niches.

Let’s illustrate all this: There is a standard treatment procedure in medicine that has to be followed for certain disease (standard archetype). As this is tested out by the doctors in actual practice sometimes it works, sometimes it seems not fit to the actual situations. The doctor at every patient’s case creates for himself the description how to do the treatment and this builds up the personal cognitive niche for this disease treatment. The doctor has learned the standards of treatment. Maybe the doctor also discusses the deviation-cases with other doctors. The cognitive niche of this doctor then incorporates and is embedded within the standard treatment niche as well as the community-defined niche. I think for the person these become inseparable. At some moments the doctor becomes aware of that the new case does not fit to his previous cognitive niche and the chances he creates as new treatment must be validated. One way of collective chance validation is seeing if someone else in the community has already tested the similar new treatment, another is making his chance case known to others for validation. In both cases the chance amplification is collectively empowered. And it is more likely that collectively empowered chances will extend the cognitive niches of many persons and thus become common and get incorporated to the arhetypical models of the community niches.



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