Department of Geography and the Human Environment
Tel Aviv University
Tel Aviv 69978, Israel
Self-organized Inter-representation networks
A proposal for the Varenius workshop on cognitive models of dynamic phenomena and their representation
The basic assumption of the IPA is that the cognitive system and the
environment are external to, and thus fully independent of, each other. In
the context of the Varenius workshop this implies that human cognitive
abilities to manipulate, interpret and store, information, and, computers
and geographical information systems, are external to each other and
co-exist in causal relations. The notion of IRN suggests a somewhat
different view, the basic tenets of which are the following:
From this perspective, the central question of the initiative, is not just a question about the relations between human cognition, geographic space and different kinds of computerised representations and display, but of the various elements of the cognitive system itself.
My contribution to the Varenius workshop will elaborate on the philosophical issues raised above and their entailed operational and modeling implications.
2. Research Abstract
The research abstract that I describe below falls into two parts. The first, philosophical, discusses the general nature of IRN and its implication to GIS, and the second, operational, shows how the abstract ideas can be cast into formal algorithms and simulation models. Some of the issues of this research abstract, (namely, sections 1, part of 2, and 6) have already been published, others will be presented here for the first time. The emphasis of my contribution to the Varenius initiative will be on the latter. The various bibliographical references made below can be found in Portugali (1996) and in Haken and Portugali (1996)
Part I: General discussion
2. Some empirical examples for its operation. The latter include, among others, (i) the Bartlett scenarios of serial reproduction, as devised by him in his study of remembering, (ii) city-games, which are public-collective serial reproductions that we have devised within the context of our IRN research. (iii) Golani et al. experiments with rats, which according to our interpretation show how the external and internal spaces are simultaneously constructed, and, (iv) several experiments we are currently conducting on emotional effects during navigation. The latter examine the hypothesis that externally represented body effects participate in the process of learning and navigation. These (and other) experiments illustrate the interplay between internal and external representations, and the self-organizing nature of the process.
3. On the relations between IRN and the foundations of cognitive mapping. In this section I show that the notion of IRN is already implicit in the elementary ideas of the founding fathers of cognitive mapping. On the one hand, in Tollman, who pioneered the concept cognitive map as an internal representation within the frame of externally represented behaviorism. On the other, in Lynch who, in his The Image of the City, has elevated the role of five elementary artifacts (paths, areas, junctions, nodes, landmarks) as legible external representations with which one builds the image (i.e. internal representation) of the city.
4. IRN and the computer metaphor. Iíll show that the very Turing Machine, which is so central to the IPA and computationism, is essentially an IRN Machine. This claim is based on a new reading of Turing.
5. On the biological dimension of IRN. Iíll show that the relations between internal and external representations that is central to IRN, typify also other biological systems. Iím refering to the relations between genotypes and phenotypes as presented by Dawkins in connection with his concepts memes and extended phenotypes.
7. The proper method of representation. Like all neural networks the structure of Hakenís synergetic computer and consequently of our SIRN model, metaphorically mimics the neural structure of the brain. As long as one deals with internal representations only, this is indeed an advantage. However, when external representations are added as integral elements of the model, we are facing a problem. On the one hand, we have a neural net that enfolds information, while on the other, artifacts (buildings, cities computer systems, etc.) that enfold information. The challenge is to go beyond neural nets and artifacts and define a medium of representation that is appropriate for both.
8. The cultural code. The variable cultural code attempts to go beyond neural nets and artifacts and can thus be a medium of representation that is appropriate to both. It defines each individual cognitive system (each human individual) by means of internal and external representation, as in our SIRN model, and each representation, internal or external, by means of a cultural code, reminiscence of a genetic code. Technically, the cultural code can be defined by a Boolean vector. The theoretical foundation to this analogy between genetic and cultural codes is based on section 5 above. A graphical representation of this model is given below.