The Development of Scientific Concepts and their Embodiment in the Representational Activities of Cognitive Systems.

ly speaking, the genetic code changes over time and gives rise to a newly structured representational space (= synaptic weight space, potential space of possible representational configurations of an organism) and, thus, to a new set of behavioral strategies. In the course of ontogenesis this space is searched, as has been described in section 4.1. A particular state in the weight space gives rise to a space of potential representations and behavioral strategies (= activation spaceY? The goal of the phyloand ontogenetic dynamics is to turn the robot's arm in such a way that its tips find the region of functionally fitting solutions, behaviours, or knowledge. What .can these interactions between evolutionary and ontogenetic representational dynamics teach us for our problem of paradigmatic shifts? The right side of figure 4 shows how the dynamics of paradigms and theories fits into this picture. In section 4.2 it has been shown that "normal science;! (in the sense of Kuhn (1970)) can be characterized as a search and optimization process in theory space. It is embedded in the adaptation, learning, and construction processes of the whole neural representation system. The goal is to find consistency between the environment and the theories which are generated (Le., moving points in DEVELOPMENT OF SCIENTIFIC CONCEPTS 161 theory space) within the context and boundaries of the chosen paradigm. In this terminology a paradigmatic shift can be described as the construction of a whole new theory space. It consists of different dimensions, new and different semantics in the dimensions, and different representational and behavioral dynamics. This newly constructed theory space, of course, is embedded in the neural substratum and represents a whole new space of potential representational constructs, relationships, and behavioral patterns. In order to test this potential space of new theories, this new theory space has to be explored, as described in section 4.2. Looking at examples from history of science, one can see that the introduction of new paradigms often brought about some kind of surprise about the new way of looking at and structuring well known phenomena. It is the "irrational" and unexpected character which makes paradigmatic shifts so interesting whereas in normal science most results are rather predictable and the theories being responsible for them have to undergo only minor adaptations • Contrary to already established paradigms, newly constructed and unexplored paradigms are based on completely new concepts, basic assumptions, terminologies, and methods in most cases. This "irrational" character suggests that the (cognitive) processes being involved in generating paradigmatic shifts might have evolutionary character: a new paradigm is brought forth in a trial-&-error manner. It is even more hypothetical than the generation of a new theory in the context of an already established theory space/paradigm. This is due to the fact that at the moment of the introduction of a new p-aradigm it means only to suggest and generate a completely hypothetical framework and space of potential theories. Hence, there is relatively high risk involved in this process. It can be compared to the process of expressing a gene which has undergone some kind of mutation. It is completely unclear, whether the resulting organism and its potential representational structures and behaviours will be capable of surviving. Similarly,at the moment of the conception of a new paradigm, a to taIl y new potential theory space is created which has to be explored by the process of "normal science" it is not at all clear, whether this space of potential theories will be successful or not. The mechanisms being involved in paradigmatic shifts can be compared to evolutionary operators which are applied to cognitive/representational structures. The introduction of completely new and unexpected categories, making use of metaphors, combining aspects from different 162 MARKUS F. PESCHL theories, etc. have a lot in common with random mutations, cross over operators, etc. By applying these operators, a completely new theory space is established as can be seen in figure 4 (right), the goal is to rotate the robot arm into the region of functionally fitting theories. From this perspective it is also clear that two or more different theories can account for the same phe~omenon. I.e., the same area of functionally fitting solutions can be reached with different robot-arm configurations ( = different conceptual systems). In other words, a phenomenon is approached from two or more different sides or angles. As the goal is not to create an image or 1: 1 mapping of the environment, but to construct consistencies in the form of functionally fitting behaviour, it is no contradiction that two or more theories can account for the same phenomenon by making use of differ.ent' representational categories. In any case, the interaction between evolutionary mechanisms and ontogenetic representational dynamics could shed some light on the mysterious phenomenon of paradigmatic shifts in science. Evolutionary operators act as' "paradigm generators"; each of these paradigms establishes a space of potential theories which has to be searched according to the rules having been outl ined in sections 4.2 and 4. 1. Critics of such an evolutionary perspectiye of growth and development of scientific knowledge (e.g., Thagard 1988) are right in stating that a purely blind search for new scientific concepts is not an adequate model. That is why the focus of this paper is not only on phylogenetic processes, but also on ontogenetic learning/adaptation (see section 4). The important point is the interaction between the rather directed ontogenetic and neurally based learning, adaptation, and construction processes and the "blind" phylogenetic processes. Evolutionary variation "blindly" brings forth a completely hypothetical space of knowledge/strategies (paradigm) which is explored in a directed manner in the course of ontogenetic development. In this process oi-exploration the new paradigm will prove its in-/adequacy very soon. Contrary to Thagard's view(1988) that "the biological roots of the human information processing system are not directly relevant to the task of developing a model for the growth of scientific knowledge" (p 105), the presented concepts suggest that so-called scientific processes are not at all abstract processes occurring in a detached system called science. Rather, they are embedded in and results of the activities and dynamics of one or a group of neural systems and, thus, follow a similar dynamics. DEVELOPMENT OF SCIENTIFIC CONCEPTS 163 6. Implications and Conclusion The approach being suggested in this paper does not aim at replacing traditional theories and concepts in philosophy of science. Rather, the goal is to give (the explanation and theory about) science a new foundation scientific processes are embedded in cognitive processes. Starting from this very basic assumption, it might be possible to view scientific processes and traditional approaches in philosophy of science from a new (cognitive) perspective which might lead to a reformulation of these theories. I am aware that there is still a very long way to go, until satisfactory theories about the cognitive foundation of science will be available. Hence, the goal of this paper was not to suggest such a detailed theory, but rather to show, how basic principles and concepts which have emerged in last decade in the fields of cognitive science, computational neuroscience, and artificial life can be used as tools for enabling the construction. of such a theory'. Both cognitive systems and science have the representation of the world as their most important task. As has been shown, neither neural nor scientific representation aims at depicting or mapping the world. Of course, the obvious goal of science is to describe and explain environmental phenomena however, as has been discussed in the context of the constructivist concepts, the epistemological status of theories (and any kind of knowledge, in general) is cQnstructive rather than descriptive. Hence, theories, even if they have "descriptive character", are the result of complex processes of construction, which, in principle, can not produce more than functionally fitting representations of the world. The. goal is to construct strategies which are capable of adequately predicting and coping with the environmental dynamics. Even if it seems that our cognitive or nervous system provides us with "pictures of the environment" , it can be shown that these pictures are the result of active processes of construction being embodied in the architecture of the neural representation system. The goal of these construction processes is not to reconstruct the environmental structure as accurately as possible, but to provide the organism with relevant information and representations for generating adequate behaviour, making reasonable decisions, etc. The representations or theories only have to fit into the environment. This constructive character of representations becomes even more obvious in the case of scientific theories. In most cases they do not speak about entities which 164 MARKUS F. PESCHL can be perceived by our sensory systems theoretic entities are constructs about hidden mechanisms which fit (as explanations or predictions) into the perceivable environmental dynamics. In this sense knowledge and/or theories become tools which are used for predicting, controlling, and manipulating the environment. In the common sense domain we use representational entities, such as concepts, symbols, language, etc., as means for manipulating and influencing the environmental dynamics and/or the (representational) dynamics of other cognitive systems. Although theoretic or scientific entities, such as concepts in p'hysics (partiCles, waves, force fields, etc.), biology, or psycholo