Not Everything We Know We Learned

This is foremost a methodological contribution. It focuses on the foundation of anticipation and the pertinent implications that anticipation has on learning (theory and experiments). By definition, anticipation does not exhaust all the forms through which the future affects human activity. Accordingly, guessing, expectation, prediction, forecast, and planning will be defined in counter-distinction to anticipation. The background against which these distinctions are made is explicit in the operational thesis advanced: Anticipation and reaction can be considered only in their unity. The interrelation of anticipation and reaction corresponds to the integrated nature of the physical and the living. Finally, an agent architecture for a hybrid control mechanism is suggested as a possible implementation. Context and Reference Einstein [1] observed that, “No problem can be solved from the same consciousness that created it. We must learn to see the world anew.” As my own work in anticipatory computing evolved, I have constantly faced attitudes varying between skepticism and sheer enmity from outside the small community of researchers dedicated to the study of anticipation. Every example of anticipation my colleagues or I advanced was whittled down to the reactive explanations of the deterministic causeand-effect sequence, that is, to a particular form of causality. Even the reviewers of this paper could not agree among themselves on a line of argument that in the final analysis suggests that there is more to causality than what the Cartesian rationality that we learned in school and have practiced since then preaches. (A good source of information on this topic is http://www.culture.com.au/brain_proj/Descartes.htm.) Given this difficult, but not surprising situation, I shall proceed in a more didactic manner than I would otherwise be inclined. The intention is to clarify terminology before working with the concepts—a practice that Charles S. Peirce [2] defined as the “ethics of terminology,” a prerequisite of any scientific endeavor. From among the many definitions of anticipation advanced since the pioneering work of Robert Rosen [3, 4, 5] and my own early work [6], I would like to focus on the following operational definitions: 1. An anticipatory system is a system whose current state is defined by a future state. Eventually, a change was introduced in this definition [7] so to imply that in addition to the future state, a current or even past state could affect the state of the system (cf. Definition 9 in [8]). 2. An anticipatory system is a system containing a predictive model of itself and/or of its environment, which allows it to change state at an instant in accord with the model’s predictions pertaining to a later instant, in faster than real time (cf. Definition 3 in [8]). 3. Anticipation is the result of the competition among a number of mind models. Reward mechanisms explain the dynamics of this competition [cf. Definition 2 in [8]; see also [9]). In the end, I adopted the viewpoint according to which anticipation is a characteristic of the living. At the foundation of this perspective lie the work of Rosen, especially [4] and [5] and Elsasser [10]. Moreover, in discussing the neural basis of deciding, choosing and acting, Jeffrey D. Schall [11] distinguishes between external forces (explaining the “movements of physical bodies, such as rocks”) and “reasons” explaining “many human movements” (actions directed towards a goal). Anticipatory behavior—such as the recognition of the prey’s trajectory—is not the only expression of anticipation. We can mention design, creative activities, and conceptual elaborations as pertaining to the rich forms expressing anticipation. In respect to learning, it is rather difficult to limit oneself to a reduced number of definitions. The richness of the forms of learning and of the expressions of learning makes the attempt tenuous at best. However, given the focus of this volume— adaptive learning systems—a first pruning of the rich tree of learning definitions becomes possible. Computational learning theory (COLT) focuses on “the design and analysis of algorithms for making predictions about the future based on past experiences” (cf. Freund and Schapire, www.learningtheory.org). This definition falls within the reactive paradigm. An applied definition, originating in studies in time series prediction [12] sees learning as an emulation of the structure of time series, i.e., of the dynamics of processes we intend to understand, control, and automate. In this case, the knowledge about the unfolding in time of a process is eventually used in order to produce some desired behavior or to avoid undesired behavior. Lastly, for the purpose of this paper, learning as the pursuit of regularities or patterns in processes involving the physical/inanimate as well as the living is of special interest to me. This extraction (in forms such as data-mining, perception, knowledge mapping, etc.), by humans, by machines, or by hybrid entities, of regularities or patterns can be subjected to further analysis, i.e., further learning. Learning is a necessary condition of adaptive processes in the living, in machines, and in hybrid systems. In what follows, this methodological section remains the only reference. Not everything we know was learned. This holds true whether “knowledge is seen as socially situated or whether it is considered to be an individual extraction (cf. Ernest [13]). Taken at face value, it maintains that the process of knowledge acquisition is complemented by knowledge production. Moreover, in addition to what we learn, there is innate knowledge—that space that “has to exist before data,” as it was defined by some researchers (cf. Novak et al [14]) focusing on language and its active role in learning. There are genetically defined processes (such as language acquisition, (cf. Nadin [15] pp. 77ff) or seeing [15] pp. 321ff). But there are also anticipatory processes through which not only what is is acknowledged, but also what might possibly be is generated (cf. [10] p. 5), i.e., the principle of creative selection. My focus in this study is on particular processes, which can be defined as anticipatory, through which the complementarity between learning and the activity of knowledge production is accomplished. With this focus in mind, I shall refer to experimental evidence. (More scientific reports based on such evidence are available at www.anticipation.info) Evidence from experiments does not in itself replace a broader understanding. What examples can do, especially when associated with data resulting from experiments, is to make us aware of an unusual or unexpected outcome of some process.