This paper aims at understanding modular organization in multivariate dynamical data. In contrast to information-theoretic approaches, the authors start from the complementary point of view of statistical modeling and prediction of dynamical systems. They arrive at the conclusion that modularity is not necessarily an objective property of a system’s organization but rather is inferred by cognitive systems as it can simplify learning and lead to gains in predictive power. This conclusion may prove useful for constructivist approaches as the paper establishes in formal ways that learning agents may perceive modularity and correlations among variables in their environments, even when such variables are actually dependent on others.

The degree of interdependence between intracranial electroencephalographic (EEG) channels was investigated in epileptic patients with temporal lobe seizures during interictal (between seizures) periods.With a novelmethod to characterize nonlinear cross-predictability, that is, the predictability of one channel using another channel as data base, we demonstrated here a possibility to extract information on the spatio-temporal organization of interactions between multichannel recording sites. This method determines whether two channels contain common activity, and often, whether one channel contains activity induced by the activity of the other channel. In particular, the technique and the comparison with surrogate data demonstrated that transient large-scale nonlinear entrainments by the epileptogenic region can be identified, this with or without epileptic activity. Furthermore, these recurrent activities related with the epileptic foci occurred in well-defined spatio-temporal patterns. This suggests that the epileptogenic region can exhibit very subtle influences on other brain regions during an interictal period and raises the possibility that the cross-predictability analysis of interictal data may be used as a significant aid in locating epileptogenic foci.

Key words: Temporal lobe epilepsy, Intracranial EEG, Epileptogenic focus, Nonlinear cross-prediction, Surrogate data

In this article, we review the nature of the functional and causal relationship between neurophysiologically/psychologically generated states of emotional feeling and action tendencies and extrapolate a novel perspective. Emotion theory, over the past century and beyond, has tended to regard feeling and action tendency as independent phenomena: attempts to outline the functional and causal relationship that exists between them have been framed therein. Classically, such relationships have been viewed as unidirectional, but an argument for bidirectionality rooted in a dynamic systems perspective has gained strength in recent years whereby the feeling–action tendency relationship is viewed as a composite whole. On the basis of our review of somatic–visceral theories of feelings, we argue that feelings are grounded upon neural-dynamic representations (elevated and stable activation patterns) of action tendency. Such representations amount to predictions updated by cognitive and bodily feedback. Specifically, we view emotional feelings as minimalist predictions of the action tendency (what the agent is physiologically and cognitively primed to do) in a given situation. The essence of this point is captured by our exposition of action tendency prediction–feedback loops which we consider, above all, in the context of emotion regulation, and in particular, of emotional regulation of goal-directed behavior. The perspective outlined may be of use to emotion theorists, computational modelers, and roboticists. Relevance: The paper presents an enactive/cybernetic/dynamical systems perspective on embodied emotion theory (James, Damasio, etc).

This article presents a novel computational framework for modeling cognitive development. The new modeling paradigm provides a language with which to compare and contrast radically different facets of children’s knowledge. Concepts from the study of machine learning are used to explore the power of connectionist networks that construct their own architectures during learning. These so-called generative algorithms are shown to escape from Fodor’s (1980) critique of Constructivist development. We describe one generative connectionist algorithm (cascade-correlation) in detail. We report on the successful use of the algorithm to model cognitive development on balance scale phenomena; seriation; the integration of velocity, time, and distance cues; prediction of effect sizes from magnitudes of causal potencies and effect resistances; and the acquisition of English personal pronouns. The article demonstrates that computer models are invaluable for illuminating otherwise obscure discussions.

The paper proposes a way of bridging the gapbetween physical processes in the brain and the “felt” aspect of sensory experience. The approach is based onthe idea that experience is not generated by brainprocesses themselves, but rather is constituted by theway these brain processes enable a particular form of “give-and-take” between the perceiver and theenvironment. From this starting-point we are able tocharacterize the phenomenological differences betweenthe different sensory modalities in a more principledway than has been done in the past. We are also ableto approach the issues of visual awareness andconsciousness in a satisfactory way. Finally we consider a number of testable empirical consequences, one of which is the striking prediction of thephenomenon of “change blindness.”

This paper addresses the questions concerning the relationship between scientific and cognitive processes. The fact that both, science and cognition, aim at acquiring some kind of knowledge or representation about the “world” is the key for establishing a link between these two domains. It turns out that the constructivist framework represents an adequate epistemological foundation for this undertaking, as its focus of interest is on the (constructive) relationship between the world and its representation. More specifically, it will be shown how cognitive processes and their primary concern to construct a representation of the environment and to generate functionally fitting behavior can act as the basis for embedding the activities and dynamics of the process of science in them by making use of constructivist concepts, such as functional fitness, structure determinedness, etc. Cognitive science and artificial life provide the conceptual framework of representational spaces and their interaction between each other and with the environment enabling us to establish this link between cognitive processes and the development/dynamics of scientific theories. The concepts of activation, synaptic weight, and genetic (representational) spaces are powerful tools which can be used as “explanatory vehicles” for a cognitive foundation of science, more specifically for the “context of discovery” (i.e., the development, construction, and dynamics of scientific theories and paradigms). Representational spaces do not only offer us a better understanding of embedding science in cognition, but also show, how the constructivist framework, both, can act as an adequate epistemological foundation for these processes and can be instantiated by these representational concepts from cognitive science. The final part of this paper addresses some more fundamental questions concerning the positivistic and constructivist understanding of science and human cognition. Among other things it is asked, whether a purely functionalist and quantitative view of the world aiming almost exclusively at its prediction and control is really satisfying for our intellect (having the goal of achieving a profound understanding of reality).

Key words: cognitive science, functional fitness, human person, knowledge (representation), (natural) science, representational space

Analysis of electroencephalographic signals and several brain imaging studies suggest that a preictal state precedes the onset of seizures. In this study, we used phenomenological strategies to detect modifications in patients’ experience before their seizures. We observed that patients with partial epilepsy feeling an aura (n = 9) frequently experienced prodromes (n = 6). Prodromes were subtle preictal symptoms, varying among patients and having common negative features. They were generally continuous before seizures and could last hours, whereas auras were sudden and intermittent. All patients were able to recognize facilitating factors. We also found that patients spontaneously develop cognitive countermeasures to avoid facilitating factors (n = 6), to prevent a seizure (n = 1) or to interrupt a seizure (n = 5). Prodromes are not specific enough for clinical use, but could refine the behavioral strategies used in the treatment of epilepsy and the pathophysiology of the preictal state.

Key words: Neurophenomenology, phenomenology, epilepsy, seizure anticipation, seizure prediction, aura, prodrome, control of epileptic seizures, behavioral treatment of epilepsy

Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent’s predictive capabilities. Therefore, we argue that the ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goaldirected: the mind serves to coordinate with the future.

Key words: anticipation, expectation, internal model, prediction, representation, simulation, goal.

Purpose: This article investigates the emergence of subsystems in societies as a solution to the double contingency problem. Context: There are two underlying paradigms: one is radical constructivism in the sense that perturbations are at the centre of the self-organising processes; the other is Luhmann’s double contingency problem, where agents learn anticipations from each other. Approach: Central to our investigation is a computer simulation where we place agents into an arena. These agents can learn to (a) collect food and/or (b) steal food from other agents. In order to analyse subsystem formation, we investigate whether agents use both behaviours or just one of these, which is equivalent to determining the number of self-referential loops. This is detected with a novel measure that we call “prediction utilisation.” Results: During the simulation, symmetry breaking is observed. The system of agents divides itself up into two subsystems: one where agents just collect food and another one where agents just steal food from other agents. The ratio between these two populations is determined by the amount of food available.

Key words: Social systems, constructivist paradigm, cybernetics, double contingency, symmetry breaking, emergence

The notions of prediction and retrodiction, and the role they play in the natural sciences, are discussed. These notions derive from our perception of the fundamental category of time, as an ordering scheme for our experiential world. The issue of philosophical determinism vs. human free will is examined from a perspective of radical constructivism, and contrasted with the issue of solipsism vs. shared experience; and it is argued that both philosophical determinism and solipsism may be rejected, on the same (existential, not logical) grounds. Both prediction and retrodiction are discussed, in the context of some sciences (notably, classical and quantum physics), and are shown to be realisable only to a very limited extent. Some consequences of this, for the ability of science to forecast what will happen in the future, or to infer what has happened in the past, are reviewed. It is concluded that scientific knowledge, of both the past and the future, is (and must be) constructed in the present, based on presently observed data and theoretical arguments. Hence there can be no conception of true knowledge, either of the future or of the past.