While structured as an autobiography, this memoir exemplifies ways in which classic contributions to cybernetics (e.g., by Wiener, McCulloch & Pitts, and von Neumann) have fed into a diversity of current research areas, including the mathematical theory of systems and computation, artificial intelligence and robotics, computational neuroscience, linguistics, and cognitive science. The challenges of brain theory receive special emphasis. Action-oriented perception and schema theory complement neural network modeling in analyzing cerebral cortex, cerebellum, hippocampus, and basal ganglia. Comparative studies of frog, rat, monkey, ape and human not only deepen insights into the human brain but also ground an EvoDevoSocio view of “how the brain got language.” The rapprochement between neuroscience and architecture provides a recent challenge. The essay also assesses some of the social and theological implications of this broad perspective.

Key words: action-oriented perception, ape, architecture, artificial intelligence, automata theory, basal ganglia, brain theory, cerebellum, cerebral cortex, cognitive science, computational neuroscience, cybernetics, frog, hippocampus, human, language evolution, li

The computational theory of cognition, or computationalism, holds that cognition is a form of computation. Two issues related to this view are comprised by the goal of this paper: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches support non-representational theories; B) Recently, a sociocultural theory against computationalism was proposed with the aim of ontologically reducing computing to cognition. We defend, however, that cognition and computation are in action, thus cognition is just a form of computing and that cognition is the explanatory basis for computation. We state that: 1. Representational theories of computing recurring to intentional content run into metaphysical problems. 2. Functional non-representational theories do not incur this metaphysical problem when describing computing in terms of the abstract machine. 3. Functional theories are consistent with enactive in describing computing machines not in a strictly functional way, but especially in terms of their organization. 4. Enactive cognition is consistent with the computationalism in describing Turing machines as functionally and organizationally closed systems. 5. The cognitive explanatory basis for computing improves the computational theory of cognition. When developed in the human linguistic domain, computer science is seen as a product of human socionatural normative practices, however, cognition is just an explanatory, not ontological, basis for computing. The paper concludes by supporting that computation is in action, that cognition is just one form of computing in the world and the explanatory basis for computation.

Key words: cognitive systems, enaction, computing, socio-natural practices.

This paper contrasts conservative and liberal interpretations of the extended mind hypothesis. The liberal view, defended here, considers cognition to be socially extensive, in a way that goes beyond the typical examples (involving notebooks and various technologies) rehearsed in the extended mind literature, and in a way that takes cognition to involve enactive processes (e.g., social affordances), rather than functional supervenience relations. The socially extended mind is in some cases constituted not only in social interactions with others, but also in ways that involve institutional structures, norms, and practices. Some of the common objections to the extended mind are considered in relation to this liberal interpretation. Implications for critical social theory are explored.

Key words: Extended mind, parity principle, institutions, enactivism, social affordances, critical theory

We introduce an approach to simulate the early mechanisms of emergent cognition based on theories of enactive cognition and on constructivist epistemology. The agent has intrinsic motivations implemented as inborn proclivities that drive the agent in a proactive way. Following these drives, the agent autonomously learns regularities afforded by the environment, and hierarchical sequences of behaviors adapted to these regularities. The agent represents its current situation in terms of perceived affordances that develop through the agent’s experience. This situational representation works as an emerging situation awareness that is grounded in the agent’s interaction with its environment and that in turn generates expectations and activates adapted behaviors. Through its activity and these aspects of behavior (behavioral proclivity, situation awareness, and hierarchical sequential learning), the agent starts to exhibit emergent sensibility, intrinsic motivation, and autonomous learning. Following theories of cognitive development, we argue that this initial autonomous mechanism provides a basis for implementing autonomously developing cognitive systems.

Key words: motivation, autonomous learning, cognitive development, enactive cognition, affordances, constructivism, cognitive architecture.

For cognitive systems, embodiment appears to be of crucial importance. Unfortunately, nobody seems to be able to define embodiment in a way that would prevent it from also covering its trivial interpretations such as mere situatedness in complex environments. The paper focuses on the definition of embodiment, especially whether physical embodiment is necessary and/or sufficient for cognitive systems. Cognition is characterized as a continuous complex process rather than ahistorical logical capability. Furthermore, the paper investigates the relationship between cognitive embodiment and the issues of understanding, representation and task specification.

Key words: Complexity, constructivism, design, embeddedness, representation, teleonomy, understanding

Embodiment has become the raison d’etre for much of the new ‘cognitive robotics’. It fills a gap in the non-interactivist approach of traditional artificial intelligence (AI) in which ‘intelligence’ is viewed as the manipulation of symbols in a vacuum. However, a foundational question for the new AI is, can embodiment lead to a strong AI, i.e. a robot mind? To address this question, two extreme poles of embodiment are distinguished here, mechanistic and phenomenal. A detailed exploration of each type of embodiment is provided together with an appraisal of whether strong embodiment is possible for robotics, or whether robotics merely provides a tool for scientific exploration and modelling, i.e. weak embodiment? It is argued that strong embodiment, either mechanistic or phenomenal, is not possible for present day robots. However, weak embodiment may provide an enlightened approach to using robots for modelling cognition.

Key words: artificial intelligence, autopoiesis, cognitive robotics, mechanistic embodiment, phenomenal embodiment

Cognitive systems research has predominantly been guided by the historical distinction between emotion and cognition, and has focused its efforts on modelling the “cognitive” aspects of behaviour. While this initially meant modelling only the control system of cognitive creatures, with the advent of “embodied” cognitive science this expanded to also modelling the interactions between the control system and the external environment. What did not seem to change with this embodiment revolution, however, was the attitude towards affect and emotion in cognitive science. This paper argues that cognitive systems research is now beginning to integrate these aspects of natural cognitive systems into cognitive science proper, not in virtue of traditional “embodied cognitive science,” which focuses predominantly on the body’s gross morphology, but rather in virtue of research into the interoceptive, organismic basis of natural cognitive systems.

Key words: proper embodiment, embodied cognition, affective neuroscience, interoception, internal robotics, enactive cognitive science.

We examine Dubois’s (2003) distinction between weak anticipation and strong anticipation. Anticipation is weak if it arises from a model of the system via internal simulations. Anticipation is strong if it arises from the system itself via lawful regularities embedded in the system’s ordinary mode of functioning. The assumption of weak anticipation dominates cognitive science and neuroscience and in particular the study of perception and action. The assumption of strong anticipation, however, seems to be required by anticipation’s ubiquity. It is, for example, characteristic of homeostatic processes at the level of the organism, organs, and cells. We develop the formal distinction between strong and weak anticipation by elaboration of anticipating synchronization, a phenomenon arising from time delays in appropriately coupled dynamical systems. The elaboration is conducted in respect to (a) strictly physical systems, (b) the defining features of circadian rhythms, often viewed as paradigmatic of biological behavior based in internal models, (c) Pavlovian learning, and (d) forward models in motor control. We identify the common thread of strongly anticipatory systems and argue for its significance in furthering understanding of notions such as “internal”, “model” and “prediction”.

Key words: anticipation, synchronization, delayed feedback, homeostasis.