Brain functions are stochastic processes without intentionality whereas mind emerges from brain functions as a Hegelian “change from quantity”, that is, on the order of 1012 profusely interconnected neurons, “into a new quality”: the collective phenomenon of the brain's self-experience. This self-referential and self-observing quality we have in mind is capable of (recursively) observing its self-observations, i.e., interpreting change that is meaningful in relation to itself. The notion of self-interpretation embodies the idea of a “hermeneutic circle”, that is, (in interpretation (...) theory) the treatment of the whole in relation to its parts. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models.Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition. (shrink)

Human behavior is the joint product of (i) contingencies of survival responsible for natural selection, and (ii) contingencies of reinforcement responsible for the repertoires of individuals, including (iii) the special contingencies maintained by an evolved social environment. Selection by consequences is a causal mode found only in living things, or in machines made by living things. It was first recognized in natural selection: Reproduction, a first consequence, led to the evolution of cells, organs, and organisms reproducing themselves under increasingly diverse (...) conditions. The behavior functioned well, however, only under conditions similar to those under which it was selected.Reproduction under a wider range of consequences became possible with the evolution of processes through which organisms acquired behavior appropriate to novel environments. One of these, operant conditioning, is a second kind of selection by consequences: New responses could be strengthened by events which followed them. When the selecting consequences are the same, operant conditioning and natural selection work together redundantly. But because a species which quickly acquires behavior appropriate to an environment has less need for an innate repertoire, operant conditioning could replace as well as supplement the natural selection of behavior.Social behavior is within easy range of natural selection, because other members are one of the most stable features of the environment of a species. The human species presumably became more social when its vocal musculature came under operant control. Verbal behavior greatly increased the importance of a third kind of selection by consequences, the evolution of social environments or cultures. The effect on the group, and not the reinforcing consequences for individual members, is responsible for the evolution of culture. (shrink)

Given the personal acquaintance between Alan M. Turing and W. Ross Ashby and the partial proximity of their research fields, a comparative view of Turing’s and Ashby’s work on modelling “the action of the brain” (letter from Turing to Ashby, 1946) will help to shed light on the seemingly strict symbolic/embodied dichotomy: While it is clear that Turing was committed to formal, computational and Ashby to material, analogue methods of modelling, there is no straightforward mapping of these approaches onto symbol-based (...) AI and embodiment-centered views respectively. Instead, it will be demonstrated that both approaches, starting from a formal core, were at least partly concerned with biological and embodied phenomena, albeit in revealingly distinct ways. (shrink)

Evolution's Arrow argues that evolution is directional and progressive, and that this has major consequences for humanity. Without resort to teleology, the book demonstrates that evolution moves in the direction of producing cooperative organisations of greater scale and evolvability - evolution has organised molecular processes into cells, cells into organisms, and organisms into societies. The book founds this position on a new theory of the evolution of cooperation. It shows that self-interest at the level of the genes does not prevent (...) cooperation from increasing as evolution unfolds. Evolution progresses by discovering ways to build cooperative organisations out of self-interested individuals. The book also shows that evolution itself has evolved. Evolution has progressively improved the ability of evolutionary mechanisms to discover effective adaptations. And it has produced new and better mechanisms. Evolution's Arrow uses this understanding of the direction of evolution to identify the next great steps in the evolution of life on earth - the steps that humanity must take if we are to continue to be successful in evolutionary terms. A key step for humanity is to increase the scale and evolvability of our societies, eventually forming a unified and cooperative society on the scale of the planet. We must also transform ourselves psychologically to become self-evolving organisms - organisms that are able to escape their biological and cultural past by adapting in whatever directions are necessary to achieve future evolutionary success. (shrink)

The question whether cognition is subserved by internal processes in the brain (internalism) or extends in to the world (active externalism) has been vigorously debated in recent years. I show how internalist and externalist ideas can be pursued in a common framework, using (1) open dynamical systems, which allow for separate analysis of an agent's intrinsic and embodied dynamics, and (2) supervenience functions, which can be used to study how low-level dynamical systems give rise to higher-level dynamical structures.

I identify three lessons from Kenneth Craik’s landmark book “The Nature of Explanation” for contemporary debates surrounding the existence, extent, and nature of mental representation: first, an account of mental representations as neural structures that function analogously to public models; second, an appreciation of prediction as the central component of intelligence in demand of such models; and third, a metaphor for understanding the brain as an engineer, not a scientist. I then relate these insights to discussions surrounding the representational status (...) of predictive processing – which, I argue, provides a contemporary vindication of Craik’s extremely prescient “hypothesis on the nature of thought.”. (shrink)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which (...) simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation. (shrink)

From the middle of the twentieth‐century onwards, there has been a growing emphasis on the importance of relationality in what it means to be human, which we call a “relational turn.” This is found in various domains, including philosophical psychology, psychoanalysis, and theological anthropology. Many have seen a close connection between relationality and personhood. In the second half of the article, we consider the implications of this trend for artificial intelligence (AI) and robotics. So far, AI has largely neglected relational (...) intelligence, though that could perhaps be about to change. Cybernetics was rendered more open to assumptions about the contextuality of intelligence by its rather different assumptions from AI. Social robotics increasingly requires relational intelligence, and promising steps might be found in computational modeling of human relationships. Questions about whether robots can achieve personhood are difficult to resolve, though the possibility should not be ruled out. (shrink)

The Cartesian concept of nature, which has determined modern thinking until the present time, has become obsolete. It shall be shown that Hegel's objective-idealistic conception of nature discloses, in comparison to that of Descartes, new perspectives for the comprehension of nature and that this, in turn, results in possibilities of actualizing Hegel's philosophy of nature. If the argumentation concerning philosophy of nature is intended to catch up with the concrete Being-of-nature and to meet it in its concretion, then this is (...) impossible for the finite spirit in a strictly a priori sense – this is the thesis supported here which is not at all close to Hegel. As the argumentation rather has to consider the conditions of realization concerning the Being-of-nature, too, it is compelled to take up empirical elements – concerning the organism, for instance, system-theoretical aspects, physical and chemical features of the nervous system, etc. With that, on the one hand, empirical-scientific premises are assumed (e.g. the lawlikeness of nature), which on the other hand become (now close to Hegel) possibly able to be founded in the frame of a Hegelian-idealistic conception. In this sense, a double strategy of empirical-scientific concretization and objective-idealistic foundation is followed up, which represents the methodical basic principle of the developed considerations. In the course of the undertaking, the main aspects of the whole Hegelian design concerning the philosophy of nature are considered – space and time, mass and motion, force and law of nature, the organism, the pro-blem of evolution, psychic being – as well as Hegel's basic thesis concerning the philosophy of nature, that therein a tendency toward coherence and idealization manifests itself in the sense of a (categorically) gradually rising succession of nature: from the separateness of space to the ideality of sensation. In the sense of the double strategy of concretization and foundation it is shown that on the one hand possibilities of philosophical penetration concerning actual empirical-scientific results are opened, and on the other hand – in turn – a re-interpretation of Hegel's theorem on the basis of physical, evolution-theoretical and system-theoretical argumentation also becomes possible. In this mutual crossing-over and elucidation of empirical and Hegelian argumentation not only do perspectives of a new comprehension of nature become visible, but also, at the same time – as an essential consequence of this methodical principle – thoughts on the possibilities of actualizing Hegel's philosophy of nature. (shrink)

The general inorganic nature of traditional selection theory (based on differential growth between any two systems) is pointed out, wherefrom it follows that this theory cannot provide explanations for the characteristics of organic evolution. Specific biophysical aspects enter with the complexity of macro-molecules: vital physical conditions for the perpetuation of the system, irrevocable extinction (= death) and random change leading to novelty, are the result of complexity per se. Further biophysical properties are a direct function of the pathway along which (...) random mutation in nucleic acids is converted into continuous protein — (specifically enzyme) — function, from there into organismic phenotype with fitness components which may, or may not, correspond to identifiable structural units in DNA. The general machine-like properties of enzymes, in that there is no additive relationship between structural (amino acid) composition and functional output, is discussed in more detail. The continuous growth functions of molecular concentrations, directed by enzyme turnover, determine simple laws of growth and morphogenesis in the organic hierarchy and thus of phenotype. Thus, the combined effect of DNA-structure and of environmental parameters (temperature, pressure, pH, etc.) on protein function determines ultimately the actual phenotype and hence, quality and intensity of genotypic selection. (shrink)

In this paper we will demonstrate that a computational system can meet the criteria for autonomy laid down by classical enactivism. The two criteria that we will focus on are operational closure and structural determinism, and we will show that both can be applied to a basic example of a physically instantiated Turing machine. We will also address the question of precariousness, and briefly suggest that a precarious Turing machine could be designed. Our aim in this paper is to challenge (...) the assumption that computational systems are necessarily heteronomous systems, to try and motivate in enactivism a more nuanced and less rigid conception of computational systems, and to demonstrate to computational theorists that they might find some interesting material within the enactivist tradition, despite its historical hostility towards computationalism. (shrink)

A blockchain can be considered a technological phenomenon that is made up of different interconnected and autonomous systems. Such systems are referred to here as cyber-physical systems: complex interconnections of cyber and physical components. When cyber-physical systems are interconnected, a new whole consisting of a system of systems is created by the autonomous systems and their intercommunication and interaction. In a blockchain, individual systems can independently make decisions on joint information transactions. The decision-making procedures needed for this are executed based (...) on fault-tolerant communication and voting and consensus procedures, while the results of these decision-making procedures are stored in distributed ledgers. Due to the intercommunication, interaction, and independent decision making by autonomous systems, the new whole of a blockchain is a complex entity. Complexity science rather than the usual reductionist scientific approach can help us better understand the behaviour of the new and continuously developing whole of a blockchain as a technological phenomenon. (shrink)

In cognitive science, the dynamical systems theory (DST) has recently been advocated as an approach to cognitive modeling that is better suited to the dynamics of cognitive processes than the symbolic/computational approaches are. Often, the differences between DST and the symbolic/computational approach are emphasized. However, alternatively their commonalities can be analyzed and a unifying framework can be sought. In this paper, the possibility of such a unifying perspective on dynamics is analyzed. The analysis covers dynamics in cognitive disciplines, as well (...) as in physics, mathematics and computer science. The unifying perspective warrants the development of integrated approaches covering both DST aspects and symbolic/computational aspects. The concept of a state-determined system, which is based on the assumption that properties of a given state fully determine the properties of future states, lies at the heart of DST. Taking this assumption as a premise, the explanatory problem of dynamics is analyzed in more detail. The analysis of four cases within different disciplines (cognitive science, physics, mathematics, computer science) shows how in history this perspective led to numerous often used concepts within them. In cognitive science, the concepts desire and intention were introduced, and in classical mechanics the concepts momentum, energy and force. Similarly, in mathematics a number of concepts have been developed to formalize the state-determined system assumption [e.g. derivatives (of different orders) of a function, Taylor approximations]. Furthermore, transition systems - a currently popular format for specification of dynamical systems within computer science - can also be interpreted from this perspective. One of the main contributions of the paper is that the case studies provide a unified view on the explanation of dynamics across the chosen disciplines. All approaches to dynamics analyzed in this paper share the state-determined system assumption and the (explicit or implicit) use of anticipatory state properties. Within cognitive science, realism is one of the problems identified for the symbolic/computational approach - i.e. how do internal states described by symbols relate to the real world in a natural manner. As DST is proposed as an alternative to the symbolic/computational approach, a natural question is whether, for DST, realism of the states can be better guaranteed. As a second main contribution, the paper provides an evaluation of DST compared to the symbolic/computational approach, which shows that, in this respect (i.e. for the realism problem), DST does not provide a better solution than the other approaches. This shows that DST and the symbolic/computational approach not only have the state-determined system assumption and the use of anticipatory state properties in common, but also the realism problem. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition. (shrink)

In a recent paper, Paul Coates defends a sophisticated dispositional account which allegedly resolves the sceptical paradox developed by Kripke in his monograph on Wittgenstein's treatment of following a rule (Kripke, 1982). Coates' account appeals to a notion of 'homeostasis', unpacked as a subject's second-order disposition to maintain a consistent pattern of extended first-order dispositions regarding her linguistic behavior. This kind of account, Coates contends, provides a naturalistic model for the normativity of intentional properties and thus resolves Kripke's sceptical paradox. (...) In this paper I argue that Coates' second-order dispositional account cannot solve the sceptic's problems regarding meaning and normativity. My main contention is that in order for second-order dispositions to be able to effectively regulate the coordinated responses constitutive of first-order dispositions, those first order dispositions must be independently identifiable. Yet that's precisely what Kripke's sceptical argument calls into question. I shall also argue, in a more positive fashion, that Coates' own appeal to practical breakdowns may suggest a different —and more effective— response to the sceptic's concern. (shrink)