“Strong computationalism” holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s “Representation & Reality”, which if correct, has important implications for Turing machine functionalism and the prospect of “conscious” machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that “everything implements the specific machine Q on a particular input set (x)”. Then, equating Q (x) to any putative AI program, I will show that conceding the “strong AI” thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g., grass, rocks, etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. Relevance: This paper critiques the computational accounts of mind and cognition using a construction borrowed from Putnam.

It’s sometimes said, and even more often assumed, that life is necessary for mind. If so, and if A-Life promises to throw light on the nature of life as such, then A-Life is in principle highly relevant to the philosophy of mind and cognitive science. However, very few philosophers have attempted to argue for the relation between life and mind. It’s usually taken for granted. Even those (mostly in the Continental tradition, including some with a following in A-Life) who have insisted on the linkage have stated it rather than justified it. If an evolutionary account of intentionality is acceptable, then perhaps biological life ‘makes room’ for mind. But that claim is problematic, since it’s not clear that the type of self-organization involved in life-as-such must necessarily include evolution. Even if it does, it’s a further step to show that life is strictly necessary for mind.

Key words: life, self-organization, evolution, intentionality, a-life

Contemporary science seems to be caught in a strange predicament. On the one hand, it holds a firm and reasonable commitment to a healthy naturalistic methodology, according to which explanations of natural phenomena should never overstep the limits of the natural itself. On the other hand, contemporary science is also inextricably and now inevitably dependent on ever more complex technologies, especially Information and Communication Technologies, which it exploits as well as fosters. Yet such technologies are increasingly “artificialising” or “denaturalising” the world, human experiences and interactions, as well as what qualifies as real. So the search for the ultimate explanation of the natural seems to rely upon, and promote, the development of the artificial, seen here as an instantiation of the non-natural. In this article, I would like to try and find a way out of this apparently strange predicament. I shall argue that the naturalisation of our knowledge of the world is either philosophically trivial (naturalism as anti-supernaturalism and anti-preternaturalism), or mistaken (naturalism as anti-constructionism) First, I shall distinguish between different kinds of naturalism. Second, I shall remind the reader that the kinds of naturalism that are justified today need to be protected and supported pragmatically, but they are no longer very interesting conceptually. We know how to win the argument. We just have to keep winning it. Whereas the kind of naturalism that is still interesting today is now in need of revision in order to remain acceptable. Such a kind of naturalism may be revised on the basis of a realistic philosophy of information, according to which knowing is a constructive activity, through which we do not merely represent the phenomena we investigate passively, but create more or less correct informational models (semantic artefacts) of them, proactively and interactively. I shall conclude that the natural is in itself artefactual (a semantic construction), and that the information revolution is disclosing a tension not between the natural and the non-natural, but a deeper one between a user’s and a producer’s interpretation of knowledge. The outcome is a philosophical view of knowledge and science in the information age that may be called constructionist and a revival of philosophy as a classic, foundationalist enterprise.

Key words: constructionism, closure, level of abstraction, naturalism, naturalisation, open question argument, reasonably reiterable query test.

In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer. //We show that in a first step it is necessary to study and investigate the different levels and forms of representation that are involved in the interaction processes between computers and human cognitive systems. Only if designers have achieved some understanding about these representational mechanisms, user interfaces enabling individual experiences and skill development can be designed. In this paper we review mechanisms and processes for knowledge representation on a conceptual, epistemological, and methodological level, and sketch some ways out of the identified dilemmas for cognitive modeling in the domain of human-computer interaction.

Key words: cognitive modeling, cognitive systems, human-computer interaction knowledge engineering, knowledge representation, knowledge-based user interfaces.

The strategies of action employed by a human subject in order to perceive simple 2-D forms on the basis of tactile sensory feedback have been modelled by an explicit computer algorithm. The modelling process has been constrained and informed by the capacity of human subjects both to consciously describe their own strategies, and to apply explicit strategies; thus, the strategies effectively employed by the human subject have been influenced by the modelling process itself. On this basis, good qualitative and semi-quantitative agreement has been achieved between the trajectories produced by a human subject, and the traces produced by a computer algorithm. The advantage of this “reciprocal modelling” option, besides facilitating agreement between the algorithm and the empirically observed trajectories, is that the theoretical model provides an explanation, and not just a description, of the active perception of the human subject.

Key words: active perception, computer modelling, sensory-motor invariants, visual tactile sensory substitution.

Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing’s account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them.

This article addresses a classical question: Can a machine use language meaningfully and if so, how can this be achieved? The first part of the paper is mainly philosophical. Since meaning implies intentionality on the part of the language user, artificial systems which obviously lack intentionality will be `meaningless’ (pace e.g. Dennett). There is, however, no good reason to assume that intentionality is an exclusively biological property (pace e.g. Searle) and thus a robot with bodily structures, interaction patterns and development similar to those of human beings would constitute a system possibly capable of meaning – a conjecture supported through a Wittgenstein-inspired thought experiment. The second part of the paper focuses on the empirical and constructive questions. Departing from the principle of epigenesis stating that during every state of development new structure arises on the basis of existing structure plus various sorts of interaction, a model of human cognitive and linguistic development is proposed according to which physical, social and linguistic interactions between the individual and the environment have their respective peaks in three consecutive stages of development: episodic, mimetic and symbolic. The transitions between these stages are qualitative, and bear a similarity to the stages in phylogenesis proposed by Donald (1991) and Deacon (1997). Following the principle of epigenetic development, robotogenesis could possibly recapitulate ontogenesis, leading to the emergence of intentionality, consciousness and meaning.