In this thesis I present a virtual laboratory which implements five different models for controlling animats: a rule-based system, a behaviour-based system, a concept-based system, a neural network, and a Braitenberg architecture. Through different experiments, I compare the performance of the models and conclude that there is no best model, since different models are better for different things in different contexts. The models I chose, although quite simple, represent different approaches for studying cognition. Using the results as an empirical philosophical (...) aid, I note that there is no best approach for studying cognition, since different approaches have all advantages and disadvantages, because they study different aspects of cognition from different contexts. This has implications for current debates on proper approaches for cognition: all approaches are a bit proper, but none will be proper enough. I draw remarks on the notion of cognition abstracting from all the approaches used to study it, and propose a simple classification for different types of cognition. (shrink)

This paper explicates the interaction between the implicit and explicit learning processes in skill acquisition, contrary to the common tendency in the literature of studying each type of learning in isolation. It highlights the interaction between the two types of processes and its various effects on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit processes; moreover, it embodies a bottom-up approach (first learning implicit knowledge and then (...) explicit knowledge on its basis) towards skill learning. The paper shows that this approach accounts for various effects in the process control task data, in addition to accounting for other data reported elsewhere. (shrink)

This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its effects on learning. The work presents an (...) integrated model of skill learning that takes into account both implicit and explicit processes and both top-down and bottom-up learning. We examine and simulate human data in the Tower of Hanoi task. The paper shows how the quantitative data in this task may be captured using either top-down or bottom-up approaches, although top-down learning is a more apt explanation of the human data currently available. These results illustrate the two different directions of learning (top-down versus bottom-up), and thereby provide a new perspective on skill learning. Ó 2003 Elsevier B.V. All rights reserved. (shrink)

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule- based reasoning and whether they involve distributed or localist representations. The bene®ts and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used (...) for cognitive modelling. q 2001 Elsevier Science Ltd. All rights reserved. (shrink)

This thesis is an examination and critique of naturalistic representational theories of phenomenal character. Phenomenal character refers to the distinctive quality that perceptual and sensational experiences seem to have; it is identified with 'what it is like' to undergo experiences. The central claims of representationalism are that phenomenal character is identical with the content of experience and that all representational states, bearing appropriate relations to the cognitive system, are conscious experiences. These claims are taken to explain both how conscious experiential (...) states arise and their nature. After examining the desiderata for naturalistic explanations, I argue that theories which ascribe nonconceptual content to experiences are the most plausible versions of representationalism. Further, causal covariation and teleological theories yield distinctive and interesting representationalist positions, hence, they become the focus of this study. To assess representationalism, I investigate whether all differences in phenomenal character can be correlated with differences in content. I claim that a useful distinction can be drawn between implicit and explicit content, which allows one to best describe the phenomena of perfect and relative pitch. I then argue that ambiguous figures show that two experiences can have the same content but different phenomenal character. I explicate the Inverted Earth hypothesis and claim that to identify content and phenomenal character, representationalists either have to condone the possibility of philosophical zombies, or hold that people lack authoritative first-person knowledge of their current experiences. Both these positions are unpalatable. Finally, I argue that representationalists cannot ascribe contents to experiences of novel colours to account for their phenomenal character. I also question, in light of dissociation phenomena, whether there is one distinctive relationship that all experiences bear to the cognitive system. I conclude that phenomenal character cannot be identical with the type of content under investigation, and that naturalistic representationalist theories cannot fully explain conscious experience. (shrink)

Theories of cognition and theories of social practices and institutions have often each separately acknowledged the relevance of the other; but seldom have there been consistent and sustained attempts to synthesize these two areas within one explanatory framework. This is precisely what my dissertation aims to remedy. I propose that certain recent developments and themes in philosophy of mind and cognitive science, when understood in the right way, can explain the emergence and dynamics of social practices and institutions. Likewise, the (...) view I construct explains how social practices and institutions shape the character of cognition of their constituent agents. Moreover, I explain both cognitive and social agency under the single explanatory framework provided by Dynamic Systems Theory. Drawing upon the phenomenological tradition, "embodied, "extended", "embedded", "enactive", and "ecological" approaches to cognition, as well as the conceptual resources of Dynamic Systems Theory, I construct a theory of agency that sees cognitive and social agents as far-from-equilibrium, open, recursively self-maintenant dynamic systems. Depending on the specifics of concrete circumstances, such systems, which I call "Dynamic Embodied Agents", may develop and possess emergent capacities for error-detection, flexible learning, normative behavior, representation, self-reflection, various modes of pattern-recognition, a temporal sense of self, and even moral responsibility. Some such systems are also sensitive to perceived social influences ; while reciprocally constituting and causally affecting them. (shrink)

As brightly shown by Mainzer [24], the science of complexity has many distinct origins in many disciplines. Those various origins has led to “an interdisciplinary methodology to explain the emergence of certain macroscopic phenomena via the nonlinear interactions of microscopic elements” (ibid.). This paper suggests that the parallel and strong expansion of modeling and simulation - especially after the Second World War and the subsequent development of computers - is a rationale which also can be counted as an explanation of (...) this emergence. With the benefit of hindsight, one can find three periods in the methodologies of modeling in the empirical sciences: 1st the simple modeling of the simple, 2nd the simple modeling of the complex, 3rd the complex modeling and simulation of the complex. Our main thesis is that the current spreading (since the 90’s) of complex computer simulations of systems of models (where a simulation is no more a step by step calculus of a unique logico-mathematical model) is another promising dimension of the science of complexity. Following this claim, we propose to distinguish three different types of computer simulations in the context of complex systems’ modeling. Finally, we show that these types of simulations lead to three different types of weak emergence, too. (shrink)

In Connectionism and the Philosophy of Psychology, Horgan and Tienson (1996) argue that cognitive processes, pace classicism, are not governed by exceptionless, “representation-level” rules; they are instead the work of defeasible cognitive tendencies subserved by the non-linear dynamics of the brain’s neural networks. Many theorists are sympathetic with the dynamical characterisation of connectionism and the general (re)conception of cognition that it affords. But in all the excitement surrounding the connectionist revolution in cognitive science, it has largely gone unnoticed that connectionism (...) adds to the traditional focus on computational processes, a new focus – one on the vehicles of mental representation, on the entities that carry content through the mind. Indeed, if Horgan and Tienson’s dynamical characterisation of connectionism is on the right track, then so intimate is the relationship between computational processes and representational vehicles, that connectionist cognitive science is committed to a resemblance theory of mental content. (shrink)