A Connectionist Theory of Phenomenal Experience

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, while there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways

Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature

What Is Cognitive Psychology?

What Is Cognitive Psychology? identifies the theoretical foundations of cognitive psychology—foundations which have received very little attention in modern textbooks. Beginning with the basics of information processing, Michael R. W. Dawson explores what experimental psychologists infer about these processes and considers what scientific explanations are required when we assume cognition is rule-governed symbol manipulation. From these foundations, psychologists can identify the architecture of cognition and better understand its role in debates about its true nature. This volume offers a deeper understanding of cognitive psychology and presents ideas for integrating traditional cognitive psychology with more modern fields like cognitive neuroscience.Publishe

Towards artificial intelligence : advances, challenges, and risks

This text contains some reflections on artificial intelligence (AI). First, we distinguish between strong and weak AI, as well as the concepts related to general and specific AI. Following this, we briefly describe the main current AI models and discuss the need to provide common-sense knowledge to machines in order to advance towards the goal of a general AI. Next, we talk about the current trends in AI based on the analysis of large amounts of data, which has recently allowed experts to make spectacular progress. Finally, we discuss other topics which, now and in the future, will continue to be key in AI, before closing with a brief reflection on the risks of AI

Wittgenstein’s Remarks on Technology and Mental Mechanisms

This article provides a survey of Wittgenstein’s remarks in which he discusses various kinds of technology. I argue that throughout his career, his use of technological examples displays a thematic unity: technologies are invoked in order to illustrate a certain mechanical conception of the mind. I trace how his use of such examples evolved as his views on the mind and on meaning changed. I also discuss an important and somewhat radical anti-mechanistic strain in his later thought and suggest that Wittgenstein’s attitude to mechanistic explanations in psychology was ultimately quite ambivalent

Giriş gösterimlerinin kendini örgütleyen ve aşağıdan yukarıya çalışan akor şemalarına etkisi

Effects of input representation on self-organizing and bottom-up model of chord schema were investigated. A single layer self-organizing map was trained with 12 major and 12 minor chords. Training was repeated five times, each time with a different input representation. In this model, activation of chords is determined by the representation and activation of pitches that compose the chords. Important findings of chord perception were simulated with this model. Simulation results showed that input representation was critical to simulate all of the findings.Giriş gösterimlerinin kendini örgütleyen ve aşağıdan yukarıya çalışan akor şemalarına etkisi incelenmiştir. Tek katmanlı kendini örgütleyen ağ 12 major ve 12 minör akor ile eğitilmiştir. Eğitim her seferinde başka bir giriş gösterimi kullanılarak beş kez tekrar edilmiştir. Bu modelde akor gösterimlerinin seviyesi sadece akoru oluşturan seslerin gösterimleri ve seviyesi ile belirlenmektedir. Akor algısının önemli bulguları bu model ile benzetilmiştir. Benzetim sonuçları göstermektedir ki bütün bulguların modellenebilmesi için uygun giriş gösterimlerinin seçilmesi gerekmektedir

The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges