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

Biologically Plausible Connectionist Prediction of Natural Language Thematic Relations

In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIO theta PRED (BIOlogically plausible thematic (theta) symbolic-connectionist PREDictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIO theta PRED is designed to ""predict"" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.Fapesp - Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, Brazil[2008/08245-4

The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research

An analysis of intertheoretical connections in the interdisciplinary field

Background: Interdisciplinarity is one of the current trends in the scientific world today, that began with the uneasiness about the loss of the unity of science. This trend also opens possibilities for explaining complex phenomena more comprehensively and creating more advanced applications and implementations of scientific theories. One of the biggest challenges to conducting interdisciplinary research is theoretical integration, how can we combine theories from various disciplines such that the combination is fruitful? Method: This dissertation attempts to answer this challenge by analyzing the intertheoretical connections of some theories from various disciplines for some real interdisciplinary research. The structuralist metatheory of science is applied as the basic theory to model the intertheoretical connections formally. This research begins with modeling the scientific theories in question before modeling the intertheoretical connections and some modifications needed. This research focuses on some researches in cognitive science that involve psychology, neuroscience, and artificial intelligence. The first research is the research conducted by van Veen et al., who research the activity of neurons in a brain's field called dorsal Anterior Cingulate Cortex during a phase of dissonance between cognitions. This research serves as a context for the analysis of intertheoretical reduction between the Festinger theory of cognitive dissonance and the Hawkins-Kandel computational neuroscientific theory. The other research is the consonance model of simulation built by Shultz and Lepper, which implements the Hopfield network to build a simulation of the cognitive dissonance. The third research is the connectionist model of simulation built by van Overwalle and Jordens, which implements the two-layers feed-forward perceptron and the delta rule as its learning rule to build a simulation of forced compliance dissonance. Result: Through this research, the author concludes that the structuralist metatheory of science can be applied for modeling and analyzing intertheoretical connections in the same discipline and between disciplines in real scientific research. The structuralist metatheory of science enables us to model and analyze the structure of the theories and their intertheoretical connections with great detail and brings very fruitful results. This research delivers some results not only for the structuralist theory science itself but also for the philosophy of science in general and interdisciplinary researches, especially cognitive science. First, by analyzing the models, a revision of the definition of intertheoretical specialization, and specialization of the concept of theory-holon according to the structuralist theory of science, called the V-pattern and strategy for combining scientific theories, are proposed. These V-pattern and strategy can serve as a tool for combining scientific theories. Second, unlike other approaches on intertheoretical reduction such as the GNS model, the structuralist metatheory of science does not intend to formulate a generalized model of reduction nor focus only on intertheoretical reduction. It provides us powerful tools for modeling various intertheoretical relations, including intertheoretical reduction, case by case. Although the intertheoretical reduction in the structuralist model is more epistemological than ontological, the structuralist models show how the reduction has empirical claims and intended applications by applying the r* function that maps the T-theoretical level to T-non-theoretical level. Third, related to the notion of the unity of science, this dissertation still sees that the unity of science is still a plausible and essential agenda for the philosophy of science and the scientific world in general. This dissertation's idea of the unity of science proposed does not assume essentialism, reductionism, and epistemological monism. This dissertation sees that the unity of science is closely related to scientific practice. Fourth, for interdisciplinary research, primarily cognitive science, this dissertation proposes an approach to model and analyze intertheoretical connections for any scientific research or any philosophical school in philosophy of science related to the idea of intertheoretical relation. This dissertation is the first example of such modeling and analysis

An analysis of intertheoretical connections in the interdisciplinary field

Background: Interdisciplinarity is one of the current trends in the scientific world today, that began with the uneasiness about the loss of the unity of science. This trend also opens possibilities for explaining complex phenomena more comprehensively and creating more advanced applications and implementations of scientific theories. One of the biggest challenges to conducting interdisciplinary research is theoretical integration, how can we combine theories from various disciplines such that the combination is fruitful? Method: This dissertation attempts to answer this challenge by analyzing the intertheoretical connections of some theories from various disciplines for some real interdisciplinary research. The structuralist metatheory of science is applied as the basic theory to model the intertheoretical connections formally. This research begins with modeling the scientific theories in question before modeling the intertheoretical connections and some modifications needed. This research focuses on some researches in cognitive science that involve psychology, neuroscience, and artificial intelligence. The first research is the research conducted by van Veen et al., who research the activity of neurons in a brain's field called dorsal Anterior Cingulate Cortex during a phase of dissonance between cognitions. This research serves as a context for the analysis of intertheoretical reduction between the Festinger theory of cognitive dissonance and the Hawkins-Kandel computational neuroscientific theory. The other research is the consonance model of simulation built by Shultz and Lepper, which implements the Hopfield network to build a simulation of the cognitive dissonance. The third research is the connectionist model of simulation built by van Overwalle and Jordens, which implements the two-layers feed-forward perceptron and the delta rule as its learning rule to build a simulation of forced compliance dissonance. Result: Through this research, the author concludes that the structuralist metatheory of science can be applied for modeling and analyzing intertheoretical connections in the same discipline and between disciplines in real scientific research. The structuralist metatheory of science enables us to model and analyze the structure of the theories and their intertheoretical connections with great detail and brings very fruitful results. This research delivers some results not only for the structuralist theory science itself but also for the philosophy of science in general and interdisciplinary researches, especially cognitive science. First, by analyzing the models, a revision of the definition of intertheoretical specialization, and specialization of the concept of theory-holon according to the structuralist theory of science, called the V-pattern and strategy for combining scientific theories, are proposed. These V-pattern and strategy can serve as a tool for combining scientific theories. Second, unlike other approaches on intertheoretical reduction such as the GNS model, the structuralist metatheory of science does not intend to formulate a generalized model of reduction nor focus only on intertheoretical reduction. It provides us powerful tools for modeling various intertheoretical relations, including intertheoretical reduction, case by case. Although the intertheoretical reduction in the structuralist model is more epistemological than ontological, the structuralist models show how the reduction has empirical claims and intended applications by applying the r* function that maps the T-theoretical level to T-non-theoretical level. Third, related to the notion of the unity of science, this dissertation still sees that the unity of science is still a plausible and essential agenda for the philosophy of science and the scientific world in general. This dissertation's idea of the unity of science proposed does not assume essentialism, reductionism, and epistemological monism. This dissertation sees that the unity of science is closely related to scientific practice. Fourth, for interdisciplinary research, primarily cognitive science, this dissertation proposes an approach to model and analyze intertheoretical connections for any scientific research or any philosophical school in philosophy of science related to the idea of intertheoretical relation. This dissertation is the first example of such modeling and analysis

Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given