Connectionism Reconsidered: Minds, Machines and Models

In this paper the issue of drawing inferences about biological cognitive systems on the basis of connectionist simulations is addressed. In particular, the justification of inferences based on connectionist models trained using the backpropagation learning algorithm is examined. First it is noted that a justification commonly found in the philosophical literature is inapplicable. Then some general issues are raised about the relationships between models and biological systems. A way of conceiving the role of hidden units in connectionist networks is then introduced. This, in combination with an assumption about the way evolution goes about solving problems, is then used to suggest a means of justifying inferences about biological systems based on connectionist research

Cog and the Creativity of God

The construction of a humanoid robot may be within reach. The science of artificial intelligence (AI) offers new understandings to contemporary Christian theology. First of all, the emerging field of embodied intelligence discloses the wholeness of the human being, correcting the tendency in Christian theology toward an anthropological dualism of body and soul. Secondly, artificial intelligence offers fresh understandings of the human mind, with implications for how human creativity reflects the creativity of God

Models of Cognition: Neurological possibility does not indicate neurological plausibility

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible

DScentTrail: A new way of viewing deception

The DScentTrail System has been created to support and demonstrate research theories in the joint disciplines of computational inference, forensic psychology and expert decision-making in the area of counter-terrorism. DScentTrail is a decision support system, incorporating artificial intelligence, and is intended to be used by investigators. The investigator is presented with a visual representation of a suspect‟s behaviour over time, allowing them to present multiple challenges from which they may prove the suspect guilty outright or receive cognitive or emotional clues of deception. There are links into a neural network, which attempts to identify deceptive behaviour of individuals; the results are fed back into DScentTrail hence giving further enrichment to the information available to the investigator

Renewing the link between cognitive archeology and cognitive science

In cognitive archeology, theories of cognition are used to guide interpretation of archeological evidence. This process provides useful feedback on the theories themselves. The attempt to accommodate archeological data helps shape ideas about how human cognition has evolved and thus—by extension—how the modern form functions. But the implications that archeology has for cognitive science particularly relate to traditional proposals from the field involving modular decomposition, symbolic thought and the mediating role of language. There is a need to make a connection with more recent approaches, which more strongly emphasize information, probabilistic reasoning and exploitation of embodiment. Proposals from cognitive archeology, in which evolution of cognition is seen to involve a transition to symbolic thought need to be realigned with theories from cognitive science that no longer give symbolic reasoning a central role. The present paper develops an informational approach, in which the transition is understood to involve cumulative development of information-rich generalizations