Online research in philosophy

Keywords: computational neuroscience, neural coding, brain function, neural modeling, cognitive modeling, computation, representation, neuroscience, neuropsychology, semantics, theoretical psychology, theoretical neuroscience.

The need for representations and computations over their contents in psychological explanations is often cited as both the mark of the genuinely cognitive and a source of skepticism about the reducibility of cognitive theories to neuroscience. A generic version of this anti-reductionist argument is rejected in this paper as unsound, since (i) current thinking about associative learning emphasizes the need for cognitivist resources in theories adequate to explain even the simplest form of this phenomena (Pavlovian conditioning), and yet (ii) the most widely accepted recent theory of associative learning, which utilizes cognitivist resources, has already been reduced to a purely neurophysiological account. Psychoneural reduction of genuinely cognitivist theories is thus already an accomplished scientific fact, despite pronouncements by anti-reductionists about its conceptual impossibility or empirical implausibility. In addition, the specific form of reduction involved in this case (“combinatorial” reduction) provides a promising model for further cognitivist-to-neuroscience theory reductions.

Allen Newell (1973) once observed that psychology researchers were playing “twenty questions with nature,” carving up human cognition into hundreds of individual phenomena but shying away from the difficult task of integrating these phenomena with unifying theories. We argue that research on cognitive control has followed a similar path, and that the best approach toward unifying theories of cognitive control is that proposed by Newell, namely developing theories in computational cognitive architectures. Threaded cognition, a recent theory developed within the ACT-R cognitive architecture, offers promise as a unifying theory of cognitive control that addresses multitasking phenomena for both laboratory and applied task domains.

Cognitive science is the interdisciplinary investigation of mind and intelligence, embracing psychology, neuroscience, anthropology, artificial intelligence, and philosophy. There are many important philosophical questions related to this investigation, but this short chapter will focus on the following three. What is the nature of the explanations and theories developed in cognitive science? What are the relations among the five disciplines that comprise cognitive science? What are the implications of cognitive science research for general issues in the philosophy of science? I will argue that cognitive theories and explanations depend on representations of mechanisms and that the relations among the five disciplines, especially psychology and neuroscience, depend on relations between kinds of mechanisms. These conclusions have implications for central problems in general philosophy of science such as the nature of theories, explanations, and reduction between theories at different levels.

According to some philosophers, computational explanation is proprietary
to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that are being developed by a number of authors.

As the commentaries reveal, cognitive neuroscience's first steps toward a theory of development are marked by vigorous debate, ranging from basic points of definition to the fine details of mechanism. In this Response, we present the neural constructivist position on this broad spectrum of issues, from basic questions such as what sets constructivism apart from other theories (particularly selectionism) to its relation to behavioral theories and to its underlying mechanisms. We conclude that the real value of global theories at this stage of cognitive neuroscience is not just their answers but the new set of research questions they pose.

In this article, I review recent findings in cognitive neuroscience in learning, particularly in the learning of mathematics and of reading. I argue that while cognitive neuroscience is in its infancy as a field, theories of learning will need to incorporate and account for this growing body of empirical data.

Thirty years ago, grounded cognition had roots in philosophy, perception, cognitive linguistics, psycholinguistics, cognitive psychology, and cognitive neuropsychology. During the next 20 years, grounded cognition continued developing in these areas, and it also took new forms in robotics, cognitive ecology, cognitive neuroscience, and developmental psychology. In the past 10 years, research on grounded cognition has grown rapidly, especially in cognitive neuroscience, social neuroscience, cognitive psychology, social psychology, and developmental psychology. Currently, grounded cognition appears to be achieving increased acceptance throughout cognitive science, shifting from relatively minor status to increasing importance. Nevertheless, researchers wonder whether grounded mechanisms lie at the heart of the cognitive system or are peripheral to classic symbolic mechanisms. Although grounded cognition is currently dominated by demonstration experiments in the absence of well-developed theories, the area is likely to become increasingly theory driven over the next 30 years. Another likely development is the increased incorporation of grounding mechanisms into cognitive architectures and into accounts of classic cognitive phenomena. As this incorporation occurs, much functionality of these architectures and phenomena is likely to remain, along with many original mechanisms. Future theories of grounded cognition are likely to be heavily influenced by both cognitive neuroscience and social neuroscience, and also by developmental science and robotics. Aspects from the three major perspectives in cognitive science—classic symbolic architectures, statistical/dynamical systems, and grounded cognition—will probably be integrated increasingly in future theories, each capturing indispensable aspects of intelligence.

Social cognitive neuroscience examines social phenomena and processes using cognitive neuroscience research tools such as neuroimaging and neuropsychology. This review examines four broad areas of research within social cognitive neuroscience: (a) understanding others, (b) understanding oneself, (c) controlling oneself, and (d) the processes that occur at the interface of self and others. In addition, this review highlights two core-processing distinctions that can be neurocognitively identified across all of these domains. The distinction between automatic versus controlled processes has long been important to social psychological theory and can be dissociated in the neural regions contributing to social cognition. Alternatively, the differentiation between internally-focused processes that focus on one's own or another's mental interior and externally-focused processes that focus on one's own or another's visible features and actions is a new distinction. This latter distinction emerges from social cognitive neuroscience investigations rather than from existing psychological theories demonstrating that social cognitive neuroscience can both draw on and contribute to social psychological theory.

I examine the branch of evolutionary epistemology which tries to account for the character of cognitive mechanisms in animals and humans by extending the biological theory of evolution to the neurophysiological substrates of cognition. Like Plotkin, I construe this branch as a struggling science, and attempt to characterize the sort of theory one might expect to find this truly interdisciplinary endeavor, an endeavor which encompasses not only evolutionary biology, cognitive psychology, and developmental neuroscience, but also and especially, the computational modeling of artificial life programming; I suggest that extending Schaffner''s notion of interlevel theories to include both horizontal and vertical levels of abstraction best fits the theories currently being developed in cognitive science. Finally, I support this claim with examples drawn from computational modeling data using the genetic algorithm.