Brain functions are stochastic processes without intentionality whereas mind emerges from brain functions as a Hegelian “change from quantity”, that is, on the order of 1012 profusely interconnected neurons, “into a new quality”: the collective phenomenon of the brain's self-experience. This self-referential and self-observing quality we have in mind is capable of (recursively) observing its self-observations, i.e., interpreting change that is meaningful in relation to itself. The notion of self-interpretation embodies the idea of a “hermeneutic circle”, that is, (in interpretation (...) theory) the treatment of the whole in relation to its parts. (shrink)

What is needed today is a biologically grounded explanation of behavior, one that moves beyond the so?called mind?body problem. Yet no solution will be found by philosophers who refuse to learn about how brains and bodies work, or by neuroscientists pursuing experimental research based on outmoded or blatantly anti?biological theories. Churchland's book proposes a solution: to come by a unified theory of the mind?brain philosophers have to work together with neuroscientists. Yet Churchland's vision of a unified theory is based on (...) an assumption that, while widely held, may not adequately reflect brain functioning in the production of behavior, namely, the assumption that brain processes represent. The present paper proposes an alternative view, suggesting that patterns of neural activity do not ?represent? anything, that brains do not ?read? or ?transform? representations, and that brains do not require representations to produce goal?directed behavior. Representations are replaced by self?organizing neural processes that achieve a certain end?state of interaction between the organism and its environment in a flexible and adaptive manner. Some of the implications of this view for neuroscientific research and the philosophy of mind are outlined. (shrink)

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)