It is commonly believed that human perceptual experiences can be, and usually are, multimodal. What is more, a stronger thesis is often proposed that some perceptual multimodal characters cannot be described simply as a conjunction of unimodal phenomenal elements. If it is the case, then a question arises: what is the additional mode of combination that is required to adequately describe the phenomenal structure of multimodal experiences? The paper investigates what types of audio–visual experiences have phenomenal character that cannot be (...) analysed as a mere conjunction of visual and auditory elements; and how can we properly characterise the required, additional mode of perceptual combination. Three main modes of combination are considered: instantiation, parthood, and grouping. It is argued that some phenomena involving intermodal relations, like spatial and temporal ventriloquism, can be analysed in terms of audio–visual, perceptual grouping. On the other hand, cases of intermodal binding need a different treatment. Experiences involving audio–visual binding should be analysed as experiences presenting objects or events which instantiate, or which have a proper part instantiating, both visually and auditorily determined properties. (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)

Cognitive functions like perception, memory, language, or consciousness are based on highly parallel and distributed information processing by the brain. One of the major unresolved questions is how information can be integrated and how coherent representational states can be established in the distributed neuronal systems subserving these functions. It has been suggested that this so-called ''binding problem'' may be solved in the temporal domain. The hypothesis is that synchronization of neuronal discharges can serve for the integration of distributed neurons into (...) cell assemblies and that this process may underlie the selection of perceptually and behaviorally relevant information. As we intend to show here, this temporal binding hypothesis has implications for the search of the neural correlate of consciousness. We review experimental results, mainly obtained in the visual system, which support the notion of temporal binding. In particular, we discuss recent experiments on the neural mechanisms of binocular rivalry which suggest that appropriate synchronization among cortical neurons may be one of the necessary conditions for the buildup of perceptual states and awareness of sensory stimuli. (shrink)

This is a bibliography of books and articles on consciousness in philosophy, cognitive science, and neuroscience over the last 30 years. There are three main sections, devoted to monographs, edited collections of papers, and articles. The first two of these sections are each divided into three subsections containing books in each of the main areas of research. The third section is divided into 12 subsections, with 10 subject headings for philosophical articles along with two additional subsections for articles in cognitive (...) science and neuroscience. Of course the division is somewhat arbitrary, but I hope that it makes the bibliography easier to use. This bibliography has first been compiled by Thomas Metzinger and David Chalmers to appear in print in two philosophical anthologies on conscious experience . From 1995 onwards it has been continuously updated by Thomas Metzinger, and now is freely available as a PDF-, RTF-, or HTML-file. This bibliography mainly attempts to cover the Anglo-Saxon and German debates, in a non-annotated, fully formatted way that makes it easy to "cut and paste" from the original file. To a certain degree this bibliography also contains items in other languages than English and German - all submissions in other languages are welcome. Last update of current version: July 13th, 2001. (shrink)

In attempts to formulate a computational understanding of brain function, one of the fundamental concerns is the data structure by which the brain represents information. For many decades, a conceptual framework has dominated the thinking of both brain modelers and neurobiologists. That framework is referred to here as "classical neural networks." It is well supported by experimental data, although it may be incomplete. A characterization of this framework will be offered in the next section. Difficulties in modeling important functional aspects (...) of the brain on the basis of classical neural networks alone have led to the recognition that another, general mechanism must be invoked to explain brain function. That mechanism I call " binding." Binding by neural signal synchrony had been mentioned several times in the liter ature before it was fully formulated as a general phenomenon. Although experimental evidence for neural syn chrony was soon found, the idea was largely ignored for many years. Only recently has it become a topic of animated discussion. In what follows, I will summarize the nature and the roots of the idea of binding, especially of temporal binding, and will discuss some of the objec tions raised against it. (shrink)