Speakers frequently perform representational gestures to depict concepts in an iconic fashion. For example, a speaker may hold her index finger and thumb apart to indicate the size of a matchstick. However, the process by which a physical handshape is mentally transformed into abstract spatial information is not well understood. We present a series of experiments that investigate how people decode the physical form of an articulator to derive imaginary geometrical constructs, which we call “gesture form.” We provide quantitative evidence (...) for several key properties that play a role in this process. First, “profiling,” the ability to focus on a structural subunit within the complex form of the physical hand. Second, “perspective,” for which we show that one and the same handshape seen from different perspectives can lead to different spatial interpretations. Third, “selectivity,” the fact that gestures focus on specific spatial features at the expense of others. Our results provide a first step toward mapping out the process of how representational gestures make the communication of spatial information possible. (shrink)

This paper offers a review of research on demonstratives from an interdisciplinary perspective. In particular, we consider the role of demonstratives in current research on language universals, language evolution, language acquisition, multimodal communication, signed language, language and perception, language in interaction, spatial imagery, and discourse processing. Traditionally, demonstratives are analyzed as a particular class of spatial deictics. Yet, a number of recent studies have argued that space is largely irrelevant to deixis and that demonstratives are primarily used for social and (...) interactive purposes. Synthesizing findings in the literature, we conclude that demonstratives are a very special class of linguistic items that are foundational to both spatial and social aspects of language and cognition. (shrink)

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...) from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (shrink)