Recent empirical and case study evidence documents a strong association between the adoption of computers and increased use of college educated or non-production workers. With few exceptions, the conceptual link explaining how computer technology complements skilled labor or substitutes for unskilled labor is less well developed. This paper applies an understanding of what computers do – the execution of procedural or rules-based logic – to develop and test a simple model of how the widespread adoption of computers in the workplace might alter workplace skill demands. Two essential contentions of our framework are that computer capital (1) substitutes for a limited and well-defined set of human activities, those involving routine (repetitive) cognitive and manual tasks; and (2) complements a second set of activities, those involving non-routine problem solving and interactive tasks. Under the assumption that routine and non-routine tasks are imperfect substitutes, the task framework implies measurable changes in the task content of employment. We examine these changes using representative samples of workers from 1960 to 1998 where individual characteristics are augmented with Dictionary of Occupational Title variables describing their occupations ’ requirements for routine and non-routine cognitive and manual skills. We find that computerization is associated with declining relative industry

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The string matching problem for strings in which one should find the occurrences of a pattern string within a text, is well-studied in the past literature. The problem can be solved efficiently, e.g., by using so-called bit-parallel algorithms. We adapt the bit-parallel approach to music information retrieval. We consider a situation where the pattern is monophonic and the text (the musical source) is polyphonic, that is, the pattern is a sequence of symbols, while the source is a sequence of sets of symbols (i.e., chords). The application of the bit-parallel approach is straightforward, if the transposition invariance is not allowed in the matching. However, the problem becomes trickier with transposition invariance, a necessary property in practice. We present algorithms for both cases. Our main contribution is a linear-time transposition-invariant filtering-algorithm for static music databases. We show by experiments that, if the average size of the chords keeps reasonably low, o...

We examine the question of which aspects of language are uniquely human and uniquely linguistic in light of recent arguments by Hauser, Chomsky, and Fitch that the only such aspect is syntactic recursion, the rest of language being either specific to humans but not to language (e.g., words and concepts) or not specific to humans (e.g., speech perception). We find this argument problematic. It ignores the many aspects of grammar that are not recursive, such as phonology, morphology, case, and agreement. It is inconsistent with the anatomy and neural control of the human vocal tract. And it is weakened by experiments showing that speech perception cannot be reduced to primate audition, that word learning cannot be reduced to fact learning, and that at least one gene involved in speech and language was evolutionarily selected in the human lineage but is not specific to recursion. The recursion-only claim, we suggest, is motivated by Chomsky’s recent approach to syntax, the Minimalist Program, which de-emphasizes the same aspects of language. The approach, however, is sufficiently problematic that it cannot be used to support claims about evolution. We contest other arguments from Chomsky that language is not an adaptation, namely that it is “perfect, ” nonredundant, unusable in any partial form, and badly designed for communication. The hypothesis that language is a complex adaptation for communication which evolved piecemeal avoids all these problems.

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Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff formulated four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard ’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. 2 Content

The goal of this position paper is to contribute to a clear understanding of the profound differences between the association-rule discovery and the classification tasks. We argue that the classification task can be considered an ill-defined, nondeterministic task, which is unavoidable given the fact that it involves prediction; while the standard association task can be considered a well-defined, deterministic, relatively simple task, which does not involve prediction in the same sense as the classification task does.

A suitable project for the new Millenium is to radically reconfigure our image of human rationality. Such a project is already underway, within the Cognitive Sciences, under the umbrellas of work in Situated Cognition, Distributed and Decentralized Cognition, Real-world Robotics and Artificial Life. Such approaches

The term “aphasia ” refers to acute or chronic impairment of language, an acquired condition that is most often associated with damage to the left side of the brain, usually due to trauma or stroke. We have known about the link between left-hemisphere damage and language loss for more than a century (Goodglass, 1993). For almost as long, we have also known that the lesion/symptom correlations observed in adults do not appear to hold for very young children (Basser, 1962; Lenneberg, 1967). In fact, in the absence of other complications, infants with congenital damage to one side of the brain (left or right) usually go on to acquire language abilities that are well within the normal range (Eisele & Aram, 1995; Feldman, Holland, & Janosky, 1992; Vargha-Khadem, Isaacs, & Muter,