scispace - formally typeset
Search or ask a question
Journal ArticleDOI

The Act of Creation

01 Jan 1966-Vol. 19, Iss: 1, pp 31
TL;DR: Koestler as mentioned in this paper examines the idea that we are at our most creative when rational thought is suspended, for example, in dreams and trancelike states, and concludes that "the act of creation is the most creative act in human history".
Abstract: While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.
Citations
More filters
Book
25 Oct 1999
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Abstract: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

20,196 citations

Journal ArticleDOI
TL;DR: In this paper, the authors identify key dimensions of absorptive capacity and offer a reconceptualization of this construct, and distinguish between a firm's potential and realized capacity, and then advance a model outlining the conditions when the firm's realized capacities can differentially influence the creation and sustenance of its competitive advantage.
Abstract: Researchers have used the absorptive capacity construct to explain various organizational phenomena. In this article we review the literature to identify key dimensions of absorptive capacity and offer a reconceptualization of this construct. Building upon the dynamic capabilities view of the firm, we distinguish between a firm's potential and realized capacity. We then advance a model outlining the conditions when the firm's potential and realized capacities can differentially influence the creation and sustenance of its competitive advantage.

8,648 citations

MonographDOI
01 Dec 2014
TL;DR: This chapter discusses the emergence of learning activity as a historical form of human learning and the zone of proximal development as the basic category of expansive research.
Abstract: 1. Introduction 2. The emergence of learning activity as a historical form of human learning 3. The zone of proximal development as the basic category of expansive research 4. The instruments of expansion 5. Toward an expansive methodology 6. Epilogue.

5,768 citations

Journal ArticleDOI
TL;DR: A perceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal symbol systems and implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.
Abstract: Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

5,259 citations

Journal ArticleDOI
TL;DR: In this article, the authors integrated a number of streams of research on the antecedents of innovation to develop and test a model of individual innovative behavior, and they used structural equation analysis to test the parameters of the proposed model simultaneously and also explored the moderating effect of task characteristics.
Abstract: The present study integrated a number of streams of research on the antecedents of innovation to develop and test a model of individual innovative behavior. Hypothesizing that leadership, individual problem-solving style, and work group relations affect innovative behavior directly and indirectly through their influence on perceptions of the climate for innovation, we used structural equation analysis to test the parameters of the proposed model simultaneously and also explored the moderating effect of task characteristics. The model explained approximately 37 percent of the variance in innovative behavior. Task type moderated the relationship between leader role expectations and innovative behavior.

4,615 citations

References
More filters
Book
25 Oct 1999
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Abstract: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

20,196 citations

Journal ArticleDOI
TL;DR: In this paper, the authors identify key dimensions of absorptive capacity and offer a reconceptualization of this construct, and distinguish between a firm's potential and realized capacity, and then advance a model outlining the conditions when the firm's realized capacities can differentially influence the creation and sustenance of its competitive advantage.
Abstract: Researchers have used the absorptive capacity construct to explain various organizational phenomena. In this article we review the literature to identify key dimensions of absorptive capacity and offer a reconceptualization of this construct. Building upon the dynamic capabilities view of the firm, we distinguish between a firm's potential and realized capacity. We then advance a model outlining the conditions when the firm's potential and realized capacities can differentially influence the creation and sustenance of its competitive advantage.

8,648 citations

MonographDOI
01 Dec 2014
TL;DR: This chapter discusses the emergence of learning activity as a historical form of human learning and the zone of proximal development as the basic category of expansive research.
Abstract: 1. Introduction 2. The emergence of learning activity as a historical form of human learning 3. The zone of proximal development as the basic category of expansive research 4. The instruments of expansion 5. Toward an expansive methodology 6. Epilogue.

5,768 citations

Journal ArticleDOI
TL;DR: A perceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal symbol systems and implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.
Abstract: Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

5,259 citations

Journal ArticleDOI
TL;DR: In this article, the authors integrated a number of streams of research on the antecedents of innovation to develop and test a model of individual innovative behavior, and they used structural equation analysis to test the parameters of the proposed model simultaneously and also explored the moderating effect of task characteristics.
Abstract: The present study integrated a number of streams of research on the antecedents of innovation to develop and test a model of individual innovative behavior. Hypothesizing that leadership, individual problem-solving style, and work group relations affect innovative behavior directly and indirectly through their influence on perceptions of the climate for innovation, we used structural equation analysis to test the parameters of the proposed model simultaneously and also explored the moderating effect of task characteristics. The model explained approximately 37 percent of the variance in innovative behavior. Task type moderated the relationship between leader role expectations and innovative behavior.

4,615 citations