This paper examines human planning abilities, using as its inspiration planning techniques developed in artificial intelligence. AI research has shown that in certain problems partial‐order planners, which manipulate partial plans while not committing to a particular ordering of those partial plans, are more efficient than total‐order planners, which represent all partial plans as totally ordered. This research asks whether total‐order planning and/or partial‐order planning are accurate descriptions of human planning, and if different populations use different planning techniques. Using a simple (...) planning task modeled after tasks designed in artificial intelligence we tested 7–8 year‐old children, 11–13 year‐old children, adult controls, and adults with damage to the prefrontal cortex. We found that adults and older children exhibited performance on planning tasks of varying complexity which matched that of artificial partial‐order planners, and that this pattern of performance did not vary with multiple presentations of the planning task. In contrast, young children and adults with damage to the prefrontal cortex exhibited performance matching that of artificial total‐order planners. This pattern of performance did vary, however, with multiple presentations of the planning task, with the young children and adults with cortical damage displaying aspects of total‐order planning. In a further study we found that adolescents who had sustained damage to the prefrontal cortex as children displayed two different patterns of performance; when measures of reaction time were analyzed they revealed a pattern of performance suggestive of partial‐order plan representations. However, analyses of the adolescents' protocols revealed a pattern of performance suggestive of total‐order plan representations. The significance of these results to psychology, neuroscience, and artificial intelligence are discussed. (shrink)

The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its performance (...) can be related to human performance (e.g., solution trajectory, error patterns, time‐on‐task). Furthermore, CAB's bounded working memory allows it to account for the inherent capacity limitations of human processing. CAB's strengths are its parsimony, transparency of operations, and ability to generate performance predictions. In this paper, CAB is evaluated against benchmark phenomena from the analogy literature. (shrink)

How is it possible to think new thoughts? What is creativity and can science explain it? And just how did Coleridge dream up the creatures of The Ancient Mariner? When The Creative Mind: Myths and Mechanisms was first published, Margaret A. Boden's bold and provocative exploration of creativity broke new ground. Boden uses examples such as jazz improvisation, chess, story writing, physics, and the music of Mozart, together with computing models from the field of artificial intelligence to uncover the nature (...) of human creativity in the arts. The second edition of The Creative Mind has been updated to include recent developments in artificial intelligence, with a new preface, introduction and conclusion by the author. It is an essential work for anyone interested in the creativity of the human mind. (shrink)

An essential work for anyone interested in the creativity of the human mind, "The Creative Mind" has been updated to include recent developments in artificial ...