We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities (...) have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high‐dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small‐world statistics and power‐law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks. (shrink)

Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individual’s semantic memory. The authors analyze the structure of semantic networks that they sampled from individuals through a new snowball sampling paradigm during approximately 6 weeks of 1-hr daily sessions. The semantic networks of individuals have a small-world structure with short distances between words and high clustering. The distribution of links follows a power law truncated by an exponential cutoff, meaning that most words (...) are poorly connected and a minority of words has a high, although bounded, number of connections. Existing aggregate networks mirror the individual link distributions, and so they are not scale-free, as has been previously assumed; still, there are properties of individual structure that the aggregate networks do not reflect. A simulation of the new sampling process suggests that it can uncover the true structure of an individual’s semantic memory. (shrink)

A divide exists in the creativity literature as to whether relatively more or less executive processing is beneficial to creative thinking. To explore this issue, we employ an individual differences perspective informed by dual-process theories in which it is assumed that people vary in the extent to which they rely on autonomous or controlled processing . We find that those more willing and/or able to engage Type 2 processing are more likely to successfully make creative connections in tasks requiring the (...) unification of disparate elements and the novelty of generated items, but not in some other indices of creativity, namely, cognitive flexibility and fluency. Implications for the role of executive processing in creative thinking are discussed in the context of DPTs. We situate the ability to make remote connections alongside other advanced higher order thinking capabilities that are unique to humans. (shrink)

In three experiments, the relation between different modes of thought and the generation of “creative” and original ideas was investigated. Participants were asked to generate items according to a specific instruction . They either did so immediately after receiving the instruction, or after a few minutes of conscious thought, or after a few minutes of distraction during which “unconscious thought” was hypothesized to take place. Throughout the experiments, the items participants listed under “unconscious thought” conditions were more original. It was (...) concluded that whereas conscious thought may be focused and convergent, unconscious thought may be more associative and divergent. (shrink)