According to Mednick's (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by “flat” (broader associations) instead of “steep” (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related to each other by their association correlations—overlap of similar associative responses (“association clouds”). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick's (1962) theory and the feasibility of using network science paradigms to investigate high level cognition.

Although neuroimaging and human lesion studies agree that the medial parietal region plays a critical role in episodic memory, many neuroimaging studies have also implicated lateral parietal cortex, leading some researchers to suggest that the lateral region plays a heretofore underappreciated role in episodic memory. Because there are very few extant lesion data on this matter, we examined memory in six cases of focal lateral parietal damage, using both clinical and experimental measures, in which we distinguished between recollection and familiarity. The patients did not have amnesia, but they did show evidence of disrupted recollection on an anterograde memory task. Although the exact mechanisms remain to be elucidated, lateral parietal damage appears to impair some aspects of episodic memory.

Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We propose a novel approach to computing semantic distance, based on network science methodology. Path length in a semantic network represents the amount of steps needed to traverse from 1 word in the network to the other. We examine whether path length can be used as a measure of semantic distance, by investigating how path length affect performance in a semantic relatedness judgment task and recall from memory. Our results show a differential effect on performance: Up to 4 steps separating between word-pairs, participants exhibit an increase in reaction time (RT) and decrease in the percentage of word-pairs judged as related. From 4 steps onward, participants exhibit a significant decrease in RT and the word-pairs are dominantly judged as unrelated. Furthermore, we show that as path length between word-pairs increases, success in free- and cued-recall decreases. Finally, we demonstrate how our measure outperforms computational methods measuring semantic distance (LSA and positive pointwise mutual information) in predicting participants RT and subjective judgments of semantic strength. Thus, we provide a computational alternative to computing semantic distance. Furthermore, this approach addresses key issues in cognitive theory, namely the breadth of the spreading activation process and the effect of semantic distance on memory retrieval. (PsycINFO Database Record

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Creativity is mainly viewed by current theories as either a bottom-up or top-down cognitive process. However, a growing body of research indicates that both processes contribute to creative ability. Furthermore, in both accounts the structure of the mental lexicon plays a key component, either as directly related to creative ability (bottom-up) or as the basis upon which top-down processes operate (top-down). Thus, the examination of the mental lexicon structure as related to both types of processes can shed further light on the nature of creative ability. In this study, we use network science methodology to examine how fluid intelligence and creative achievement are related to the structure of the mental lexicon. A large sample of participants completed a semantic verbal fluency task and was divided into 4 groups, based on their performance on intelligence and creative achievement measures. A network science methodology was then used to extract and compare the lexical network structure of the semantic category between the 4 groups. The results of this analysis revealed that while fluid intelligence was more related to structural properties of the lexical network, creative achievement was more related to flexible properties of the lexical network. Furthermore, we found that the lexical network of the high-fluid-intelligence and high-creative-achievement group exhibited a combination of both effects. These findings provide insight into structural and functional properties of semantic networks, and they demonstrate the utility of network science in examining high-level cognitive phenomena, such as creativity and intelligence.