Abstract
Previous studies have shown how individual differences in creativity relate to differences in the structure of semantic memory. However, the latter is only one aspect of the whole mental lexicon, a repository of conceptual knowledge that is considered to simultaneously include multiple types of conceptual similarities. In the current study, we apply a multiplex network approach to compute a representation of the mental lexicon combining semantics and phonology and examine how it relates to individual differences in creativity. This multiplex combination of 150,000 phonological and semantic associations identifies a core of words in the mental lexicon known as viable cluster, a kernel containing simpler to parse, more general, concrete words acquired early during language learning. We focus on low (N = 47) and high (N = 47) creative individuals’ performance in generating animal names during a semantic fluency task. We model this performance as the outcome of a mental navigation on the multiplex lexical network, going within, outside, and in-between the viable cluster. We find that low and high creative individuals differ substantially in their access to the viable cluster during the semantic fluency task. Higher creative individuals tend to access the viable cluster less frequently, with a lower uncertainty/entropy, reaching out to more peripheral words and covering longer multiplex network distances between concepts in comparison to lower creative individuals. We use these differences for constructing a machine learning classifier of creativity levels, which leads to an accuracy of 65 . 0 ± 0 . 9 % and an area under the curve of 68 . 0 ± 0 . 8 % , which are both higher than the random expectation of 50%. These results highlight the potential relevance of combining psycholinguistic measures with multiplex network models of the mental lexicon for modelling mental navigation and, consequently, classifying people automatically according to their creativity levels.
Highlights
The creative process—generating novel and useful ideas—has been shown to involve participant’s search processes to “move away” from prototypical ideas [1] and has been related to individual differences in semantic memory structure and executive processes that guide such search processes [2,3,4,5,6,7]
We apply a multiplex network approach to compute a representation of the mental lexicon combining semantics and phonology and examine how it relates to individual differences in creativity
In the current study we investigate how a multiplex representation of the mental lexicon relates to the way low and high creative individuals retrieve animal category members in a semantic fluency task
Summary
The creative process—generating novel and useful ideas—has been shown to involve participant’s search processes to “move away” from prototypical ideas [1] and has been related to individual differences in semantic memory structure and executive processes that guide such search processes [2,3,4,5,6,7]. The associative theory of creativity [6] argues that creativity involves the connection of weakly related, remote concepts into novel and applicable concepts This theory argues that low and high creative individuals differ in their structure of semantic memory, where high creative individuals have a structure that facilitates such a process [6]. In the current study we apply a computational multiplex network analysis [9,10] to examine how low and high creative individuals search through their memory to retrieve animal names. We outline one such approach, based on the application of network science methodologies [11]. The application of network science to study cognitive phenomena is steadily developing, providing quantitative means to study the structure and dynamics of cognitive systems [11] and the interplay between cognition and language in terms of modelling word learning [12], picture naming [13], semantic relatedness [14], stance detection [15], and personality traits [16]
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