Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

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.

Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing

“…Associations recalled more than x ≥ 11 times were considered, in order for the whole layer to feature as many links as the phonological one. Also for consistency with the phonological layer, this layer was treated as undirected and unweighted, as done in previous cognitive computing approaches 27,30,35 .…”

“…The resulting multiplex lexical network was minimal in the sense that, differently from other approaches 29,30 , it considered only one layer of semantic relationships coupled with a phonological one. Indeed, free associations identify recalls from semantic memory 11 that might be related not only to semantics but also to sound similarity or visual features.…”

“…its largest connected component (LCC) includes all 15886 words in the multiplex network. Like in past approaches [26][27][28]30 , two words are connected if a multiplex path exists between them, i.e. a network path combining free associations and phonological similarities connected the words.…”

“…Although it is appealing to investigate robustness of single aspects of the mental lexicon to degradation, this approach would neglect the extensive evidence indicating how failed lexical retrieval crucially depends on the interplay between semantic and phonological conceptual similarities 3,6,8,26,27 . In order to embrace multiple aspects of the mental lexicon in a single network representation, this work suggests studying lexicon robustness through a minimal representation of semantic and phonological associations for 16000 English words as a multiplex lexa) Electronic mail: massimo.stella@inbox.com ical network 26,[28][29][30][31] . Such representation is used as a quantitative framework for exploring the large-scale robustness of the mental lexicon under progressive word failure through the formalism of network-based attacks 20,22,23,25 .…”

“…The largest viable component of the mental lexicon was identified 26 as a network core for the mental lexicon, thus facilitating network navigation, and emerging through an explosive phase transition in children around age 7-8 yrs. Subsequent work 30 identified the largest viable cluster as a relevant component of the mental lexicon, made of more frequent, easier to identify, earlier acquired words, of relevance also for knowledge exploration across groups with different levels of creativity. Building upon such evidence for the cognitive relevance of the largest viable component in the mental lexicon, also its size will be adopted as a metric for robustness under attack.…”