“Forward flow”: A new measure to quantify free thought and predict creativity.

Gray, Kurt; Anderson, Stephen A.; Chen, Eric Evan; Kelly, John; Christian, Michael S.; Patrick, John; Huang, Laura; Kenett, Yoed N.; Lewis, Kevin

doi:10.1037/amp0000391

Cited by 119 publications

(122 citation statements)

References 72 publications

(78 reference statements)

Supporting

Mentioning

113

Contrasting

Unclassified

Order By: Relevance

“…Second, we found that higher creative individuals can cover longer distances over the multiplex network when jumping from one response to the next (i.e., higher coverage per response). This finding is analogous to previous findings [1,72] about higher creative individuals being able to cover longer network distances when searching through their mental lexicon, findings that currently have only been shown at the semantic level [1,30,31].…”

Section: Discussionsupporting

confidence: 90%

“…Hence, in addition to lower creative individuals providing more responsive in the LVC, this finding indicates that lower creative individuals tend to exploit more words in the LVC as "bridges" for their mental exploration in comparison to higher creative individuals, who exploit more concepts outside of the language kernel represented by the LVC. This finding may be related to previous studies demonstrating how higher creative individuals' search processes being broader and allowing them to reach more weakly connected nodes, potentially words that are outside the LVC [6,30,31].…”

Section: Discussionsupporting

confidence: 74%

“…In line with the associative theory of creativity, the authors found that random walks over the semantic network of high creative individuals visits more unique and weaker nodes. Finally, a recent study proposed a method to quantify the semantic distance between associative responses generated by participants in a chained free association task [31]. The authors found that high creative individuals reach farther distances than low creative individuals, providing empirical support to the findings of [30].…”

Section: Introductionmentioning

confidence: 67%

See 2 more Smart Citations

Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

Stella¹,

Kenett

2019

BDCC

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 67%

See 1 more Smart Citation

Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

Stella¹,

Kenett

2019

BDCC

Self Cite

View full text Add to dashboard Cite

show abstract

“…Application of LSA in creativity research is rooted in the associative theory of creativity (Kenett, 2019;Mednick, 1962) which proposes that creative thinking requires making connections between seemingly "remote" concepts. The associative theory has received increasing support from several recent computational modeling studies showing that high-creative individuals, defined by performance on a battery of creativity tasks, show a more flexible semantic network structure, characterized by low modularity and high connectivity between concepts (Christensen, Kenett, Cotter, Beaty, & Silvia, 2018;Gray et al, 2019;Kenett, Anaki, & Faust, 2014;Kenett et al, 2018;Kenett & Faust, 2019). According to Kenett and colleagues, this flexible (or small-world) semantic network architecture is conducive to creative thinking because it allows people to form conceptual combinations between concepts that are typically represented further apart (e.g., hammer and tissue).…”

Section: Automating Creativity Assessmentmentioning

confidence: 99%

Automating Creativity Assessment with SemDis: An Open Platform for Computing Semantic Distance

Beaty¹,

Johnson²

2020

Preprint

View full text Add to dashboard Cite

Creativity research requires assessing the quality of ideas and products. In practice, conducting creativity research often involves asking several human raters to judge participants’ responses to creativity tasks, such as judging the novelty of ideas from the alternate uses task (AUT). Although such subjective scoring methods have proved useful, they have two inherent limitations—labor cost (raters typically code thousands of responses) and subjectivity (raters vary on their perceptions and preferences)—raising classic psychometric threats to reliability and validity. We sought to address the limitations of subjective scoring by capitalizing on recent developments in automated scoring of verbal creativity via semantic distance, a computational method that uses natural language processing to quantify the semantic relatedness of texts. In five studies, we compare the top performing semantic models (e.g., GloVe, continuous bag of words) previously shown to have the highest correspondence to human relatedness judgements. We assessed these semantic models in relation to human creativity ratings from a canonical verbal creativity task (AUT; Study 1-3) and novelty/creativity ratings from two word association tasks (Study 4-5). We find that a latent semantic distance factor—comprised of the common variance from five semantic models—reliably and strongly predicts human creativity and novelty ratings across a range of creativity tasks. We also replicate an established experimental effect in the creativity literature (i.e., the serial order effect) and show that semantic distance correlates with other creativity measures, demonstrating convergent validity. We provide an open platform to efficiently compute semantic distance, including tutorials and documentation (semdis.wlu.psu.edu).

show abstract

“…For example, Kenett and colleagues conducted a network analysis of free association data to compare the semantic network structure of high-and low-creative individuals-defined by performance on a battery of creative thinking tasks-and found that, compared to the low-creative group, the high creative group showed a more "flexible" network structure characterized by higher connectivity and lower overall distances between concepts in their semantic network (Kenett et al, 2014), likely permitting more efficient spreading activation processes to unfold. These findings have since been replicated and extended in other group-based analyses (Gray et al, 2019;Kenett et al, 2018), as well as at the individual level, including a study that estimated semantic networks and examined how properties of individual's semantic networks relate to creative performance (Benedek et al, 2017). A similar network structure was also reported in a recent study of openness to experience-a personality trait linked to creative behavior and cognition (Christensen et al, 2018)-providing further support for the role of semantic memory in creativity (Kenett & Faust, 2019;Volle, 2018).…”

mentioning

confidence: 57%

A Fan Effect for Creative Thought: Semantic Richness Facilitates Idea Quantity but Constrains Idea Quality

Beaty¹,

Kenett²,

Rw³

et al. 2019

Preprint

View full text Add to dashboard Cite

Creative thinking has long been associated with spreading of activation through concepts within semantic memory. Despite its theoretical importance, little is yet known about how semantic memory structure facilitates and constrains idea production. We examine one potential influence on spreading activation during divergent thinking known as the fan effect: increasing knowledge about a concept leads to increasing interference from conceptually related information. Specifically, we tested whether cue association size—an index of semantic richness reflecting the average number of elements associated with a given concept—impacts the quantity (fluency) and quality (originality) of responses generated during the alternate uses task (AUT). We hypothesized that low-association AUT cues should benefit originality at the cost of fluency because such cues are embedded within a semantic network with fewer conceptual elements, thus yielding lesser interference from closely-related concepts. This hypothesis was confirmed in three experiments. Furthermore, we found an interaction with individual differences in fluid intelligence in the low-association AUT cues, suggesting that constraints of sparse semantic knowledge can be overcome with top-down intervention. The findings indicate that semantic richness differentially impacts the quality and quantity of generated ideas, and that cognitive control processes can facilitate idea production when conceptual knowledge is limited.

show abstract

“Forward flow”: A new measure to quantify free thought and predict creativity.

Cited by 119 publications

References 72 publications

Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

Automating Creativity Assessment with SemDis: An Open Platform for Computing Semantic Distance

A Fan Effect for Creative Thought: Semantic Richness Facilitates Idea Quantity but Constrains Idea Quality

Contact Info

Product

Resources

About