Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

Christensen, Alexander P.; Kenett, Yoed N.

doi:10.31234/osf.io/eht87

Cited by 15 publications

(19 citation statements)

References 63 publications

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“…The present work contributes to the growing study of creativity in the context of semantic networks (Christensen & Kenett, 2019;Kenett & Faust, 2019;Zemla, Cao, Mueller, & Austerweil, 2020). Kenett and colleagues have published several recent papers empirically validating the longstanding associative theory of creativity (Mednick, 1962), which posits that creative thinking involves making connections between remote concepts in semantic memory.…”

Section: Summary Limitations and Future Directionsmentioning

confidence: 79%

Automating creativity assessment with SemDis: An open platform for computing semantic distance

2020

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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 limitationslabor 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; Studies 1-3) and novelty/creativity ratings from two word association tasks (Studies 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 (https://osf.io/gz4fc/).

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Section: Summary Limitations and Future Directionsmentioning

confidence: 79%

Automating creativity assessment with SemDis: An open platform for computing semantic distance

2020

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“…Thus, we constructed a word-correlation matrix between all the pairs of words for each group. Here, we applied the cosine similarity measure with the following formula (using the SemNeT package; ( Christensen and Kenett 2019 ) ):

where A j indicates the column of response a, and B j indicates the vector of response b . Despite the fact that Pearson’s correlation was used in prior work ( Kenett et al 2014 ; Kenett et al 2016a ), we followed the reasoning of Christensen and colleagues ( Christensen et al 2018 ) according to which the resulting associations using the cosine similarity are all positively valued (ranging from 0 to 1), giving the advantage of not assuming a negative association between two responses.…”

Section: Methodsmentioning

confidence: 99%

“…The following network parameters were calculated for each network using the SemNeT ( Christensen and Kenett 2019 ) and NetworkToolbox ( Christensen 2019 ) packages in R: the clustering coefficient (CC) ( Watts and Strogatz 1998 ) the average shortest path length (ASPL), the modularity index (Q) ( Newman 2006 ), and the small-world-ness measure (S) ( Humphries and Gurney 2008 ). Based on previous studies ( Borodkin et al 2016 ; Christensen et al 2018 ; Kenett et al 2014 ), we empirically examined the validity of our findings by applying two reciprocal approaches.…”

Section: Methodsmentioning

confidence: 99%

Revealing the Role of Divergent Thinking and Fluid Intelligence in Children’s Semantic Memory Organization

2020

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The current theories suggest the fundamental role of semantic memory in creativity, mediating bottom-up (divergent thinking) and top-down (fluid intelligence) cognitive processes. However, the relationship between creativity, intelligence, and the organization of the semantic memory remains poorly-characterized in children. We investigated the ways in which individual differences in children’s semantic memory structures are influenced by their divergent thinking and fluid intelligence abilities. The participants (mean age 10) were grouped by their levels (high/low) of divergent thinking and fluid intelligence. We applied a recently-developed Network Science approach in order to examine group-based semantic memory graphs. Networks were constructed from a semantic fluency task. The results revealed that divergent thinking abilities are related to a more flexible structure of the semantic network, while fluid intelligence corresponds to a more structured semantic network, in line with the previous findings from the adult sample. Our findings confirm the crucial role of semantic memory organization in creative performance, and demonstrate that this phenomenon can be traced back to childhood. Finally, we also corroborate the network science methodology as a valid approach to the study of creative cognition in the developmental population.

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“…Responses consisting of non-category members were excluded, and root variations were converged (e.g., dogs to dog) from the final analysis using the SemNetCleaner package (Christensen & Kenett, 2019) in R (R Core Team, 2019). Responses were analyzed categorically, with "1" corresponding to a valid response and "0" corresponding to no response.…”

Section: Broad Retrieval Ability (Gr)mentioning

confidence: 99%

Intelligence and creativity share a common cognitive and neural basis.

Frith¹,

Elbich²,

Christensen³

et al. 2021

Journal of Experimental Psychology: General

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Are intelligence and creativity distinct abilities, or do they rely on the same cognitive and neural systems?We sought to quantify the extent to which intelligence and creative cognition overlap in brain and behavior by combining machine learning of fMRI data and latent variable modeling of cognitive ability data in a sample of young adults (N = 186) who completed a battery of intelligence and creative thinking tasks. The study had three analytic goals: (a) to assess contributions of specific facets of intelligence (e.g., fluid and crystallized intelligence) and general intelligence to creative ability (i.e., divergent thinking originality), (b) to model whole-brain functional connectivity networks that predict intelligence facets and creative ability, and (c) to quantify the degree to which these predictive networks overlap in the brain. Using structural equation modeling, we found moderate to large correlations between intelligence facets and creative ability, as well as a large correlation between general intelligence and creative ability (r = .63). Using connectomebased predictive modeling, we found that functional brain networks that predict intelligence facets overlap to varying degrees with a network that predicts creative ability, particularly within the prefrontal cortex of the executive control network. Notably, a network that predicted general intelligence shared 46% of its functional connections with a network that predicted creative ability-including connections linking executive control and salience/ventral attention networks-suggesting that intelligence and creative thinking rely on similar neural and cognitive systems.

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Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

Cited by 15 publications

References 63 publications

Automating creativity assessment with SemDis: An open platform for computing semantic distance

Automating creativity assessment with SemDis: An open platform for computing semantic distance

Revealing the Role of Divergent Thinking and Fluid Intelligence in Children’s Semantic Memory Organization

Intelligence and creativity share a common cognitive and neural basis.

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