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

Abstract

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.

Simulated visual hallucinations in virtual reality enhance cognitive flexibility

“…We found that DeepDream exposure significantly influenced the network organization, leading to a reduction of the shortest path between nodes and an increase S, with respect to the OR condition. Consistent with our findings, a higher value of S in the semantic has been previously associated with high creative individuals 15,47,57 . Yet, in our results we found significantly higher connectivity in the OR over DD, suggesting that the reduced ASPL, in DD over OR, was the driving effect of these results, augmenting the chances of reaching a wider number of semantic connections 56,58 .…”

“…The flexibility of the AUT responses is usually measured by a panel of judges, bringing with it all the issues related to subjectivity and therefore the replicability of results. Therefore, here we adopt a method recently developed based on network science methodology and percolation theory to examine CF 4,15,46 , yet extensively used to investigate how semantic memory organization may aid flexible thinking 14,47 . Thus, characterized by higher connectivity and shorter overall distances between concepts, the semantic network allows for more efficient spreading of activation processes throughout the semantic space, which may contribute to the generation of more distinctive ideas 4,15 .…”

Simulated visual hallucinations in virtual reality enhance cognitive flexibility

“…The flexibility of the AUT responses is usually measured by a panel of judges, bringing with it all the issues related to subjectivity and therefore the replicability of results. Therefore, here we adopted a method recently developed based on network science methodology and percolation theory to examine CF 4 , 15 , 46 , yet extensively used to investigate how semantic memory organization may aid flexible thinking 14 , 47 . Thus, characterized by higher connectivity and shorter overall distances between concepts, the semantic network allows for more efficient spreading of activation processes throughout the semantic space, which may contribute to the generation of more distinctive ideas 4 , 15 .…”

Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks.

“…Key questions for simulations to answer are: What sample sizes are necessary for semantic network analysis? Some studies have used as few as 30 people per group (Rastelli, Greco, & Finocchiaro, 2020), while others have used more than 200 people per group (Christensen, Kenett, Cotter, Beaty, & Silvia, 2018). To our knowledge, there are no quantitative procedures available that can determine power for network analysis.…”

“…This approach at least keeps consistency between investigations and can allow for between-sample inferences. Some work, for example, has used group-level correlation-based networks of fluency data for the semantic networks of highly creative people, finding they have more interconnected (higher ASPL) and flexible (lower Q) structures (Kenett, Anaki, & Faust, 2014;Kenett, Beaty, Silvia, Anaki, & Faust, 2016;Li, Kenett, Hu, & Beaty, 2021;Rastelli, Greco, & Finocchiaro, 2020). Although our software contributes to standardizing the semantic network pipeline, we urge researchers to work towards developing best practices to guide new and expert researchers on what methods to use.…”