Creativity is linked to two different semantic memory processes

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Coming up with a creative idea requires us to draw on all our previous knowledge. But how does this happen in our mind and in our brain?

Emmanuelle Volle’s group (Inserm) at the Frontlab of the Paris Brain Institute, in collaboration with the Universities of Graz (Austria) and Warwick (UK), and the Israel Institute of Technology, has identified two semantic memory search processes involved in creativity.

Being creative does not come out of nowhere. Yet the birth of a creative idea in our brain is still an unknown phenomenon. Current theories suggest that it is based in part on the on the organization of our knowledge stored in semantic memory and how we search for concepts in it.

“What actually happens when we look for a new idea? Until now, we didn’t have a clear idea about the processes that allow us to navigate our semantic memory and be creative,” explains Marcela Ovando-Tellez, a postdoctoral fellow at Frontlab and first author of the study.

Semantic memory and creativity

Semantic memory can be studied as a network of associations of objects and concepts that are linked together to a greater or lesser extent. For example, the word “apple” will be strongly connected to the broader set of “fruit” but will also be connected to the concepts of “sweet”, “vegetable” or even to more distant words such as “fairytale” (if you have read Snow White). It is all these concepts, stored in our semantic memory, that allow us to make sense of the world.

Creativity is intimately linked to the structure of this network and the way we navigate within it linked to executive control processes. If the semantic associations are organised in such a way that links between distant objects are easily established, it is easier to generate original ideas.

The components of the semantic memory search process: clustering and switching

In order to understand how we navigate along this network of semantic associations to unearth creative thoughts, Emmanuelle Volle’s group (Inserm) and their collaborators constructed a free semantic association task which consists of giving a cue word to a participant and asking them for all the associates that come to mind in relation to the proposed word.

“The specificity here was that the cue words were polysemous, i.e., they had several possible meanings,” explains Emmanuelle Volle (Inserm), the study’s last author.

“This ambiguity results in the activation of several meanings of the cue words, which allowed us to classify the responses according to the related meaning, and to distinguish two interacting components of the memory search process: clustering and switching.”

What are clustering and switching? Taking the example of a word generation task involving the category “Animals”, clustering would consist of listing successively number of names of a subcategory of animals such as birds, while switching would involve moving from one subcategory to another, from birds to amphibians or mammals.

The task developed by the group of scientists contained, for example, the French word “rayon”, which can have several meanings: the rays of the sun, the supermarket shelves, or the bicycle spokes. Thus, if a participant proposes words associated with “ray” in relation to the weather in a row, he or she adopts a clustering type of memory search, whereas if he or she alternates between words associated with the weather and the supermarket, his or her memory search now is of a switching type.

The researchers combined this association task with a whole series of other tests measuring creativity, the judgment of semantic associations, and executive control (i.e., inhibition, working memory, etc.).

Thanks to these data, they were able to reconstruct the structure of the semantic network of each participant and relate the two components of memory search to creativity, semantic memory organization, and executive control abilities.

Finally, functional imaging MRI acquisitions have enabled us to explore the underlying neural correlates.

Creativity, memory search and cognitive control

The first result obtained by the team is that clustering and switching are indeed related to creativity, but differently. Clustering is linked to divergent thinking, i.e., the free generation of ideas, while switching is related to the ability to combine distant associations between concepts. In addition, the switching component was also related to the organization of the concepts in memory and executive control abilities.

The researchers then were able to predict both clustering and switching from the participant’s brain functional connectivity and show that the two components have different brain correlates.

Clustering was predicted by connectivity patterns between brain networks related to attention and executive control, suggesting that persisting on a semantic category – all the names of mammals that come to mind, for example – involves attentional processes and may be involved in creative idea generation.

Switching, on the other hand, was predicted by connectivity patterns involving mainly the default network and the control network. This pattern of connectivity may support executive control processes interacting with semantic memory to explore and combine distant elements of memory.

Taken together, these results show how the alternations between exploratory search and focused attention support creativity, and provide new insights on the neurocognitive correlates of memory search related to creative cognition.


Industrial designers are commonly assumed to be more creative than other people, because design requires creativity (Sarkar and Chakrabarti, 2011). Design ability is often applied to developing new products (Er Biyikli and Gulen, 2018; Lazar, 2018), which requires the innovative use of variables in the environment (physical objects, behaviors, rules, etc.) to result in favorable outcomes for the executor. There is a close relationship between creativity and design, and for a long time, creativity has been regarded as an important criterion in the evaluation of designers’ proficiency (Sundström and Zika-Viktorsson, 2003). Therefore, many design programs have set up courses to improve students’ creativity (Cheung et al., 2003, 2006; Wang, 2008). However, instructors in most training programs conceptualize creativity as a whole without examining its component parts, or they emphasize some components and not others (Sarkar and Chakrabarti, 2011). Research on this topic can help in developing a design curriculum that covers all areas of creativity.

As early as 1950, Guilford conceptualized creativity as a combination of two forms of thinking, namely, divergent thinking and convergent thinking (Guilford, 1950). Divergent thinking broadens the representational research space while convergent thinking is used to identify the best ideas for the task at hand (Cortes et al., 2019). These dual processes influence the overall process of creation (Barr, 2018), although there may be times when one form of creativity is more influential than the other (Sarkar and Chakrabarti, 2011; Lubart, 2016; Webb et al., 2017; Zhang et al., 2020). The designer can switch between the two forms of thinking according to the actual task requirements (Lazar, 2018). The alternating pattern of divergent thinking and convergent thinking forms the creative process (de Vries and Lubart, 2017).

The creative process can be divided into two stages, with divergent thinking being prominent early in the process and convergent thinking being prominent later in the process. First, in the idea generation stage, the designer uses mostly divergent thinking to put forward as many abstract ideas, forms, and design schemes as possible (Forthmann et al., 2019). During this first stage, distraction is beneficial and creative generation may depend on the availability of unfiltered, low-level perceptual information (Weinberger et al., 2017). Second, in the idea evaluation stage, the designer uses mostly convergent thinking to evaluate these ideas and to determine a solution, resulting in an answer that is not just novel but also useful for the purposes at hand (Guilford, 1957). In this stage, concentration is needed to evaluate the rationality and feasibility of the design scheme (Mohamed, 2016). This stage requires task-directed thoughts and the integration of semantically distant concepts (Weinberger et al., 2017).

Although both divergent and convergent thinking are thought to be important for creativity, there are two reasons to expect that design training might help in developing divergent thinking more than convergent thinking. The first reason is that training programs give more attention to divergent thinking (Rao et al., 2021). Some have asserted that a cognitive process can be judged as creative only if it causes divergent thinking (Finke et al., 1992). Therefore, some early evaluations of creativity focused on the novelty, fluency, and flexibility of ideas (Haritaipan et al., 2018).

The results of two recent studies on the creativity of first-year and senior engineering students suggest that the training emphasis on one thinking may come at the expense of improvements in another thinking. First-year students scored significantly higher on the design thinking scale, while senior students performed significantly better on the integrative thinking scale (Coleman et al., 2020). First-year students also generated significantly more solutions than seniors and showed higher activation in the brain region associated with cognitive flexibility and divergent thinking (Hu et al., 2021).

Design students may perform better on tasks of divergent than convergent thinking because divergent thinking can be taught even in a short period, but there is no corresponding evidence that convergent thinking can be taught. Tran et al. (2020) conducted a 14-week undergraduate creative methods course and found that the participants demonstrated significant promotion in divergent thinking at the post-test. Similarly, Rao et al. (2021) found that training in design thinking significantly increased ideational fluency and elaboration in a divergent thinking task. Another study documented that analogies training improved design consultants’ innovations and divergent thinking (Kalogerakis et al., 2010). Finally, training in cognitive flexibility, which is correlated with divergent thinking (Benedek et al., 2012; Zabelina et al., 2012), has been shown to have direct and near transfer effects (van Bers et al., 2020).

The second reason to expect senior design students to show better divergent than convergent thinking is that the use of divergent thinking (encouraged in training programs) may inhibit convergent thinking. Research in cognitive psychology has shown that a person’s cognitive style is mainly characterized by either divergent or convergent thinking, suggesting that one approach to thinking may hinder the other approach (Kuypers et al., 2016). The implication for design training is that the enhancement of divergent thinking may inhibit the development of convergent thinking (Yue and Gong, 1999; Hommel et al., 2011; Kuypers et al., 2016).

The aim of the current research was to address this question: What is the specific impact of design training on design majors’ convergent and divergent thinking? Our general assumption was that design training would improve both types of creativity, but this would be especially evident in their divergent thinking. To test this assumption, we compared three groups of university students: senior design students (graduate students who already had at least 4years of design training); junior design students (undergraduates in the first year of a design program); and students who were not majoring in design. The three groups were compared on tests of divergent thinking, convergent thinking, and nonverbal abstract reasoning.

Since divergent thinking might benefit from a minimum cognitive-control state and so that the individual can easily “jump” from one thought to the other. However, convergent thinking is likely to benefit from strong top-down cognitive-control state and so that the individual can quickly conduct subsequent performance in tasks. The training of one thinking may impair the performance of the other (Hommel et al., 2011). The specific hypotheses were (1) senior design students will perform better on divergent thinking tasks rather than convergent thinking tasks, when compared with junior design students and non-specific majors; (2) the difference in creativity between senior design students and the other two groups of students will be greater for divergent thinking than convergent thinking.

reference link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520923/


Original Research: Open access.
An investigation of the cognitive and neural correlates of semantic memory search related to creative ability” by Marcela Ovando-Telez et al. Communications Biology

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