skip to main content


Title: Flexible semantic network structure supports the production of creative metaphor
Metaphors are a common way to express creative language, yet the cognitive basis of figurative language production remains poorly understood. Previous studies found that higher creative individuals can better comprehend novel metaphors, potentially due to a more flexible semantic memory network structure conducive to remote conceptual combination. The present study extends this domain to creative metaphor production and examined whether the ability to produce creative metaphors is related to variation in the structure of semantic memory. Participants completed a creative metaphor production task and two verbal fluency tasks. They were divided into two equal groups based on their creative metaphor production score. The semantic networks of these two groups were estimated and analyzed based on their verbal fluency responses using a computational network science approach. Results revealed that the semantic networks of high-metaphor producing individuals were more flexible, clustered, and less rigid than that of the low-metaphor producing individuals. Importantly, these results replicated across both semantic categories. The findings provide the first evidence that a flexible, clustered, and less rigid semantic memory structure relates to people’s ability to produce figurative language, extending the growing literature on the role of semantic networks in creativity to the domain of metaphor production.  more » « less
Award ID(s):
1920653
NSF-PAR ID:
10285535
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Creativity Research Journal
ISSN:
1040-0419
Page Range / eLocation ID:
1 to 15
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    We examined the relationship between metaphor comprehension and verbal analogical reasoning in young adults who were either typically developing (TD) or diagnosed with Autism Spectrum Disorder (ASD). The ASD sample was highly educated and high in verbal ability, and closely matched to a subset of TD participants on age, gender, educational background, and verbal ability. Additional TD participants with a broader range of abilities were also tested. Each participant solved sets of verbal analogies and metaphors in verification formats, allowing measurement of both accuracy and reaction times. Measures of individual differences in vocabulary, verbal working memory, and autistic traits were also obtained. Accuracy for both the verbal analogy and the metaphor task was very similar across the ASD and matched TD groups. However, reaction times on both tasks were longer for the ASD group. Additionally, stronger correlations between verbal analogical reasoning and working memory capacity in the ASD group indicated that processing verbal analogies was more effortful for them. In the case of both groups, accuracy on the metaphor and analogy tasks was correlated. A mediation analysis revealed that after controlling for working memory capacity, the inter‐task correlation could be accounted for by the mediating variable of vocabulary knowledge, suggesting that the primary common mechanisms linking the two tasks involve language skills.

     
    more » « less
  2. null (Ed.)
    Impairments related to figurative language understanding have been considered to be one of the diagnostic and defining features of autism. Metaphor comprehension and production in autism spectrum disorder (ASD) as compared to typically developing (TD) individuals have been investigated for around thirty years, generally showing an overall advantage for TD groups. We present a preregistered systematic review and meta-analysis including a total of 15 studies that fulfilled our set of inclusion criteria (notably, ASD and TD groups matched in chronological age and verbal- or full-scale IQ). Along with accuracy, we also analyzed group differences in reaction time in the studies that reported them. The results revealed a medium-to-large group difference favoring TD over ASD groups based on accuracy measures, as well as a similar overall advantage for TD groups based on reaction times. There was reliable heterogeneity in effect sizes for group differences in accuracy, which was mostly explained by the effect of verbal intelligence, with differences in metaphor processing being smaller for participants with better verbal skills. Some of the variation in effect sizes may also be attributed to differences in types of metaphor processing tasks. We also evaluated the quality of the studies included in the meta-analysis, and the evidence relating to the potential presence of publication bias. 
    more » « less
  3. Abstract

    Novel metaphorical language use exemplifies human creativity through production and comprehension of meaningful linguistic expressions that may have never been heard before. Available electrophysiological research demonstrates, however, that novel metaphor comprehension is cognitively costly, as it requires integrating information from distantly related concepts. Herein, we investigate if such cognitive cost may be reduced as a factor of prior domain knowledge. To this end, we asked engineering and nonengineering students to read for comprehension literal, novel metaphorical, and anomalous sentences related to engineering or general knowledge, while undergoing EEG recording. Upon reading each sentence, participants were asked to judge whether or not the sentence was original in meaning (noveltyjudgment) and whether or not it made sense (sensicalityjudgment). When collapsed across groups, our findings demonstrate a gradual N400 modulation with N400 being maximal in response to anomalous, followed by metaphorical, and literal sentences. Between‐group comparisons revealed a mirror effect on the N400 to novel metaphorical sentences, with attenuated N400 in engineers and enhanced N400 in non‐engineers. Critically, planned comparisons demonstrated reduced N400 amplitudes to engineering novel metaphors in engineers relative to non‐engineers, pointing to an effect of prior knowledge on metaphor processing. This reduction, however, was observed in the absence of a sentence type × knowledge × group interaction. Altogether, our study provides novel evidence suggesting that prior domain knowledge may have a direct impact on creative language comprehension.

     
    more » « less
  4. Using poetic metaphors in the Serbian language, we identified systematic variations in the impact of fluid and crystalized intelligence on comprehen-sion of metaphors that varied in rated aptness and familiarity. Overall, comprehension scores were higher for metaphors that were high rather than low in aptness, and high rather than low in familiarity. A measure of crystalized intelligence was a robust predictor of comprehension across the full range of metaphors, but especially for those that were either relatively unfamiliar or more apt. In contrast, individual differences associated with fluid intelligence were clearly found only for metaphors that were low in aptness. Superior verbal knowledge appears to be particularly important when trying to find meaning in novel metaphorical expressions, and also when exploring the rich interpretive potential of apt metaphors. The broad role of crystalized intelligence in metaphor comprehension is consistent with the view that metaphors are largely understood using semantic integration processes continuous with those that operate in understanding literal language. 
    more » « less
  5. Abstract

    Graph and language embedding models are becoming commonplace in large scale analyses given their ability to represent complex sparse data densely in low-dimensional space. Integrating these models’ complementary relational and communicative data may be especially helpful if predicting rare events or classifying members of hidden populations—tasks requiring huge and sparse datasets for generalizable analyses. For example, due to social stigma and comorbidities, mental health support groups often form in amorphous online groups. Predicting suicidality among individuals in these settings using standard network analyses is prohibitive due to resource limits (e.g., memory), and adding auxiliary data like text to such models exacerbates complexity- and sparsity-related issues. Here, I show how merging graph and language embedding models (metapath2vecanddoc2vec) avoids these limits and extracts unsupervised clustering data without domain expertise or feature engineering. Graph and language distances to a suicide support group have little correlation (ρ< 0.23), implying the two models are not embedding redundant information. When used separately to predict suicidality among individuals, graph and language data generate relatively accurate results (69% and 76%, respectively) but have moderately large false-positive (25% and 21%, respectively) and false-negative (38% and 27%, respectively) rates; however, when integrated, both data produce highly accurate predictions (90%, with 10% false-positives and 12% false-negatives). Visualizing graph embeddings annotated with predictions of potentially suicidal individuals shows the integrated model could classify such individuals even if they are positioned far from the support group. These results extend research on the importance of simultaneously analyzing behavior and language in massive networks and efforts to integrate embedding models for different kinds of data when predicting and classifying, particularly when they involve rare events.

     
    more » « less