Using Network Science to Understand the Aging Lexicon: Linking Individuals' Experience, Semantic Networks, and Cognitive Performance

Sparking creativity: Encouraging creative idea generation through automatically generated word recommendations

Creative block is a familiar foe to any who attempt to create and is especially related to "writers block". While significant effort has been focused on developing methods to break such blocks, it remains an active challenge. Here, we focus on the role of semantic memory structure in driving creative block, by having people get "stuck" in a certain part of their semantic memory network. We directly examine whether we can "pull out" a participant from where they got "stuck" in their semantic memory, breaking their creative impasse. Our Associative Creativity Sparker (ACS) is a cognitive network science-based online tool that aims to spark creative ideas and break creative impasse: Once a participant runs out of ideas in a creative idea generation task, word recommendations are suggested to prime new ideas. These word recommendations are either towards or away from previous ideas, as well as close or far from the target object, based on a conceptual space extracted from the participants responses using online text analysis. In Study 1, 121 participants use the ACS to generate creative alternative uses for five different objects and completed creativity and Gf tasks. In Study 2, we repeat the design of Study 1, but further examine the impact of writing experience on the ACS, by examining 120 novice and 120 experienced writers. Across both studies, our results indicate that the location of word recommendations affects the fluency and originality of one's ideas, and that novice and experienced writers differently benefit from these word recommendations.

Local but not global graph theoretic measures of semantic networks generalize across tasks

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory.

Mapping the Memory Structure of High-Knowledge Students: A Longitudinal Semantic Network Analysis

Standard learning assessments like multiple-choice questions measure what students know but not how their knowledge is organized. Recent advances in cognitive network science provide quantitative tools for modeling the structure of semantic memory, revealing key learning mechanisms. In two studies, we examined the semantic memory networks of undergraduate students enrolled in an introductory psychology course. In Study 1, we administered a cumulative multiple-choice test of psychology knowledge, the Intro Psych Test, at the end of the course. To estimate semantic memory networks, we administered two verbal fluency tasks: domain-specific fluency (naming psychology concepts) and domain-general fluency (naming animals). Based on their performance on the Intro Psych Test, we categorized students into a high-knowledge or low-knowledge group, and compared their semantic memory networks. Study 1 (N = 213) found that the high-knowledge group had semantic memory networks that were more clustered, with shorter distances between concepts-across both the domain-specific (psychology) and domain-general (animal) categories-compared to the low-knowledge group. In Study 2 (N = 145), we replicated and extended these findings in a longitudinal study, collecting data near the start and end of the semester. In addition to replicating Study 1, we found the semantic memory networks of high-knowledge students became more interconnected over time, across both domain-general and domain-specific categories. These findings suggest that successful learners show a distinct semantic memory organization-characterized by high connectivity and short path distances between concepts-highlighting the utility of cognitive network science for studying variation in student learning.

Novel embeddings improve the prediction of risk perception

We assess whether the classic psychometric paradigm of risk perception can be improved or supplanted by novel approaches relying on language embeddings. To this end, we introduce the Basel Risk Norms, a large data set covering 1004 distinct sources of risk (e.g., vaccination, nuclear energy, artificial intelligence) and compare the psychometric paradigm against novel text and free-association embeddings in predicting risk perception. We find that an ensemble model combining text and free association rivals the predictive accuracy of the psychometric paradigm, captures additional affect and frequency-related dimensions of risk perception not accounted for by the classic approach, and has greater range of applicability to real-world text data, such as news headlines. Overall, our results establish the ensemble of text and free-association embeddings as a promising new tool for researchers and policymakers to track real-world risk perception.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-024-00478-x.

Modeling the neurocognitive dynamics of language across the lifespan

Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.

Navigating the Mental Lexicon: Network Structures, Lexical Search and Lexical Retrieval

This paper examines the implications of the association patterns in our understanding of the mental lexicon. By applying the principles of graph theory to word association data, we intend to explore which measures tap better into lexical knowledge. To that end, we had different groups of English as Foreign language learners complete a lexical fluency task. Based on these empirical data, a study was undertaken on the corresponding lexical availability graph (LAG). It is observed that the aggregation (mentioned through human coding) of all lexical tokens on a given topic allows the emergence of some lexical-semantic patterns. The most important one is the existence of some key terms, featuring both high centrality in the sense of network theory and high availability in the LAG, which define a hub of related terms. These communities of words, each one organized around an anchor term, or most central word, are nicely apprehended by a well-known network metric called modularity. Interestingly enough, each module seems to describe a conceptual class, showing that the collective lexicon, at least as approximated by LA Graphs, is organised and traversed by semantic mechanisms or associations via hyponymy or hiperonymy, for instance. Another empirical observation is that these conceptual hubs can be appended, resulting in high diameters compared to same-sized random graphs; even so it seems that the small-world hypothesis holds in LA Graphs, as in other social and natural networks.

The "Small World of Words" free association norms for Rioplatense Spanish

Large-scale word association datasets are both important tools used in psycholinguistics and used as models that capture meaning when considered as semantic networks. Here, we present word association norms for Rioplatense Spanish, a variant spoken in Argentina and Uruguay. The norms were derived through a large-scale crowd-sourced continued word association task in which participants give three associations to a list of cue words. Covering over 13,000 words and +3.6 M responses, it is currently the most extensive dataset available for Spanish. We compare the obtained dataset with previous studies in Dutch and English to investigate the role of grammatical gender and studies that used Iberian Spanish to test generalizability to other Spanish variants. Finally, we evaluated the validity of our data in word processing (lexical decision reaction times) and semantic (similarity judgment) tasks. Our results demonstrate that network measures such as in-degree provide a good prediction of lexical decision response times. Analyzing semantic similarity judgments showed that results replicate and extend previous findings demonstrating that semantic similarity derived using spreading activation or spectral methods outperform word embeddings trained on text corpora.

Representing melodic relationships using network science

Music is a complex system consisting of many dimensions and hierarchically organized information-the organization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,403 nodes (sequences) and 157,429 edges connecting them. We then investigated whether mathematical graph modelling relates to musical characteristics in real-world listening situations via a behavioral experiment paralleling those used to examine language. We found that as melodic distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgements was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sensitive to the property of melodic distance in this network. More generally, our work demonstrates the similarity between music and language as complex systems, and how network science methods can be used to quantify different aspects of its complexity.

The Role of Semantic Associations as a Metacognitive Cue in Creative Idea Generation

Is my idea creative? This question directs investing in companies and choosing a research agenda. Following previous research, we focus on the originality of ideas and consider their association with self-assessments of idea generators regarding their own originality. We operationalize the originality score as the frequency (%) of each idea within a sample of participants and originality judgment as the self-assessment of this frequency. Initial evidence suggests that originality scores and originality judgments are produced by separate processes. As a result, originality judgments are prone to biases. So far, heuristic cues that lead to such biases are hardly known. We used methods from computational linguistics to examine the semantic distance as a potential heuristic cue underlying originality judgments. We examined the extent to which the semantic distance would contribute additional explanatory value in predicting originality scores and originality judgments, above and beyond cues known from previous research. In Experiment 1, we re-analyzed previous data that compared originality scores and originality judgments after adding the semantic distance of the generated ideas from the stimuli. We found that the semantic distance contributed to the gap between originality scores and originality judgments. In Experiment 2, we manipulated the examples given in task instructions to prime participants with two levels of idea originality and two levels of semantic distance. We replicated Experiment 1 in finding the semantic distance as a biasing factor for originality judgments. In addition, we found differences among the conditions in the extent of the bias. This study highlights the semantic distance as an unacknowledged metacognitive cue and demonstrates its biasing power for originality judgments.

Age differences in semantic network structure: Acquiring knowledge shapes semantic memory

Computational research suggests that semantic memory, operationalized as semantic memory networks, undergoes age-related changes. Previous work suggests that concepts in older adults' semantic memory networks are more separated, more segregated, and less connected to each other. However, cognitive network research often relies on group averages (e.g., young vs. older adults), and it remains unclear if individual differences influence age-related disparities in language production abilities. Here, we analyze the properties of younger and older participants' individual-based semantic memory networks based on their semantic relatedness judgments. We related individual-based network measures-clustering coefficient (CC; connectivity), global efficiency, and modularity (structure)-to language production (verbal fluency) and vocabulary knowledge. Similar to previous findings, we found significant age effects: CC and global efficiency were lower, and modularity was higher, for older adults. Furthermore, vocabulary knowledge was significantly related to the semantic memory network measures: corresponding with the age effects, CC and global efficiency had a negative relationship, while modularity had a positive relationship with vocabulary knowledge. More generally, vocabulary knowledge significantly increased with age, which may reflect the critical role that the accumulation of knowledge within semantic memory has on its structure. These results highlight the impact of diverse life experiences on older adults' semantic memory and demonstrate the importance of accounting for individual differences in the aging mental lexicon. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Emergence of Covid-19 as a Novel Concept Shifts Existing Semantic Spaces

Conceptual knowledge is dynamic, fluid, and flexible, changing as a function of contextual factors at multiple scales. The Covid-19 pandemic can be considered a large-scale, global context that has fundamentally altered most people's experiences with the world. It has also introduced a new concept, COVID (or COVID-19), into our collective knowledgebase. What are the implications of this introduction for how existing conceptual knowledge is structured? Our collective emotional and social experiences with the world have been profoundly impacted by the Covid-19 pandemic, and experience-based perspectives on concept representation suggest that emotional and social experiences are critical components of conceptual knowledge. Such changes in collective experience should, then, have downstream consequences on knowledge of emotion- and social-related concepts. Using a naturally occurring dataset derived from the social media platform Twitter, we show that semantic spaces for concepts related to our emotional experiences with Covid-19 (i.e., emotional concepts like FEAR)-but not for unrelated concepts (i.e., animals like CAT)-show quantifiable shifts as a function of the emergence of COVID-19 as a concept and its associated emotional and social experiences, shifts which persist 6 months after the onset of the pandemic. The findings support a dynamic view of conceptual knowledge wherein shared experiences affect conceptual structure.

Structural differences in the semantic networks of younger and older adults

Cognitive science invokes semantic networks to explain diverse phenomena, from memory retrieval to creativity. Research in these areas often assumes a single underlying semantic network that is shared across individuals. Yet, recent evidence suggests that content, size, and connectivity of semantic networks are experience-dependent, implying sizable individual and age-related differences. Here, we investigate individual and age differences in the semantic networks of younger and older adults by deriving semantic networks from both fluency and similarity rating tasks. Crucially, we use a megastudy approach to obtain thousands of similarity ratings per individual to allow us to capture the characteristics of individual semantic networks. We find that older adults possess lexical networks with smaller average degree and longer path lengths relative to those of younger adults, with older adults showing less interindividual agreement and thus more unique lexical representations relative to younger adults. Furthermore, this approach shows that individual and age differences are not evenly distributed but, rather, are related to weakly connected, peripheral parts of the networks. All in all, these results reveal the interindividual differences in both the content and the structure of semantic networks that may accumulate across the life span as a function of idiosyncratic experiences.

Comparing the semantic networks of children with cochlear implants and children with typical hearing: Effects of length of language access

PURPOSE

Kenett et al. (2013) report that the sematic networks, measured by using an oral semantic fluency task, of children with cochlear implants (CI) are less structured compared to the sematic networks of children with typical hearing (TH). This study aims to evaluate if such differences are only evident if children with CI are compared to children with TH matched on chronological age, or also if they are compared to children with TH matched on hearing age.

METHOD

The performance of a group of children with CI on a verbal fluency task was compared to the performance of a group of chronological-age matched children with TH. Subsequently, computational network analysis was used to compare the semantic network structure of the groups. The same procedure was applied to compare a group of children with CI to a group of hearing-age matched children with TH.

RESULTS

The children with CI perform on the same level on an oral semantic verbal fluency task as the children with TH matched on hearing age. There are significant differences in terms of the structure of the semantic network between the groups. The magnitude of these differences is very small and they are non-significant for a proportion of nodes included in the bootstrap analysis. This indicates that there is no true difference between the networks. Hearing age, but not age at implantation was found to be significantly positively correlated with semantic verbal fluency performance for the children with CI.

CONCLUSIONS

The results from the current study indicate that length of exposure to the tested language is an important factor for the structure of the semantic network and the performance on a semantic verbal fluency task for children with CI. Further studies are needed to explore the role of the accessibility of the language input for the development of semantic networks of children with CI.

Brands, networks, communities: How brand names are wired in the mind

Brands can be defined as psychological constructs residing in our minds. By analyzing brand associations, we can study the mental constructs around them. In this paper, we study brands as parts of an associative network based on a word association database. We explore the communities–closely-knit groups in the mind–around brand names in this structure using two community detection algorithms in the Hungarian word association database ConnectYourMind. We identify brand names inside the communities of a word association network and explain why these brand names are part of the community. Several detected communities contain brand names from the same product category, and the words in these categories were connected either to brands in the category or to words describing the product category. Based on our findings, we describe the mental position of brand names. We show that brand knowledge, product knowledge and real word knowledge interact with each other. We also show how the meaning of a product category arises and how this meaning is related to brand meaning. Our results suggest that words sharing the same community with brand names can be used in brand communication and brand positioning.

On the semantic representation of risk

What are the defining features of lay people's semantic representation of risk? We contribute to mapping the semantics of risk based on word associations to provide insight into both universal and individual differences in the representation of risk. Specifically, we introduce a mini-snowball word association paradigm and use the tools of network and sentiment analysis to characterize the semantics of risk. We find that association-based representations not only corroborate but also extend those extracted from past survey- and text-based approaches. Crucially, we find that the semantics of risk show universal properties and individual and group differences. Most notably, while semantic clusters generalize across languages, their frequency varies systematically across demographic groups, with older and female respondents showing more negative connotations and mentioning more often certain types of activities (e.g., recreational activities) relative to younger adults and males, respectively. Our work has general implications for the measurement of risk-related constructs by suggesting that "risk" can mean different things to different individuals.

Editors' Introduction to Networks of the Mind: How Can Network Science Elucidate Our Understanding of Cognition?

Thinking is complex. Over the years, several types of methods and paradigms have developed across the psychological, cognitive, and neural sciences to study such complexity. A rapidly growing multidisciplinary quantitative field of network science offers quantitative methods to represent complex systems as networks, or graphs, and study the network properties of these systems. While the application of network science to study the brain has greatly advanced our understanding of the brains structure and function, the application of these tools to study cognition has been done to a much lesser account. This topic is a collection of papers that discuss the fruitfulness of applying network science to study cognition across a wide scope of research areas from generalist accounts of memory and encoding, to individual differences, to communities, and finally to cultural and individual change.