Structure Of Semantic Memory Research Articles

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.

Changes in semantic memory structure support successful problem-solving and analogical transfer

Creative problem-solving is central in daily life, yet its underlying mechanisms remain elusive. Restructuring (i.e., reorganization of problem-related representations) is considered one problem-solving mechanism and may lead to an abstract problem-related representation facilitating the solving of analogous problems. Here, we used network science methodology to estimate participants’ semantic memory networks (SemNets) before and after attempting to solve a riddle. Restructuring was quantified as the difference in SemNets metrics between pre- and post-solving phases. Our results provide initial evidence that problem-related SemNets restructuring may be associated with the successful solving of the riddle and, subsequently, an analogous one. Solution-relevant concepts and semantically remote concepts became more strongly related in solvers. Only changes in semantically remote concepts were instrumental in actively solving the riddle while changes in solution-relevant concepts may reflect a pre-exposure to the solution.

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.

The Role of Knowledge in Creative Thinking

ABSTRACT In this invited paper, I briefly review my past, current, and future lines of research. The associative theory of creativity argues that higher creative individuals have a richer semantic memory structure that facilitates broader associative search processes, that leads to the combination of remote concepts into novel and appropriate ideas. Based on this theory, in my research I investigate the role of knowledge – or semantic memory – in high-level cognition, focusing on creativity, associative thinking, and memory search, in typical and clinical populations. To do so, I apply computational tools from network science, natural language processing, and machine learning, coupled with empirical cognitive and neural research. Such computational tools are enabling the representation and operationalization of the structure of semantic memory and the processes that operate over it. This is critical as it allows us to start quantifying issues that for a very long time were studied very subjectively in creativity research – remoteness of ideas, associative thinking, flexible/richer semantic memory structure, etc. Such work is offering unique, quantitative, ways to directly study classic theories of creativity, propelling forward our understanding of its complexity.

Методы сетевого моделирования структуры семантической памяти: теоретический обзор

The article is aimed at considering the network modeling methods for human semantic memory structure. The work provides an analytical review of existing approaches to modeling the structure of human semantic memory, as a result of which the most methodologically optimal way of modeling semantic memory is estab-lished – based on the construction and analysis of semantic networks. The authors study various methods of network modeling of semantic memory structure: verbal fluency problem, snowball problem, semantic connec-tivity problem. In addition, examples of using these methods in psychological research are given, and their methodological advantages and disadvantages are highlighted. The results of the present work can be useful in the formation of new methods for network modeling of the structure of human semantic memory, as well as in the creation of intelligent systems that process graph data.

Color blindness and semantic knowledge: Cognition of color terms from elicited lists in dichromats and normal observers

AbstractColor blindness (color vision deficiency) affects ~8% of males and ~ 0.4% females worldwide. Here we use the elicited lists method to investigate their semantic knowledge regarding “basic color terms” and their relationships. Lists were obtained from color vision deficient and normal observers. 32 dichromats (15 protanopes, 17 deuteranopes) and 32 normal trichromats (17 females, 15 males) diagnosed by a battery of color tests (Ishihara, City University Test, anomaloscope) wrote monolexemic lists of colors. Psychological salience of terms (ln(CSI)), adjacency between pairs of terms (ADJ; MDS) and the presence of clusters of terms defined on the basis of the Universals and evolution hypothesis were analyzed. All four groups of participants showed the same semantic memory structure: lists started with the cardinal primaries cluster (blue, red, yellow, green), followed by the achromatic primaries cluster (black and white), or the derived cluster (brown, orange, violet, pink, purple, and gray). After the clusters (cardinals, achromatics, and derived), a highly variable number of non‐basic terms appeared. This number was higher in normal trichromats. Non‐basic terms were not part of any cognitive cluster. The similarity in the lists of trichromats and dichromats suggest that both may acquire similar semantic knowledge about color terms. Several potential explanations are considered.

Multiple sclerosis is associated with differences in semantic memory structure.

Although language is often considered to be largely intact in multiple sclerosis (MS), word-finding difficulties are a common complaint. Recent work suggests that declines in language are not solely the result of motoric and cognitive slowing that is most strongly associated with MS. Network science approaches have been effectively used to examine network structure as it relates to clinical conditions, aging, and language. The present study utilizes a network science approach to investigate whether individuals with MS exhibit less interconnected and resilient semantic networks compared to age-matched neurotypical peers. We used semantic fluency data from 89 participants with MS and 88 neurotypical participants to estimate and analyze the semantic network structure for each participant group. Additionally, we conducted a percolation analysis to examine the resilience of each network. Network measures showed that individuals with MS had lower local and global clustering coefficients, longer average shortest path lengths, and higher modularity values compared to neurotypical peers. Small-worldness, network portrait divergence measures, and community detection analyses were consistent with these results and indicated that macroscopic properties of the two networks differed and that the semantic network for individuals with MS was more fractured than the neurotypical peer network. Moreover, a spreading activation simulation and percolation analysis suggested that the semantic networks of individuals with MS are less flexible and activation degrades faster than those of age-matched neurotypical participants. These differing semantic network structures suggest that language retrieval difficulties in MS partially result from decline in language-specific factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans

Modern large language models generate texts that are virtually indistinguishable from those written by humans and achieve near-human performance in comprehension and reasoning tests. Yet, their complexity makes it difficult to explain and predict their functioning. We examined a state-of-the-art language model (GPT-3) using lexical decision tasks widely used to study the structure of semantic memory in humans. The results of four analyses showed that GPT-3’s patterns of semantic activation are broadly similar to those observed in humans, showing significantly higher semantic activation in related (e.g., “lime–lemon”) word pairs than in other-related (e.g., “sour–lemon”) or unrelated (e.g., “tourist–lemon”) word pairs. However, there are also significant differences between GPT-3 and humans. GPT-3’s semantic activation is better predicted by similarity in words’ meaning (i.e., semantic similarity) rather than their co-occurrence in the language (i.e., associative similarity). This suggests that GPT-3’s semantic network is organized around word meaning rather than their co-occurrence in text.

Convergent thinking and insight problem solving relate to semantic memory network structure

The associative theory of creativity has long held that creative thinking involves connecting remote concepts in semantic memory. Network science tools have recently been applied to map the organization of concepts in semantic memory, and to study the link between semantic memory and creativity. Yet such work has largely overlooked the domain of convergent thinking, despite the theoretical importance of semantic memory networks for facilitating associative processes relevant for convergent problem solving (e.g., spreading activation). Convergent thinking problems, such as the Compound Remote Associates (CRA) test, can be solved with insight (the sudden “aha” experience) or analysis (deliberately and incrementally working towards the solution). In a sample of 477 participants, we adopted network science methods to compare semantic memory structure across two grouping variables: 1) convergent thinking ability (i.e., CRA accuracy), and 2) the self-reported tendency to solve problems with insight or analysis. Semantic memory networks were constructed from a semantic fluency task, and problem solving style (insight or analysis) was determined from judgments provided during solving of CRAs. We found that, compared to the low-convergent thinking group, the high-convergent thinking group exhibited a more flexible and interconnected semantic network—with short paths and many connections between concepts. Moreover, participants who primarily solved problems with insight (compared to analysis) showed shorter average path distances between concepts, even after controlling for accuracy. Our results extend the literature on semantic memory and creativity, and suggest that the organization of semantic memory plays a key role in convergent thinking, including insight problem solving.

Hands-on false memories: a combined study with distributional semantics and mouse-tracking

Although mouse-tracking has been seen as a real-time window into different aspects of human decision-making processes, currently little is known about how the decision process unfolds in veridical and false memory retrieval. Here, we directly investigated decision-making processes by predicting participants’ performance in a mouse-tracking version of a typical Deese–Roediger–McDermott (DRM) task through distributional semantic models, a usage-based approach to meaning. Participants were required to study lists of associated words and then to perform a recognition task with the mouse. Results showed that mouse trajectories were extensively affected by the semantic similarity between the words presented in the recognition phase and the ones previously studied. In particular, the higher the semantic similarity, the larger the conflict driving the choice and the higher the irregularity in the trajectory when correctly rejecting new words (i.e., the false memory items). Conversely, on the temporal evolution of the decision, our results showed that semantic similarity affects more complex temporal measures indexing the online decision processes subserving task performance. Together, these findings demonstrate that semantic similarity can affect human behavior at the level of motor control, testifying its influence on online decision-making processes. More generally, our findings complement previous seminal theories on false memory and provide insights into the impact of the semantic memory structure on different decision-making components.

An investigation of the cognitive and neural correlates of semantic memory search related to creative ability

Creative ideas likely result from searching and combining semantic memory knowledge, yet the mechanisms acting on memory to yield creative ideas remain unclear. Here, we identified the neurocognitive correlates of semantic search components related to creative abilities. We designed an associative fluency task based on polysemous words and distinguished two search components related to clustering and switching between the different meanings of the polysemous words. Clustering correlated with divergent thinking, while switching correlated with the ability to combine remote associates. Furthermore, switching correlated with semantic memory structure and executive abilities, and was predicted by connectivity between the default, control, and salience neural networks. In contrast, clustering relied on interactions between control, salience, and attentional neural networks. Our results suggest that switching captures interactions between memory structure and control processes guiding the search whereas clustering may capture attentional controlled processes for persistent search, and that alternations between exploratory search and focused attention support creativity.

Examining the relations between semantic memory structure and creativity in second language

Creativity is related to a higher flexible semantic memory structure, which could explain greater fluency of ideas. Extensive research has identified a positive connection between creativity and bi-/multilingualism mainly in contexts where two languages or more concur in daily communicative interactions. Yet, creativity has received scant attention in regard to L2 (second or foreign language) acquisition that mainly takes place in classroom situations. The scarce research points to a positive relationship between creativity and L2 fluency – understood as the number of words produced. We apply computational network science analysis and Forward Flow methods to examine lexical organization patterns of a low creativity (LC) and high creativity (HC) group of 12th grade Spanish English as a Foreign Language (EFL) learners. The participants completed two fluency tasks, where they generated animal names in their L2, and also L1 – used here as a control measure. EFL proficiency was controlled. Our analyses revealed that the HC individuals were more fluent in L1 and L2, generated more remote responses, and exhibited a more flexible and efficiently structured semantic memory in both languages, with a greater effect of creativity in L2. Contrary to previous research, the L2 semantic memory network exhibited a less random organization. Differences in the L2 learning conditions are adduced as likely causes of this result.

Investigating the network structure of domain-specific knowledge using the semantic fluency task.

Cognitive scientists have a long-standing interest in quantifying the structure of semantic memory. Here, we investigate whether a commonly used paradigm to study the structure of semantic memory, the semantic fluency task, as well as computational methods from network science could be leveraged to explore the underlying knowledge structures of academic disciplines such as psychology or biology. To compare the knowledge representations of individuals with relatively different levels of expertise in academic subjects, undergraduate students (i.e., experts) and preuniversity high school students (i.e., novices) completed a semantic fluency task with cue words corresponding to general semantic categories (i.e., animals, fruits) and specific academic domains (e.g., psychology, biology). Network analyses of their fluency networks found that both domain-general and domain-specific semantic networks of undergraduates were more efficiently connected and less modular than the semantic networks of high school students. Our results provide an initial proof-of-concept that the semantic fluency task could be used by educators and cognitive scientists to study the representation of more specific domains of knowledge, potentially providing new ways of quantifying the nature of expert cognitive representations.

Semantic memory and creativity: the costs and benefits of semantic memory structure in generating original ideas

Despite its theoretical importance, little is known about how semantic memory structure facilitates and constrains creative idea generation. We examine whether the semantic richness of a concept has both benefits and costs to creative idea generation. Specifically, we tested whether cue set size—an index of semantic richness reflecting the average number of elements associated with a given concept—impacts the quantity (fluency) and quality (originality) of responses generated during the Alternate Uses Task (AUT). Across four studies, we show that low-association, sparse, AUT cues benefit originality at the cost of fluency compared to high-association, rich, AUT cues. Furthermore, we found an interaction with individual differences in fluid intelligence in the low-association AUT cues, suggesting that constraints of sparse semantic knowledge can be overcome with top-down intervention. Our findings indicate that semantic richness differentially impacts the quality and quantity of generated ideas, and that cognitive control processes can facilitate idea production when conceptual knowledge is limited.

Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure.

Associative theories of creativity argue that creative cognition involves the abilities to generate remote associations and make useful connections between unrelated concepts in one’s semantic memory. Yet, whether and how real-life creative behavior relies on semantic memory structure and its neural substrates remains unclear. We acquired multi-echo functional magnetic resonance imaging data while participants underwent a semantic relatedness judgment task. These ratings were used to estimate their individual semantic memory networks, whose properties significantly predicted their real-life creativity. Using a connectome predictive modeling approach, we identified patterns of task-based functional connectivity that predicted creativity-related semantic memory network properties. Furthermore, these properties mediated the relationship between functional connectivity and real-life creativity. These results provide new insights into how brain connectivity patterns support real-life creative behavior via the structure of semantic memory. We also show how computational network science can be used to couple behavioral, cognitive, and neural levels of analysis.

Structure and Dynamics of Semantic Memory of Foreign Language Learners

Structure and Dynamics of Semantic Memory of Foreign Language Learners

Education shapes the structure of semantic memory and impacts creative thinking

Education is central to the acquisition of knowledge, such as when children learn new concepts. It is unknown, however, whether educational differences impact not only what concepts children learn, but how those concepts come to be represented in semantic memory—a system that supports higher cognitive functions, such as creative thinking. Here we leverage computational network science tools to study hidden knowledge structures of 67 Swiss schoolchildren from two distinct educational backgrounds—Montessori and traditional, matched on socioeconomic factors and nonverbal intelligence—to examine how educational experience shape semantic memory and creative thinking. We find that children experiencing Montessori education show a more flexible semantic network structure (high connectivity/short paths between concepts, less modularity) alongside higher scores on creative thinking tests. The findings indicate that education impacts how children represent concepts in semantic memory and suggest that different educational experiences can affect higher cognitive functions, including creative thinking.

Semantic Memory Search and Retrieval in a Novel Cooperative Word Game: A Comparison of Associative and Distributional Semantic Models.

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two-player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20-item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam-algebra) and generated a one-word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk-based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes.

Left temporal pole contributes to creative thinking via an individual semantic network.

The neural substrates that contribute to creative thinking through the recruitment of semantic memory structures remain largely unknown. This study sought to investigate the properties of semantic networks using a semantic judgment rating task at the individual level and explore the relationship among creative abilities, the topological properties of semantic networks, and their underlying brain structures. We first used a semantic judgment rating to assess individual semantic networks and computed their topological properties. The analysis confirmed a significant correlation between the creative thinking abilities assessed by an alternate uses task and all three topological properties. In addition, voxel-based morphometry was employed to assess the neural correlates of gray matter volume (GMV) related to different topological properties of the semantic network. Results revealed a positive correlation between global efficiency and the left temporal pole cortex, considered to be involved in semantic information transmission and processing. Furthermore, mediation analysis found that the global efficiency of the individual semantic network mediated the association between the left temporal pole GMV and creative thinking, showing that the relationship between left temporal pole GMV and creative thinking may be affected by the semantic networks. To the best of our knowledge, this study is the first to combine a behavioral investigation of semantic networks with magnetic resonance imaging to shed light on the cerebral structural basis of semantic memory networks, in addition to their relationship to creativity.

A Critical Review of Network-Based and Distributional Approaches to Semantic Memory Structure and Processes.

Some of the earliest work on understanding how concepts are organized in memory used a network-based approach, where words or concepts are represented as nodes, and relationships between words are represented by links between nodes. Over the past two decades, advances in network science and graph theoretical methods have led to the development of computational semantic networks. This review provides a modern perspective on how computational semantic networks have proven to be useful tools to investigate the structure of semantic memory as well as search and retrieval processes within semantic memory, to ultimately model performance in a wide variety of cognitive tasks. Regarding representation, the review focuses on the distinctions and similarities between network-based (based on behavioral norms) approaches and more recent distributional (based on natural language corpora) semantic models, and the potential overlap between the two approaches. Capturing the type of relation between concepts appears to be particularly important in this modeling endeavor. Regarding processes, the review focuses on random walk models and the degree to which retrieval processes demand attention in pursuit of given task goals, which dovetails with the type of relation retrieved during tasks. Ultimately, this review provides a critical assessment of how the network perspective can be reconciled with distributional and machine-learning-based perspectives to meaning representation, and describes how cognitive network science provides a useful conceptual toolkit to probe both the structure and retrieval processes within semantic memory.