Is the Mind a Network? Maps, Vehicles, and Skyhooks in Cognitive Network Science

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

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.

Evidence for optimal semantic search throughout adulthood

As people age, they learn and store new knowledge in their semantic memory. Despite learning a tremendous amount of information, people can still recall information relevant to the current situation with ease. To accomplish this, the mind must efficiently organize and search a vast store of information. It also must continue to retrieve information effectively despite changes in cognitive mechanisms due to healthy aging, including a general slowing in information processing and a decline in executive functioning. How effectively does the mind of an individual adjust its search to account for changes due to aging? We tested 746 people ages 25 through 69 on a semantic fluency task (free listing animals) and found that, on average, retrieval follows an optimal path through semantic memory. Participants tended to list a sequence of semantically related animals (e.g., lion, tiger, puma) before switching to a semantically unrelated animal (e.g., whale). We found that the timing of these transitions to semantically unrelated animals was remarkably consistent with an optimal strategy for maximizing the overall rate of retrieval (i.e., the number of animals listed per unit time). Age did not affect an individual's deviation from the optimal strategy given their general performance, suggesting that people adapt and continue to search memory optimally throughout their lives. We argue that this result is more likely due to compensating for a general slowing than a decline in executive functioning.

Aesthetic emotions are affected by context: a psychometric network analysis

Aesthetic emotions are defined as emotions arising when a person evaluates a stimulus for its aesthetic appeal. Whether these emotions are unique to aesthetic activities is debated. We address this debate by examining if recollections of different types of engaging activities entail different emotional profiles. A large sample of participants were asked to recall engaging aesthetic (N = 167), non-aesthetic (N = 160), or consumer (N = 172) activities. They rated the extent to which 75 candidate aesthetic emotions were evoked by these activities. We applied a computational psychometric network approach to represent and compare the space of these emotions across the three conditions. At the behavioral level, recalled aesthetic activities were rated as the least vivid but most intense compared to the two other conditions. At the network level, we found several quantitative differences across the three conditions, related to the typology, community (clusters) and core nodes (emotions) of these networks. Our results suggest that aesthetic and non-aesthetic activities evoke emotional spaces differently. Thus, we propose that aesthetic emotions are distributed differently in a multidimensional aesthetic space than for other engaging activities. Our results highlight the context-specificity of aesthetic emotions.

Neural evidence of switch processes during semantic and phonetic foraging in human memory

Humans may retrieve words from memory by exploring and exploiting in "semantic space" similar to how nonhuman animals forage for resources in physical space. This has been studied using the verbal fluency test (VFT), in which participants generate words belonging to a semantic or phonetic category in a limited time. People produce bursts of related items during VFT, referred to as "clustering" and "switching." The strategic foraging model posits that cognitive search behavior is guided by a monitoring process which detects relevant declines in performance and then triggers the searcher to seek a new patch or cluster in memory after the current patch has been depleted. An alternative body of research proposes that this behavior can be explained by an undirected rather than strategic search process, such as random walks with or without random jumps to new parts of semantic space. This study contributes to this theoretical debate by testing for neural evidence of strategically timed switches during memory search. Thirty participants performed category and letter VFT during functional MRI. Responses were classified as cluster or switch events based on computational metrics of similarity and participant evaluations. Results showed greater hippocampal and posterior cerebellar activation during switching than clustering, even while controlling for interresponse times and linguistic distance. Furthermore, these regions exhibited ramping activity which increased during within-patch search leading up to switches. Findings support the strategic foraging model, clarifying how neural switch processes may guide memory search in a manner akin to foraging in patchy spatial environments.

Associative thinking at the core of creativity

Creativity has long been thought to involve associative processes in memory: connecting concepts to form ideas, inventions, and artworks. However, associative thinking has been difficult to study due to limitations in modeling memory structure and retrieval processes. Recent advances in computational models of semantic memory allow researchers to examine how people navigate a semantic space of concepts when forming associations, revealing key search strategies associated with creativity. Here, we synthesize cognitive, computational, and neuroscience research on creativity and associative thinking. This Review highlights distinctions between free- and goal-directed association, illustrates the role of associative thinking in the arts, and links associative thinking to brain systems supporting both semantic and episodic memory - offering a new perspective on a longstanding creativity theory.

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.

Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms-fragmented across linguistics, psychology and computer science-by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.

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.

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.

Semantic flow and its relation to controlled semantic retrieval deficits in the narrative production of people with aphasia

Aphasia has had a profound influence on our understanding of how language is instantiated within the human brain. Historically, aphasia has yielded an in vivo model for elucidating the effects of impaired lexical-semantic access on language comprehension and production. Aphasiology has focused intensively on single word dissociations. Yet, less is known about the integrity of combinatorial semantic processes required to construct well-formed narratives. Here we addressed the question of how controlled lexical-semantic retrieval deficits (a hallmark of aphasia) might compound over the course of longer narratives. We specifically examined word-by-word flow of taxonomic vs. thematic semantic distance in the storytelling narratives of individuals with chronic post-stroke aphasia (n = 259) relative to age-matched controls (n = 203). We first parsed raw transcribed narratives into content words and computed inter-word semantic distances for every running pair of words in each narrative (N = 232,490 word transitions). The narratives of people with aphasia showed significant reductions in taxonomic and thematic semantic distance relative to controls. Both distance metrics were strongly predictive of offline measures of semantic impairment and aphasia severity. Since individuals with aphasia often exhibit perseverative language output (i.e., repetitions), we performed additional analyses with repetitions excluded. When repetitions were excluded, group differences in semantic distances persisted and thematic distance was still predictive of semantic impairment, although some findings changed. These results demonstrate the cumulative impact of deficits in controlled word retrieval over the course of narrative production. We discuss the nature of semantic flow between words as a novel metric of characterizing discourse and elucidating the nature of lexical-semantic access impairment in aphasia at multiword levels.

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 production. We examine whether the semantic richness of a concept has both benefits and costs to creative idea production. 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. The 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.

Knowledge Modelling and Learning through Cognitive Networks

Knowledge modelling is a growing field at the fringe of computer science, psychology and network science [...]

Knowledge Representations Derived From Semantic Fluency Data

The semantic fluency task is commonly used as a measure of one’s ability to retrieve semantic concepts. While performance is typically scored by counting the total number of responses, the ordering of responses can be used to estimate how individuals or groups organize semantic concepts within a category. I provide an overview of this methodology, using Alzheimer’s disease as a case study for how the approach can help advance theoretical questions about the nature of semantic representation. However, many open questions surrounding the validity and reliability of this approach remain unresolved.

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.

DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad–happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts.