Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.

L1 lexical attrition is the decline of L1 lexical-semantic abilities due to reduced L1 exposure and/or L2 interference. Semantic fluency tasks are central in this research, but traditional analyses are often inconclusive.

To address this, we employed an innovative network science approach to investigate the bilingual lexicon's structural properties. Semantic fluency data were collected from immersed/non-immersed late bilinguals with comparable L2 proficiencies.

Our results indicate that L2 immersion led to more integrated and efficient L2 networks. Crucially, immersion also resulted in L1 attrition (only evident in the network analysis). Immersed participants exhibited less densely connected and sparser L1 networks. Furthermore, network measures suggest that L1 lexical attrition occurs gradually, initially impacting network interconnectivity, while information flow and community structure remain more stable.

Drawing from these insights, we introduce the Lexical Attrition Foundation (LeAF) framework, offering a network-based perspective on lexical attrition development and laying the groundwork for future research.

While there are well-known demonstrations that children can use distributional information to acquire multiple components of language, the underpinnings of these achievements are unclear. In the current paper, we investigate the potential pre-requisites for a distributional learning model that can explain how children learn their first words. We review existing literature and then present the results of a series of computational simulations with Vector Space Models, a type of distributional semantic model used in Computational Linguistics, which we evaluate against vocabulary acquisition data from children. We focus on nouns and verbs, and we find that: (i) a model with flexibility to adjust for the frequency of events provides a better fit to the human data, (ii) the influence of context words is very local, especially for nouns, and (iii) words that share more contexts with other words are harder to learn.

This essay investigates the relationships among scripts, schemata, and social norms. The authors examine how social norms are triggered by particular schemata and are grounded in scripts. Just as schemata are embedded in a network, so too are social norms, and they can be primed through spreading activation. Moreover, the expectations that allow a social norm’s existence are inherently grounded in particular scripts and schemata. Using interventions that have targeted gender norms, open defecation, female genital cutting, and other collective issues as examples, the authors argue that ignoring the cognitive underpinnings of a social norm can hamper the effectiveness of behavioral interventions.

This paper discusses how patterns of construction usage, implicitly learned over lifelong experience, tune the language processing system for fluent interactive lexical, syntactic, and semantic access. It reports on three experiments that investigate online processing of Verb–Argument Constructions (VACs) and the degree to which this is effected by (i) verb frequency in the language, (ii) verb frequency in the VAC, (iii) VAC-verb contingency, and (iv) verb prototypicality in terms of centrality within the VAC semantic network. Experiment 1 tested lexical decision of VAC exemplars presented as successive verb–preposition pairs. Experiment 2 tested lexical decision of VAC exemplars presented as arbitrarily interrupted verb–adverb–preposition pairs. Experiment 3 had participants judge whether two-word utterances were meaningful or not. All of the experiments show effects of Verb Frequency and Verb-VAC frequency: learners have rich implicit statistical knowledge of verb-VAC type–token frequency that guides processing. Lexical decision is additionally driven by semantic prototypicality (but not VAC-verb contingency ΔPcw), whereas meaning judgment is affected by VAC-verb contingency ΔPcw (but not semantic prototypicality). These findings, I argue, index the spreading activation of unconscious meaning representation in lexical decision in comparison to the election of a unitary interpretation in conscious comprehension. I conclude that speeded automatic VAC processing involves rich associations, tuned by verb type and token frequencies, their contingencies of usage, and their histories of prototypical and specific interpretations which interface syntax, lexis, and semantics.

Do infants learn their early words in semantic isolation? Or do they integrate new words into an inter-connected semantic system? In an infant-friendly adaptation of the adult lexical priming paradigm, infants at 18 and 24 months-of-age heard two words in quick succession. The noun-pairs were either related or unrelated. Following the onset of the target word, two pictures were presented, one of which depicted the target. Eye movements revealed that both age groups comprehended the target word. In addition, 24-month-olds demonstrated primed picture looking in two measures of comprehension: Named target pictures preceded by a related word pair took longer to disengage from and attracted more looking overall. The finding of enhanced target recognition demonstrates the emergence of semantic organisation by the end of the second year.

The semantic fluency task is a widely used assessment tool for evaluating memory-related cognitive deficits in neurological and neuropsychiatric disorders. The present study investigates individual differences in performance on this task in a normal population. The aim is to explore handedness differences in switching and clustering tendencies when performing this task. Consistent with our prediction, when asked to produce as many animal names as possible in 1 min, mixed handers demonstrated greater switching between different subcategories of animals than strong handers. These findings are interpreted in terms of the more diffuse spread of activation among conceptual representations in the right hemisphere, and greater access to right hemisphere processes in mixed handers. The findings have implications for the research communities using the semantic fluency task, irrespective of whether or not they are looking at handedness differences per se. (JINS, 2009, 15, 1023–1027.)

A neural network model with dynamic thresholds, asymmetric connections, and clustered memories simulates spread activation that is hypothesized for semantic networks in the brain. By altering the parameters of the dynamic threshold a large range of disturbances can be generated in the model. These disturbances show metaphorical resemblance to certain general clinical descriptions of mental disturbances found in psychiatric patients engaged in various cognitive tasks. Even though the model is highly theoretical and metaphoric, it may help to gain certain insights into the relation between alterations of certain neural parameters, for example, thresholds and connectivity, and clinical symptoms in patients. (JINS, 2000, 6, 608–619.)