Word association task responses prime associations in subsequent trials

The word association task has been used extensively in psychological and linguistic research as a way of measuring connections between words in the mental lexicon. Interpretation of word association data has assumed that responses represent the strongest association between cue word and response, but there is evidence that participant behaviour can be affected by task instructions and design. This study investigated whether word association responses can be primed by the participants' own response to the preceding cue-that is, whether the order in which cues are presented alters the responses that are generated. Results showed that the proportion of participants who provide a particular association (e.g., acid-RAIN) is greater when their response to the previous cue in the list is also associated with rain (e.g., parasol-UMBRELLA). The same is not true when the two cues are presented non-consecutively. Word association tasks should be administered such that the order in which cues are presented is random for every participant so as to avoid unintentional contamination of associative strength data.

Measuring the menu, not the food: "psychometric" data may instead measure "lingometrics" (and miss its greatest potential)

This is a review of a range of empirical studies that use digital text algorithms to predict and model response patterns from humans to Likert-scale items, using texts only as inputs. The studies show that statistics used in construct validation is predictable on sample and individual levels, that this happens across languages and cultures, and that the relationship between variables are often semantic instead of empirical. That is, the relationships among variables are given a priori and evidently computable as such. We explain this by replacing the idea of "nomological networks" with "semantic networks" to designate computable relationships between abstract concepts. Understanding constructs as nodes in semantic networks makes it clear why psychological research has produced constant average explained variance at 42% since 1956. Together, these findings shed new light on the formidable capability of human minds to operate with fast and intersubjectively similar semantic processing. Our review identifies a categorical error present in much psychological research, measuring representations instead of the purportedly represented. We discuss how this has grave consequences for the empirical truth in research using traditional psychometric methods.

Cascaded Semantic Fractionation for identifying a domain in social media

Searching social media to find relevant semantic domains often results in large text files, many of which are irrelevant due to cross-domain content resulting from word polysemy, abstractness, and degree centrality. Through an iterative pruning process, Cascaded Semantic Fractionation (CSF) systematically removes these cross-domain links. The social network procedure performs community detection in semantic networks, locates the semantic groups containing the terms of interest, excludes intergroup links, and repeats community detection on the pruned intragroup network until the domain of interest is clarified. To illustrate CSF, we analyzed public Facebook posts, using the CrowdTangle app for historical data search, from February 3, 2020, to March 13, 2021, about the possible Wuhan lab leak of COVID-19 over a daily interval. The initial search using keywords located six multi-day bursts of posts of more than 500 per day among 95 K posts. These posts were network analyzed to find the domain of interest using the iterative community detection and pruning process. CSF can be applied to capture the evolutions in semantic domains over time. At the outset, the lab leak theory was presented in conspiracy theory terms. Over time, the conspiratorial elements washed out in favor of an accidental release as the issue moved from social to mainstream media and official government views. CSF identified the relevant social media semantic domain and tracked its changes.

The role of systematicity in early referent selection

Previous studies showed that word learning is affected by children's existing knowledge. For instance, knowledge of semantic category aids word learning, whereas a dense phonological neighbourhood impedes learning of similar-sounding words. Here, we examined to what extent children associate similar-sounding words (e.g., rat and cat) with objects of the same semantic category (e.g., both are animals), that is, to what extent children assume meaning overlap given form overlap between two words. We tested this by first presenting children (N = 93, Mage = 22.4 months) with novel word-object associations. Then, we examined the extent to which children assume that a similar sounding novel label, that is, a phonological neighbour, refers to a similar looking object, that is, a likely semantic neighbour, as opposed to a dissimilar looking object. Were children to preferentially fixate the similar-looking novel object, it would suggest that systematic word form-meaning relations aid referent selection in young children. While we did not find any evidence for such word form-meaning systematicity, we demonstrated that children showed robust learning for the trained novel word-object associations, and were able to discriminate between similar-sounding labels and also similar-looking objects. Thus, we argue that unlike iconicity which appears early in vocabulary development, we find no evidence for systematicity in early referent selection.

How does linguistic context influence word learning?

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.

Predication of Writing Originality Based on Computational Linguistics

Existing assessment methods of writing originality have been criticized for depending heavily on subjective scoring methods. This study attempted to investigate the use of topic analysis and semantic networks in assessing writing originality. Written material was collected from a Chinese language test administered to eighth-grade students. Two steps were performed: 1. Latent topics of essays in each writing task were identified, and essays on the same topic were treated as a refined reference group, within which an essay was to be evaluated; 2. A group of features was developed, including four categories, i.e., path distance, semantic differences, centrality, and similarity of the network drawn from each text response, which were used to quantify the differences among essays. The results show that writing originality scoring is not only related to the intrinsic characteristics of the text, but is also affected by the reference group in which it is to be evaluated. This study proves that computational linguistic features can be a predictor of originality in Chinese writing. Each feature type of the four categories can predict originality, although the effect varies across various topics. Furthermore, the feature analysis provided evidence and insights to human raters for originality scoring.

Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis

Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.

Beauty and Wellness in the Semantic Memory of the Beholder

Beauty and wellness are terms used often in common parlance, however their meaning and relation to each other is unclear. To probe their meaning, we applied network science methods to estimate and compare the semantic networks associated with beauty and wellness in different age generation cohorts (Generation Z, Millennials, Generation X, and Baby Boomers) and in women and men. These mappings were achieved by estimating group-based semantic networks from free association responses to a list of 47 words, either related to Beauty, Wellness, or Beauty + Wellness. Beauty was consistently related to Elegance, Feminine, Gorgeous, Lovely, Sexy, and Stylish. Wellness was consistently related Aerobics, Fitness, Health, Holistic, Lifestyle, Medical, Nutrition, and Thrive. In addition, older cohorts had semantic networks that were less connected and more segregated from each other. Finally, we found that women compared to men had more segregated and organized concepts of Beauty and Wellness. In contemporary societies that are pre-occupied by the pursuit of beauty and a healthy lifestyle, our findings shed novel light on how people think about beauty and wellness and how they are related across different age generations and by sex.

Perceptual Connectivity Influences Toddlers' Attention to Known Objects and Subsequent Label Processing

While recent research suggests that toddlers tend to learn word meanings with many “perceptual” features that are accessible to the toddler’s sensory perception, it is not clear whether and how building a lexicon with perceptual connectivity supports attention to and recognition of word meanings. We explore this question in 24–30-month-olds (N = 60) in relation to other individual differences, including age, vocabulary size, and tendencies to maintain focused attention. Participants’ looking to item pairs with high vs. low perceptual connectivity—defined as the number of words in a child’s lexicon sharing perceptual features with the item—was measured before and after target item labeling. Results revealed pre-labeling attention to known items is biased to both high- and low-connectivity items: first to high, and second, but more robustly, to low-connectivity items. Subsequent object–label processing was also facilitated for high-connectivity items, particularly for children with temperamental tendencies to maintain focused attention. This work provides the first empirical evidence that patterns of shared perceptual features within children’s known vocabularies influence both visual and lexical processing, highlighting the potential for a newfound set of developmental dependencies based on the perceptual/sensory structure of early vocabularies.

Architecture and evolution of semantic networks in mathematics texts

Knowledge is a network of interconnected concepts. Yet, precisely how the topological structure of knowledge constrains its acquisition remains unknown, hampering the development of learning enhancement strategies. Here, we study the topological structure of semantic networks reflecting mathematical concepts and their relations in college-level linear algebra texts. We hypothesize that these networks will exhibit structural order, reflecting the logical sequence of topics that ensures accessibility. We find that the networks exhibit strong core–periphery architecture, where a dense core of concepts presented early is complemented with a sparse periphery presented evenly throughout the exposition; the latter is composed of many small modules each reflecting more narrow domains. Using tools from applied topology, we find that the expositional evolution of the semantic networks produces and subsequently fills knowledge gaps, and that the density of these gaps tracks negatively with community ratings of each textbook. Broadly, our study lays the groundwork for future efforts developing optimal design principles for textbook exposition and teaching in a classroom setting.

When slowing down processing helps learning: Lexico-semantic structure supports retention, but interferes with disambiguation of novel object-label mappings

This project explores how children disambiguate and retain novel object-label mappings in the face of semantic similarity. Burgeoning evidence suggests that semantic structure in the developing lexicon promotes word learning in ostensive contexts, whereas other findings indicate that semantic similarity interferes with and temporarily slows familiar word recognition. This project explores how these distinct processes interact when mapping and retaining labels for novel objects (i.e., low-frequency objects that are unfamiliar to toddlers) via disambiguation from a semantically similar familiar referent in 24-month-olds (N = 65). Toddlers' log-adjusted looking to labeled target objects (relative to distractor objects) was measured in three conditions: Familiar trials (familiar label spoken while viewing semantically related familiar and novel objects), Disambiguation trials (unfamiliar label spoken while viewing semantically similar familiar and unfamiliar object), and Retention trials (unfamiliar label spoken while viewing novel object pairs). Toddlers' individual vocabulary structure was then compared to performance on each condition. Vocabulary structure was measured at two levels: category-level structure (semantic density) for experimental items, and lexicon-level structure (global clustering coefficient). The findings suggest, consistent with prior results, that semantic density interfered with known word recognition, and facilitated unfamiliar word retention. Children did not show a significant novel word preference during disambiguation, and disambiguation behavior was not impacted by semantic structure. These findings connect seemingly disparate mechanisms of semantic interference in processing and semantic leveraging in word learning. Semantic interference momentarily slows word recognition and resolution of referential uncertainty for novel label-object mappings. Nevertheless, this slowing might support retention by enabling comparison between related objects.

[Medical subject headings for the scientific groups evolution analysis on the example of academician A.I. Archakov's scientific school]

This paper proposes a method of comparative analysis of scientific trajectories based on bibliographic profiles. The bibliographic profile ("meshprint") is a list of MeSH terms (key terms used to index articles in the PubMed), indicating the relative frequency of occurrence of each term in the scientist's articles. Comparison of personalized bibliographic profiles can be represented in the form of a semantic network, where the nodes are the names of scientists, and the relationships are proportional to the calculated measures of similarity of bibliographic profiles. The proposed method was used to analyze the semantic network of scientists united by the academic school of the academician A.I. Archakov. The results of the work allowed us to show the relationship between the scientific trajectories of one scientific school and to correlate the results with world trends.

The Representation of Coordinate Relations in Lexical Semantic Memory

Two experiments examined the size of the typicality effect for true items in a category verification task as a function of the type of false item used. In Experiment 1, compared to the case where false items paired unrelated concepts ("carrot-vehicle"), the typicality effect was much larger when false items paired an exemplar with a category coordinate to its proper category ("carrot-fruit"). In contrast, when false items paired coordinate concepts ("carrot-pea") or reversed the ordering of subject and predicate terms ("All vegetables are carrots"), the typicality effect did not change in size. Further, the time to verify true sentences did not increase monotonically with the semantic similarity of the two terms used in false sentences. Experiment 2 showed that the pattern of results for coordinate items reflected semantic processing, not simply task difficulty. A combined analysis examined data across multiple experiments, increasing the power of the statistical analysis. The size of the typicality effect when coordinate false items were used was again the same as when false items paired unrelated concepts. The most straightforward explanation of this pattern of results seems to be in terms of a sparse semantic network model of lexical semantic memory, in which labeled links are used to indicate the semantic relation that exists between pairs of words.

Olfaction, Vision, and Semantics for Mobile Robots. Results of the IRO Project

Olfaction is a valuable source of information about the environment that has not been sufficiently exploited in mobile robotics yet. Certainly, odor information can contribute to other sensing modalities, e.g., vision, to accomplish high-level robot activities, such as task planning or execution in human environments. This paper organizes and puts together the developments and experiences on combining olfaction and vision into robotics applications, as the result of our five-years long project IRO: Improvement of the sensory and autonomous capability of Robots through Olfaction. Particularly, it investigates mechanisms to exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems such as object recognition and scene–activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decision-making processes. The obtained results have improved the robot capabilities in terms of efficiency, autonomy, and usefulness, as reported in our publications.

Modeling early lexico-semantic network development: Perceptual features matter most

What aspects of word meaning are important in early word learning and lexico-semantic network development? Adult lexico-semantic systems flexibly encode multiple types of semantic features, including functional, perceptual, taxonomic, and encyclopedic. However, various theoretical accounts of lexical development differ on whether and how these semantic properties of word meanings are initially encoded into young children's emerging lexico-semantic networks. Whereas some accounts highlight the importance of early perceptual versus conceptual properties, others posit that thematic or functional aspects of word meaning are primary relative to taxonomic knowledge. We seek to shed light on these debates with 2 modeling studies that explore patterns in early word learning using a large database of early vocabulary in 5,450 children, and a newly developed set of semantic features of early acquired nouns. In Study 1, we ask whether semantic properties of early acquired words relate to order in which these words are typically learned; Study 2 models normative lexico-semantic noun-feature network development compared to random network growth. Both studies provide converging evidence that perceptual properties of word meanings play a key role in early word learning and lexico-semantic network development. The findings lend support to theoretical accounts of language learning that highlight the importance of the child's perceptual experience. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The company objects keep: Linking referents together during cross-situational word learning

Learning the meanings of words involves not only linking individual words to referents but also building a network of connections among entities in the world, concepts, and words. Previous studies reveal that infants and adults track the statistical co-occurrence of labels and objects across multiple ambiguous training instances to learn words. However, it is less clear whether, given distributional or attentional cues, learners also encode associations amongst the novel objects. We investigated the consequences of two types of cues that highlighted object-object links in a cross-situational word learning task: distributional structure - how frequently the referents of novel words occurred together - and visual context - whether the referents were seen on matching backgrounds. Across three experiments, we found that in addition to learning novel words, adults formed connections between frequently co-occurring objects. These findings indicate that learners exploit statistical regularities to form multiple types of associations during word learning.

Estimating semantic networks of groups and individuals from fluency data

One popular and classic theory of how the mind encodes knowledge is an associative semantic network, where concepts and associations between concepts correspond to nodes and edges, respectively. A major issue in semantic network research is that there is no consensus among researchers as to the best method for estimating the network of an individual or group. We propose a novel method (U-INVITE) for estimating semantic networks from semantic fluency data (listing items from a category) based on a censored random walk model of memory retrieval. We compare this method to several other methods in the literature for estimating networks from semantic fluency data. In simulations, we find that U-INVITE can recover semantic networks with low error rates given only a moderate amount of data. U-INVITE is the only known method derived from a psychologically plausible process model of memory retrieval and one of two known methods that we found to be consistent estimators of this process: if semantic memory retrieval is consistent with this process, the procedure will eventually estimate the true network (given enough data). We conduct the first exploration of different methods for estimating psychologically-valid semantic networks by comparing people's similarity judgments of edges estimated by each network estimation method. To encourage best practices, we discuss the merits of each network estimation technique, provide a flow chart that assists with choosing an appropriate method, and supply code for others to employ these techniques on their own data.

Modeling Semantic Fluency Data as Search on a Semantic Network

Psychologists have used the semantic fluency task for decades to gain insight into the processes and representations underlying memory retrieval. Recent work has suggested that a censored random walk on a semantic network resembles semantic fluency data because it produces optimal foraging. However, fluency data have rich structure beyond being consistent with optimal foraging. Under the assumption that memory can be represented as a semantic network, we test a variety of memory search processes and examine how well these processes capture the richness of fluency data. The search processes we explore vary in the extent they explore the network globally or exploit local clusters, and whether they are strategic. We found that a censored random walk with a priming component best captures the frequency and clustering effects seen in human fluency data.

Scoring Divergent Thinking Tests by Computer With a Semantics-Based Algorithm

Divergent thinking (DT) tests are useful for the assessment of creative potentials. This article reports the semantics-based algorithmic (SBA) method for assessing DT. This algorithm is fully automated: Examinees receive DT questions on a computer or mobile device and their ideas are immediately compared with norms and semantic networks. This investigation compared the scores generated by the SBA method with the traditional methods of scoring DT (i.e., fluency, originality, and flexibility). Data were collected from 250 examinees using the “Many Uses Test” of DT. The most important finding involved the flexibility scores from both scoring methods. This was critical because semantic networks are based on conceptual structures, and thus a high SBA score should be highly correlated with the traditional flexibility score from DT tests. Results confirmed this correlation (r = .74). This supports the use of algorithmic scoring of DT. The nearly-immediate computation time required by SBA method may make it the method of choice, especially when it comes to moderate- and large-scale DT assessment investigations. Correlations between SBA scores and GPA were insignificant, providing evidence of the discriminant and construct validity of SBA scores. Limitations of the present study and directions for future research are offered.

Semantic networks of interests in online non-suicidal self-injury communities

People who engage in non-suicidal self-injury (NSSI) often conceal their practices, which limits examination and understanding of their engagement. The goal of this research is to utilize data from public online social networks (namely, LiveJournal, a major blogging social networking site) to observe the NSSI population in a naturally occurring setting. Specifically, the focus of this paper is the interests publicly declared by LiveJournal users. In the course of study, we collected the self-declared interests of 25,000 users who are members of or participate in 139 NSSI-related communities. We constructed a family of semantic networks of interests based on their similarity. The semantic networks are structured and contain several dense clusters—semantic domains—that include NSSI-specific interests (such as self-injury and razor), references to music performers (such as evanescence), and general daily life and creativity related interests (such as poetry and friendship). Assuming users are genuine in their declarations, the clusters reveal distinct patterns of interest and may signal keys to NSSI.

Utilizing the Wikidata system to improve the quality of medical content in Wikipedia in diverse languages: a pilot study

Background

Wikipedia is an important source of medical information for both patients and medical professionals. Given its wide reach, improving the quality, completeness, and accessibility of medical information on Wikipedia could have a positive impact on global health.

Objective

We created a prototypical implementation of an automated system for keeping drug-drug interaction (DDI) information in Wikipedia up to date with current evidence about clinically significant drug interactions. Our work is based on Wikidata, a novel, graph-based database backend of Wikipedia currently in development.

Methods

We set up an automated process for integrating data from the Office of the National Coordinator for Health Information Technology (ONC) high priority DDI list into Wikidata. We set up exemplary implementations demonstrating how the DDI data we introduced into Wikidata could be displayed in Wikipedia articles in diverse languages. Finally, we conducted a pilot analysis to explore if adding the ONC high priority data would substantially enhance the information currently available on Wikipedia.

Results

We derived 1150 unique interactions from the ONC high priority list. Integration of the potential DDI data from Wikidata into Wikipedia articles proved to be straightforward and yielded useful results. We found that even though the majority of current English Wikipedia articles about pharmaceuticals contained sections detailing contraindications, only a small fraction of articles explicitly mentioned interaction partners from the ONC high priority list. For 91.30% (1050/1150) of the interaction pairs we tested, none of the 2 articles corresponding to the interacting substances explicitly mentioned the interaction partner. For 7.21% (83/1150) of the pairs, only 1 of the 2 associated Wikipedia articles mentioned the interaction partner; for only 1.48% (17/1150) of the pairs, both articles contained explicit mentions of the interaction partner.

Conclusions

Our prototype demonstrated that automated updating of medical content in Wikipedia through Wikidata is a viable option, albeit further refinements and community-wide consensus building are required before integration into public Wikipedia is possible. A long-term endeavor to improve the medical information in Wikipedia through structured data representation and automated workflows might lead to a significant improvement of the quality of medical information in one of the world’s most popular Web resources.

Investigating the structure of semantic networks in low and high creative persons

According to Mednick's (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by "flat" (broader associations) instead of "steep" (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related to each other by their association correlations-overlap of similar associative responses ("association clouds"). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick's (1962) theory and the feasibility of using network science paradigms to investigate high level cognition.

Automating case definitions using literature-based reasoning

BACKGROUND

Establishing a Case Definition (CDef) is a first step in many epidemiological, clinical, surveillance, and research activities. The application of CDefs still relies on manual steps and this is a major source of inefficiency in surveillance and research.

OBJECTIVE

Describe the need and propose an approach for automating the useful representation of CDefs for medical conditions.

METHODS

We translated the existing Brighton Collaboration CDef for anaphylaxis by mostly relying on the identification of synonyms for the criteria of the CDef using the NLM MetaMap tool. We also generated a CDef for the same condition using all the related PubMed abstracts, processing them with a text mining tool, and further treating the synonyms with the above strategy. The co-occurrence of the anaphylaxis and any other medical term within the same sentence of the abstracts supported the construction of a large semantic network. The 'islands' algorithm reduced the network and revealed its densest region including the nodes that were used to represent the key criteria of the CDef. We evaluated the ability of the "translated" and the "generated" CDef to classify a set of 6034 H1N1 reports for anaphylaxis using two similarity approaches and comparing them with our previous semi-automated classification approach.

RESULTS

Overall classification performance across approaches to producing CDefs was similar, with the generated CDef and vector space model with cosine similarity having the highest accuracy (0.825 ± 0.003) and the semi-automated approach and vector space model with cosine similarity having the highest recall (0.809 ± 0.042). Precision was low for all approaches.

CONCLUSION

The useful representation of CDefs is a complicated task but potentially offers substantial gains in efficiency to support safety and clinical surveillance.

Semantic organization in children with cochlear implants: computational analysis of verbal fluency

PURPOSE

Cochlear implants (CIs) enable children with severe and profound hearing impairments to perceive the sensation of sound sufficiently to permit oral language acquisition. So far, studies have focused mainly on technological improvements and general outcomes of implantation for speech perception and spoken language development. This study quantitatively explored the organization of the semantic networks of children with CIs in comparison to those of age-matched normal hearing (NH) peers.

METHOD

Twenty seven children with CIs and twenty seven age- and IQ-matched NH children ages 7-10 were tested on a timed animal verbal fluency task (Name as many animals as you can). The responses were analyzed using correlation and network methodologies. The structure of the animal category semantic network for both groups were extracted and compared.

RESULTS

Children with CIs appeared to have a less-developed semantic network structure compared to age-matched NH peers. The average shortest path length (ASPL) and the network diameter measures were larger for the NH group compared to the CIs group. This difference was consistent for the analysis of networks derived from animal names generated by each group [sample-matched correlation networks (SMCN)] and for the networks derived from the common animal names generated by both groups [word-matched correlation networks (WMCN)].

CONCLUSIONS

The main difference between the semantic networks of children with CIs and NH lies in the network structure. The semantic network of children with CIs is under-developed compared to the semantic network of the age-matched NH children. We discuss the practical and clinical implications of our findings.

The ontogeny of lexical networks: toddlers encode the relationships among referents when learning novel words

Although the semantic relationships among words have long been acknowledged as a crucial component of adult lexical knowledge, the ontogeny of lexical networks remains largely unstudied. To determine whether learners encode relationships among novel words, we trained 2-year-olds on four novel words that referred to four novel objects, which were grouped into two visually similar pairs. Participants then listened to repetitions of word pairs (in the absence of visual referents) that referred to objects that were either similar or dissimilar to each other. Toddlers listened significantly longer to word pairs referring to similar objects, which suggests that their representations of the novel words included knowledge about the similarity of the referents. A second experiment confirmed that toddlers can learn all four distinct words from the training regime, which suggests that the results from Experiment 1 reflected the successful encoding of referents. Together, these results show that toddlers encode the similarities among referents from their earliest exposures to new words.

Chinese medicine: a cognitive and epistemological review*

In spite of the common belief that Chinese natural philosophy and medicine have a unique frame of reference completely foreign to the West, this article argues that they in fact have significant cognitive and epistemic similarities with certain esoteric health beliefs of pre-Christian Europe. From the standpoint of Cognitive Science, Chinese Medicine appears as a proto-scientific system of health observances and practices based on a symptomological classification of disease using two elementary dynamical-processes pattern categorization schemas: a hierarchical and combinatorial inhibiting-activating model (Yin-Yang), and a non-hierarchical and associative five-parameter semantic network (5-Elements/Agents). The concept-map of the five-parameter model amounts to a pentagram, a commonly found geomantic and spell casting sigil in a number of pre-Christian health and safety beliefs in Europe, to include the Pythagorean cult of Hygieia, and the Old Religion of Northern Europe. This non-hierarchical pattern-recognition archetype/prototype was hypothetically added to the pre-existing hierarchical one to form a hybrid nosology that can accommodate for a change in disease perceptions. The selection of five parameters rather than another number might be due to a numerological association between the integer five, the golden ratio, the geometry of the pentagram and the belief in health and wholeness arising from cosmic or divine harmony. In any case, this body of purely empirical knowledge is nowadays widely flourishing in the US and in Europe as an alternative to Western Medicine and with the claim of being a unique, independent and comprehensive medical system, when in reality it is structurally-and perhaps historically-related to the health and safety beliefs of pre-Christian Europe; and without the prospect for an epistemological rupture, it will remain built upon rudimentary cognitive modalities, ancient metaphysics, and a symptomological view of disease.