Atypical semantic cognition in schizotypal personality disorder and borderline personality disorder

Increased schizotypal traits have previously been associated with atypical semantic cognition in community samples. However, no study has yet examined whether adults diagnosed with schizotypal personality disorder (SPD) display atypical semantic fluency and memory. We hypothesized that 24 adults diagnosed with SPD would name more idiosyncratic words on the semantic fluency task and show decreased semantic recall for animal and fruit category words compared with 29 participants with borderline personality disorder (BPD) and a community sample of 96 age-matched controls. We examined whether atypical semantic cognition was specifically associated with disorganized and eccentric speech and thinking, or more broadly with pathological personality traits and personality functioning. Our main hypothesis was confirmed, as the SPD participants named more idiosyncratic words and recalled fewer semantically related words compared with controls. Surprisingly, participants with BPD likewise named more atypical words compared with controls. More idiosyncratic semantic fluency was associated with more eccentric speech and thinking. Increased idiosyncratic semantic fluency and reduced semantic recall were both coupled to increased detachment and lowered personality functioning, while reduced semantic recall further was related to increased interpersonal problems. Our findings suggest that persons with SPD, and to a lesser degree BPD, show atypical semantic cognition, which is associated with eccentric speech and thinking, and more broadly with impaired personality function, social withdrawal, and emotional flatness. The idiosyncratic semantic cognition may worsen difficulties with social reciprocity seen in SPD and BPD.

Prime saliency in semantic priming with 18-month-olds

This study investigated semantic priming in 18-month-old infants using the inter-modal priming technique, focusing on the effects of prime repetition on saliency. Our findings showed that prime repetition led to longer looking times at target referents for related primes compared to unrelated primes, supporting the existence of a structured semantic system in infants as young as 18 months. The results are consistent with both Spreading Activation and Distributed models of semantic priming. Additionally, our findings highlighted the impact of prime-target stimulus onset asynchronies (SOAs) on priming effects, revealing positive, negative, or no priming effects depending on the chosen SOA. A post-hoc explanation of this finding points to negative priming as a possible mechanism. The study also demonstrated the utility of the inter-modal priming task in studying lexical-semantic structure in younger infants with its diverse measures of infant behaviour.

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

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

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.

The human language effective connectome

To advance understanding of brain networks involved in language, the effective connectivity between 26 cortical regions implicated in language by a community analysis and 360 cortical regions was measured in 171 humans from the Human Connectome Project, and complemented with functional connectivity and diffusion tractography, all using the HCP multimodal parcellation atlas. A (semantic) network (Group 1) involving inferior cortical regions of the superior temporal sulcus cortex (STS) with the adjacent inferior temporal visual cortex TE1a and temporal pole TG, and the connected parietal PGi region, has effective connectivity with inferior temporal visual cortex (TE) regions; with parietal PFm which also has visual connectivity; with posterior cingulate cortex memory-related regions; with the frontal pole, orbitofrontal cortex, and medial prefrontal cortex; with the dorsolateral prefrontal cortex; and with 44 and 45 for output regions. It is proposed that this system can build in its temporal lobe (STS and TG) and parietal parts (PGi and PGs) semantic representations of objects incorporating especially their visual and reward properties. Another (semantic) network (Group 3) involving superior regions of the superior temporal sulcus cortex and more superior temporal lobe regions including STGa, auditory A5, TPOJ1, the STV and the Peri-Sylvian Language area (PSL) has effective connectivity with auditory areas (A1, A4, A5, Pbelt); with relatively early visual areas involved in motion, e.g., MT and MST, and faces/words (FFC); with somatosensory regions (frontal opercular FOP, insula and parietal PF); with other TPOJ regions; and with the inferior frontal gyrus regions (IFJa and IFSp). It is proposed that this system builds semantic representations specialising in auditory and related facial motion information useful in theory of mind and somatosensory / body image information, with outputs directed not only to regions 44 and 45, but also to premotor 55b and midcingulate premotor cortex. Both semantic networks (Groups 1 and 3) have access to the hippocampal episodic memory system via parahippocampal TF. A third largely frontal network (Group 2) (44, 45, 47l; 55b; the Superior Frontal Language region SFL; and including temporal pole TGv) receives effective connectivity from the two semantic systems, and is implicated in syntax and speech output.

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

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience‐dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large‐scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network‐analytical approaches.

Whether it is possible to define a rational standard in decision making and, if yes, whether such a standard is achievable by finite agents (such as humans) has been a hotly debated issue. This special issue offers an overview of some promising modern approaches to these questions, taking advantage of the latest developments in decision theory.

We review evidence that suggests links between individual experiences, semantic representations, and age differences in cognitive performance, and present an empirical agenda and pilot data involving the assessment of large‐scale, individual semantic networks.

COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland

Mobile health (mHealth) technologies, such as symptom tracking apps, are crucial for coping with the global pandemic crisis by providing near real-time, in situ information for the medical and governmental response. However, in such a dynamic and diverse environment, methods are still needed to support public health decision-making. This paper uses the lens of strong structuration theory to investigate networks of COVID-19 symptoms in the Belfast metropolitan area. A self-supervised machine learning method measuring information entropy was applied to the Northern Ireland COVIDCare app. The findings reveal: (1) relevant stratifications of disease symptoms, (2) particularities in health-wealth networks, and (3) the predictive potential of artificial intelligence to extract entangled knowledge from data in COVID-related apps. The proposed method proved to be effective for near real-time in-situ analysis of COVID-19 progression and to focus and complement public health decisions. Our contribution is relevant to an understanding of SARS-COV-2 symptom entanglements in localised environments. It can assist decision-makers in designing both reactive and proactive health measures that should be personalised to the heterogeneous needs of different populations. Moreover, near real-time assessment of pandemic symptoms using digital technologies will be critical to create early warning systems of emerging SARS-CoV-2 strains and predict the need for healthcare resources.

Italian Twitter semantic network during the Covid-19 epidemic

The Covid-19 pandemic has had a deep impact on the lives of the entire world population, inducing a participated societal debate. As in other contexts, the debate has been the subject of several d/misinformation campaigns; in a quite unprecedented fashion, however, the presence of false information has seriously put at risk the public health. In this sense, detecting the presence of malicious narratives and identifying the kinds of users that are more prone to spread them represent the first step to limit the persistence of the former ones. In the present paper we analyse the semantic network observed on Twitter during the first Italian lockdown (induced by the hashtags contained in approximately 1.5 millions tweets published between the 23rd of March 2020 and the 23rd of April 2020) and study the extent to which various discursive communities are exposed to d/misinformation arguments. As observed in other studies, the recovered discursive communities largely overlap with traditional political parties, even if the debated topics concern different facets of the management of the pandemic. Although the themes directly related to d/misinformation are a minority of those discussed within our semantic networks, their popularity is unevenly distributed among the various discursive communities.

Visual and Affective Multimodal Models of Word Meaning in Language and Mind

One of the main limitations of natural language‐based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic‐level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed.

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.

Semantic memory: A review of methods, models, and current challenges

Adult semantic memory has been traditionally conceptualized as a relatively static memory system that consists of knowledge about the world, concepts, and symbols. Considerable work in the past few decades has challenged this static view of semantic memory, and instead proposed a more fluid and flexible system that is sensitive to context, task demands, and perceptual and sensorimotor information from the environment. This paper (1) reviews traditional and modern computational models of semantic memory, within the umbrella of network (free association-based), feature (property generation norms-based), and distributional semantic (natural language corpora-based) models, (2) discusses the contribution of these models to important debates in the literature regarding knowledge representation (localist vs. distributed representations) and learning (error-free/Hebbian learning vs. error-driven/predictive learning), and (3) evaluates how modern computational models (neural network, retrieval-based, and topic models) are revisiting the traditional "static" conceptualization of semantic memory and tackling important challenges in semantic modeling such as addressing temporal, contextual, and attentional influences, as well as incorporating grounding and compositionality into semantic representations. The review also identifies new challenges regarding the abundance and availability of data, the generalization of semantic models to other languages, and the role of social interaction and collaboration in language learning and development. The concluding section advocates the need for integrating representational accounts of semantic memory with process-based accounts of cognitive behavior, as well as the need for explicit comparisons of computational models to human baselines in semantic tasks to adequately assess their psychological plausibility as models of human semantic memory.

Quantifying flexibility in thought: The resiliency of semantic networks differs across the lifespan

Older adults tend to have a broader vocabulary compared to younger adults - indicating a richer storage of semantic knowledge - but their retrieval abilities decline with age. Recent advances in quantitative methods based on network science have investigated the effect of aging on semantic memory structure. However, it is yet to be determined how this aging effect on semantic memory structure relates to its overall flexibility. Percolation analysis provides a quantitative measure of the flexibility of a semantic network, by examining how a semantic memory network is resistant to "attacks" or breaking apart. In this study, we incorporated percolation analyses to examine how semantic networks of younger and older adults break apart to investigate potential age-related differences in language production. We applied the percolation analysis to 3 independent sets of data (total N = 78 younger, 78 older adults) from which we generated semantic networks based on verbal fluency performance. Across all 3 datasets, the percolation integrals of the younger adults were larger than older adults, indicating that older adults' semantic networks were less flexible and broke down faster than the younger adults'. Our findings provide quantitative evidence for diminished flexibility in older adults' semantic networks, despite the stability of semantic knowledge across the lifespan. This may be one contributing factor to age-related differences in language production.

A semantic network approach to measuring sentiment

Sentiment research is dominated by studies that assign texts to positive and negative categories. This classification is often based on a bag-of-words approach that counts the frequencies of sentiment terms from a predefined vocabulary, ignoring the contexts for these words. We test an aspect-based network analysis model that computes sentiment about an entity from the shortest paths between the sentiment words and the target word across a corpus. Two ground-truth datasets in which human annotators judged whether tweets were positive or negative enabled testing the internal and external validity of the automated network-based method, evaluating the extent to which this approach’s scoring corresponds to the annotations. We found that tweets annotated as negative had an automated negativity score that was nearly twice as strong than positivity, while positively annotated tweets were six times stronger in positivity than negativity. To assess the predictive validity of the approach, we analyzed sentiment associated with coronavirus coverage in television news from January 1 to March 25, 2020. Support was found for the four hypotheses tested, demonstrating the utility of the approach. H1: broadcast news expresses less sentiment about coronavirus, panic, and social distancing than non-broadcast news outlets. H2: there is a negative bias in the news across channels. H3: sentiment increases are associated with an increased volume of news stories. H4: sentiment is associated with uncertainty in news coverage of coronavirus over time. We also found that as the type of channel moved from broadcast network news to 24-h business, general, and foreign news sentiment increased for coronavirus, panic, and social distancing.

Semantic and attentional networks in bilingual processing: fMRI connectivity signatures of translation directionality

Comparisons between backward and forward translation (BT, FT) have long illuminated the organization of bilingual memory, with neuroscientific evidence indicating that FT would involve greater linguistic and attentional demands. However, no study has directly assessed the functional interaction between relevant mechanisms. Against this background, we conducted the first fMRI investigation of functional connectivity (FC) differences between BT and FT. In addition to yielding lower behavioral outcomes, FT was characterized by increased FC between a core semantic hub (the left anterior temporal lobe, ATL) and key nodes of attentional and vigilance networks (left inferior frontal, left orbitofrontal, and bilateral parietal clusters). Instead, distinct FC patterns for BT emerged only between the left ATL and the right thalamus, a region implicated in automatic relaying of sensory information to cortical regions. Therefore, FT seems to involve enhanced coupling between semantic and attentional mechanisms, suggesting that asymmetries in cross-language processing reflect dynamic interactions between linguistic and domain-general systems.

False memories in relapsing remitting multiple sclerosis patients: A preliminary investigation with the DRM paradigm

BACKGROUND

Memory impairment is one of the most frequently and early detected impairment in multiple sclerosis (MS) patients. Several authors have argued that when a failure occurs in the retrieval of lexical information, this might be due to a reduction of the lexical pool, related to semantic memory. Here we further investigated memory alteration in MS patients, by focusing on memory distortions (i.e., false memories) for semantically-related material.

METHODS

A group of 40 consecutive relapsing remitting MS (RRMS) patients and a matched control group of 40 healthy controls performed the Deese-Roediger-McDermott (DRM), a false memory task for lists of associated words.

RESULTS

At recall, RRMS patients reported a reduced number of false recalls for semantically-related but non-presented items (i.e., critical false recalls) compared to HCs; at recognition, RRMS patients showed a reduced level of confidence for false recognitions of critical items.

CONCLUSION

We found a reduced susceptibility to false memories in RRMS patients compared to HCs. The potential mechanisms underlying this effect are discussed in light of the alterations in the structure of semantic memory.

SNAFU: The Semantic Network and Fluency Utility

The verbal fluency task—listing words from a category or words that begin with a specific letter—is a common experimental paradigm that is used to diagnose memory impairments and to understand how we store and retrieve knowledge. Data from the verbal fluency task are analyzed in many different ways, often requiring manual coding that is time intensive and error-prone. Researchers have also used fluency data from groups or individuals to estimate semantic networks—latent representations of semantic memory that describe the relations between concepts—that further our understanding of how knowledge is encoded. However computational methods used to estimate networks are not standardized and can be difficult to implement, which has hindered widespread adoption. We present SNAFU: the Semantic Network and Fluency Utility, a tool for estimating networks from fluency data and automatizing traditional fluency analyses, including counting cluster switches and cluster sizes, intrusions, perseverations, and word frequencies. In this manuscript, we provide a primer on using the tool, illustrate its application by creating a semantic network for foods, and validate the tool by comparing results to trained human coders using multiple datasets.

"Association splitting" versus cognitive remediation in obsessive-compulsive disorder: A randomized controlled trial

Studies have confirmed the efficacy of the cognitive intervention Association Splitting (AS) in obsessive-compulsive disorder (OCD) when applied as a self-help technique. AS aims to alter symptom-provoking automated cognitive networks of OC-related stimuli by building new or strengthening established but weak neutral associations. The aim of this study was to investigate the acceptance and benefits of therapist-assisted AS as an add-on to cognitive behavioral therapy (CBT). One hundred and nine patients with OCD who were undergoing CBT were randomly assigned to either AS or cognitive remediation (CR). Both groups were assessed at baseline, 4 weeks and 6 months later. The primary measure was the Yale-Brown Obsessive Compulsive Scale. Although patients' acceptance of AS was good, AS was not better than CR regarding overall symptom severity. However, a larger decrease was found from baseline to 6 months follow-up in AS regarding avoidance. Moreover, subsidiary analyses excluding control patients who had obtained information about AS indicated its superiority. Because superiority of AS was found in post hoc analyses excluding control patients who had obtained information on AS, we suggest that contagion effects deserve consideration.

Association Splitting: A randomized controlled trial of a new method to reduce craving among inpatients with alcohol dependence

Association Splitting, a novel cognitive intervention, was tested in patients with alcohol dependence as an add-on intervention in an initial randomized controlled trial. Preliminary support for Association Splitting has been found in patients with obsessive-compulsive disorder, as well as in an online pilot study of patients with alcohol use disorders. The present variant sought to reduce craving by strengthening neutral associations with alcohol-related stimuli, thus, altering cognitive networks. Eighty-four inpatients with verified diagnoses of alcohol dependence, who were currently undergoing inpatient treatment, were randomly assigned to Association Splitting or Exercise Therapy. Craving was measured at baseline, 4-week follow-up, and six months later with the Obsessive-Compulsive Drinking Scale (primary outcome) and the Alcohol Craving Questionnaire. There was no advantage for Association Splitting after three treatment sessions relative to Exercise Therapy. Among Association Splitting participants, 51.9% endorsed a subjective decline in craving and 88.9% indicated that they would use Association Splitting in the future. Despite high acceptance, an additional benefit of Association Splitting beyond standard inpatient treatment was not found. Given that participants were concurrently undergoing inpatient treatment and Association Splitting has previously shown moderate effects, modification of the study design may improve the potential to detect significant effects in future trials.

Mining integrated semantic networks for drug repositioning opportunities

Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.

Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury

Objective

Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury.

Methods

Our approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances.

Results

99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers.

Conclusion

We created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI.

Toddlers encode similarities among novel words from meaningful sentences

Toddlers can learn about the meanings of individual words from the structure and semantics of the sentences in which they are embedded. However, it remains unknown whether toddlers encode similarities among novel words based on their positions within sentences. In three experiments, two-year-olds listened to novel words embedded in familiar sentence frames. Some novel words consistently occurred in the subject position across sentences, and others in the object position across sentences. An auditory semantic task was used to test whether toddlers encoded similarities based on sentential position, for (a) pairs of novel words that occurred within the same sentence, and (b) pairs of novel words that occurred in the same position across sentences. The results suggest that while toddlers readily encoded similarity based on within-sentence occurrences, only toddlers with more advanced grammatical knowledge encoded the positional similarities of novel words across sentences. Moreover, the encoding of these cross-sentential relationships only occurred if the exposure sentences included a familiar verb. These studies suggest that the types of lexical relationships that toddlers learn depend on the child's current level of language development, as well as the structure and meaning of the sentences surrounding the novel words.

Suggestions for a Web based universal exchange and inference language for medicine

Mining biomedical and pharmaceutical data generates huge numbers of interacting probabilistic statements for inference, which can be supported by mining Web text sources. This latter can also be probabilistic, in a sense described in this report. However, the diversity of tools for probabilistic inference is troublesome, suggesting a need for a unifying best practice. Physicists often claim that quantum mechanics is the universal best practice for probabilistic reasoning. We discuss how the Dirac notation and algebra suggest the form and algebraic and semantic meaning of XML-like Web tags for a clinical and biomedical universal exchange language formulated to make sense directly to the eye of the physician and biomedical researcher.

Cognitive consequences of individual differences in arousal asymmetry

Prior research has demonstrated that semantic organization in the right hemisphere (RH) is more diffuse and specialized for distant semantic associates than is semantic organization in the left hemisphere (LH). The present research explored individual differences in this regard. If the RH is more specialized for distant semantic associates, then individuals with a more active RH should display greater activation of distant semantic associations. Two experiments were conducted to examine this issue. In both studies a line bisection task was used to assess arousal asymmetry. In Experiment 1, greater RH activation was associated with the ability to generate remote associates to three word stimuli. In Experiment 2, relatively greater RH activation was associated with enhanced priming of distant semantic associates. Taken together, these experiments demonstrate that arousal asymmetry is an individual difference variable that is related to variability in semantic organization and retrieval.

Semantic organisation and handedness: mixed-handedness is associated with more diffuse activation of ambiguous word associates

Research indicates that there are individual differences in the flexibility and ease with which one retrieves and uses concepts stored in memory. Based on prior research suggesting that mixed-handedness is associated with greater cognitive flexibility, it was hypothesised that mixed-handers have access to a relatively diffuse associative network, where link strengths for closely related and distantly related concepts are not as disparate as in the case of strong-handers. This idea was explored using ambiguous words for stimuli, as ambiguous words are known to have both strong (concepts related via dominant meaning) and weak associates (concepts related via subordinate meaning). Consistent with the prediction, mixed-handers showed equal ease in accessing both strongly and weakly related concepts. In Experiment 1 mixed-handers exhibited equivalent priming for dominant and subordinate associates, while strong-handers exhibited priming for dominant associates only. In Experiment 2 ratings of strength of association for dominant versus subordinate associates were examined. Mixed-handedness was associated with lesser disparity of dominant and subordinate association ratings.