Semantic Abilities Research Articles

Hubungan Kecerdasan Linguistik dengan Kemampuan Semantik Anak Gangguan Neurodevelopmental di Sekolah Luar Biasa Kota Surakarta

Background: Neurodevelopmental disorders are associated with disorders that lead to brain development characterized by an inability to reach normal limits of cognitive, emotional and motor development. This is also characterized by a delay in the language development process. Cognitive-linguistic is the ability to use language, both written and spoken, in various forms to express ideas. One of them is semantic skill. Semantics is the part of language structure that is related to the meaning of expressions and the meaning structure of speech. Objectives: This study aims to determine the relationship between cognitive-linguistic and semantic skill in children with neurodevelopmental disorders in special schools in Surakarta City. Methods: This research is a quantitative research with a descriptive correlational research design. The sampling technique used is purposive sampling with 60 respondents. The research instrument uses a linguistic intelligence test (Cognitive Linguistic Evaluation) and a semantic ability questionnaire. The statistical test used in this study is Kendall's Tau. Results: The results of the Kendall's Tau test showed a ρ value of 0.000 (ρ < 0.05) and a correlation strength of 0.417. This means that there is a relationship between cognitive-linguistic and semantic skill, with a moderate correlation strength and a positive correlation direction. This means that the higher the child's cognitive-linguistic, the higher their semantic skill. Conclusion: There is a relationship between cognitive-linguistic and semantic skill in children with neurodevelopmental disorders in special schools in Surakarta City. Keywords: Cognitive-Linguistic, Semantic, Neurodevelopmental Disorders

Cross-linguistic dissociations in Kannada-English bilingual persons with aphasia and alexia

Individuals with alexia manifest reading impairments comparable to spoken language impairments. With escalating dependency on communication through texts, emails, and other social media sources, these individuals express their interest in improving written language skills as well. Alexia in bilinguals in the Indian scenario is of special interest, owing to diverse geographical, cultural, traditional, and linguistic demarcations in India. Although substantial research is reported on bilingual aphasia, evidence on bilingual alexia is scarce. The study aimed to explore the cross-linguistic dissociations in Kannada-English bilingual individuals with alexia in post-stroke survivors. Thirteen Kannada-English bilingual individuals (10 males and 3 females) with reading and language impairments post ictus, above 18 years of age were recruited. Participants were subjected to neurobehavioral linguistic and reading tasks in both Kannada and English. The performance of linguistic tasks and reading tasks were analysed for cross-linguistic distinctions, linguistics versus reading, and correlation between linguistics and reading abilities. Results revealed evident cross-linguistic dissociations, wherein participants outperformed in Kannada (L1) in both linguistics and reading domains. All performed superior in the linguistics domain compared to reading. In both languages, semantic abilities were best performed within the linguistic domains. Oral reading abilities fared poor scores relative to reading comprehension. The correlation analysis revealed strong correlation between oral reading and semantics > phonology > syntax. Reading comprehension strongly correlated to syntax > phonology > semantics. The study proved convincing linguistic influences on reading abilities in Kannada- English bilingual context. Most investigations have predominantly centred on case observations, and have often lacked thorough pre- and post-rehabilitation assessments of linguistic and reading impairments using equivalent tests in a bilingual context. This study proves to be a preliminary attempt in this context. A much larger bilingual alexia cohort would aid in substantiating the reading impairments in a variant of subgrouping of aphasia.

The Two Word Test as a semantic benchmark for large language models

Large language models (LLMs) have shown remarkable abilities recently, including passing advanced professional exams and demanding benchmark tests. This performance has led many to suggest that they are close to achieving humanlike or “true” understanding of language, and even artificial general intelligence (AGI). Here, we provide a new open-source benchmark, the Two Word Test (TWT), that can assess semantic abilities of LLMs using two-word phrases in a task that can be performed relatively easily by humans without advanced training. Combining multiple words into a single concept is a fundamental linguistic and conceptual operation routinely performed by people. The test requires meaningfulness judgments of 1768 noun-noun combinations that have been rated as meaningful (e.g., baby boy) or as having low meaningfulness (e.g., goat sky) by human raters. This novel test differs from existing benchmarks that rely on logical reasoning, inference, puzzle-solving, or domain expertise. We provide versions of the task that probe meaningfulness ratings on a 0–4 scale as well as binary judgments. With both versions, we conducted a series of experiments using the TWT on GPT-4, GPT-3.5, Claude-3-Optus, and Gemini-1-Pro-001. Results demonstrated that, compared to humans, all models performed relatively poorly at rating meaningfulness of these phrases. GPT-3.5-turbo, Gemini-1.0-Pro-001 and GPT-4-turbo were also unable to make binary discriminations between sensible and nonsense phrases, with these models consistently judging nonsensical phrases as making sense. Claude-3-Opus made a substantial improvement in binary discrimination of combinatorial phrases but was still significantly worse than human performance. The TWT can be used to understand and assess the limitations of current LLMs, and potentially improve them. The test also reminds us that caution is warranted in attributing “true” or human-level understanding to LLMs based only on tests that are challenging for humans.

The interconnection of orthographic, phonetic, and semantic skills with arithmetic fluency.

Arithmetic fluency is considered considers highly rely on language processing, encompassing essential skills. However, the independent predictive power of phonetic, semantic, or orthographic skills in relation to arithmetic fluency remains an unresolved query. This study introduces the common component hypothesis to elucidate the inconsistent findings in previous research. The hypothesis posits that significant correlations between language and mathematics hinge on whether the language and mathematics utilized in a given task share a common component. According to this hypothesis, processing skills for each of the three fundamental language elements (i.e., phonetic, semantic, orthographic) should correlate with arithmetic fluency, as these elements are also integral to simple arithmetic processing. A cohort of one hundred and ninety-eight primary school students participated in the study, undertaking a battery of tests assessing general cognitive abilities, psycholinguistic elements, and arithmetic fluency. The results showed that orthographic, phonetic, and semantic abilities independently predicted arithmetic fluency, even after accounting for all other cognitive predictors. These findings substantiate the common component hypothesis, providing empirical support for explaining the association between language and mathematics. This evidence contributes to addressing the interplay between language and mathematics in educational contexts.

Sensitivity to moral and conventional rules in temporal lobe epilepsy

ObjectivesSensitivity to moral and conventional rules (SMCR) is supported by bilateral brain networks and psychosocial input both of which may be altered in temporal lobe epilepsy (TLE). This study evaluated the components of SMCR in patients with TLE, aiming to clarify their preservation and link to psychopathological and cognitive aspects. MethodsAdult patients with unilateral TLE and healthy controls were evaluated using neuropsychological tests for SMCR, memory, language, and executive functions, the Empathy Questionnaire (EQ), and the Symptom Checklist-90-R (SCL-90-R). ResultsThe SMCR test items showed good reliability and validity, yielding the Severity and Rules factors distinct from the Executive, Lexical and Memory factors. Patients with right TLE scored worse in moral rules recognition than controls, but this difference was nullified by a significant influence for age and sex. The Severity and Rules factors related to semantic fluency and age and, respectively, TLE side and psychoticism. However, these factors did predict TLE membership. ConclusionsIn adult patients with TLE, the SMCR test reflects a distinct cognitive domain. Conventional rules are well-retained, while moral reasoning may be only affected in right TLE if unfavorable demographics coexist. Although age, TLE side, semantic abilities, and psychoticism cooperate to determine SMCR, impairment of such domain is not a distinctive feature of TLE.

The relationship between semantics, phonology, and naming performance in aphasia: a structural equation modeling approach

ABSTRACT The exploration of naming error patterns in aphasia provides insights into the cognitive processes underlying naming performance. We investigated how semantic and phonological abilities correlate and how they influence naming performance in aphasia. Data from 296 individuals with aphasia, drawn from the Moss Aphasia Psycholinguistics Project Database, were analyzed using a structural equation model. The model incorporated latent variables for semantics and phonology and manifest variables for naming accuracy and error patterns. There was a moderate positive correlation between semantics and phonology after controlling for overall aphasia severity. Both semantic and phonological abilities influenced naming accuracy. Semantic abilities negatively related to semantic, mixed, unrelated errors, and no responses. Interestingly, phonology positively affected semantic errors. Additionally, phonological abilities negatively related to each of phonological and neologism errors. These results highlight the role of semantic and phonological skills on naming performance in aphasia and reveal a relationship between these cognitive processes.

Irregular word reading as a marker of semantic decline in Alzheimer’s disease: implications for premorbid intellectual ability measurement

BackgroundIrregular word reading has been used to estimate premorbid intelligence in Alzheimer’s disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities.MethodSix hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer’s Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory.ResultsEMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances.ConclusionsWhile the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum.

Patterns of performance on the animal fluency task in logopenic variant of primary progressive aphasia: A reflection of phonological and semantic skills

PurposeThis study aimed to characterize the quantitative (total number of correct words generated) and qualitative (psycholinguistic properties of correct words generated) performance patterns on the animal fluency task in individuals with the logopenic variant of primary progressive aphasia and to investigate the influence of phonological and semantic abilities to these patterns. MethodsFifteen participants with lvPPA and twenty neurotypical adults completed the animal fluency task and an assessment battery to characterize their phonological and semantic abilities. We recorded the total number of correct words produced and their psycholinguistic properties. Group differences were analyzed using independent samples t-tests and analysis of covariance. Stepwise and multiple linear regression analyses were implemented to investigate the contribution of psycholinguistic properties on word generation as well as the role of phonological and semantic abilities on performance. We also investigated the mediating role of phonological and semantic abilities on the relationship between relevant psycholinguistic properties and word generation output. ResultsCompared to neurotypical controls, participants with lvPPA produced fewer correct responses and more words with lower age of acquisition. The total number of correct words generated was predicted by the age of word acquisition, such that individuals who generated more responses, produced words acquired later in life. Phonology and semantics influenced the number of correct words generated and their frequency, age of acquisition, and semantic neighborhood density. Familiarity and arousal were driven by semantic abilities. Phonological abilities partially mediated the relationship between age of acquisition and word generation output. ConclusionsThis study provides valuable insights into the performance patterns of the animal fluency task in lvPPA. Individuals with lvPPA with more intact phonological and semantic abilities generated greater number of words with more complex psycholinguistic properties. Our findings contribute to the understanding of language processes underlying word retrieval in lvPPA.

Event Knowledge in Large Language Models: The Gap Between the Impossible and the Unlikely.

Word co-occurrence patterns in language corpora contain a surprising amount of conceptual knowledge. Large language models (LLMs), trained to predict words in context, leverage these patterns to achieve impressive performance on diverse semantic tasks requiring world knowledge. An important but understudied question about LLMs' semantic abilities is whether they acquire generalized knowledge of common events. Here, we test whether five pretrained LLMs (from 2018's BERT to 2023's MPT) assign a higher likelihood to plausible descriptions of agent-patient interactions than to minimally different implausible versions of the same event. Using three curated sets of minimal sentence pairs (total n = 1215), we found that pretrained LLMs possess substantial event knowledge, outperforming other distributional language models. In particular, they almost always assign a higher likelihood to possible versus impossible events (The teacher bought the laptop vs. The laptop bought the teacher). However, LLMs show less consistent preferences for likely versus unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow-up analyses, we show that (i) LLM scores are driven by both plausibility and surface-level sentence features, (ii) LLM scores generalize well across syntactic variants (active vs. passive constructions) but less well across semantic variants (synonymous sentences), (iii) some LLM errors mirror human judgment ambiguity, and (iv) sentence plausibility serves as an organizing dimension in internal LLM representations. Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events.

Focusing on the locus of the breakdown for treatment of anomia: a pilot study

ABSTRACT The primary goal of this study was to evaluate the treatment effects of semantic feature analysis (SFA) and phonological components analysis (PCA) on word retrieval processing in persons with aphasia (PWAs). After identifying the locus of the breakdown in lexical retrieval processing, 15 monolingual native Persian speakers with aphasia were divided into two groups. After three naming trials, participants with dominant semantic deficits received SFA, and participants with primary phonological deficits were provided with PCA three times a week for eight weeks. Both approaches improved participants’ naming and performance on language tests, including spontaneous speech, repetition, comprehension, and semantic processing. However, the correct naming of treated and untreated items was higher in mild-to-moderate participants, with mostly circumlocution and semantic paraphasias in the SFA group. The same holds for mild-to-moderate participants with mostly phonemic paraphasia who received PCA therapy. Moreover, the results showed that participants’ baseline naming performance and semantic abilities could be associated with the treatment outcomes. Although limited by a lack of a control group, this study provided evidence supporting the possible benefits of focusing on the locus of the breakdown for treating anomia through SFA and PCA approaches, specifically in participants with mild to moderate aphasia. However, for those with severe aphasia, the treatment choice may not be as straightforward because several variables are likely to contribute to this population’s word-finding difficulties. Replication with larger, well-stratified samples, use of a within-subjects alternating treatment design and consideration of treatments’ long-term effects are required to better ascertain the effects of focusing on the locus of breakdown for treatment of anomia.

Lexical difficulties in the acquisition of German

This study is focused on interference-related errors in German students' written papers. These errors are caused mostly by the differently structured non-linguistic reality, which results in asymmetries of language signs. Mistakes occur particularly in polysemous words, words with different semantic ability to combine, fixed phrases, and prepositional phrases. Concerning foreign words, interference-related errors arise from their asymmetric occurrence in the mother and the target languages, and also from their different meanings in both languages.

Common predictors of spoken and written language performance in aphasia, alexia, and agraphia.

Language performance requires support from central cognitive/linguistic abilities as well as the more peripheral sensorimotor skills to plan and implement spoken and written communication. Both output modalities are vulnerable to impairment following damage to the language-dominant hemisphere, but much of the research to date has focused exclusively on spoken language. In this study we aimed to examine an integrated model of language processing that includes the common cognitive processes that support spoken and written language, as well as modality-specific skills. To do so, we evaluated spoken and written language performance from 87 individuals with acquired language impairment resulting from damage to left perisylvian cortical regions that collectively constitute the dorsal language pathway. Comprehensive behavioral assessment served to characterize the status of central and peripheral components of language processing in relation to neurotypical controls (n = 38). Performance data entered into principal components analyses (with or without control scores) consistently yielded a strong five-factor solution. In line with a primary systems framework, three central cognitive factors emerged: semantics, phonology, and orthography that were distinguished from peripheral processes supporting speech production and allographic skill for handwriting. The central phonology construct reflected performance on phonological awareness and manipulation tasks and showed the greatest deficit of all the derived factors. Importantly, this phonological construct was orthogonal to the speech production factor that reflected repetition of words/non-words. When entered into regression analyses, semantics and phonological skill were common predictors of language performance across spoken and written modalities. The speech production factor was also a strong, distinct predictor of spoken naming and oral reading, in contrast to allographic skills which only predicted written output. As expected, visual orthographic processing contributed more to written than spoken language tasks and reading/spelling performance was strongly reliant on phonological and semantic abilities. Despite the heterogeneity of this cohort regarding aphasia type and severity, the marked impairment of phonological skill was a unifying feature. These findings prompt greater attention to clinical assessment and potential treatment of underlying phonological skill in individuals with left perisylvian damage.

Design of Knowledge Graph Retrieval System for Legal and Regulatory Framework of Multilevel Latent Semantic Indexing.

Latent semantic analysis (LSA) is a natural language statistical model, which is considered as a method to acquire, generalize, and represent knowledge. Compared with other retrieval models based on concept dictionaries or concept networks, the retrieval model based on LSA has the advantages of strong computability and less human participation. LSA establishes a latent semantic space through truncated singular value decomposition. Words and documents in the latent semantic space are projected onto the dimension representing the latent concept, and then the semantic relationship between words can be extracted to present the semantic structure in natural language. This paper designs the system architecture of the public prosecutorial knowledge graph. Combining the graph data storage technology and the characteristics of the public domain ontology, a knowledge graph storage method is designed. By building a prototype system, the functions of knowledge management, knowledge query, and knowledge push are realized. A named entity recognition method based on bidirectional long-short-term memory (bi-LSTM) combined with conditional random field (CRF) is proposed. Bi-LSTM-CRF performs named entity recognition based on character-level features. CRF can use the transition matrix to further obtain the relationship between each position label, so that bi-LSTM-CRF not only retains the context information but also considers the influence between the current position and the previous position. The experimental results show that the LSTM-entity-context method proposed in this paper improves the representation ability of text semantics compared with other algorithms. However, this method only introduces relevant entity information to supplement the semantic representation of the text. The order in the case is often ignored, especially when it comes to the time series of the case characteristics, and the “order problem” may eventually affect the final prediction result. The knowledge graph of legal documents of theft cases based on ontology can be updated and maintained in real time. The knowledge graph can conceptualize, share, and perpetuate knowledge related to procuratorial organs and can also reasonably utilize and mine many useful experiences and knowledge to assist in decision-making.

CharAs-CBert: Character Assist Construction-Bert Sentence Representation Improving Sentiment Classification.

In the process of semantic capture, traditional sentence representation methods tend to lose a lot of global and contextual semantics and ignore the internal structure information of words in sentences. To address these limitations, we propose a sentence representation method for character-assisted construction-Bert (CharAs-CBert) to improve the accuracy of sentiment text classification. First, based on the construction, a more effective construction vector is generated to distinguish the basic morphology of the sentence and reduce the ambiguity of the same word in different sentences. At the same time, it aims to strengthen the representation of salient words and effectively capture contextual semantics. Second, character feature vectors are introduced to explore the internal structure information of sentences and improve the representation ability of local and global semantics. Then, to make the sentence representation have better stability and robustness, character information, word information, and construction vectors are combined and used together for sentence representation. Finally, the evaluation and verification are carried out on various open-source baseline data such as ACL-14 and SemEval 2014 to demonstrate the validity and reliability of sentence representation, namely, the F1 and ACC are 87.54% and 92.88% on ACL14, respectively.

A2TPNet: Alternate Steered Attention and Trapezoidal Pyramid Fusion Network for RGB-D Salient Object Detection

RGB-D salient object detection (SOD) aims at locating the most eye-catching object in visual input by fusing complementary information of RGB modality and depth modality. Most of the existing RGB-D SOD methods integrate multi-modal features to generate the saliency map indiscriminately, ignoring the ambiguity between different modalities. To better use multi-modal complementary information and alleviate the negative impact of ambiguity among different modalities, this paper proposes a novel Alternate Steered Attention and Trapezoidal Pyramid Fusion Network (A2TPNet) for RGB-D SOD composed of Cross-modal Alternate Fusion Module (CAFM) and Trapezoidal Pyramid Fusion Module (TPFM). CAFM is focused on fusing cross-modal features, taking full consideration of the ambiguity between cross-modal data by an Alternate Steered Attention (ASA), and it reduces the interference of redundant information and non-salient features in the interactive process through a collaboration mechanism containing channel attention and spatial attention. TPFM endows the RGB-D SOD model with more powerful feature expression capabilities by combining multi-scale features to enhance the expressive ability of contextual semantics of the model. Extensive experimental results on five publicly available datasets demonstrate that the proposed model consistently outperforms 17 state-of-the-art methods.

Construction and Research on Chinese Semantic Mapping Based on Linguistic Features and Sparse Self-Learning Neural Networks.

In this paper, we adopt the algorithms of linguistic feature Rong and sparse self-learning neural network to conduct an in-depth study and analysis of Chinese semantic mapping, which complements the emotion semantic representation ability of traditional word embedding and fully explores the emotion semantic information contained in the text in the task preprocessing stage. We incorporate various semantic features such as lexical information and location information to make the model have richer emotion semantic expression, and the model also uses an attention mechanism to allow various features to interact and abstract deeper contextual internal semantic associations to improve the model's sentiment classification performance. Finally, experiments are conducted on two publicly available English sentiment classification corpora, and the results prove that the model outperforms other comparison models and effectively improves the sentiment classification performance. The model uses deep memory networks and capsule networks to construct a transfer learning framework and effectively leverages the transfer learning properties of capsule networks to transfer knowledge embedded in large-scale labeled data from similar domains to the target domain, improving the classification performance on small data sets. The use of multidimensional combined features compensates for the lack of a one-dimensional feature attention mechanism, while multiple domain category-based attention computation layers are superimposed to obtain deeper domain-specific sentiment feature information. Based on the segmented convolutional neural network, the model first introduces the dependent subtree of relational attributes to obtain the position weights of each word in the sentence, then introduces domain ontology knowledge in the output layer to constrain the extraction results, and conducts experimental comparison through the data set to verify the validity of the model, which ensures the accuracy of the network term entity and relational attribute recognition extraction and makes the knowledge map constructed in this paper. It ensures the accuracy of the extraction rate of the web term entities and relationship attributes and makes the knowledge map constructed in this paper more factual.

Automatic Recognition Method of Machine English Translation Errors Based on Multisignal Feature Fusion.

The current automatic recognition method of machine English translation errors has poor semantic analysis ability, resulting in low accuracy of recognition results. Therefore, this paper designs an automatic recognition method for machine English translation errors based on multifeature fusion. Manually classify and summarize the real error sentence pairs, falsify a large amount of data by means of data enhancement, enhance the effect and robustness of the machine translation error detection model, and add the source text to translation length ratio information and the translation language model PPL into the model input. The score feature information can further improve the classification accuracy of the error detection model. Based on this error detection scheme, the detection results can be used for subsequent error correction and can also be used for error prompts to provide translation user experience; it can also be used for evaluation indicators of machine translation effects. The experimental results show that the word posterior probability features calculated by different methods have a significant impact on the classification error rate, and adding source word features based on the combination of word posterior probability and linguistic features can significantly reduce the classification error rate, to improve the translation error detection ability.

Playing Games with Ais: The Limits of GPT-3 and Similar Large Language Models

This article contributes to the debate around the abilities of large language models such as GPT-3, dealing with: firstly, evaluating how well GPT does in the Turing Test, secondly the limits of such models, especially their tendency to generate falsehoods, and thirdly the social consequences of the problems these models have with truth-telling. We start by formalising the recently proposed notion of reversible questions, which Floridi & Chiriatti (2020) propose allow one to ‘identify the nature of the source of their answers’, as a probabilistic measure based on Item Response Theory from psychometrics. Following a critical assessment of the methodology which led previous scholars to dismiss GPT’s abilities, we argue against claims that GPT-3 completely lacks semantic ability. Using ideas of compression, priming, distributional semantics and semantic webs we offer our own theory of the limits of large language models like GPT-3, and argue that GPT can competently engage in various semantic tasks. The real reason GPT’s answers seem senseless being that truth-telling is not amongst them. We claim that these kinds of models cannot be forced into producing only true continuation, but rather to maximise their objective function they strategize to be plausible instead of truthful. This, we moreover claim, can hijack our intuitive capacity to evaluate the accuracy of its outputs. Finally, we show how this analysis predicts that a widespread adoption of language generators as tools for writing could result in permanent pollution of our informational ecosystem with massive amounts of very plausible but often untrue texts.

Neural sensitivity to semantic neighbors is stable across the adult lifespan

As we age, language reflects patterns of both stability and change. On the one hand, vocabulary and semantic abilities are largely stable across the adult lifespan, yet lexical retrieval is often slower and less successful (i.e., slower picture naming times, increased tip of the tongue incidents). Although the behavioral bases of these effects have been well established, less is known about the brain regions that support these age-related differences. We used functional Magnetic Resonance Imaging (fMRI) to examine the neural basis of picture naming. Specifically, we were interested in whether older adults would be equally sensitive to semantic characteristics, specifically the number of semantic near neighbors. Near neighbors, defined here as items with a high degree of semantic feature overlap, were of interest as these are thought to elicit competition among potential candidates and increase naming difficulty. Consistent with prior reports, pictures with more semantic near neighbors were named more slowly and less accurately for all adults. Additionally, this interference for naming times was larger as age increased, starting around 30 years old. In contrast to the age-related behavioral slowing, the neural basis of these effects was stable across adulthood. Across all adults, a number of language-relevant regions including left posterior middle temporal gyrus and left inferior frontal gyrus, pars triangularis were sensitive to the number of near neighbors. Our results suggest that although middle-aged and older adults’ picture naming is more slowed by increased semantic competition, the brain regions supporting semantic processes remain stable across the adult lifespan.

Gli esiti dello stroke ischemico del bambino: afasia e talamo

Left thalamic stroke in adults commonly results in thalamic aphasia, which is characterized by defects in lexical and semantic abilities associated with fluency and comprehension disabilities and intact word repetition. Although few cases have been reported, aphasia with similar features can also be present as a consequence of left-brain hemisphere stroke in children, which has usually a poorer prognosis if compared with that of the adult population. The paper reports the case of a 20-month-old female child that developed aphasia after an ischemic stroke that involved the left anteromedial thalamus. In these cases it is necessary to initiate an early rehabilitation treatment, possibly supported by augmentative and alternative communication techniques.