47 research outputs found

    Information-processing skills related to working memory in individuals with Asperger\u27s disorder

    Get PDF
    The present study examined information-processing (IP) deficits specific to the ability of individuals with Asperger\u27s disorder (AD) to interpret and respond to nonverbal and verbal information inherent in social relationships as it relates to working memory capacity. Sixty boys between the ages of 11.0 and 15.7 years (30 diagnosed with AD [Group A], and 30 typically developing, same-age peers [Group T]) were assessed using the Working Memory Test Battery for Children (WMTB-C). The present study combined theories from cognitive, neurological, and clinical psychology, isolated specific working memory components, and identified a connection between working memory capacity and the social skill deficiencies of individuals with AD. Three working memory scores (i.e., verbal [PL], visual-spatial [VSSP], and complex [CE]) were compared between the two groups using ANOVA. All working memory differences examined between the two groups in the present study were statistically different. The effect sizes of differences between Groups A and T for PL, VSSP, and CE were .397, .279, and .627, respectively. The results of the present study support the hypothesis that working memory is a specific IP deficit of individuals with AD. Findings suggest that by targeting remedial efforts to enhance working memory capacity, individuals with AD can more effectively engage in complex IP tasks, participate in reciprocal social interactions, and thereby create social change. Future research needs to expand upon the connection between working memory capacity and the social deficiencies of individuals with Asperger\u27s disorder

    Semantic radical consistency and character transparency effects in Chinese: an ERP study

    Get PDF
    BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin

    Cognitive processes and neural correlates of reading in languages with graded levels of orthographic transparency: Spanish, English and Hebrew

    Get PDF
    This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis examined the cognitive processes and neural correlates involved in reading Spanish (a transparent orthography), English (an intermediate orthography) and Hebrew (an opaque orthography) by bilinguals and trilinguals. The main objectives of the five experiments were to: (i) extend previous findings which demonstrated that orthographic transparency influences the degree of reliance on lexical and sublexical processing, and (ii) assess the effects of orthographic transparency and language proficiency on strategies employed for reading in a second and third language. Word/non-word naming tasks undertaken by Spanish-English bilinguals, Hebrew-English bilinguals and English monolinguals, where frequency, length and lexicality were manipulated, showed a predominant reliance on sublexical processing in Spanish, lexical processing in Hebrew, and a balanced interplay in English. Effects of language proficiency were also observed as slower naming and lower accuracy in English as a second language. Concurrently, while showing an efficient adaptation of reading strategy to the level of orthographic transparency of English, Hebrew bilinguals appeared to show stronger reliance on sublexical processing than Spanish bilinguals, suggesting a compensatory mechanism. fMRI experiments showed that reading in all languages was associated with a common network of predominantly left-lateralised cerebral regions. Reading in each language was associated with some preferential activation within regions implicated in lexical and sublexical processing, in keeping with their graded levels of orthographic transparency. Effects of language proficiency were demonstrated as increased activation within medial frontal regions implicated in attentional processes as well as right-lateralised homologous language-processing regions. Furthermore, the patterns of activation seen in Hebrew readers in English strengthened the notion of a compensatory mechanism. Finally, a trilingual experiment replicated findings observed in bilinguals, revealed the acute complexity of reading in Hebrew as an additional language and further strengthened the concept of a compensatory mechanism in English and Spanish. The present findings further contribute to current knowledge on teaching methods, diagnostic tools and therapeutic strategies for developmental and acquired reading disorders

    INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION

    Get PDF
    This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems

    Structural and effective connectivity of lexical-semantic and naming networks in patients with chronic aphasia

    Full text link
    Given the difficulty in predicting outcomes in persons with stroke-induced aphasia (PWA), neuroimaging-based biomarkers of recovery could provide invaluable predictive power to stroke models. However, the neural patterns that constitute beneficial neural organization of language in PWA remain debated. Thus, in this work, we propose a novel network theory of aphasia recovery and test our overarching hypothesis, i.e., that task-specific language processing in PWA requires the dynamic engagement of intact tissue within a bilateral network of anatomically-segregated but functionally and structurally connected language-specific and domain-general brain regions. We first present two studies in which we examined left frontotemporal connectivity during different language tasks (i.e., picture naming and semantic feature verification). Results suggest that PWA heavily rely on left middle frontal gyrus (LMFG)-driven connectivity for tasks requiring lexical-semantic processing and semantic control whereas controls prefer models with input to either LMFG or left inferior frontal gyrus (LIFG). Both studies also revealed several significant associations between spared tissue, connectivity and language skills in PWA. In the third study, we examined bilateral frontotemporoparietal connectivity and tested a lesion- and connectivity-based hierarchical model of chronic aphasia recovery. Between-group comparisons showed controls exhibited stronger left intra-hemispheric task-modulated connectivity than did PWA. Connectivity and language deficit patterns most closely matched predictions for patients with primarily anterior damage whereas connectivity results for patients with other lesion types were best explained by the nature of the semantic task. In the last study, we investigated the utility of lesion classification based on gray matter (GM) only versus combined GM plus white matter (WM) metrics. Results suggest GM only classification was sufficient for characterizing aphasia and anomia severity but the GM+WM classification better predicted naming treatment outcomes. We also found that fractional anisotropy of left WM association tracts predicted baseline naming and treatment outcomes independent of total lesion volume. Finally, results of a preliminary multimodal prediction analysis suggest that combined structural and functional metrics reflecting the integrity of regions and connections comprise optimal predictive models of behavior in PWA. To conclude this dissertation, we discuss how multimodal network models of aphasia recovery can guide future investigations.2020-10-23T00:00:00

    TMS application in both health and disease

    Get PDF
    Transcranial magnetic stimulation (TMS) can be useful for therapeutic purposes for a variety of clinical conditions. Numerous studies have indicated the potential of this noninvasive brain stimulation technique to recover brain function and to study physiological mechanisms. Following this line, the articles contemplated in this Research Topic show that this field of knowledge is rapidly expanding and considerable advances have been made in the last few years. There are clinical protocols already approved for Depression (and anxiety comorbid with major depressive disorder), Obsessive compulsive Disorder (OCD), migraine headache with aura, and smoking cessation treatment but many studies are concentrating their efforts on extending its application to other diseases, e.g., as a treatment adjuvant. In this Research Topic we have the example of using TMS for pain, post-stroke depression, or smoking cessation, but other diseases/injuries of the central nervous system need attention (e.g., tinnitus or the surprising epilepsy). Further, the potential of TMS in health is being explored, in particular regarding memory enhancement or the mapping of motor control regions, which might also have implications for several diseases. TMS is a non-invasive brain stimulation technique that can be used for modulating brain activation or to study connectivity between brain regions. It has proven efficacy against neurological and neuropsychiatric illnesses but the response to this stimulation is still highly variable. Research works devoted to studying the response variability to TMS, as well as large-scale studies demonstrating its efficacy in different sub-populations, are therefore of utmost importance. In this editorial, we summarize the main findings and viewpoints detailed within each of the 12 contributing articles using TMS for health and/or disease applications.publishe

    Object-based modelling for representing and processing speech corpora

    Get PDF
    This thesis deals with modelling data existing in large speech corpora using an object-oriented paradigm which captures important linguistic structures. Information from corpora is transformed into objects and are assigned properties regarding their behaviour. These objects, called speech units, are placed onto a multi-dimensional framework and have their relationships to other units explicitly defined through the use of links. Frameworks that model temporal utterances or atemporal information like speaker characteristics and recording conditions can be searched efficiently for contextual matches. Speech units that match desired contexts are the result of successful linguistically motivated queries and can be used in further speech processing tasks in the same computational environment. This allows for empirical studies of speech and its relation to linguistic structures to be carried out, and for the training and testing of applications like speech recognition and synthesis. Information residing in typical speech corpora is discussed first, followed by an overview of object-orientation which sets the tone for this thesis. Then the representation framework is introduced which is generated by a compiler and linker that rely on a set of domain-specific resources that transform corpus data into speech units. Operations on this framework are then presented along with a comparison between a relational and object-oriented model of identical speech data. The models described in this work are directly applicable to existing large speech corpora, and the methods developed here are tested against relational database methods. The object-oriented methods outperform the relational methods for typical linguistically relevant queries by about three orders of magnitude as measured by database search times. This improvement in simplicity of representation and search speed is crucial for the utilisation of large multi-lingual corpora in basic research on the detailed properties of speech, especially in relation to contextual variation.reviewe

    Disentangling the molecular landscape of genetic variation of neurodevelopmental and speech disorders

    Get PDF
    corecore