Detecting order-disorder transitions in discourse : implications for schizophrenia

Abstract Several psychiatric and neurological conditions affect the semantic organization and content of a patient's speech. Specifically, the discourse of patients with schizophrenia is frequently characterized as lacking coherence. The evaluation of disturbances in discourse is often used in diagnosis and in assessing treatment efficacy, and is an important factor in prognosis. Measuring these deviations, such as “loss of meaning” and incoherence, is difficult and requires substantial human effort. Computational procedures can be employed to characterize the nature of the anomalies in discourse. We present a set of new tools derived from network theory and information science that may assist in empirical and clinical studies of communication patterns in patients, and provide the foundation for future automatic procedures. First we review information science and complex network approaches to measuring semantic coherence, and then we introduce a representation of discourse that allows for the computation of measures of disorganization. Finally we apply these tools to speech transcriptions from patients and a healthy participant, illustrating the implications and potential of this novel framework

Lexiland: A Tablet-based Universal Screener for Reading Difficulties in the School Context

First published online January 27, 2022Massive and timely screening of the student population for early signs of reading difficulties is needed to implement timely effective remediation of these difficulties. However, traditional approaches are costly and hard to apply. Here, we present Lexiland, a tablet-based reading assessment tool for kindergarten and primary school children developed to be applied in school settings with minimal personnel intervention. Following a story line, players help a character of the game perform several tasks that measure different predictors of reading outcomes. Most of the tasks that usually involve a verbal response were switched to receptive tasks to demand a touch-screen response only. The tablet application was administered to a sample of N = 616 5-yo kindergarten children and to a sub-sample of these children twice during the following two years (First and Second Grades). Applying logistic regression and cross-validation, we selected a reduced subset of tasks that can predict with great sensitivity and specificity, whether a five-year-old child will have reading difficulties by the end of first grade (sensitivity 90% and specificity 76%) and two years later (sensitivity 90% and specificity 61%). Importantly, Lexiland is a scalable tool to implement universal screening, given the increasing availability of devices able to run android and iOS applications.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by ANII FSED_2_2015_1_120741 and ANII FSED_2_2016_1_131230 grants to Juan Valle-Lisboa and Manuel Carreiras. Camila Zugarramurdi received a PhD Scholarship from Fundación Carolin

Deep networks as cognitive models: the case of reading in different orthographies

Although Artificial Neural Networks were born as Neurocognitive models, the architectures used nowadays in AI are not conceived as models of the brain. In the last few years, Deep networks have been fitted to brain activity to use them as neural models, but their use as cognitive models is less prevalent. Here we use a transformer model complemented with a simplified visual input to model reading acquisition. First, we trained the network to recognize the speech input. After that, we use letter sounds and letter visual representations to train the network to output the correct letters. We apply this model to our empirical previous results, comparing learning in a transparent (Spanish) and an opaque (French) orthography as in transparent orthographies, phonological awareness is much less important than in opaque orthographies as a predictor of reading. We show that the difficulty of training correlates with opaqueness, and interpret the results

Relative meaning frequencies for 578 homonyms in two Spanish dialects: A cross-linguistic extension of the English eDom norms

Published online: 15 August 2015Relative meaning frequency is a critical factor to consider in studies of semantic ambiguity. In this work, we examined how this measure may change across the European and Rioplatense dialects of Spanish, as well as how the overall distributional properties differ between Spanish and English, using a computer-assisted norming approach based on dictionary definitions (Armstrong, Tokowicz, & Plaut, 2012). The results showed that the two dialects differ considerably in terms of the relative meaning frequencies of their constituent homonyms, and that the overall distributions of relative frequencies vary considerably across languages, aswell. These results highlight the need for localized norms to design powerful studies of semantic ambiguity and suggest that dialectal differences may be responsible for some discrepant effects related to homonymy. In quantifying the reliability of the norms, we also established that as few as seven ratings are needed to converge on a highly stable set of ratings. This approach is therefore a very practical means of acquiring essential data in studies of semantic ambiguity, relative to past approaches, such as those based on the classification of free associates. The norms also present new possibilities for studying semantic ambiguity effects within and between populations who speak one or more languages. The norms and associated software are available for download at http://edom.cnbc.cmu.edu/ or http://www.bcbl.eu/ databases/edom/.B.C.A. was supported by a Marie Curie International Incoming Fellowship (IIF) (No. PIIF-GA-2013-689 627784). C.Z., A.C., and J.V.L. have been supported by CSIC-UDELAR, and CZ was supported by ANII

Dynamic searching in the brain

Cognitive functions rely on the extensive use of information stored in the brain, and the searching for the relevant information for solving some problem is a very complex task. Human cognition largely uses biological search engines, and we assume that to study cognitive function we need to understand the way these brain search engines work. The approach we favor is to study multi-modular network models, able to solve particular problems that involve searching for information. The building blocks of these multimodular networks are the context dependent memory models we have been using for almost 20 years. These models work by associating an output to the Kronecker product of an input and a context. Input, context and output are vectors that represent cognitive variables. Our models constitute a natural extension of the traditional linear associator. We show that coding the information in vectors that are processed through association matrices, allows for a direct contact between these memory models and some procedures that are now classical in the Information Retrieval field. One essential feature of context-dependent models is that they are based on the thematic packing of information, whereby each context points to a particular set of related concepts. The thematic packing can be extended to multimodular networks involving input-output contexts, in order to accomplish more complex tasks. Contexts act as passwords that elicit the appropriate memory to deal with a query. We also show toy versions of several ‘neuromimetic’ devices that solve cognitive tasks as diverse as decision making or word sense disambiguation. The functioning of these multimodular networks can be described as dynamical systems at the level of cognitive variables