Juan Carlos Valle Lisboa

When participants are engaged with auditory narratives, physiological and neural signals exhibit temporal correlations between subjects. The intersubject correlation (ISC) increases when attention is directed to the stories, suggesting that shared neural and bodily dynamics arise from a similar processing of the narratives. In this study, we investigated whether the ISC of the EEG elicited by auditory narratives is driven by low-level acoustic (envelope, spectrogram) and/or higher-level linguistic information (word onset, word surprisal) in two groups of healthy participants during passive, attentive and distracted listening. We use temporal response functions (TRFs) for acoustic, and linguistic features to assess the contribution of each feature to the ISC, measured using correlated component analysis (CorrCA). TRFs derived for acoustic features explained a larger fraction of variance in the EEG than linguistic features and were the main contributors to the ISC. The attention-related increase in ISC was driven by all features. Importantly, word surprisal had an effect on ISC only during active story engagement, with timing and scalp distribution consistent with language processing. Notably, the linear responses captured by TRFs only explained a small amount of the overall ISC, suggesting that ISC is largely driven by nonlinear responses to the narratives. Identifying the factors that drive these common neural responses, is clinically relevant for interpreting EEG ISC exhibited in unresponsive patients. We propose that the combined use of ISC and TRFs has the potential to provide meaningful markers of language processing in patients with disorders of consciousness, and we suggest practical recommendations for implementing these approaches in clinical settings.

Para construir un programa informático que resuelva un problema, este debe modelarse técnicamente. Previamente a dicho modelado técnico, se debe elaborar un modelo cognitivo, es decir, identificar y relacionar los principales elementos constitutivos del problema que guíen la construcción de la solución y representen su semántica. Este trabajo de tipo exploratorio, analiza las características del modelado cognitivo realizado por estudiantes de secundaria, el cual se expresa mediante la escritura en lenguaje natural. Para analizar el modelo cognitivo de los estudiantes, se emplearon herramientas estadísticas, así como algoritmos de inteligencia artificial que muestran las relaciones semánticas del discurso. Concluimos que aquellos discursos que hacen referencia a los elementos estructurantes del problema de forma coherente se asocian con programas viables; modelos que también muestran relaciones semánticas complejas. Por otro lado, las construcciones lingüísticas empobrecidas, que no hacen referencia a elementos disciplinarios fuertes y por tanto representan un modelo pobre, generalmente se asocian soluciones deficientes.

When participants are engaged with auditory narratives, physiological and neural signals exhibit temporal correlations between subjects. The intersubject correlation (ISC) increases when attention is directed to the stories, suggesting that shared neural and bodily dynamics arise from a similar processing of the narratives. Identifying the factors that drive these common responses is clinically relevant for interpreting EEG ISC exhibited in unresponsive patients. In this study, we investigated whether the ISC of the EEG elicited by auditory narratives is driven by low-level acoustic (envelope, spectrogram) and/or higher-level linguistic information (word onset, word surprisal) in two groups of healthy participants during passive, attentive and distracted listening. We use temporal response functions (TRFs) for acoustic, and linguistic features to assess the contribution of each feature to the ISC, measured using correlated component analysis (CorrCA). TRFs derived for acoustic features explained a larger fraction of variance in the EEG than linguistic features and were the main contributors to the ISC. The attention-related increase in ISC was driven by all features. Importantly, word surprisal had an effect on ISC only during active story engagement, with timing and scalp distribution consistent with language processing. Notably, the linear responses captured by TRFs only explained a small amount of the overall ISC, suggesting that ISC is largely driven by nonlinear responses to the narratives. We propose that the combined use of ISC and TRFs has the potential to provide meaningful markers of language processing in patients with disorders of consciousness, and we suggest practical recommendations for their implementation.

A significant portion of Brazilian students fail to meet basic reading standards. The 2017 National Common Curricular Base (NCCB) marked a pivotal effort by the government to prioritize recognizing phonics in early reading instruction. However, the NCCB lacked clear directives for implementation, resulting in limited adoption in classrooms where teachers were accustomed to constructivist approaches to literacy. To address this shortcoming, we adapted the open‐source Kalulu phonics method to Brazilian Portuguese and tested its effectiveness as a supplementary learning workstation during classroom reading instruction. The Kalulu method is available at kalulu.excellolab.org . 184 Brazilian children (97 girls) from 1st year participated, aged 6 to 8 years ( M = 6.59, SD = 0.50). Five schools were assigned randomly to the Kalulu intervention group or a “business‐as‐usual” control group, and evaluated two times in 2022. Kalulu group showed significant progress, reading an average of 4.9 more words per minute compared to control classes. Improvements were also observed in phoneme verbal fluency and a memory task. Given the strong scientific support for phonics and our encouraging results, we urge Brazil's educational leaders, teachers, and parents to advocate for increased phonics instruction as part of early literacy methods. Explicit phonics instruction, like the Kalulu method, could provide essential help for overcoming the country's ongoing literacy challenges.

We introduce both conceptual and empirical findings arising from the amalgamation of a robotics cognitive architecture with an embedded physics simulator, aligning with the principles outlined in the intuitive physics literature. The employed robotic cognitive architecture, named CORTEX, leverages a highly efficient distributed working memory known as deep state representation. This working memory inherently encompasses a fundamental ontology, state persistency, geometric and logical relationships among elements, and tools for reading, updating, and reasoning about its contents. Our primary objective is to investigate the hypothesis that the integration of a physics simulator into the architecture streamlines the implementation of various functionalities that would otherwise necessitate extensive coding and debugging efforts. Furthermore, we categorize these enhanced functionalities into broad types based on the nature of the problems they address. These include addressing challenges related to occlusion, model-based perception, self-calibration, scene structural stability, and human activity interpretation. To demonstrate the outcomes of our experiments, we employ CoppeliaSim as the embedded simulator and both a Kinova Gen3 robotic arm and the Open-Manipulator-P as the real-world scenarios. Synchronization is maintained between the simulator and the stream of real events. Depending on the ongoing task, numerous queries are computed, and the results are projected into the working memory. Participating agents can then leverage this information to enhance overall performance.

The precision of cortical tracking of auditory rhythmic stimuli in low frequency ranges coding for prosodic (delta) and syllabic (theta) amplitude modulations in the speech signal has been proposed to contribute to the development of phonological processing and reading acquisition. The present study investigates the role of low frequency cortical tracking of non-verbal auditory stimuli in reading acquisition through a longitudinal design from before the onset of formal reading instruction until one year afterwards. At time one, 40 prereading children performed a passive listening task of amplitude modulated white noise presented in the delta (2 Hz) and theta (4 Hz) frequency bands, while their neural activity was recorded via EEG. At time two, at the end of first grade, children’s reading skills were assessed. Results show significant cortical tracking in prereading children at both frequencies, with larger responses for theta than for delta rate non-verbal auditory stimuli. Importantly, only pre-reding cortical tracking measures for delta rate stimuli predicted reading acquisition one year later. These findings underscore the role of early neural synchronization to delta rate rhythmic auditory stimuli to reading acquisition and support the potential important role of early prosodic processing for the development of future reading skills.

Explaining the foundation of cognitive abilities in the processing of information by neural systems has been in the beginnings of biophysics since McCulloch and Pitts pioneered work within the biophysics school of Chicago in the 1940s and the interdisciplinary cybernetists meetings in the 1950s, inseparable from the birth of computing and artificial intelligence. Since then, neural network models have traveled a long path, both in the biophysical and the computational disciplines. The biological, neurocomputational aspect reached its representational maturity with the Distributed Associative Memory models developed in the early 70 s. In this framework, the inclusion of signal-signal multiplication within neural network models was presented as a necessity to provide matrix associative memories with adaptive, context-sensitive associations, while greatly enhancing their computational capabilities. In this review, we show that several of the most successful neural network models use a form of multiplication of signals. We present several classical models that included such kind of multiplication and the computational reasons for the inclusion. We then turn to the different proposals about the possible biophysical implementation that underlies these computational capacities. We pinpoint the important ideas put forth by different theoretical models using a tensor product representation and show that these models endow memories with the context-dependent adaptive capabilities necessary to allow for evolutionary adaptation to changing and unpredictable environments. Finally, we show how the powerful abilities of contemporary computationally deep-learning models, inspired in neural networks, also depend on multiplications, and discuss some perspectives in view of the wide panorama unfolded. The computational relevance of multiplications calls for the development of new avenues of research that uncover the mechanisms our nervous system uses to achieve multiplication.

Large-scale word association datasets are both important tools used in psycholinguistics and used as models that capture meaning when considered as semanti

Reading acquisition is based on a set of preliteracy skills that lay the foundation for future reading abilities. Phonological awareness-the ability to identify and manipulate the sound units of oral language-has been reported to play a central role in reading acquisition. However, current evidence is mixed with respect to its universal contribution to reading acquisition across orthographies. This longitudinal study examines the development and contribution of phonological awareness to early reading skills in Spanish, a transparent orthography. The results of a comprehensive battery of phonological awareness skills in a large sample of children (Time 1 n = 616, 296 females, mean age 5.6, from middle to high socioeconomic backgrounds; Time 2 n = 397) with no reading experience at study onset suggest that the development of phonological awareness is delayed in Spanish. Furthermore, our results show that phonological awareness does not contribute to the prediction of reading acquisition above and beyond other preliteracy skills. Letter knowledge indexes children's ability to identify phonemes and thus takes a more central role in the prediction of early reading skills. Therefore, we underscore the need to thoughtfully address the distinctive features of the reading acquisition process across orthographies, which should be taken into account in models of reading and learning to read. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Massive 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.