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La política educativa franquista tuvo como uno de sus principales objetivos la formación de la clase obrera, por lo que una de las principales instituciones educativas creadas en la época fueron las Universidades Laborales. Este artículo se centra en analizar las finalidades que perseguían estas macroinstituciones al formar los futuros obreros españoles, empleando para su estudio diversas fuentes primarias —documentales, archivísticas, gráficas y jurídicas—. Las conclusiones señalan que los objetivos de las Universidades Laborales fueron, por un lado, la formación profesional especializada y, por el otro lado, su adoctrinamiento en los principios ideológicos propugnados por el régimen. One of the main objectives of Franco’s educational policy was the training of the working class, which is why one of the principal educational institutions created during that period were the Universidades Laborales. This article is focused on analyzing the ends sought by these macroinstitutions in preparing the future workers of Spain, and it makes use of a variety of primary sources —documentary, archival, graphic and juridical— in order to study the subject. The conclusions indicate that the objectives of the Universidades Laborales were, on the one hand, specialized professional training and, on the other hand, their indoctrination in the ideological principles advocated by the regime. A política educativa franquista teve como um de seus principais objetivos a formação da classe operária, razão pela qual uma das principais instituições educativas criadas na época foram as universidades trabalhistas. Este artigo se centraliza em analisar as finalidades que essas macroinstituições perseguiam ao formar os futuros operários espanhóis, empregando para seu estudo diversas fontes primárias —documentais, de arquivo, gráficas e jurídicas—. As conclusões indicam que os objetivos das universidades trabalhistas foram, por um lado, a formação profissional especializada e, por outro, seu doutrinamento nos princípios ideológicos defendidos pelo regime.

The received view of computation is methodologically bifurcated: it offers different accounts of computation in the mathematical and physical cases. But little in the way of argument has been given for this approach. This paper rectifies the situation by arguing that the alternative, a unified account, is untenable. Furthermore, once these issues are brought into sharper relief we can see that work remains to be done to illuminate the relationship between physical and mathematical computation.

When reading a text describing an everyday activity, comprehenders build a model of the situation described that includes prior knowledge of the entities, locations, and sequences of actions that typically occur within the event. Previous work has demonstrated that such knowledge guides the processing of incoming information by making event boundaries more or less expected. In the present ERP study, we investigated whether comprehenders’ expectations about event boundaries are influenced by how elaborately common events are described in the context. Participants read short stories in which a common activity (e.g., washing the dishes) was described either in brief or in an elaborate manner. The final sentence contained a target word referring to a more predictable action marking a fine event boundary (e.g., drying) or a less predictable action, marking a coarse event boundary (e.g., jogging). The results revealed a larger N400 effect for coarse event boundaries compared to fine event boundaries, but no interaction with description length. Between 600 and 1000 ms, however, elaborate contexts elicited a larger frontal positivity compared to brief contexts. This effect was largely driven by less predictable targets, marking coarse event boundaries. We interpret the P600 effect as indexing the updating of the situation model at event boundaries, consistent with Event Segmentation Theory (EST). The updating process is more demanding with coarse event boundaries, which presumably require the construction of a new situation model. Electronic supplementary material The online version of this article (10.3758/s13421-017-0766-4) contains supplementary material, which is available to authorized users.

Abstract Ten years ago, researchers using event‐related brain potentials (ERPs) to study language comprehension were puzzled by what looked like a Semantic Illusion: Semantically anomalous, but structurally well‐formed sentences did not affect the N400 component—traditionally taken to reflect semantic integration—but instead produced a P600 effect, which is generally linked to syntactic processing. This finding led to a considerable amount of debate, and a number of complex processing models have been proposed as an explanation. What these models have in common is that they postulate two or more separate processing streams, in order to reconcile the Semantic Illusion and other semantically induced P600 effects with the traditional interpretations of the N400 and the P600. Recently, however, these multi‐stream models have been called into question, and a simpler single‐stream model has been proposed. According to this alternative model, the N400 component reflects the retrieval of word meaning from semantic memory, and the P600 component indexes the integration of this meaning into the unfolding utterance interpretation. In the present paper, we provide support for this “Retrieval–Integration (RI)” account by instantiating it as a neurocomputational model. This neurocomputational model is the first to successfully simulate the N400 and P600 amplitude in language comprehension, and simulations with this model provide a proof of concept of the single‐stream RI account of semantically induced patterns of N400 and P600 modulations.

We investigate the underexplored question of when speakers make use of the omission phenomenon verb phrase ellipsis (VPE) in English given that the full form is also available to them. We base the interpretation of our results on the well-established information-theoretic Uniform Information Density (UID) hypothesis: Speakers tend to distribute processing effort uniformly across utterances and avoid regions of low information by omitting redundant material through, e.g., VPE. We investigate the length of the omittable VP and its predictability in context as sources of redundancy which lead to larger or deeper regions of low information and an increased pressure to use ellipsis. We use both naturalness rating and self-paced reading studies in order to link naturalness patterns to potential processing difficulties. For the length effects our rating and reading results support a UID account. Surprisingly, we do not find an effect of the context on the naturalness and the processing of VPE. We suggest that our manipulation might have been too weak or not effective to evidence such an effect.

In this article, we argue for a change in how professionals in speech-language pathology think and talk about dialect diversity in the US and elsewhere. Our recommendation is evidence-based and reflects a change we have made to better serve children and advocate for the field of communication disorders. The change involves replacing the phrase dialect vs. disorder with disorder within dialect. While this change in wording may seem superficial, it generates dramatically different types of conversations a professional can have with others about childhood language impairment across different dialects of English.

<span>The work presents new theoretical equipment for the representation of natural languages (NL) in computers. Linguistics: morphology, semantics, and syntax are also presented as components of subtle computer science that form. A structure and an integrated data system. The presented useful theory of language is a new method to learn the language by separating the fields of semantics and syntax.</span>

The COVID-19 pandemic become a critical challenge for the higher education sector worldwide. Under such a circumstance, the exploration of the capacity of this sector to adapt to such a state of uncertainty has become more of huge importance. In this chapter, we critically reflect on the Cape Verdean teaching experience during the early COVID-19 lockdown in this country. This is an exploratory case study based on a qualitative approach with an aim to reflect about new practices of teaching under a pandemic emergency. Based on the teaching experience of teaching in Santiago University, we explain how this university has changed from a face-to-face to an online teaching system and stress the challenges and opportunities that appear from this transition process. This chapter concludes that this strategy has become an opportunity to the university since it consistently raised the number of international students cooperating with them and also that the more adaptive and resilient approaches to online teaching were also a success. info:eu-repo/semantics/publishedVersion

Natural language semantics has recently sought to combine the complementary strengths of formal and distributional approaches to meaning. More specifically, proposals have been put forward to augment formal semantic machinery with distributional meaning representations, thereby introducing the notion of semantic similarity into formal semantics, or to define distributional systems that aim to incorporate formal notions such as entailment and compositionality. However, given the fundamentally different 'representational currency' underlying formal and distributional approaches - models of the world versus linguistic co-occurrence - their unification has proven extremely difficult. Here, we define a Distributional Formal Semantics that integrates distributionality into a formal semantic system on the level of formal models. This approach offers probabilistic, distributed meaning representations that are also inherently compositional, and that naturally capture fundamental semantic notions such as quantification and entailment. Furthermore, we show how the probabilistic nature of these representations allows for probabilistic inference, and how the information-theoretic notion of "information" (measured in terms of Entropy and Surprisal) naturally follows from it. Finally, we illustrate how meaning representations can be derived incrementally from linguistic input using a recurrent neural network model, and how the resultant incremental semantic construction procedure intuitively captures key semantic phenomena, including negation, presupposition, and anaphoricity. To appear in: Information and Computation (WoLLIC 2019 Special Issue)

Despite the ubiquity of knowledge attribution in human social cognition, its associated neural and cognitive mechanisms are poorly documented. A wealth of converging evidence in cognitive neuroscience has identified independent perspective-taking and inhibitory processes for belief attribution, but the extent to which these processes are shared by knowledge attribution isn't presently understood. Here, we present the findings of an EEG study designed to directly address this shortcoming. These findings suggest that belief attribution is not a component process in knowledge attribution, contra a standard attitude taken by philosophers. Instead, observed differences in P3b amplitude indicate that knowledge attribution doesn't recruit the strong self-perspective inhibition characteristic of belief attribution. However, both belief and knowledge attribution were observed to display a late slow wave widely associated with mental state attribution, indicating that knowledge attribution also shares in more general processing of others' mental states. These results provide a new perspective both on how we think about knowledge attribution, as well as Theory of Mind processes generally.