When an adult claims he cannot sleep without his teddy bear, people tend to react surprised. Language interpretation is, thus, influenced by social context, such as who the speaker is. The present study reveals inter-individual differences in brain reactivity to social aspects of language. Whereas women showed brain reactivity when stereotype-based inferences about a speaker conflicted with the content of the message, men did not. This sex difference in social information processing can be explained by a specific cognitive trait, one's ability to empathize. Individuals who empathize to a greater degree revealed larger N400 effects (as well as a larger increase in γ-band power) to socially relevant information. These results indicate that individuals with high-empathizing skills are able to rapidly integrate information about the speaker with the content of the message, as they make use of voice-based inferences about the speaker to process language in a top-down manner. Alternatively, individuals with lower empathizing skills did not use information about social stereotypes in implicit sentence comprehension, but rather took a more bottom-up approach to the processing of these social pragmatic sentences.
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The current electroencephalography study investigated the relationship between the motor and (language) comprehension systems by simultaneously measuring mu and N400 effects. Specifically, we examined whether the pattern of motor activation elicited by verbs depends on the larger sentential context. A robust N400 congruence effect confirmed the contextual manipulation of action plausibility, a form of semantic congruency. Importantly, this study showed that: (1) Action verbs elicited more mu power decrease than non-action verbs when sentences described plausible actions. Action verbs thus elicited more motor activation than non-action verbs. (2) In contrast, when sentences described implausible actions, mu activity was present but the difference between the verb types was not observed. The increased processing associated with a larger N400 thus coincided with mu activity in sentences describing implausible actions. Altogether, context-dependent motor activation appears to play a functional role in deriving context-sensitive meaning.
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In an event related potential (ERP) experiment using written language materials only, we investigated a potential modulation of the N400 by the modality switch effect. The modality switch effect occurs when a first sentence, describing a fact grounded in one modality, is followed by a second sentence describing a second fact grounded in a different modality. For example, "A cellar is dark" (visual), was preceded by either another visual property "Ham is pink" or by a tactile property "A mitten is soft." We also investigated whether the modality switch effect occurs for false sentences ("A cellar is light"). We found that, for true sentences, the ERP at the critical word "dark" elicited a significantly greater frontal, early N400-like effect (270-370 ms) when there was a modality mismatch than when there was a modality-match. This pattern was not found for the critical word "light" in false sentences. Results similar to the frontal negativity were obtained in a late time window (500-700 ms). The obtained ERP effect is similar to one previously obtained for pictures. We conclude that in this paradigm we obtained fast access to conceptual properties for modality-matched pairs, which leads to embodiment effects similar to those previously obtained with pictorial stimuli.
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In this study, we used electroencephalography to investigate the influence of discourse-level semantic coherence on electrophysiological signatures of local sentence-level processing. Participants read groups of four sentences that could either form coherent stories or were semantically unrelated. For semantically coherent discourses compared to incoherent ones, the N400 was smaller at sentences 2–4, while the visual N1 was larger at the third and fourth sentences. Oscillatory activity in the beta frequency range (13–21 Hz) was higher for coherent discourses. We relate the N400 effect to a disruption of local sentence-level semantic processing when sentences are unrelated. Our beta findings can be tentatively related to disruption of local sentence-level syntactic processing, but it cannot be fully ruled out that they are instead (or also) related to disrupted local sentence-level semantic processing. We conclude that manipulating discourse-level semantic coherence does have an effect on oscillatory power related to local sentence-level processing.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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Oscillatory neural dynamics have been steadily receiving more attention as a robust and temporally precise signature of network activity related to language processing. We have recently proposed that oscillatory dynamics in the beta and gamma frequency ranges measured during sentence-level comprehension might be best explained from a predictive coding perspective. Under our proposal we related beta oscillations to both the maintenance/change of the neural network configuration responsible for the construction and representation of sentence-level meaning, and to top-down predictions about upcoming linguistic input based on that sentence-level meaning. Here we zoom in on these particular aspects of our proposal, and discuss both old and new supporting evidence. Finally, we present some preliminary magnetoencephalography data from an experiment comparing Dutch subject- and object-relative clauses that was specifically designed to test our predictive coding framework. Initial results support the first of the two suggested roles for beta oscillations in sentence-level language comprehension.
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There is a growing literature investigating the relationship between oscillatory neural dynamics measured using electroencephalography (EEG) and/or magnetoencephalography (MEG), and sentence-level language comprehension. Recent proposals have suggested a strong link between predictive coding accounts of the hierarchical flow of information in the brain, and oscillatory neural dynamics in the beta and gamma frequency ranges. We propose that findings relating beta and gamma oscillations to sentence-level language comprehension might be unified under such a predictive coding account. Our suggestion is that oscillatory activity in the beta frequency range may reflect both the active maintenance of the current network configuration responsible for representing the sentence-level meaning under construction, and the top-down propagation of predictions to hierarchically lower processing levels based on that representation. In addition, we suggest that oscillatory activity in the low and middle gamma range reflect the matching of top-down predictions with bottom-up linguistic input, while evoked high gamma might reflect the propagation of bottom-up prediction errors to higher levels of the processing hierarchy. We also discuss some of the implications of this predictive coding framework, and we outline ideas for how these might be tested experimentally.
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To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
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This article draws on Robinson, McNeill and Maruna’s argument (2012) about the adaptability of community sanctions and measures, observed through four distinctive penal narratives, in order to shed light on the regional development of community service in Wroclaw, Poland. While the managerial adaptation of community sanctions is underpinned by an inter-agency cooperation to fulfil the goals of the system, the contemporary rehabilitation iteration has become a toolkit of measures predominantly phrased around risk management, the reparative discourse seeks various means to repair harm, and the punitive orientation represent the turn to desert-based and populist sentencing frameworks. In this article, the first three are reflected upon along with the emerging, restorative adaptation of community sanctions. The last one is added to expand on the findings of previous research, which suggests the viability of the restorative orientation for community service in Poland (Matczak, 2018). A brief discussion of how punishment, probation and restorative justice can be reconciled is followed by the introduction of Polish Probation and the role of probation officers in delivering community service in Poland. Although the penal narratives are visible in the Wrocław model to different degrees and in various combinations, more research is required to evaluate the viability of a progressive orientation to punishment during a gradual optimisation of community orders. Originally published: Anna Matczak, The penal narratives of community sentence and the role of probation: The case of the Wrocław model of community service, European journal of probation (Vol. 13 nr. 1) pp. 72-88. Copyright © 2021year (The Author). DOI: 10.1177/2066220320976105
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