On the application of graph neural networks for indoor positioning systems.

Due to the inability of GPS (or other GNSS methods) to provide satisfactory precision for the indoor location scenario, indoor positioning systems resort to other signals already available on site, typically Wi-Fi given its ubiquity. However, instead of relying on an error-prone propagation model as in ranging methods, the popular fingerprinting positioning technique considers a more direct data-driven approach to the problem. First of all, the area of interest is divided into zones, and then a machine learning algorithm is trained to map, for instance, power measurements (RSSI) from APs to the localization zone, thus effectively turning the problem into a classification one. However, although the positioning problem is a geometrical one, virtually all methods proposed in the literature disregard the underlying structure of the data, using generic machine learning algorithms. In this chapter we consider instead a graph-based learning method, Graph Neural Networks, a paradigm that has emerged in the last few years and that constitutes the state of the art for several problems. After presenting the pertinent theoretical background, we discuss two possibilities to construct the underlying graph for the positioning problem. We then perform a thorough evaluation of both possibilities and compare it with some of the most popular machine learning alternatives. The main conclusion is that these graph-based methods obtain systematically better results, particularly with regard to practical aspects (e.g., gracefully tolerating faulty APs), which makes them a serious candidate to consider when deploying positioning systems

On the application of graph neural networks for indoor positioning systems

Due to the inability of GPS (Global Positioning System) or other GNSS (Global Navigation Satellite System) methods to provide satisfactory precision for the indoor localization scenario, indoor positioning systems resort to other signals already available on-site, typically Wi-Fi given its ubiquity. However, instead of relying on an error-prone propagation model as in ranging methods, the popular fingerprinting positioning technique considers a more direct data-driven approach to the problem. First of all, the area of interest is divided into zones, and then a machine learning algorithm is trained to map between, for instance, power measurements from Access Points (APs) to the localization zone, thus effectively turning the problem into a classification one. However, although the positioning problem is a geometrical one, virtually all methods proposed in the literature disregard the underlying structure of the data, using generic machine learning algorithms. In this work we consider instead a graph-based learning method, Graph Neural Networks, a paradigm that has emerged in the last few years and constitutes the state-of-the-art for several problems. After presenting the pertinent theoretical background, we discuss two possibilities to construct the underlying graph for the positioning problem. We then perform a thorough evaluation of both possibilities, and compare it with some of the most popular machine learning alternatives. The main conclusion is that these graph-based methods obtain systematically better results, particularly with regards to practical aspects (e.g. gracefully tolerating faulty APs), which makes them a serious candidate to consider when deploying positioning systems.Debido a la incapacidad del GPS (Global Positioning System o Sistema de Posicionamiento Global) o de otros métodos de navegación por satélite (GNSS por sus siglas en inglés) de proporcionar un posicionamiento en espacios interiores con suficiente precisión, se suele recurrir a otras señales ya disponibles en el lugar, típicamente Wi-Fi por su gran adopción. Existen diversas técnincas que utilizan la señal de Wi-Fi para realizar el posicionamiento modelando la propagación de la señal para alcanzar el objetivo. Sin embargo, debido a su alta complejidad, estos modelos de propagación son propensos a errores. Una alternativa que se popularizó es el posicionamiento en base a huellas (fingerprinting) que considera un enfoque basado en datos más directo al problema. El método consiste en dividir el área de interés en zonas y entrenar un algoritmo de aprendizaje automático para establecer una relación entre, por ejemplo, las mediciones de potencia de los puntos de acceso (Access Points o APs) y la zona de localización, convirtiéndose así en un problema de clasificación. Si bien el problema de posicionamiento es en última instancia un problema geométrico, prácticamente todos los métodos propuestos en la literatura ignoran la estructura subyacente de los datos, utilizando para su resolución algoritmos genéricos de aprendizaje automático. Este trabajo propone utilizar un método de aprendizaje basado en grafos (Graph Neural Networks o GNN), un paradigma que ha surgido en los últimos años y que constituye el estado del arte para varios problemas. Tras presentar el marco teórico pertinente, discutimos dos posibilidades para construir el grafo subyacente al problema de posicionamiento. A continuación realizamos una evaluación exhaustiva de ambas posibilidades y las comparamos con algunas de las alternativas más populares de aprendizaje automático. La principal conclusión es que estos métodos basados en grafos obtienen sistemáticamente mejores resultados, especialmente en lo que respecta a los aspectos prácticos (por ejemplo, tolerar fallas en APs), lo que los convierte en excelentes candidatos a considerar a la hora de diseñar e implementar sistemas de posicionamiento

Preliminary evidence about the effects of meditation on interoceptive sensitivity and social cognition

Background: Interoception refers to the conscious perception of body signals. Mindfulness is a meditation practice that encourages individuals to focus on their internal experiences such as bodily sensations, thoughts, and emotions. In this study, we selected a behavioral measure of interoceptive sensitivity (heartbeat detection task, HBD) to compare the effect of meditation practice on interoceptive sensitivity among long term practitioners (LTP), short term meditators (STM, subjects that completed a Mindfulness-Based Stress Reduction (MBSR) program) and controls (non-meditators). All participants were examined with a battery of different tasks including mood state, executive function and social cognition tests (emotion recognition, empathy and theory of mind). Findings: Compared to controls, both meditators’ groups showed lower levels of anxiety and depression, but no improvement in executive function or social cognition performance was observed (except for lower scores compared to controls only in the personal distress dimension of empathy). More importantly, meditators’ performance did not differ from that of nonmeditators regarding cardiac interoceptive sensitivity. Conclusion: Results suggest no influence of meditation practice in cardiac interoception and in most related social cognition measures. These negative results could be partially due to the fact that awareness of heartbeat sensations is not emphasized during mindfulness/vipassana meditation and may not be the best index of the awareness supported by the practice of meditation.Fil: Melloni, Margherita. Instituto de Neurologia Cognitiva. Laboratorio de Psicologia Experimental y Neurociencia; Argentina. Universidad Favaloro. Facultad de Medicina. Instituto de Neurociencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sedeño, Lucas. Instituto de Neurologia Cognitiva. Laboratorio de Psicologia Experimental y Neurociencia; Argentina. Universidad Favaloro. Facultad de Medicina. Instituto de Neurociencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Couto, Juan Blas Marcos. Instituto de Neurologia Cognitiva. Laboratorio de Psicologia Experimental y Neurociencia; Argentina. Universidad Favaloro. Facultad de Medicina. Instituto de Neurociencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Reynoso, Martín. Universidad Favaloro. Facultad de Medicina. Instituto de Neurociencias; Argentina. Instituto de Neurologia Cognitiva. Laboratorio de Psicologia Experimental y Neurociencia; ArgentinaFil: Gelormini Lezama, Carlos. Instituto de Neurologia Cognitiva. Laboratorio de Psicologia Experimental y Neurociencia; Argentina. Universidad Favaloro. Facultad de Medicina. Instituto de Neurociencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Favaloro, Roberto. Universidad Favaloro. Facultad de Medicina. Instituto de Neurociencias; Argentina. Instituto de Neurologia Cognitiva. Laboratorio de Psicologia Experimental y Neurociencia; ArgentinaFil: Canales Johnson, Andres. Universidad Diego Portales; ChileFil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina. Universidad Torcuato Di Tella; ArgentinaFil: Manes, Facundo Francisco. Instituto de Neurologia Cognitiva. Laboratorio de Psicologia Experimental y Neurociencia; Argentina. Universidad Favaloro. Facultad de Medicina. Instituto de Neurociencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ibanez Barassi, Agustin Mariano. Universidad Favaloro. Facultad de Medicina. Instituto de Neurociencias; Argentina. Instituto de Neurologia Cognitiva. Laboratorio de Psicologia Experimental y Neurociencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Erratum to: The Overt Pronoun Constraint Across Three Dialects of Spanish

The overt pronoun constraint (OPC) states that, in null subject languages, overt pronoun subjects of embedded clauses cannot be bound by wh- or quantifier antecedents. Through the administration of two written questionnaires, we examined the OPC in 246 monolingual native speakers of three dialects of Spanish, spoken in Barranquilla (Colombia), Santiago (Chile), and Buenos Aires (Argentina). We tested separately the predictions that overt pronouns cannot be bound by wh- antecedents (Experiment 1) and that they cannot be bound by quantifier antecedents (Experiment 2). We found that the OPC was not operative in any of these dialects. In Experiment 1, the percentage of bound answers was approximately the same as the percentage of anaphoric answers. In Experiment 2, the percentage of bound answers was significantly higher than the percentage of anaphoric answers. Implications both for theories of pronoun resolution in null subject languages and for theories of first and second language acquisition are discussed.The original version of this article unfortunately contained an error in the author affiliation. The second affiliation was submitted and published incorrectly. The affiliation is corrected with this erratum.ErratumThe online version of the original article can be found under doi: http://dx.doi.org/10.1007/s10936-016-9426-

N400 ERPs for actions: Building meaning in context

Converging neuroscientific evidence suggests the existence of close links between language and sensorimotor cognition. Accordingly, during the comprehension of meaningful actions, our brain would recruit semantic-related operations similar to those associated with the processing of language information. Consistent with this view, electrophysiological findings show that the N400 component, traditionally linked to the semantic processing of linguistic material, can also be elicited by action-related material. This review outlines recent data from N400 studies that examine the understanding of action events. We focus on three specific domains, including everyday action comprehension, co-speech gesture integration, and the semantics involved in motor planning and execution. Based on the reviewed findings, we suggest that both negativities (the N400 and the action-N400) reflect a common neurocognitive mechanism involved in the construction of meaning through the expectancies created by previous experiences and current contextual information. To shed light on how this process is instantiated in the brain, a testable contextual fronto-temporo-parietal model is proposed.Fil: Amoruso, Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Gelormini Lezama, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Aboitiz, Francisco. Pontificia Universidad Católica de Chile; ChileFil: Alvarez Gonzalez, Miguel Angel. Universidad de La Habana; CubaFil: Manes, Facundo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Cardona Londoño, Juan Felipe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Ibáñez Barassi, Agustín Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; Argentina. Universidad Diego Portales; Chil

Action-verb processing in Parkinson’s disease: new pathways for motor–language coupling

Recent studies suggest that action-verb processing is particularly affected in early stage Parkinson´s disease (PD), highlighting the potential role of subcortical areas in language processing and in the semantic integration of actions. However, this disorder-related language impairment is frequently unrecognized by clinicians and often remains untreated. Early detection of action-language processing deficits could be critical for diagnosing and developing treatment strategies for PD. In this article, we review how action-verb processing is affected in PD and propose a model in which multiple and parallel frontotemporal circuits between the cortex and the basal ganglia provide the anatomic substrate for supporting action-language processing. We hypothesize that contextual coupling of action-language networks are partially dependent on cortical-subcortical integration, and not only on somatotopic motor cortical organization or in a mirror neuron system. This hypothesis is supported by both experimental and clinical evidence. Then, we identify further research steps that would help to determine the reliability of action-language impairments as an early marker of PD. Finally, theoretical implications for clinical assessment and for models of action-language interaction (action-perception cycle theories, mirror system models of language, and embodied cognition approaches to language) are discussed.Fil: Cardona Londoño, Juan Felipe. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Universidad Favaloro; ArgentinaFil: Gershanik, Oscar Samuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Universidad Favaloro; ArgentinaFil: Gelormini Lezama, Carlos. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; ArgentinaFil: Lee Houck, Alexander. University of Tennessee; Estados UnidosFil: Cardona, Sebastián. Universidad de Caldas; ColombiaFil: Kargieman, Lucila. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; ArgentinaFil: Trujillo, Natalia. Universidad de Antioquía; ColombiaFil: Arévalo, Analía. Universidad de la República; UruguayFil: Amoruso, Lucía. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Universidad Favaloro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Manes, Facundo Francisco. Universidad Favaloro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; ArgentinaFil: Ibáñez Barassi, Agustín Mariano. Instituto de Neurología Cognitiva. Laboratorio de Psicología Experimental y Neurociencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; Argentin

How embodied is action language? Neurological evidence from motor diseases

Although motor-language coupling is now being extensively studied, its underlying mechanisms are not fully understood. In this sense, a crucial opposition has emerged between the non-representational and the representational views of embodiment. The former posits that action language is grounded on the non-brain motor system directly engaged by musculoskeletal activity – i.e., peripheral involvement of ongoing actions. Conversely, the latter proposes that such grounding is afforded by the brain’s motor system – i.e., activation of neural areas representing motor action. We addressed this controversy through the action-sentence compatibility effect (ACE) paradigm, which induces a contextual coupling of motor actions and verbal processing. ACEs were measured in three patient groups – early Parkinson’s disease (EPD), neuromyelitis optica (NMO), and acute transverse myelitis (ATM) patients – as well as their respective healthy controls. NMO and ATM constitute models of injury to non-brain motor areas and the peripheral motor system, whereas EPD provides a model of brain motor system impairment. In our study, EPD patients exhibited impaired ACE and verbal processing relative to healthy participants, NMO, and ATM patients. These results indicate that the processing of action-related words is mainly subserved by a cortico-subcortical motor network system, thus supporting a brain-based embodied view on action language. More generally, our findings are consistent with contemporary perspectives for which action/verb processing depends on distributed brain networks supporting context-sensitive motor-language coupling.Fil: Cardona Londoño, Juan Felipe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Tecnológica de Pereira; ColombiaFil: Kargieman, Lucila. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; ArgentinaFil: Sinay, Vladimiro. Instituto de Neurología Cognitiva; ArgentinaFil: Gershanik, Oscar Samuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; ArgentinaFil: Gelormini Lezama, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; ArgentinaFil: Amoruso, Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; ArgentinaFil: Roca, Martin. Instituto de Neurología Cognitiva; ArgentinaFil: Pineda, David. Universidad de Antioquia; ColombiaFil: Trujillo Arias, Natalia. Universidad de Antioquia; ColombiaFil: Michon, Maeva. Universidad Diego Portales; ChileFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; ArgentinaFil: Szenkman, Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; ArgentinaFil: Bekinschtein, Tristán Andrés. Medical Research Council; Reino UnidoFil: Manes, Facundo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; Argentina. Australian Research Council; AustraliaFil: Ibáñez Barassi, Agustín Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Neurología Cognitiva; Argentina. Universidad Diego Portales; Chile. Universidad Autónoma del Caribe. Barranquilla; Colombi