Combining statistics: the role of phonotactics on cross-situational word learning

Abstract Language learners can rely on phonological and semantic information to learn novel words. Using a cross-situational word learning paradigm, we explored the role of phonotactic probabilities on word learning in ambiguous contexts. Brazilian-Portuguese speaking adults (N = 30) were exposed to two sets of word-object pairs. Words from one set of labels had slightly higher phonotactic probabilities than words from the other set. By tracking co-occurrences of words and objects, participants were able to learn word-object mappings similarly across both sets. Our findings contrast with studies showing a facilitative effect of phonotactic probability on word learning in non-ambiguous contexts.

Premio de Investigación SCHOT 2020: desarrollo y validación de un modelo multivariables de predicción de estadía hospitalaria en pacientes mayores de 65 años sometidos artroplastia total de cadera electiva en Chile utilizando aprendizaje de máquinas / 2020 SCHOT Research award: Development an a multivariable prediction model of hospital stay in elderly chilean patients undergoing elective total hip arthroplasty using machine learning

INTRODUCCIÓN La predicción de la estadía hospitalaria luego de una artroplastia total de cadera (ATC) electiva es crucial en la evaluación perioperatoria de los pacientes, con un rol determinante desde el punto de vista operacional y económico. Internacionalmente, se han empleado macrodatos (big data, en inglés) e inteligencia artificial para llevar a cabo evaluaciones pronósticas de este tipo. El objetivo del presente estudio es desarrollar y validar, con el empleo del aprendizaje de máquinas (machine learning, en inglés), una herramienta capaz de predecir la estadía hospitalaria de pacientes chilenos mayores de 65 años sometidos a ATC por artrosis. MATERIALES Y MÉTODOS Empleando los registros electrónicos de egresos hospitalarios anonimizados del Departamento de Estadísticas e Información de Salud (DEIS), se obtuvieron los datos de 8.970 egresos hospitalarios de pacientes sometidos a ATC por artrosis entre los años 2016 y 2018. En total, 15 variables disponibles en el DEIS, además del porcentaje de pobreza de la comuna de origen del paciente, fueron incluidos para predecir la probabilidad de que un paciente presentara una estadía acortada (< 3 días) o prolongada (> 3 días) luego de la cirugía. Utilizando técnicas de aprendizaje de máquinas, 8 algoritmos de predicción fueron entrenados con el 80% de la muestra. El 20% restante se empleó para validar las capacidades predictivas de los modelos creados a partir de los algoritmos. La métrica de optimización se evaluó y ordenó en un ranking utilizando el área bajo la curva de característica operativa del receptor (area under the receiver operating characteristic curve, AUC-ROC, en inglés), que corresponde a cuan bien un modelo puede distinguir entre dos grupos. RESULTADOS El algoritmo XGBoost obtuvo el mejor desempeño, con una AUC-ROC promedio de 0,86 (desviación estándar [DE]: 0,0087). En segundo lugar, observamos que el algoritmo lineal de máquina de vector de soporte (support vector machine, SVM, en inglés) obtuvo una AUC-ROC de 0,85 (DE: 0,0086). La importancia relativa de las variables explicativas demostró que la región de residencia, el servicio de salud, el establecimiento de salud donde se operó el paciente, y la modalidad de atención son las variables que más determinan el tiempo de estadía de un paciente. DISCUSIÓN El presente estudio desarrolló algoritmos de aprendizaje de máquinas basados en macrodatos chilenos de libre acceso, y logró desarrollar y validar una herramienta que demuestra una adecuada capacidad discriminatoria para predecir la probabilidad de estadía hospitalaria acortada versus prolongada en adultos mayores sometidos a ATC por artrosis. CONCLUSIÓN Los algoritmos creados a traves del empleo del aprendizaje de máquinas permiten predecir la estadía hospitalaria en pacientes chilenos operado de artroplastia total de cadera electiva

Introduction The prediction of the length of hospital stay after elective total hip arthroplasty (THA) is crucial in the perioperative evaluation of the patients, and it plays a decisive role from the operational and economic point of view. Internationally, big data and artificial intelligence have been used to perform prognostic evaluations of this type. The present study aims to develop and validate, through the use of artificial intelligence (machine learning), a tool capable of predicting the hospital stay of patients over 65 years of age undergoing THA for osteoarthritis. Material and Methods Using the electronic records of hospital discharges de-identified from the Department of Health Statistics and Information (Departamento de Estadísticas e Información de Salud, DEIS, in Spanish), the data of 8,970 hospital discharges of patients who had undergone THA for osteoarthritis between 2016 and 2018 were obtained. A total of 15 variables available in the DEIS registry, in addition to the poverty rate in the patient's borough of origin were included to predict the probability that a patient would have a shortened (< 3 days) or prolonged (> 3 days) stay after surgery. By using machine learning techniques, 8 prediction algorithms were trained with 80% of the sample. The remaining 20% was used to validate the predictive capabilities of the models created from the algorithms. The optimization metric was evaluated and ranked using the area under the receiver operating characteristic curve (AUC-ROC), which corresponds to how well a model can distinguish between two groups. Results The XGBoost algorithm had the best performance, with an average AUC-ROC of 0.86 (standard deviation [SD]: 0.0087). Secondly, we observed that the linear support vector machine (SVM) algorithm obtained an AUC-ROC of 0.85 (SD: 0.0086). The relative importance of the explanatory variables showed that the region of residence, the administrative health service, the hospital where the patient was operated on, and the care modality are the variables that most determine the length of stay. Discussion The present study developed machine learning algorithms based on freeaccess Chilean big data, which helped create and validate a tool that demonstrates an adequate discriminatory capacity to predict shortened versus prolonged hospital stay in elderly patients undergoing elective THA. Conclusion The algorithms created through the use of machine learning allow to predict the hospital stay in Chilean patients undergoing elective total hip arthroplasty Introduction The prediction of the length of hospital stay after elective total hip arthroplasty (THA) is crucial in the perioperative evaluation of the patients, and it plays a decisive role from the operational and economic point of view. Internationally, big data and artificial intelligence have been used to perform prognostic evaluations of this type. The present study aims to develop and validate, through the use of artificial intelligence (machine learning), a tool capable of predicting the hospital stay of patients over 65 years of age undergoing THA for osteoarthritis. Material and Methods Using the electronic records of hospital discharges de-identified from the Department of Health Statistics and Information (Departamento de Estadísticas e Información de Salud, DEIS, in Spanish), the data of 8,970 hospital discharges of patients who had undergone THA for osteoarthritis between 2016 and 2018 were obtained. A total of 15 variables available in the DEIS registry, in addition to the poverty rate in the patient's borough of origin were included to predict the probability that a patient would have a shortened (< 3 days) or prolonged (> 3 days) stay after surgery. By using machine learning techniques, 8 prediction algorithms were trained with 80% of the sample. The remaining 20% was used to validate the predictive capabilities of the models created from the algorithms. The optimization metric was evaluated and ranked using the area under the receiver operating characteristic curve (AUC-ROC), which corresponds to how well a model can distinguish between two groups. Results The XGBoost algorithm had the best performance, with an average AUC-ROC of 0.86 (standard deviation [SD]: 0.0087). Secondly, we observed that the linear

Efecto del contenido en la resolución de tareas de conjunción probabilísticas / Content effects in probability judgments

El error de conjunción (Tversky & Kahneman, 1983) se estudió en dos escenarios de probabilidad que suponen distintos contenidos en las tareas a resolver: ficcional y realista (Teigen, Martinussen & Lund, 1996). Participaron voluntariamente 83 sujetos de ambos sexos, alumnos de Psicología de la Universidad de Buenos Aires, quienes resolvieron ambas tareas. Las diferencias halladas en las cantidades de errores de conjunción al comparar las ejecuciones en los dos escenarios fueron altamente significativas. Los resultados reflejan una disminución de los errores cuando se presentan tareas realistas en lugar de ficcionales. Tales hallazgos indican la relevancia de considerar elementos socioecológicos tanto en razonamientos probabilísticos (Hertwig & Gigerenzer, 1999) como en las estrategias didácticas de enseñanza de probabilidad.

Projeção da dinâmica da floresta natural de Terra-firme, região de Manaus-AM, com o uso da cadeia de transição probabilística de Markov / Dynamics of the Terra-firme primary forest in Manaus-AM region using the Markov probabilistic transition matrix

Os fatores que envolvem os processos da dinâmica da floresta influenciam a sua biodiversidade e, portanto, a qualidade da floresta. A definição de estratégias que envolve a proteção e o uso adequado da floresta manejada e a recuperação de áreas já degradadas tornam-se possível com o estudo da estrutura e dinâmica da floresta primária por meio de informações como a mortalidade, o recrutamento e a permanência das árvores no sistema florestal. Este trabalho teve como objetivo avaliar a dinâmica de uma floresta não perturbada e fazer projeções da dinâmica florestal usando a matriz de transição probabilística (Cadeia de Markov). As taxas de recrutamento, mortalidade e incremento foram determinadas a partir de inventários florestais realizados em dois transectos, nos sentidos Norte-Sul e Leste-Oeste (20 x 2500 m cada, totalizando 10 ha), localizados no km 50 da BR 174, na estrada vicinal ZF-2, Manaus/AM, nos anos de 2000 e 2004. A floresta acumulou 8,34 t.ha-1.ano-1 de biomassa fresca acima do solo. De acordo com projeção para 2008, o número total de árvores diminuirá em 2,67 por cento (de 5987 indivíduos (2004) para 5827 (2008)) e a mortalidade será 15 por cento maior (de 264 (2004) para 311 (2008)). O teste Qui-quadrado mostrou que não há diferença significativa (1 por cento de probabilidade) entre as informações coletadas e projetadas. Esses resultados permitem concluir que a Cadeia de Markov é um eficiente instrumento para projetar a dinâmica da floresta natural, contribuindo para o planejamento em curto prazo das atividades que utilizam os recursos florestais.

To combine protection and utilization of forest resources in the tropics, the understanding of forest dynamics is essential. It is also important in the definition of strategies for rehabilitation of degraded areas. In Forestry, forest dynamics could be translated as the understanding of recruitment, mortality and biomass increment rates over time. For this study, these rates were estimated based on measurements carried out in 2000 and 2004 over two transects measuring 20 by 2500 m (5 hectares) each, in Manaus region. This paper deals with forest dynamics of a pristine forest based on the probabilistic transition matrix (the first-order Markov Chain) approach. The main objective is to report 4-year (2000 to 2004) changes in the forest structure. Diameter distribution and tree mortality will be projected ahead to 2008 (t+2), based upon a 4-year period of observations completed in 2004 (t+1) and its immediate past in 2000 (t). In terms of fresh aboveground biomass, this site accumulated 8.34 t.ha-1.ano-1. The chi ² test has shown no statistical difference (p = 0.01) between observed diameter frequency and the expected projected by Markov Chain. This result indicates that the Markov Chain approach is a reliable tool to project the forest dynamics on a short-term basis. In 2008, the total number of individuals will have a decrease of 2.7 percent, and the mortality rate will 15 percent higher than in 2004.