ID Consecutivo : 1742

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Resumen
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action–outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors.

 

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Coleccion ASCOFAME: Evaluacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

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ID Consecutivo : 1742

Titulo

Editorial

 

Año de publicación Edición Primera Segunda Tercera Cuarta Quita Sexta Septima Octava Novena Decima Selecione una

Paginas Existencias 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Resumen
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action–outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors.

 

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Tipo de Acceso

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Palabras clave

Coleccion ASCOFAME: Evaluacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

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ID Consecutivo : 1742

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Tipo : Ley Decreto Norma Resolucion Circular Directiva

 

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Resumen
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action–outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors.

 

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Tipo de Acceso

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Libre

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Palabras clave

Coleccion ASCOFAME: Evaluacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

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ID Consecutivo : 1742

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Resumen
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action–outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors.

 

Adjunte la portada del libro Si no tiene una imagen, descargue aqui la imagen de Ascofame

Idioma Español Inglés Portugués Francés Alemán

 

Tipo de Acceso

Restringido(ASCOFAME)

Libre

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Palabras clave

Coleccion ASCOFAME: Evaluacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

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ID Consecutivo : 1742

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Resumen
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action–outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors.

 

Adjunte la portada del libro Si no tiene una imagen, descargue aqui la imagen de Ascofame

Idioma Español Inglés Portugués Francés Alemán

 

Tipo de Acceso

Restringido(ASCOFAME)

Libre

Registrado

Palabras clave

Coleccion ASCOFAME: Evaluacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

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ID Consecutivo : 1742

Titulo videos

Año de publicación

Resumen
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action–outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors.

 

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Coleccion ASCOFAME: Evaluacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331622

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Publico	Pregrado	Posgrado	Decanatura
Objetivo:	Docentes	Médicos	Investigadores
	Clínicos	Educadores Médicos	Otros profesionales de la salud
	Evaluadores

 

ID Consecutivo : 1742

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Titulo Revista

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Resumen
Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action–outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors.

 

Base de datos bibliográfica Base de datos bibliográfica Seleccione una Access Medicine Biblioteca VirtualenSalud(BVS) Cochrane Central Dialnet Lilacs-Literatura Latinoamericana en Ciencias de la Salud Medline-PubMed Redalyc ScienceDirect SciELO-Scientific Electronic Library Online WHOLIS Otra

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Idioma Español Inglés Portugués Francés Alemán

 

Tipo de Acceso

Restringido(ASCOFAME)

Libre

Registrado

Palabras clave

Coleccion ASCOFAME: Evaluacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

Cargue el Documento

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Publico	Pregrado	Posgrado	Decanatura
Objetivo:	Docentes	Médicos	Investigadores
	Clínicos	Educadores Médicos	Otros profesionales de la salud
	Evaluadores