Self-interpretability of human cognition: How reportable knowledge emerges in le...
Current artificial intelligence (AI) surpasses human-level performance in a vast range of tasks. However, its decisional processes are opaque, referred to as the AI interpretability problem. Humans, on the other hand, can verbally...
Current artificial intelligence (AI) surpasses human-level performance in a vast range of tasks. However, its decisional processes are opaque, referred to as the AI interpretability problem. Humans, on the other hand, can verbally describe their decisional processes and strategies. The accuracy of these reports varies, especially in complex environments. Yet, people often come up with reasonably accurate explanations for their decisions, thereby allowing knowledge transfer in society. However, the mechanisms of accurate verbal report generation remain unclear. Therefore, the main research objective of the REPORT-IT project is to study how humans generate adequate reportable knowledge during learning through experience. Inspired by the recent findings from research on metacognition (i.e., insight into one's own cognition) and cognition-emotion interaction, I will test the novel hypothesis that metacognition and learning-related affect support the emergence of reportable knowledge. In two experiments modeling complex learning environments (implicit category learning and probabilistic reward learning tasks), I will track the development of metacognition, affect, and reportable knowledge over time. This will allow me to evaluate the temporal relationships between these components and predict the emergence of reportable knowledge. In the final step of the project, I will study the behavior of deep neural networks (DNNs) in the exact same tasks and test whether DNNs can generate temporal patterns of metacognition and affect, as observed in humans. Thereby, the REPORT-IT project combines my expertise in implicit learning and affective science with expertise in neuroscience of consciousness and DNNs at the host institute (University of Amsterdam). This way, REPORT-IT will contribute to understanding how people generate reportable knowledge and, at the same time, provide new approaches for explainable AI.ver más
Seleccionando "Aceptar todas las cookies" acepta el uso de cookies para ayudarnos a brindarle una mejor experiencia de usuario y para analizar el uso del sitio web. Al hacer clic en "Ajustar tus preferencias" puede elegir qué cookies permitir. Solo las cookies esenciales son necesarias para el correcto funcionamiento de nuestro sitio web y no se pueden rechazar.
Cookie settings
Nuestro sitio web almacena cuatro tipos de cookies. En cualquier momento puede elegir qué cookies acepta y cuáles rechaza. Puede obtener más información sobre qué son las cookies y qué tipos de cookies almacenamos en nuestra Política de cookies.
Son necesarias por razones técnicas. Sin ellas, este sitio web podría no funcionar correctamente.
Son necesarias para una funcionalidad específica en el sitio web. Sin ellos, algunas características pueden estar deshabilitadas.
Nos permite analizar el uso del sitio web y mejorar la experiencia del visitante.
Nos permite personalizar su experiencia y enviarle contenido y ofertas relevantes, en este sitio web y en otros sitios web.