A Goal-Directed Bayesian Framework for Categorization

Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis

DEVELOPMENT OF RESEARCH AND STUDY PATHS IN THE PRE-SERVICE TEACHER EDUCATION

In this paper, we present results of an implementation of research and study course carried out into a teacher training course in the University. The framework of the Anthropological Theory of the Didactic (ATD) is adopted, and a co-disciplinary Research and Study Path (RSP) whose generative question requires studying mathematics and physics together is carried out by training teachers of Mathematics at University. Some conclusions concerning on the conditions, restrictions and relevance of introducing the RSP in teachers training courses at the university are presented.  Article visualizations

From 'scientific revolution' to 'unscientific revolution': an analysis of approaches to the history of generative linguistics

This paper is devoted to the challenge that generative linguistics poses for linguistic historiography. As a first step, it presents a systematic overview of 19 approaches to the history of generative linguistics. Second, it analyzes the approaches overviewed by asking and answering the following questions: (a) To what extent and how are the views at issue biased? (b) What central topics do the approaches discuss, how successfully do they tackle them, and how do the various standpoints converge and diverge? (c) How do the approaches relate to general trends in the philosophy and history of science? The concluding step summarizes our findings with respect to Chomsky’s impact on linguistic historiography

Rich environments for active learning: a definition

Rich Environments for Active Learning, or REALs, are comprehensive instructional systems that evolve from and are consistent with constructivist philosophies and theories. To embody a constructivist view of learning, REALs: promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making, and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher-order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning-to-learn within authentic contexts using realistic tasks and performances. REALs provide learning activities that engage students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, REALs are a response to educational practices that promote the development of inert knowledge, such as conventional teacher-to-student knowledge-transfer activities. In this article, we describe and organize the shared elements of REALs, including the theoretical foundations and instructional strategies to provide a common ground for discussion. We compare existing assumptions underlying education with new assumptions that promote problem-solving and higher-level thinking. Next, we examine the theoretical foundation that supports these new assumptions. Finally, we describe how REALs promote these new assumptions within a constructivist framework, defining each REAL attribute and providing supporting examples of REAL strategies in action

The Field-tested and Grounded Technological Rule as Product of Mode 2 Management Research �

management, research