Unnatural pedagogy : a computational analysis of children\u27s learning to learn from other people.

Infants rely on others for much of what they learn. People are a ready source of quick information, but people produce data differently than the world. Data from a person are a result of that person\u27s knowledgeability and intentions. People may produce inaccurate or misleading data. On the other hand, if a person is knowledgeable about the world and intends to teach, that person may produce data that are more useful than simply accurate data: data that are pedagogical. This idea that people have special innate methods for efficient information transfer lies at the heart of recent proposals regarding what makes humans such powerful knowledge accumulators. These innate assumptions result in developmental patterns observed in epistemic trust research. This research seeks to create a computational account of the development of these abilities. We argue that pedagogy is not innate, but rather that people learn to learn from others. We employ novel computational models to show that there is sufficient data early on from which infants may learn that people choose data pedagogically, that the development of children\u27s epistemic trust is primarily a result of their decreasing beliefs that all informants are helpful, and that innate pedagogy would not lead to more rapid learning. We connect results from the pedagogy and epistemic trust literatures across tasks and development, showing that these are different manifestations of the same underlying abilities, and show that pedagogy need not be innate to have powerful implications for learning

From simple to complex categories: how structure and label information guides the acquisition of category knowledge

Categorization is a fundamental ability of human cognition, translating complex streams of information from the all of different senses into simpler, discrete categories. How do people acquire all of this category knowledge, particularly the kinds of rich, structured categories we interact with every day in the real-world? In this thesis, I explore how information from category structure and category labels influence how people learn categories, particular for the kinds of computational problems that are relevant to real-world category learning. The three learning problems this thesis covers are: semi-supervised learning, structure learning and category learning with many features. Each of these three learning problems presents a different kinds of learning challenge, and through a combination of behavioural experiments and computational modeling, this thesis illustrates how the interplay between structure and label information can explain how humans can acquire richer kinds of category knowledge.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Psychology, 201