Unnatural pedagogy : a computational analysis of children's learning to learn from other people.
Date on Master's Thesis/Doctoral Dissertation
Psychological and Brain Sciences
Committee Co-Chair (if applicable)
Cognitive learning; Social learning; Learning, Psychology of
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's 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's 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.
Eaves, Baxter S. 1984-, "Unnatural pedagogy : a computational analysis of children's learning to learn from other people." (2014). Electronic Theses and Dissertations. Paper 1714.