Directors: Antonio M. Battro, Kurt W. Fischer and Diego Golombek
Program officer: María Lourdes Majdalani
Interactions between human cognitive architecture, the challenges of complex causal induction in Science learning, and the affordances of a digital world
Reasoning effectively about a complex world requires cognitive flexibility in how we structure patterns and in what we perceive as the parameters of a problem space. For instance, it requires the ability to look beyond immediate constraints and events to reason about extended time and spatial frames and about processes and steady states (Grotzer, Kamarainen, Tutwiler, Metcalf & Dede, 2013). However, human cognitive architecture might not readily support these abilities. The cognitive science literature has debated the modes of causal induction humans engage in as they attempt to understand their world. Three bodies of literature have made strong contributions to our understanding of how everyday causal reasoning works: Causal Bayes Nets (CBN) theories (e.g. Gopnik & Glymour, 2002; Gopnik & Schulz, 2007); specific generative transmission notions of mechanism (e.g. Atran, 1995; Keil, 1994) and the role of testimony from others (e.g. Harris, 2002; 2012). In various ways, each of these modes alone falls short given the evidence available in a complex problem space. This presentation analyzes these difficulties and how these modes on induction may interact in cases with specific features of causal complexity. It offers examples of how digital supports in science learning can be used to research complex causal reasoning patterns as well as enable students to learn new ones.