Causal Systems Categories: Differences in Novice and Expert Categorization of Causal Phenomena

Abstract

We investigated the understanding of causal systems categories-categories defined by common causal structure rather than by common domain content-among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: The novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures.

On the acquisition of abstract knowledge: Structural alignment and explication in learning causal system categories

“…Instead of examining how people learn and use the causal relations that govern a particular phenomenon, we ask how people learn abstract categories of causal systems that apply across domains and phenomena. In our prior work we developed a sorting task aimed at assessing people's propensity to notice key causal patterns amidst competing information (Rottman, Gentner, & Goldwater, 2012). In this task, the Ambiguous Sorting Task (AST), subjects are asked to sort descriptions of causal phenomena into categories.…”

Ontologies and Databases – Knowledge Engineering for Materials Informatics

“…However, the realization of these expectations is dependent on creating real value by acquiring knowledge and engineering utility of that knowledge, not by simply accumulating even greater stores of data than that which is overwhelming us today. The reader is referred to a broad array of references that provide a very useful introduction to the complexities, challenges, and strategies for information management (Chomsky, 1957;Alexander et al, 1986;Senge, 1990;Mayer, 1992;Ashenhurst, 1996;Butler et al, 1997;Behringer, 2001;Berners-Lee et al, 2001;Pfeifer and Scheier, 2001;Baader et al, 2003;Schieritz, 2003;Sica, 2006;Chickowski, 2008;Matsuka et al, 2008;Spivak, 2011;Gromiha and Huang, 2012;Rottman et al, 2012).…”

Models as Relational Categories

“…Moreover, it has been suggested that science teaching enhances finding relational commonalities across domains and this is based on the ability to perceive the commonalities 5 (Goldwater and Gentner 2015;Rottman et al 2012). Thus, finding the relational commonalities is not just about being more knowledgeable on some domain or across multiple domains but also about possessing general causal patterns and being inclined to look for these patterns.…”

“…While for example, Ohm's law, U = RI, typically has a causal reading (voltage causes current), Kirchhoff's current law, ∑I k = 0, may be best described as a constraining equation. There are also various relational patterns, which refer to the ways in which the variables are linked to each other (such as linear causality, common cause, feedback loops, and cyclic causalities) (for examples, see Kokkonen and Mäntylä 2017;Perkins and Grotzer 2005;Rottman et al 2012).…”