Publication Date

2015

Document Type

Thesis

Committee Members

Dragana Claflin (Committee Member), John Flach (Committee Member), Valerie Shalin (Committee Chair)

Degree Name

Master of Science (MS)

Abstract

The following study assessed how contingency and delay influence people's reasoning strategy and outcomes after interacting with a representation of a discrete and continuous system environment, in the context of controlling hypertension. The related causal reasoning and system dynamics research adopt different measurement paradigms and employ different system dynamics, making it difficult to resolve the empirical findings. Specifically, the causal reasoning literature has traditionally considered systems in which previous inputs do not influence future outcomes (e.g., a discrete system condition) while the system dynamics literature removes this constraint (e.g., a continuous system condition). Also, the system dynamics literature has focused on the ability to control pre-specified systems, whereas the causal reasoning literature has focused on the ability to discover and identify causal relationships. To examine reasoning under conditions comparable to hypertension management, I asked participants to consider causal scenarios involving causal variables (e.g., treatment options) with different amounts of contingency and delay in relation to a known outcome variable (i.e., level of blood pressure) with the representation of either a discrete or continuous system condition. The findings address the relationship between causal attribution and system control, highlighting the effect of the system representation and dynamics on both reasoning behavior and outcomes, and challenging whether the efforts to build reasoning theory based on the combination of simplified paradigms paradoxically result in artificially complex problems and misleading theory. Participants' use of more observation-dependent intervention strategies with the discrete system condition indicates that they were aware of and responding to salient information. Additionally, differences in information accessibility explain why more extreme causal attributions were observed with the continuous system condition. Independent of system condition, specific intervention strategies (observation-independent and treatment-biased strategies) led to higher causal attributions, again reinforcing that system representation and underlying system dynamics directs reasoning outcomes.

Page Count

314

Department or Program

Department of Psychology


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