student profile: Ms Julie Chow


Thesis work

Thesis title: The role of contingency learning in beliefs about medical treatment

Supervisors: Ben COLAGIURI , Evan LIVESEY

Thesis abstract:

My research aims to assess how people come to learn about the relationship between a potential cause or action and an outcome, and the factors that may bias them to form a false causal belief. An example of this is the illusion of causality: the erroneous belief that two unrelated events are causally associated, such that the occurrence of one causes or prevents the occurrence of the other. One possible reason people are often biased to these causal illusions is the failure to accurately encode the frequency of the cue and outcome co-occurring. Failure to correctly identify null relationships were also found when participants were unable to calculate cue-outcome contingencies on-line due to high cognitive load.

I am interested in looking at factors contributing to the development of false cue-outcome associations, as well as how failure to learn about contingencies in general may influence subsequent beliefs about the cue-outcome relationship. For example, I am interested in investigating how people come to form opinions and make important decisions, particularly in a health-related context, when there are conflicting evidence regarding the putative cue-outcome relationship (e.g. complementary and alternative medicine), or if there are unable to experience all potential cue-outcome combinations (e.g. when deciding between a few courses of treatment for a medical illness). Similarly, I am interested in investigating whether people learn about identical relationships (e.g. between a drug and a treatment outcome) when presented with different considerations, such as getting on a new drug versus getting off a current prescription. Extending from that, whether medical professionals with high health literacy show improved ability (relative to laypeople) in ignoring secondary side effects from primary treatment outcomes when making causal judgements about treatment efficacy.

The overall aim of the project is to provide a better understanding of the mechanism behind human causal learning, particularly in a health-related context. This includes sampling from a broad range of contexts (fictitious and realistic) and population sample, in order to identify any parallels between what we know about contingency learning in the lab with real-world scenarios, where there are greater implications to making an inaccurate causal judgement.

Note: This profile is for a student at the University of Sydney. Views presented here are not necessarily those of the University.