Over the last eight years, my research interests have focused on how people understand and utilize information to make judgments and decisions. Of particular interest are the mechanisms which underlie general abilities to reason through complex information when uncertainty is involved. In these types of situations, the data needed to make a decision are often presented as complicated statistics, which are notoriously difficult to understand. In my research, I employ a combination of quantitative and qualitative research methods and analyses to evaluate how people process statistical data, which has strong theoretical contributions for discerning how people may perceive and utilize statistics in reasoning and decision making. This research also has valuable practical implications as statistical reasoning is one of the foundational pillars required for scientific thinking. I plan to continue this research via several avenues in both theoretical and applied contexts.
As a stepping stone toward understanding statistics, we use Bayesian reasoning tasks to assess reasoner’s ability to understanding nested information (e.g., Talboy & Schneider, 2017, 2018a, 201b, under review). In these types of tasks, there are difficulties with representing the inherently nested structure of the problem in a way that clearly elucidates the correct reference class needed to determine the solution. Additionally, computational demands compound these representation difficulties, contributing to generally low levels of accuracy.
In my own research, we have tackled the representational difficulties of reasoning by fundamentally altering how information is presented and which reference classes are elucidated in the problem structure (Talboy & Schneider, 2017, 2018a, 2018b). In a related line, we break down the computational difficulties into the component processes of identification, computation, and application of values from the problem to the solution (Talboy & Schneider, in progress). In doing so, we discovered a general bias in which reasoners tend to select values that are presented in the problem text as the answer even when computations are required (Talboy & Schneider, 2018a, in progress).
I parlayed this previous research on representational and computational difficulties into the foundation for my dissertation, with an eye toward how reference dependence affects uninitiated reasoners’ abilities to overcome these obstacles (Talboy, dissertation). Additionally, I evaluated the general value selection bias to determine the circumstances in which uninitiated reasoners revert to selecting values from the problem rather than completing computations (Talboy & Schneider, 2018a, in progress). I plan to extend this line of research to further evaluate the extent to which a value selection bias is utilized in other types of reasoning tasks involving reference classes, such as relative versus absolute risk. Moving forward, I also plan to apply the advances made in understanding how people work through the complicated nested structure of Bayesian reasoning tasks to the more difficult nested structure of statistical testing.
Although my research focuses on the theoretical underpinnings of cognitive processes involved in reasoning about inherently nested problem structures, I also have a parallel line of research that translates what we learn from research to application in everyday scenarios. We recently published a paper geared toward the medical community that takes what we learned about Bayesian reasoning and applies it to understanding the outcomes of medical diagnostic testing, and how patients would use that information to make future medical decisions (Talboy & Schneider, 2018b). I also led an interdisciplinary team on a collaborative project to evaluate how younger and older adults evaluate pharmaceutical pamphlet information to determine which treatment to use (Talboy, Aylward, Lende, & Guttmann, 2016; Talboy & Guttmann, in progress). I plan to continue researching how information presented in medical contexts can be more clearly elucidated to improve individual health literacy, as well as general health decision making and reasoning.
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