The Differential Influence of Strategy Selection and Implementation on Probability Matching in a Binary Prediction Task
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Date
2017-12-08
Authors
James, Greta
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Publisher
University of Waterloo
Abstract
Probability Matching is a common and suboptimal strategy often used by participants in a Binary Prediction Task. A plethora of research has examined why participants engage in Probability Matching and also what factors improve the optimality of their choices. However, understanding of Probability Matching has been limited by a failure to differentiate between Strategy Choice (the conscious Strategy that a participant chooses to implement) and Strategy Implementation (the efficacy with which participants implement their chosen strategy). As a result, manipulations that primarily or partially cause effects by impacting Strategy Implementation have instead been attributed to modifying Strategy Choice. In particular, this thesis examines how two factors currently known to impact behaviour in Binary Prediction Tasks (feedback on the accuracy of participants’ predictions and implementation effort) provide evidence for the distinction between Strategy Choice and Implementation. The first three experiments test the impact that feedback on the accuracy of predictions has on choice. I replicate earlier findings in the literature that feedback increases selection of the more probable outcome, but provide evidence that this increase is the result of factors influencing Strategy Implementation and not Strategy Choice. The final of these 3 experiments also examines the differential impacts of describing the contingencies in the problem versus having participants learn these contingencies through the aforementioned feedback. Here I find a large benefit to optimal performance from having the contingencies described.
An additional three experiments examine the impact of working memory load on participants’ behaviour in a Binary Prediction Task. I find evidence that working memory load increases selection of the more probable outcome when Probability Matching is more difficult to implement than Maximizing. I also find preliminary evidence that the opposite may be true when the implementation effort associated with each strategy is equal. In both cases, I find evidence that improvements in optimal responding, traditionally attributed to changes in Strategy Choice, are more likely the result of factors influencing participants’ ability to implement their chosen strategy. Finally, I discuss what ramifications the distinction between Strategy Choice and Implementation has for future research on Binary Prediction Tasks.
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Keywords
Probability Matching, Binary Choice Task, Binary Prediction Task, Probability Maximizing