Abstract
Bayesian reasoning is a manner of thinking about probability that speci lies how people should integrate given evidence with existing knowledge in order to produce the most accurate likelihood judgement. I However, much research has shown that people tend to make a systematic error in Bayesian reasoning, known as Base Rate Neglect, whereby the prior probability of an events occurrence, irrespective of the evidence, is not considered. I his PhD is concerned with a recent theoretical account of probabilistic judgement: the causal Bayesian framework of Krynski and Tenenbaum (2007), which posits that accurate Bayesian reasoning is contingent upon the reasoner being able to intuitively represent the evidence in terms of a sufficient causal model representation.Chapter I reviews the literature on base rate neglect, introducing the reader to Bayesian reasoning and key accounts of base rate neglect. The chapter concludes by evaluating the causal Bayesian framework, highlighting several issues with the work of Krynski and Tenenbaum (2007), before outlining the main research aims of the PhD. Four experiments are described in Chapter 2. Experiments 1 and 2 aimed to test the intuitiveness of causal facilitation by using a dualtask paradigm. Whilst neither causal information nor secondary load was found to have an overall effect on reasoning, Experiments 3 and 4  where no loading procedure was employed  replicated the causal facilitation effect and observed similar levels of causal facilitation to the dual task experiments, leading to the overall conclusion that the effect is not related to working memory. Chapter 3 explores whether clarifying the causal basis of the given evidence in fact encourages reasoners to employ a nested sets rather than a strictly causal representation. Whilst the results of Experiment 5 are ambivalent, results from Experiment 6 showed that the causal facilitation effect was present only in the absence of an additional intervention designed to highlight the set relations. Reasoners, then, may not employ a strictly causal model, but may instead represent different causes as interrelated sets of data. Chapter 4 investigated the role of mathematical ability in probabilistic reasoning. Experiments 7a and 7b demonstrated more Bayesian responses in a sample likely to be of high mathematical ability than in a lower ability sample, although the size of the causal facilitation effect was equivalent in both samples. However, Experiment 8 showed that causal facilitation is limited to reasoners of sufficiently high numeracy.
Whilst each chapter closes with a general discussion of the results reported therein, Chapter 5 presents an overall synthesis of the major findings of the PhD. Overall, the thesis furthers our understanding of how mental representations can positively influence judgements over the classical, purely statistical approach. Clarifying the causal basis of the given evidence can help reasoners of good numerical ability to intuitively recognise the set relations between data, leading to significant improvements in performance.
Date of Award  Jul 2013 

Original language  English 
Awarding Institution 

Supervisor  Aidan Feeney (Supervisor) & Teresa McCormack (Supervisor) 