Causal structure and base rate neglect in statistical reasoning

• Main Author: McNair, Simon John
• Published: Queen's University Belfast 2013
• Subjects: 519.54
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.602584

This PhD is concerned with the causal Bayesian framework account of probabilistic judgement (Krynski & Tenenbaum, 2007). which posits that accurate Bayesian reasoning is contingent upon the reasoner being able to intuitively represent evidence in terms of a sufficient causal model. According to this account, base rate neglect whereby reasoners fail to consider the prior probability of the hypothesis, irrespective of the evidence - can be overcome by explicitly clarifying the causal basis of all of the given evidence. Chapter 1 reviews the literature on base rate neglect; details the main accounts of base rate neglect in Bayesian reasoning; considers some issues with the causal Bayesian framework; and finally outlines the main aims of the PhD. Chapter 2 presents two dual-task experiments which aimed to test the intuitiveness of causal facilitation. Whilst secondary load was not found to have an overall effect on reasoning, subsequent non-load experiments in the chapter found similar levels of causal facilitation to the dual-task experiments. leading to the overall conclusion that facilitation occurs outside of working memory considerations. Chapter 3 indicated that the causal facilitation effect was present only • in the absence of an additional intervention designed to highlight nested set relations in the data, indicating that reasoners may not employ a strictly causal model, but instead represent different causes as interrelated sets of data. Chapter 4 demonstrated that the causal facilitation effect was limited to reasoners of sufficiently high numeracy, which explained the consistently lower levels of Bayesian responding reported throughout the rest 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.