Mapping the intuitive investigation: Seeking, evaluating and explaining the evidence

The human mind has developed numerous cognitive tools to allow us to navigate the uncertainty of the world and make sense of situations and events. In this thesis I present a descriptive account of some of these tools by probing people’s ability to: evaluate, seek, and explain evidence and information. This was achieved by appraising people’s behaviour in controlled experiments – predominantly representing legal-investigative scenarios – utilising normative causal models (e.g., causal Bayesian networks), and uncovering the alternative strategies that people employed when reasoning under uncertainty. In Chapter 4, I investigate people’s ability to engage in a pattern of reasoning termed ‘explaining away’ and propose, and find empirical support towards, intuitive theories that address why the observed inference errors were made. In Chapter 5, I outline how people search for, and evaluate, evidence in a sequential investigative information-seeking paradigm – finding that people do not seek information simply to maximize a given utility function but rather are driven by additional strategies which are sensitive to factors such as demands of the task and a novel form of risk aversion. I extend these findings to forensic professionals, and utilise a naturalistic study employing mobile eye-trackers during a mock crime scene investigation to elucidate the key role that ‘asking the right questions’ plays when engaging in sense-making practices ‘in the wild’. In Chapter 6, I explore people’s preferences for certain types of information relating to opportunity and motive at various stages of the legal-investigative process. Here, I demonstrate that people prefer ‘motive’ accounts of crimes (analogous to a teleology preference) at different stages of the investigative process. In an additional two studies I demonstrate that these preferences are context-sensitive: namely, that ‘motive’ information tends to be moreincriminating and less exculpatory. In a final set of experiments, outlined in Chapter 7, I investigate how drawing causal models of competing explanations of the evidence affects how these same explanations are evaluated – arguing that graphically representing the evidence bolsters people’s understanding of the probabilistic and logical significance of the causal structures drawn. In sum, this thesis provides a rich descriptive account of how people engage in various aspects of sense-making and decision-making under uncertainty. The work presented in this thesis ultimately aims to increase the ecological and descriptive validity of normative causal frameworks utilised in the cognitive sciences – whilst informing ways to formalise decision-making practices in real-world specialised domains