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This paper examines the role that lived experience plays in the human capacity to reason about uncertainty. Previous research shows that people are more likely to provide accurate responses in Bayesian tasks when the data are presented in natural frequencies, the problem in question describes a familiar event, and the values of the data are in line with beliefs. Precisely why these factors are important remains open to debate. We elucidate the issue in two ways. Firstly, we hypothesize that in a task that requires people to reason about conditional probabilities, they are more likely to respond accurately when the values of the problem reflect their own lived experience, than when they reflect the experience of the average participant. Secondly, to gain further understanding of the underlying reasoning process, we employ a novel interaction analysis method that tracks mouse movements in an interactive web application and applies transition analysis to model how the approach to reasoning differs depending on whether data are presented using percentages or natural frequencies. We find (1) that the closer the values of the data in the problem are to people's self-reported lived experience, the more likely they are to provide a correct answer, and (2) that the reasoning process employed when data are presented using natural frequencies is qualitatively different to that employed when data are presented using percentages. The results indicate that the benefits of natural frequency presentation are due to a clearer representation of the relationship between sets and that the prior humans acquire through experience has an overwhelming influence on their ability to reason about uncertainty. [ABSTRACT FROM AUTHOR]

Does reading a description of an artwork affect how a person subsequently views it? In a controlled study, we show that in most cases, textual description does not influence how people subsequently view paintings, contrary to participants' self-report that they believed it did. To examine whether the description affected transition behaviour, we devised a novel analysis method that systematically determines Units of Interest (UOIs), and calculates transitions between these, to quantify the effect of an external factor (a descriptive text) on the viewing pattern of a naturalistic stimulus (a painting). UOIs are defined using a grid-based system, where the cell-size is determined by a clustering algorithm (DBSCAN). The Hellinger distance is computed for the distance between two Markov chains using a permutation test, constructed from the transition matrices (visual shifts between UOIs) of the two groups for each painting. Results show that the description does not affect the way in which people transition between UOIs for all but one of the paintings -- an abstract work -- suggesting that description may play more of a role in determining transition behaviour when a lack of semantic cues means it is unclear how the painting should be interpreted. The contribution is twofold: to the domain of art/curation, we provide evidence that descriptive texts do not effect how people view paintings, with the possible exception of some abstract paintings; to the domain of eye-movement research, we provide a method with the potential to answer questions across multiple research areas, where the goal is to determine whether a particular factor or condition consistently affects viewing behaviour of naturalistic stimuli. [ABSTRACT FROM AUTHOR]