ii Acknowledgements First, I extend my thanks to my committee members for their time, consideration, and guidance. I would especially like to thank my advisors, Rich Shiffrin and Mike Jones, for first convincing me to come to Indiana, making me feel welcome, and guiding me into the world I now inhabit. I must also thank Isaiah Harbison, Michael Dougherty, and Sharona Atkins for introducing me to the very idea of mathematical psychology and for teaching me and trusting me. show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process.
Proponents of preregistration argue that, among other benefits, it improves the diagnosticity of statistical tests [1]. In the strong version of this argument, preregistration does this by solving statistical problems, such as family-wise error rates. In the weak version, it nudges people to think more deeply about their theories, methods, and analyses. We argue against both: the diagnosticity of statistical tests depend entirely on how well statistical models map onto underlying theories, and so improving statistical techniques does little to improve theories when the mapping is weak. There is also little reason to expect that preregistration will spontaneously help researchers to develop better theories (and, hence, better methods and analyses).
Roberts (2020) discussed research claiming honeybees can do arithmetic. Some readers of this research might regard such claims as unlikely. The present authors used this example as a focus for a debate on the criterion that ought to be used for publication of results that could be viewed as unlikely by a significant number of readers. The resulting dialogue contains interesting discussion of non-human cognition, whether honeybee arithmetic should be considered unlikely, and the role of replication in such cases.
Graphical perception is a crucial part of scientific endeavour, and the interpretation of graphical information is increasingly important among the lay public, who are often presented with graphs of data in support of different policy positions. However, graphs are multidimensional and data in graphs are comprised not only of overall global trends but also local perturbations. We presented a novel function estimation task in which scatterplots of noisy data that varied in the number of data points, the scale of the data, and the true generating function were shown to observers. Observers were asked to draw the function which they believe generated the data. Our results indicated not only a general influence of various aspects of the presented graph (e.g., increasing the number of data points results in smoother generated functions), but also clear individual differences, with some observers tending to generate functions which track the local changes in the data and others following global trends in the data.
This chapter discusses key features of computational models of event memory, also called “episodic” memory. Models aim to capture the representations and processes that enable us to perform a variety of episodic memory tasks, including recognition and free, cued, and serial recall. We review different ways in which models distinguish between the content and context of events; how different models represent event memories, including networks and vectors; and the types of processes that operate on memory representations to accomplish retrieval goals, including matching and search. Finally, we discuss how modeling serves broader scientific goals and can help bridge levels of explanation between the cognitive processes involved in memory and their neural instantiation.
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