Experiment and bias: the case of parsimony in comparative cognitionReportar como inadecuado


Experiment and bias: the case of parsimony in comparative cognition


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Abstract

Comparative cognition is the interdisciplinary field of animal cognition and behavior studies, which includes comparative psychology and branches of ethology, biology, and neuroscience. My dissertation shows that the quasi-epistemic value of parsimony plays a problematic role in the experimental setting of comparative cognition. More specifically, I argue that an idiosyncratic interpretation of the statistical hypothesis-testing method, known as the Neyman-Pearson Method NPM, embeds an Occamist parsimony preference into experimental methodology in comparative cognition, which results in an underattribution bias, or a bias in favor of allegedly simple cognitive ontologies. I trace this parsimony preference to the content of the null hypothesis within the NPM, and defend a strategy for modifying the NPM to guard against the underattribution bias. I recommend adopting an evidence-driven strategy for choosing the null hypothesis. Further, I suggest a role for non-empirical values, such as ethical concerns, in the weighting of Type I and Type II error-rates. I contend that statistical models are deeply embedded in experimental practice and are not value-free. These models provide an often overlooked door through which values, both epistemic and non-epistemic, can enter scientific research. Since statistical models generally, and the NPM in particular, play a role in a wide variety of scientific disciplines, this dissertation can also be seen as a case study illustrating the importance of attending to the choice a particular statistical model. This conclusion suggests that various philosophical investigations of scientific practice - from inquiry into the nature of scientific evidence to analysis of the role of values in science - would be greatly enriched by increased attention to experimental methodology, including the choice and interpretation of statistical models.

Boston University Theses and Dissertations -



Autor: Meketa, Irina - -

Fuente: https://open.bu.edu/







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