Abstract
We model the response times for word recognition collected in experimental trials conducted on four subjects. Because of the sequential nature of the experiment and the fact that several replications of similar trials were conducted on each subject, the assumption of i.i.d. response times within experimental conditions (often encountered in the psychology literature) is untenable. We consider Bayesian hierarchical models in which the response times are described as conditionally independent Weibull random variables given the parameters of the Weibull distribution. The sequential dependencies, as well as the effects of response accuracy, word characteristics, and subject specific learning processes are incorporated via a linear regression model for the logarithm of the scale parameter of the Weibull distribution. We compare the inferences from our analysis with those obtained by means of instruments that are commonly used in the cognitive psychology arena. We pay close attention to the quality of the fits, the adequacy of the assumptions, and their impact on the inferential conclusions.
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Peruggia, M., Van Zandt, T., Chen, M. (2002). Was it a car or a cat I saw? An Analysis of Response Times for Word Recognition. In: Gatsonis, C., et al. Case Studies in Bayesian Statistics. Lecture Notes in Statistics, vol 167. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2078-7_17
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DOI: https://doi.org/10.1007/978-1-4612-2078-7_17
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