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A popular method for indexing numerical representations is to compute anindividual estimate of a response time effect, such as the SNARC effect or thenumerical distance effect. Classically, this is done by estimating individuallinear regression slopes and then either pooling the slopes to obtain agroup-level slope estimate, or using the individual slopes as predictors ofother phenomena. In this paper, I develop a hierarchical Bayesian model forsimultaneously estimating group-level and individual-level slope parameters. Ishow examples of using this modeling framework to assess two common effects innumerical cognition: the SNARC effect and the numerical distance effect.Finally, I demonstrate that the Bayesian approach can result in bettermeasurement fidelity than the classical approach, especially with smallsamples.

A hierarchical Bayesian model for measuring individual-level and group-level numerical representations