Bayesian Modelling of Rising Decision Thresholds in DDMs using BlueBEAR

In this case study we hear from Sophie Wetz, a Masters student in Psychology, who has been using BlueBEAR to investigate whether a dynamic, rising boundary improved the fit of the full Drift Diffusion Model (DDM) to Random Dot Kinematogram (RDK) task data. I’m a Master’s student on the Computational Neuroscience stream in the School … Continue reading “Bayesian Modelling of Rising Decision Thresholds in DDMs using BlueBEAR”

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