Tugba ­ Hato

I am a Ph.D. student at the DFG Research Training Group "Statistical Modeling in Psychology" (SMiP). My main research interests lie in cognitive modeling and Bayesian statistics.

• Application of cognitive models to large datasets
  I am currently working on a project that aims to predict dementia outcomes by combining evidence accumulation models and machine learning approaches.

• Model misspecification through simulation
  I investigated the effects of model misspecification by examining whether the Diffusion Model can adequately capture data generated by alternative processes such as Lévy Flights. This research focuses on potential biases introduced when the assumed model does not reflect the true data-generating process.
  (See paper: Lévy versus Wiener)

• Memory modeling
  In another lifetime, I was interested in memory models, particularly the Temporal Context Model. My Master's thesis focused on the interplay of associative asymmetry in associative memory and the contiguity effect.
  (See thesis: Testing Encoding and Retrieval Dynamics of Pairs Using Probed Recall Task)

Across these projects, I mainly use simulation-based inference via a Python package called BayesFlow and apply Bayesian statistical methods throughout my work.

If you are interested in collaborating or writing your master’s thesis in this area, feel free to reach out!