%0 Generic
%A Radev, Stefan
%C Heidelberg
%D 2021
%F heidok:30807
%R 10.11588/heidok.00030807
%T Deep Learning Architectures for Amortized Bayesian Inference in Cognitive Modeling
%U https://archiv.ub.uni-heidelberg.de/volltextserver/30807/
%X Mathematical models are becoming increasingly important for describing, explaining, and predicting human behavior in terms of underlying mechanisms and systems of mechanisms. Although the ontology of such mechanisms remains largely unknown, their epistemic value and inferential power are now widely acknowledged throughout the behavioral sciences. Broadly speaking, whenever an assumed mechanism transforms information into behavior, it is referred to as a cognitive process. Cognitive processes are the conceptual fabric used to fill the explanatory gap between the mysterious firing of neurons and the mundane recognition of a long-forgotten acquaintance in the morning train. Consequently, modelers of cognitive processes earn their livelihood in an attempt to make the “ghost in a machine” tractable by replacing the ghost with hidden parameters embedded in an abstract functional framework.  The purpose of such parametric models is twofold. On the one hand, they can be viewed as formal expedients for understanding the messy and noisy human data in much the same way as the models physicists employ to make sense of the data coming from spiral galaxies and interstellar clouds. On the other hand, parametric models can be viewed as behavioral simulators and used to mimic the output of cognitive processes by generating synthetic behavior. Interestingly, there is a strange asymmetry in the challenges surrounding these two goals. Simulating behavior requires only specifying a cognitive model as a computer program and running the program with a desired parameter configuration. It is thus a generative process mainly constrained by the creativity and imagination of individual modelers.  Differently, reverse engineering human data to recover hidden parameters is hampered by two external factors: the resolution and abundance of data and the availability of universal and efficient inferential methods.  As for the latter, behavioral scientists have often sacrificed fidelity and complexity in order to adjust their models not to reality but to the limitations of existing inferential methods. Such a strategy is definitely viable in the early (often linear and beguilingly clear) stages of scientific inquiry, but it does not live up to the challenges and questions posed by later (often non-linear and disconcertingly fuzzy) stages.  The main argument of this thesis is that questions of inferential tractability are of secondary importance for enhancing our understanding of the processes under study. Accordingly, the core purpose of this thesis is to develop frameworks which leave such questions to specialized ``black-box'' artificial neural networks and enable researchers to focus on developing and validating faithful ``white-box'' models of cognition. Instead of a ready-made solution, the thesis explores a beginning of a solution. It presents a potentially fruitful coupling between human and artificial intelligence, an approach which is expected to gain more and more momentum as the world fills with artificial agents. Ultimately, this thesis strives to increase creativity by embracing complexity.