Deterministic differential equation based recurrent neural networks (RNNs) can be used to replicate, on a small scale, the processes of the human brain. RNNs are typically assumed to be fully predictable given complete knowledge of initial conditions. This project investigates how an RNN, when coupled with biologically accurate noise and amplification mechanisms, can nonetheless exhibit practical unpredictability on behaviorally realistic timescales while remaining responsive to structured inputs (“reasons”). The objective of this project is to suggest that human thought, when modeled by recurrent neural networks (RNNs), can be both unpredictable and still exhibit normative agency, creating implications for fields of computational neuroscience and quantitative philosophy.