# Biological Basis of the Code ## Introduction The provided code is modeling a process within a computational framework that bridges Bayesian inference with neurobiological phenomena through a hierarchical Gaussian filter (HGF) applied to a multi-armed bandit task. This model is designed to capture the hierarchical nature of learning and decision-making in the brain, emphasizing the brain's ability to process uncertainty and adapt its beliefs based on environmental changes. ## Biological Concepts ### Hierarchical Bayesian Inference - **Hierarchical Bayesian Models**: The model in the code represents the brain's inference mechanism as a hierarchy, aligning with the understanding that the brain processes information through multiple layers, with each layer responsible for different levels of abstraction. - **Levels of Representation**: The code models multiple levels (`n_levels`) of representation, which may correspond to different neural circuits processing information at varying scales of complexity and abstraction. The levels could be thought of as resembling neural processing from sensory inputs to higher-order cognitive functions. ### Prediction and Error Processing - **Predictions and Prediction Errors**: At the core of the model is the concept of predictions and prediction errors, key components in theories such as predictive coding. The brain is hypothesized to continuously generate predictions about sensory inputs and experiences, with prediction errors (discrepancies between predictions and actual inputs) being used to update beliefs. - **Volatility and Precision**: The model takes into account the learning about environmental volatility, represented by parameters like `v` (weighting factors) and `w` (volatility prediction errors). This aligns with neuromodulatory systems in the brain, such as those involving neurotransmitter regulation, which modulate learning rates and the precision of prediction errors based on the assessed variability of the environment. ### Neurophysiological Correlates - **Neuromodulation**: Parameters such as `phi`, `ka`, `om`, and `th` connect to neuromodulatory influences (e.g., dopamine, acetylcholine) in the brain that adjust learning rates, volatility assessments, and confidence levels in predictions. These parameters could be analogs to biological processes where neuromodulatory systems adapt to uncertainty and influence decision-making. - **Adaptive Learning**: The model embodies a dynamic learning process where the brain’s beliefs are updated efficiently in response to the variability and uncertainty of the environment. This reflects the adaptive nature of synaptic plasticity and neural circuitry, which adjust based on experience to optimize behavior and decision-making strategies. ## Conclusion The code models a computational framework that reflects critical biologically inspired processes, namely hierarchical information processing, prediction error signaling, and adaptive learning under uncertainty. These processes are rooted in how the brain handles complex interactions with its environment and are supported by a variety of neural and neuromodulatory systems that work together to optimize behavior. By simulating these processes using an AR(1)-HGF in a multi-armed bandit task, the code helps to elucidate how humans update their beliefs and make decisions based on changing information.