# Biological Basis of the Code The provided MATLAB code is part of a computational model that uses the Hierarchical Gaussian Filter (HGF) framework to simulate perceptual inference and learning. It draws from the field of computational neuroscience to model how organisms update their beliefs based on sensory input over time. The code specifically focuses on estimating the trajectories of these beliefs and the associated uncertainties (variances) during perceptual tasks. ## Key Biological Concepts ### Hierarchical Gaussian Filter (HGF) The HGF is an influential framework used to model hierarchical levels of cognitive processes. It primarily aims to capture the way individuals infer latent states of the world through noisy sensory observations. This model is biological in its approach as it mimics the hierarchical nature of neural processing in the brain: 1. **Hierarchical Structure**: The HGF architecture is reminiscent of cortical processing, where higher-order cortical areas infer more abstract features while lower-order areas deal with raw sensory input. 2. **Bayesian Inference**: The model uses Bayesian principles, which align closely with how neurons might integrate sensory information to update beliefs about the environment. ### Biological Relevance #### Sensory Processing and Belief Updating The task of inferring environmental states from sensory inputs is analogous to the way biological systems learn about their surroundings. This code models trajectory plots of latent states (`muhat`) and their associated uncertainties (`sahat`): - **`muhat`**: Represents the estimated means of hidden states. This can be akin to the neural encoding of expected stimulus properties. - **`sahat`**: Represents variance or uncertainty associated with the beliefs. In a biological context, this aligns with neural uncertainty estimation processes, influencing how sensory information is integrated and how attention is allocated. #### Volatility Estimation Volatility, modeled as `mu3` in the code, refers to the estimated variability or changeability of the environment, akin to a form of sensory uncertainty. Volatility estimates are crucial for adaptive behavior, reflecting how organisms adjust their learning rates based on how stable or unpredictable they perceive their world to be. This mechanism is comparable to how higher cognitive functions, such as those in the prefrontal cortex, adjust predictions and decisions based on environmental stability. ### Neural Mechanisms While the code abstractly represents these processes without simulating specific neuronal dynamics, the underlying concepts are influenced by how populations of neurons might communicate and adapt based on synaptic changes. This includes: - **Probabilistic Synaptic Transmission**: Reflecting how unreliable synaptic transmission can be modeled as noise in neural responses, akin to the system's consideration of uncertainty. - **Plasticity**: Volatility estimates influence learning rates, paralleling synaptic plasticity mechanisms (e.g., LTP and LTD), where synaptic strengths are adjusted through experience. ### Conclusion In summary, the `tapas_hgf_whatworld_plotTraj` code represents a higher-level abstraction of perceptual processing and learning akin to brain functions, focusing on Bayesian inference, hierarchical information processing, and adaptive learning in a dynamic environment. This provides insights into biological processes that determine how organisms adaptively adjust their internal models in response to changing external conditions.