## Biological Basis of the Computational Model The provided code is part of a computational model that uses hierarchical Gaussian filtering (HGF) to analyze behavioral data. HGF is a Bayesian model used for inferring hidden states from observed data, particularly in the context of perception and decision-making processes. Here's a breakdown of the biological basis of the code: ### Hierarchical Gaussian Filter (HGF) HGF is a generative model that aims to capture how humans or animals perceive and respond to stimuli over time. It models cognitive processes such as belief updating in a hierarchical manner, simulating how the brain might integrate and interpret noisy sensory inputs. #### Levels of Inference 1. **Sensory Input (Level 1):** The lowest level involves direct sensory input or observations (e.g., accuracy in response to stimulus conditions). The variable `y2` is constructed from test accuracy data, capturing the observed performance under different stimulus conditions. 2. **Latent States (Levels 2 and 3):** Higher levels represent inferred latent states or beliefs about the world. These levels model the updating of beliefs about environmental and internal states, akin to what might occur in the brain. - **Belief Updating:** The model adjusts beliefs over time based on sensory inputs and prior expectations, reflecting biological processes such as synaptic plasticity and neural adaptation. - **Trajectory of Beliefs:** The trajectory of beliefs, represented by `muhat(:,2)` and other columns, suggest how certain neural processes (e.g., synaptic weights) evolve as new information is integrated. ### Neurobiological Relevance - **Perceptual and Decision-making Processes:** By mapping observed behaviors (like accuracy in task performance) to latent states, the HGF model provides insights into the underlying perceptual and decision-making processes. This is relevant for understanding how the brain continuously updates its model of the environment to make predictions and decisions. - **Cognitive Function Modeling:** The ability to simulate and visualize trajectories of these processes (via `tapas_hgf_binary_condhalluc_plotTraj`) offers a way to hypothesize about brain functions, such as attention, expectation, and error processing. ### Model Configurations - **Configurations Used:** - The model uses `tapas_hgf_binary_config`, which suggests a structure for modeling binary outcomes in decision-making scenarios. - The `tapas_condhalluc_obs_config` suggests the model can incorporate conditions related to altered perception, such as hallucinations, potentially implicating dysregulated learning or prediction error processes. ### Summary The code provides a glimpse into how computational models can dissect and simulate complex cognitive processes. By aligning behavioral data with perceptual decision-making models like the HGF, researchers aim to illuminate how the brain processes, updates, and reacts to information, thus offering a closer look at the biological underpinnings of cognition and behavior.