The provided code is a part of a computational neuroscience model simulating log reaction times (logRTs) based on specific physiological and cognitive processes. This model is rooted in the hierarchical Gaussian filter (HGF) framework, which is often used to understand how the brain updates beliefs about the environment through Bayesian inference. Here's a breakdown of the biological bases reflected in this code: ### Bayesian Inference and Neuromodulation 1. **Belief Updating and Prediction Errors:** - The model simulates human behavior under uncertainty by predicting reaction times using Bayesian principles. The brain is postulated to maintain probabilistic beliefs about states of the world (like the occurrence of sensory stimuli) and updates them with prediction errors — the discrepancy between predicted and observed outcomes. 2. **Surprise and Uncertainty:** - **Surprise (surp):** This reflects the Shannon information or the unlikelihood of an observed event given current beliefs. In neurobiology, surprise is thought to be linked with neural signals that drive learning, such as those involving dopaminergic neurons. - **Uncertainty Types:** - **Bernoulli Variance (bernv):** Analogous to irreducible uncertainty or risk, relating to variability in prediction without further information. - **Inferential Variance (inferv):** Also known as estimation uncertainty or ambiguity, this represents uncertainty due to limited information about the underlying state. - **Phasic Volatility (pv):** Captures changes in the environment that are unexpected, akin to environmental unpredictability. 3. **Neuromodulatory Systems:** - The model mirrors real-world cognitive processes where neuromodulators (like dopamine and norepinephrine) affect attention and learning by modulating perceived uncertainty and volatility. For example, phasic dopamine signals correlate with prediction errors and are crucial for adapting learning rates. ### Cognitive Functions - **Reaction Time Prediction:** - **Log-Reaction Time (logRT):** The primary output reflects participants' response times in cognitive tasks. This is modeled as a function of the aforementioned factors, indicating how cognitive processing and adaptations to beliefs and expectations influence behavioral outputs. - **Parameterization (be0, be1, etc.):** - These parameters can be thought of as representing individual differences in sensitivity to surprise, uncertainty, and volatility. These variations might correspond to different cognitive styles or neural processing strategies among individuals. ### Biological Signal Transformation - **Sigmoid Transformation (tapas_sgm):** - The use of a sigmoid function (tapas_sgm) reflects a transformation commonly used in modeling binary decision processes or neural signaling, converting raw states into probabilities akin to the firing rate of neurons. ### Noise and Variability - **Gaussian Noise (ze):** - The addition of Gaussian noise to the simulated logRT reflects biological variability and stochasticity present in neural processing. This noise acknowledges the inherent variability in behavior and decision-making at the neuronal level. In summary, this code models the interaction of cognitive processes such as prediction, updating beliefs under uncertainty, and resultant behavioral outputs, grounded in well-studied neuromodulatory systems and information-theoretic principles. It seeks to replicate and understand the complex dynamics of human learning and decision-making as influenced by unobservable latent variables and their biological correlates.