# Biological Basis of the Code The code provided is part of a computational model that applies principles from computational neuroscience to understand reaction times in decision-making contexts. Specifically, it focuses on a model to predict log-reaction times (log-RT) using a linear relationship with parameters that capture different aspects of decision-making under uncertainty. The model was developed in collaboration with researchers Louise Marshall and Sven Bestmann, presumably indicating a psychological or neurophysiological context. ## Key Biological Aspects ### 1. Reaction Times and Decision-Making - **Log-Reaction Time (Log-RT):** The model focuses on predicting the log-transformed reaction times, which is a common approach to handle variability and skewness in distribution of reaction time data. Reaction times are often used as behavioral metrics to infer underlying cognitive and neural processes in tasks requiring decision-making and response execution. ### 2. Uncertainty in Decision-Making - **Surprise, Expected Uncertainty, and Unexpected Uncertainty:** The code highlights three primary cognitive metrics: surprise, expected uncertainty, and unexpected uncertainty. These metrics are grounded in Bayesian models of brain function, which assume that the brain constantly updates its beliefs about the world in a probabilistic manner. - **Surprise:** Represents the deviation between expected and received information, reflecting a prediction error signal that prompts the organism to update its beliefs. - **Expected Uncertainty (EU):** Captures the predicted variability in the outcome, allowing the system to weigh evidence in a Bayesian manner. - **Unexpected Uncertainty (UEU):** Refers to the unpredicted variability or abrupt changes in the environment that necessitate belief updates. ### 3. Neural Substrates of Decision-Making - **Hierarchical Models of Inference:** - The model uses a hierarchical Gaussian filter (HGF) to simulate the cognitive processes and neural computations involved in integrating evidence over time and across decision-making hierarchies. - Variables such as `mu1`, `mu2`, `mu3`, and `sa2` represent inferred states and uncertainties at different hierarchical levels, potentially corresponding to layered processing in neural circuits within regions like the prefrontal cortex that are involved in complex decision-making. ### 4. Bayesian Inference in the Brain - **Probabilistic Computation:** The code embodies a Bayesian approach to cognition. The brain uses prior knowledge and sensory evidence to compute probabilities of events, assessing both volatility (unexpected changes) and uncertainty. The `tapas_sgm` function uses a sigmoidal transformation common in Bayesian models to ensure that probabilities are bounded between 0 and 1, translating neural excitability or synaptic weights into decision variables. ### 5. Parameters Linking Cognitive States to Behavior - **Coefficients (`be0`, `be1`, `be2`, `be3`):** These parameters serve to link cognitive processes (like predictive surprise and uncertainty) to behavioral outputs (reaction times). The coefficients essentially embody how strongly each cognitive state influences reaction time, likely representing underlying neurophysiological weights or synaptic strength adjustments in relevant neural pathways. ## Summary The model exemplified by the code is an attempt to bridge computational neuroscience with behavioral data. The aim is to understand how high-level cognitive processes, potentially driven by neural computations mimicking Bayesian updates, can determine flexible and adaptive behavioral responses, like reaction times, in the face of uncertainty. Such models enable insights into the mechanistic underpinnings of decision-making across various biological systems, providing an integrative view of cognition from neural circuits to observable behavior.