# Biological Basis of the Code The provided code is part of a computational model intended to simulate aspects of human cognitive neuroscience, specifically related to **spatial attention, precision, and saccadic response speed**. This model draws upon findings from a study by Vossel et al. (2013) that investigated how precision and Bayesian inference influence decision-making processes in terms of attentional selection and oculomotor behavior. ## Core Biological Aspects ### Spatial Attention and Bayesian Inference - **Spatial Attention**: This refers to the cognitive process by which the brain selects certain spatial locations for enhanced processing over others. In the context of this model, it is likely concerned with how individuals prioritize certain spatial cues over others, facilitating quicker and more efficient saccadic eye movements. - **Bayesian Inference**: The model incorporates Bayesian methods, which are used to infer the probability of certain outcomes based on prior knowledge and current evidence. In the biological context, this represents how the brain might utilize prior expectations and sensory information to make predictions about the environment, optimizing decision-making processes. ### Precision - **Precision in Neural Processing**: In neural terms, precision refers to the clarity or quality of incoming sensory information relative to noise. The model suggests that better precision allows for more accurate predictions about sensory states, influencing cognitive and motor responses such as saccadic eye movements. ### Gaussian Noise and Observation Models - **Gaussian Noise**: The model assumes that the variability in responses (e.g., reaction times or saccadic speeds) can be described by Gaussian noise, suggesting a probabilistic nature of neural responses to sensory inputs. - **Noise Variance**: The parameter "zeta" represents the noise variance, indicating the level of uncertainty or fluctuation in response times. The priors on this variance (e.g., variability around expected mean states) reflect how the model embeds assumptions about the expected precision of responses. ## Biophysical Implications - This model does not directly simulate ions, synapses, or neuronal firing patterns, but instead, it abstracts these concepts into parameters of precision and variance in cognitive responses. - The parameters involved (such as zeta) are intended to represent how neural noise might influence the variability in cognitive processes, though they do not explicitly model the underlying biophysical properties of neuronal networks. The code serves as a configuration set for a broader model simulating how precision and Bayesian inference influence human attention and motor control in a probabilistic framework. While it does not detail specific biological mechanisms at the cellular level, it captures higher-level cognitive processes and their neural correlates.