Biological Basis of the Code

The provided code snippet is derived from a computational model within the Hierarchical Gaussian Filter (HGF) toolbox, which is used in computational neuroscience to model perceptual and cognitive processes. Specifically, this code seems to focus on modeling binary decision-making processes. Here are the key biological considerations relevant to the code:

Perceptual Uncertainty and Adaptation

1. Sigmoid Function (tapas_sgm):

   • The code uses a sigmoid function (tapas_sgm) to transform certain parameters. Biologically, the sigmoid function is often used to model neuronal firing rates, which saturate at high and low input levels. This relates to how neurons can fire at different rates depending on the inputs they receive, which aligns with human perceptual decision-making processes where stimulus intensity affects perception probability.

2. Decision Parameters (v_0 and al_0):

   • The parameters v_0 and al_0 are transformed using the sigmoid function, suggesting they represent probabilities or intensities linked to decision variables.
   • v_0 is potentially related to the baseline activity or initial bias toward one of the binary outcomes in a decision-making task.
   • al_0 might relate to an adaptability or learning rate, indicating how quickly a subject adapts to new information—akin to how the nervous system adapts its responses based on experiences.

3. Exponential Transformation for Scale (S):

   • The use of an exponential transformation for the parameter S suggests it represents a scaling factor, possibly related to the variability in a perceptual judgment.
   • This could correlate to the level of noise or uncertainty in sensory neuronal responses, which impacts the reliability of perceptual inputs.

Hierarchical Modeling

• The HGF framework models perceptual and cognitive processes through a hierarchical structure, where lower levels might represent irregularities or noise in sensory input, and higher levels correspond to more abstract inferences or beliefs about the state of the world.
• The code helps parameterize the influences of external stimuli and internal cognitive processes on decision-making, echoing the hierarchical and predictive nature of brain function.

Conclusion

Overall, the code models binary decision-making processes by adjusting parameters that reflect biological processes like neuronal response properties, adaptation rates, and sensory evidence scaling. These mappings underscore the complex interplay of factors influencing perceptual decision-making in the brain.