Yes, meshes perfectly. Good. Now I have that connection to Kuramoto model I was looking for.
Me:
“Points:
4.6. Initial beliefs of agents
4.7. Time scale of communication rounds
4.8. Communication cost (e.g., time or resources required for communication)
seem as if they might relate well to hierarchical bayesian brain and ADHD, in particular Id’ read that noisy priors can be part of the reason for the constant sense of now-ness experienced by people with ADHD and their continual doubting of themselves”
GPT4:
Indeed, the points 4.6, 4.7, and 4.8 from the complexity of agreement can be related to the hierarchical Bayesian brain framework and ADHD, as both involve the updating of beliefs and the influence of noise on these processes:
4.6. Initial beliefs of agents: In the context of the hierarchical Bayesian brain, the initial beliefs or priors correspond to an individual’s expectations about the world based on previous experiences. For individuals with ADHD, these priors might be noisier or less stable, leading to difficulties in forming accurate expectations or judgments. This could contribute to the constant sense of “now-ness” and self-doubt experienced by people with ADHD.
4.7. Time scale of communication rounds: This parameter can be linked to the time scale of belief updating in the brain. For individuals with ADHD, this time scale might be shorter, as they may require more frequent updating of their beliefs due to noisy priors or fluctuating attention. This could also contribute to the “now-ness” experience and difficulties in maintaining focus on long-term goals or tasks.
4.8. Communication cost: In the context of the hierarchical Bayesian brain, communication cost can be related to the metabolic or cognitive resources required for processing and integrating information from different sources. People with ADHD may have higher communication costs due to the need for more frequent belief updating or the presence of noisy priors. This increased cost could further contribute to the challenges faced by individuals with ADHD in maintaining focus, making decisions, or managing resources efficiently.
By drawing connections between these parameters and the hierarchical Bayesian brain framework, we can gain insights into the cognitive processes underlying ADHD and how noisy priors, shorter time scales for belief updating, and higher communication costs might contribute to the symptoms and experiences of individuals with this condition.