Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Percept formation involves transforming ambiguous sensory data into coherent representations.
• Understanding the neural mechanisms of perception is crucial for cognitive science.
• Existing models often lack mechanisms for online adaptation and stochastic updates.

Purpose of the Study:

• To model the vague-to-crisp dynamics of percept formation in the brain.
• To integrate dynamic logic (DL) with operant learning for a novel computational framework.
• To explore the connection between this framework and cognitive processing, including the intentional neurodynamic cycle.

Main Methods:

• Combining dynamic logic (DL) for percept selection with an operant learning process for online updates.
• Utilizing an intrinsic reward mechanism to enable stochastic updates, replacing traditional DL optimization.
• Modeling percept formation as a form of model selection based on sampled visual evidence.

Main Results:

• A computational framework that successfully models the transition from vague to crisp percepts.
• Demonstration of how intrinsic rewards can facilitate online adaptation in percept formation.
• The framework provides a new perspective on how the brain processes visual information and forms stable percepts.

Conclusions:

• The integrated dynamic logic and operant learning framework offers a viable model for percept formation.
• The use of intrinsic rewards allows for flexible and adaptive cognitive processing.
• This approach sheds light on the intentional neurodynamic cycle and brain information processing.