Understanding Human Cognition Through Computational Modeling.

Area of Science:

• Cognitive Science
• Computational Neuroscience
• Artificial Intelligence

Background:

• Understanding the mind requires examining representational and computational capacities.
• Computational modeling is crucial for explaining and predicting human behavior and mental phenomena.

Purpose of the Study:

• Investigate information processing in learning and visual cognition using computational modeling.
• Examine perceptual representation and attention strategies through behavioral experiments and cognitive neuroscience.

Main Methods:

• Utilized neural network models to study visual system architecture's impact on cognition and expertise.
• Developed Eye Movement analysis with Hidden Markov Models (HMM) for quantifying eye movement patterns.
• Integrated deep neural networks (DNN) with HMM (DNN+HMM) to model eye movement strategy learning.

Main Results:

• Neural networks revealed how visual system architecture and expertise shape perceptual representation.
• Eye Movement analysis with HMM provided novel, cross-disciplinary findings on attention not found with traditional methods.
• DNN+HMM successfully accounted for eye movement strategy learning in visual cognition.

Conclusions:

• Computational modeling is essential for theoretical accounts of the human mind and its interactions.
• Findings advance understanding of human learning, visual cognition, and attention.
• Research facilitates AI comparability with human cognition, enhancing human-AI interaction and explainability.