Formal frameworks that use [[Mathematics|mathematical]] or [[Algorithms & Data Structures|algorithmic]] representations to simulate mental processes and neural dynamics. They provide testable predictions, allow comparison with behavioral and neural data, and bridge [[Psychology]], [[Neuroscience]], and [[AI]]. - Core Approaches - Symbolic models - rule-based, logical structures (e.g., production systems) - Connectionist models - neural networks that learn through weighted associations - Bayesian models - probabilistic inference and [[decision-making]] under uncertainty - Reinforcement learning - value-based choice and reward-driven learning - Hybrid models - integrate symbolic reasoning with network-level learning - Applications - [[Memory]] - models of storage, retrieval, and forgetting curves - [[Decision-Making]] - reinforcement learning, drift-diffusion models - [[Attention]] - resource allocation, signal-to-noise dynamics - [[Language Processing]] - parsing, semantic networks, deep learning models - [[Emotion]] - affective computing, appraisal models - Perspectives - [[Cognitive Psychology]] - explains behavior via abstract rules or processes - [[Cognitive Neuroscience]] - links algorithms to brain activity (fMRI/EEG data fitting) - [[Artificial Intelligence]] - builds intelligent systems inspired by cognition - Subtopics - [[Connectionist Models]] - [[Bayesian Models]] - [[Reinforcement Learning]] - [[Drift-Diffusion Model]] - Methods - Simulation experiments - test predictions against behavioral data - Neural network architectures - layers, weights, activation functions - Probabilistic inference - likelihood estimation, uncertainty quantification