cognitive/docs/guides/learning_paths/active_inference_cognitive_learning_path.md

title	type	status	created	complexity	processing_priority	tags	semantic_relations
Active Inference in Cognitive Science Learning Path	learning_path	stable	2024-03-15	advanced	1	active-inference cognitive-science psychology behavior	type links specializes active_inference_learning_path type links relates cognitive_architecture_learning_path cognitive_psychology_learning_path computational_psychiatry_learning_path

Active Inference in Cognitive Science Learning Path

Overview

This specialized path focuses on applying Active Inference to understand cognitive processes, behavior, and mental phenomena. It integrates psychological theory with computational modeling.

Prerequisites

1. Cognitive Science Foundations (4 weeks)

• Cognitive Psychology

  • Perception
  • Attention
  • Memory
  • Decision making

• Behavioral Science

  • Learning theory
  • Motivation
  • Emotion
  • Social cognition

• Experimental Methods

  • Research design
  • Data collection
  • Statistical analysis
  • Behavioral measures

• Computational Theory

  • Information processing
  • Mental representations
  • Cognitive architectures
  • Neural computation

2. Technical Skills (2 weeks)

• Research Tools
  • Python/R
  • Statistical packages
  • Experimental software
  • Data visualization

Core Learning Path

1. Cognitive Modeling (4 weeks)

Week 1-2: Mental State Inference

class CognitiveStateEstimator:
    def __init__(self,
                 belief_dim: int,
                 observation_dim: int):
        """Initialize cognitive state estimator."""
        self.belief_model = BeliefUpdateModel(belief_dim)
        self.obs_model = ObservationModel(belief_dim, observation_dim)
        self.beliefs = torch.zeros(belief_dim)
        
    def update_beliefs(self,
                      observation: torch.Tensor) -> torch.Tensor:
        """Update beliefs based on observation."""
        # Generate prediction
        pred_obs = self.obs_model(self.beliefs)
        
        # Compute prediction error
        error = observation - pred_obs
        
        # Update beliefs
        self.beliefs = self.belief_model.update(self.beliefs, error)
        return self.beliefs

Week 3-4: Action Selection

class BehavioralController:
    def __init__(self,
                 action_space: int,
                 goal_space: int):
        """Initialize behavioral controller."""
        self.policy = PolicyNetwork(action_space)
        self.value = ValueNetwork(goal_space)
        
    def select_action(self,
                     beliefs: torch.Tensor,
                     goals: torch.Tensor) -> torch.Tensor:
        """Select action using active inference."""
        # Generate policies
        policies = self.policy.generate_policies(beliefs)
        
        # Evaluate expected free energy
        G = torch.zeros(len(policies))
        for i, pi in enumerate(policies):
            future_beliefs = self.simulate_policy(beliefs, pi)
            G[i] = self.compute_expected_free_energy(
                future_beliefs, goals
            )
        
        # Select optimal policy
        best_policy = policies[torch.argmin(G)]
        return best_policy[0]

2. Cognitive Domains (6 weeks)

Week 1-2: Perceptual Processing

• Sensory Integration
• Feature Extraction
• Pattern Recognition
• Attention Allocation

Week 3-4: Decision Making

• Value Computation
• Risk Assessment
• Temporal Planning
• Social Decision Making

Week 5-6: Learning and Memory

• Skill Acquisition
• Knowledge Formation
• Memory Consolidation
• Habit Learning

3. Applications (4 weeks)

Week 1-2: Behavioral Tasks

class CognitiveBehaviorTask:
    def __init__(self,
                 task_type: str,
                 difficulty: float):
        """Initialize cognitive task."""
        self.type = task_type
        self.difficulty = difficulty
        self.stimuli = self.generate_stimuli()
        
    def run_trial(self,
                 agent: CognitiveAgent) -> Dict[str, Any]:
        """Run single trial of task."""
        # Present stimulus
        observation = self.present_stimulus()
        
        # Get agent response
        response = agent.process_stimulus(observation)
        
        # Evaluate performance
        results = self.evaluate_response(response)
        return results

Week 3-4: Clinical Applications

• Psychiatric Disorders
• Behavioral Therapy
• Cognitive Training
• Intervention Design

4. Advanced Topics (4 weeks)

Week 1-2: Social Cognition

class SocialCognitionModel:
    def __init__(self,
                 n_agents: int,
                 social_dim: int):
        """Initialize social cognition model."""
        self.agents = [CognitiveAgent() for _ in range(n_agents)]
        self.social_space = SocialSpace(social_dim)
        
    def simulate_interaction(self,
                           context: Dict[str, Any]) -> List[torch.Tensor]:
        """Simulate social interaction."""
        # Initialize interaction
        states = []
        for agent in self.agents:
            # Update beliefs about others
            social_obs = self.social_space.get_observations(agent)
            agent.update_social_beliefs(social_obs)
            
            # Generate social action
            action = agent.select_social_action(context)
            states.append(action)
        
        return states

Week 3-4: Metacognition

• Self-monitoring
• Confidence Estimation
• Strategy Selection
• Learning to Learn

Projects

Cognitive Projects

1. Perceptual Tasks

   • Visual Search
   • Pattern Recognition
   • Category Learning
   • Attention Tasks

2. Decision Tasks

   • Value-based Choice
   • Risk Assessment
   • Social Dilemmas
   • Sequential Planning

Clinical Projects

1. Disorder Modeling

   • Anxiety
   • Depression
   • OCD
   • ADHD

2. Intervention Design

   • Cognitive Training
   • Behavioral Therapy
   • Treatment Planning
   • Outcome Prediction

Assessment

Knowledge Assessment

1. Theoretical Understanding

   • Cognitive Processes
   • Behavioral Principles
   • Clinical Applications
   • Research Methods

2. Practical Skills

   • Experimental Design
   • Data Analysis
   • Model Implementation
   • Result Interpretation

Final Projects

1. Research Project

   • Theory Development
   • Experimental Design
   • Data Collection
   • Analysis

2. Clinical Application

   • Disorder Modeling
   • Treatment Design
   • Validation Study
   • Outcome Assessment

Resources

Academic Resources

1. Research Papers

   • Theoretical Papers
   • Empirical Studies
   • Review Articles
   • Clinical Studies

2. Books

   • Cognitive Science
   • Computational Modeling
   • Clinical Psychology
   • Research Methods

Technical Resources

1. Software Tools

   • Experimental Software
   • Analysis Packages
   • Modeling Tools
   • Visualization Libraries

2. Data Resources

   • Behavioral Datasets
   • Clinical Data
   • Model Benchmarks
   • Analysis Scripts

Next Steps

Advanced Topics

1. computational_psychiatry_learning_path
2. social_cognition_learning_path
3. metacognition_learning_path

Research Directions

1. research_guides/cognitive_science
2. research_guides/clinical_psychology
3. research_guides/computational_modeling