---
title: Active Inference in Cognitive Science Learning Path
type: learning_path
status: stable
created: 2024-03-15
complexity: advanced
processing_priority: 1
tags:
  - active-inference
  - cognitive-science
  - psychology
  - behavior
semantic_relations:
  - type: specializes
    links: [[active_inference_learning_path]]
  - type: relates
    links:
      - [[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
```python
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
```python
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
```python
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
```python
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|Computational Psychiatry]]
2. [[social_cognition_learning_path|Social Cognition]]
3. [[metacognition_learning_path|Metacognition]]

### Research Directions
1. [[research_guides/cognitive_science|Cognitive Science Research]]
2. [[research_guides/clinical_psychology|Clinical Psychology Research]]
3. [[research_guides/computational_modeling|Computational Modeling Research]]