Consciousness and Artificial Intelligence

5.

Engineering and Architectural Approaches

5.1.

Computational Models of Consciousness

5.1.1.

Simulating Neural Correlates of Consciousness (NCC)

5.1.1.1.

Mapping Biological to Artificial Systems

5.1.1.2.

Thalamocortical Models

5.1.1.3.

Cortical Simulation

5.1.2.

Implementing GWT-based Architectures

5.1.2.1.

Software Implementations

5.1.2.1.1.

CLARION Architecture

5.1.2.1.2.

ACT-R Extensions

5.1.2.1.3.

Custom GWT Systems

5.1.2.2.

Hardware Implementations

5.1.2.2.1.

Neuromorphic Computing

5.1.2.2.2.

Parallel Processing Systems

5.1.3.

Modeling IIT Principles

5.1.3.1.

Network Topologies

5.1.3.1.1.

Small-World Networks

5.1.3.1.2.

Scale-Free Networks

5.1.3.1.3.

Modular Networks

5.1.3.2.

Measuring Information Integration

5.1.3.2.1.

Phi Calculation Algorithms

5.1.3.2.2.

Approximation Methods

5.1.3.2.3.

Real-Time Assessment

5.1.4.

Predictive Processing Implementations

5.1.4.1.

Hierarchical Predictive Networks

5.1.4.2.

Temporal Prediction Models

5.1.4.3.

Active Inference Systems

5.2.

The Role of Embodiment and Robotics

5.2.1.

Situated Cognition

5.2.1.1.

Environmental Interaction

5.2.1.2.

Ecological Psychology

5.2.1.3.

Affordances and Action

5.2.2.

Sensory-Motor Loops

5.2.2.1.

Perception-Action Cycles

5.2.2.2.

Sensorimotor Contingencies

5.2.2.3.

Motor Control Systems

5.2.3.

The Body as a Foundation for Self-Model

5.2.3.1.

Proprioception and Self-Localization

5.2.3.2.

5.2.3.3.

5.2.3.4.

Multimodal Integration

5.2.4.

Robotic Implementations

5.2.4.1.

Humanoid Robots

5.2.4.2.

Cognitive Robotics

5.2.4.3.

Developmental Robotics

5.3.

Artificial Qualia

5.3.1.

Representing Sensory Information

5.3.1.1.

Multimodal Integration

5.3.1.2.

Cross-Modal Plasticity

5.3.1.3.

Sensory Substitution

5.3.2.

The Symbol Grounding Problem

5.3.2.1.

Linking Symbols to Percepts

5.3.2.2.

Embodied Symbol Grounding

5.3.2.3.

Categorical Perception

5.3.3.

Creating an "Inner World" Model

5.3.3.1.

Internal Simulation

5.3.3.2.

5.3.3.3.

Virtual Environments

5.3.3.4.

Imagination and Creativity

5.3.4.

Phenomenal Concepts

5.3.4.1.

Concept Formation

5.3.4.2.

Categorical Representation

5.3.4.3.

Prototype Theory

5.4.

Architectures for Self-Awareness

5.4.1.

Recursive Self-Modeling

5.4.1.1.

Self-Representation Layers

5.4.1.2.

Meta-Cognitive Architectures

5.4.1.3.

Self-Referential Systems

5.4.2.

Metacognitive Monitoring and Control

5.4.2.1.

Error Detection and Correction

5.4.2.2.

Confidence Assessment

5.4.2.3.

Strategy Selection

5.4.3.

Internal Speech and Narrative Generation

5.4.3.1.

Self-Talk in AI

5.4.3.2.

Narrative Construction

5.4.3.3.

Autobiographical Memory

5.4.3.4.

Identity Formation

5.4.4.

Theory of Mind Systems

5.4.4.1.

Belief Attribution

5.4.4.2.

Intention Recognition

5.4.4.3.

Social Cognition

5.5.

Memory and Temporal Consciousness

5.5.1.

Working Memory Systems

5.5.1.1.

Attention and Consciousness

5.5.1.2.

Capacity Limitations

5.5.1.3.

Temporal Dynamics

5.5.2.

Episodic Memory

5.5.2.1.

Autobiographical Memory

5.5.2.2.

Mental Time Travel

5.5.2.3.

Self-Continuity

5.5.3.

Temporal Binding

5.5.3.1.

Unity of Consciousness

5.5.3.2.

Temporal Integration

5.5.3.3.

Stream of Consciousness

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4. Theoretical Frameworks for Artificial Consciousness

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6. The Problem of Verification: Testing for Machine Consciousness