AI and Cognitive Science

About Course

This interdisciplinary course explores the profound, symbiotic relationship between artificial intelligence and cognitive science. Students will delve into how computational models of the human mind can inform and lead to more robust, human-like AI systems. Conversely, they will discover how advanced AI models serve as powerful tools for unlocking the intricate mechanisms of human thought and behavior. The curriculum emphasizes designing AI systems inspired by established cognitive processes such as learning, memory, perception, and reasoning, while rigorously evaluating these AI models as compelling, testable theories of cognition.

Critically evaluate fundamental principles and methodologies across classical and contemporary cognitive science.
Analyze the intricate connections between human cognition and advanced artificial intelligence paradigms.
Apply specific cognitive architectures and models (e.g., ACT-R, SOAR, Global Workspace Theory) to enhance AI system design and functionality.
Implement biologically-inspired AI architectures, including spiking neural networks and neuromorphic computing principles, for specific cognitive tasks.
Develop and utilize psychometric evaluation methods to assess AI systems as robust models of human cognitive processes.
Design and develop hybrid intelligent systems that effectively integrate human decision-making with artificial intelligence capabilities.

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Lesson Modules
1. Foundations of Cognitive Science Explore core theories and approaches in cognitive science, including symbolic vs. connectionist models, mental representations, cognitive architectures (e.g., ACT-R, SOAR), and the computational theory of mind. Review modern research methods such as fMRI, EEG, and behavioral experiments, and analyze the historical interplay between cognitive science and AI.
2. Human and Machine Learning
Compare and contrast human and machine learning processes, distinguishing between statistical, symbolic, and deep learning paradigms. Examine advanced topics like one-shot and few-shot learning, meta-learning, transfer learning, and analogical reasoning. Understand developmental trajectories in learning and the crucial role of inductive biases and priors in both human and machine domains.
3. Perception and Attention
Investigate visual processing in humans and machines, focusing on biologically-inspired object recognition (e.g., hierarchical visual processing) and scene understanding. Delve into attention mechanisms in human cognition (e.g., selective, sustained) and deep learning (e.g., self-attention,
cross-attention). Explore multimodal perception, cross-modal integration, and predictive processing frameworks.
4. Memory, Knowledge, and Reasoning
Explore human memory systems (e.g., episodic, semantic, working memory) and their implications for artificial knowledge representation. Analyze forms
of reasoning in humans and AI, including causal reasoning, deductive reasoning, mental models, simulation, and common-sense knowledge.
Implement practical approaches for analogical reasoning in AI systems.
5. Language and Communication
Study human language acquisition and processing, incorporating insights from cognitive linguistics for advanced Natural Language Processing (NLP).
Address pragmatics, context, and intention in human-AI communication, as well as social aspects of language. Examine techniques for grounding
language in perception and action, and analyze mental representations of language and their computational counterparts.
6. Consciousness, Emotion, and Social Cognition
Examine leading theories of consciousness (e.g., Global Workspace Theory) and their relevance to AI. Cover affective computing, computational models of emotion, and emotional intelligence in AI. Discuss theory of mind in humans and AI systems, the ethical implications of consciousness-like properties in AI, and methods for evaluating social cognition and interaction.

Capstone Project
Students will design and implement an AI system directly inspired by specific cognitive processes, such as a neural network model of human visual processing, a reinforcement learning agent exhibiting human-like decision biases, or a computational model that accurately simulates aspects of human memory retrieval. Alternatively, students may leverage AI techniques to model and investigate complex cognitive phenomena, such as creating an NLP system that learns language with human-like developmental stages. The project will include comprehensive implementation details, a rigorous evaluation methodology comparing the system's performance to human cognition, and a thorough analysis of its cognitive fidelity and potential implications.

AI and Cognitive Science

About Course

What Will You Learn?

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AI and Cognitive Science

AI and Cognitive Science

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What Will You Learn?

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