The paradigms i’d settled on for CogSci so far put next to SIMILAR BUT NOT IDENTICAL AI paradigms.
Cognitive Science vs Artificial Intelligence
Dynamic Systems Theory vs Reinforcement Learning
Human cognition emerges from the complex interactions of multiple systems and adapts dynamically to changing conditions. vs AI agents learn by interacting with their environment, receiving rewards or punishments, and adjusting their behavior to maximize their total reward.
Predictive Coding vs Bayesian Inference
The brain constantly generates and updates predictions about the world, adjusting its beliefs based on the difference between its predictions and sensory input. vs AI systems use Bayesian inference to update their models and predictions based on new data, aiming to maximize the likelihood of the observed data given the model.
Neurophenomenology vs AI Explainability/Interpretability
Combines neuroscience with first-person subjective experience to understand consciousness. vs AI Explainability seeks to understand and articulate the internal decision-making processes of complex AI models, akin to giving the AI a ‘voice’ to explain its reasoning.
Embodied Cognitive Ecology vs Embodied AI
Our cognition is deeply rooted in our bodies and our interactions with the environment. vs Embodied AI refers to AI agents that interact with their environment through a body – either physical (robotics) or virtual (simulation).
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These comparisons highlight the intersection of human cognition (as understood through cognitive science) and artificial intelligence (AI), as well as their parallel concepts and methodologies. Let’s take a deeper look:
1. Cognitive Science vs Artificial Intelligence: While both cognitive science and AI are concerned with intelligent behaviors, cognitive science is a multi-disciplinary study of the mind and intelligence, which includes elements of psychology, neuroscience, philosophy, linguistics, and anthropology. On the other hand, AI is a branch of computer science that aims to create machines that mimic human intelligence.
2. Dynamic Systems Theory vs Reinforcement Learning: Dynamic Systems Theory is a way of describing how a person’s cognition changes and adapts over time as a result of complex interactions between different cognitive and environmental systems. Reinforcement Learning, on the other hand, is an AI technique where an agent learns to make decisions by taking actions in an environment to achieve the maximum cumulative reward.
3. Predictive Coding vs Bayesian Inference: Predictive coding is a theory about how the brain processes information, suggesting that the brain is a predictive machine that constantly updates its models of the world based on incoming sensory data. Bayesian inference is a method of statistical inference used in AI and machine learning, where a model’s predictions are updated as new evidence is incorporated, much like the predictive coding theory.
4. Neurophenomenology vs AI Explainability/Interpretability: Neurophenomenology combines subjective experience with objective measures to understand consciousness and cognition. AI Explainability/Interpretability, by contrast, aims to articulate the internal decision-making processes of complex AI models, providing a way for AI to explain its reasoning, making it more understandable and trustworthy.
5. Embodied Cognitive Ecology vs Embodied AI: Embodied Cognitive Ecology emphasizes the importance of our body and environment in shaping our cognitive processes. Similarly, Embodied AI, whether in physical robots or simulated agents, posits that intelligent behavior emerges from the interaction of an agent’s brain (control algorithms), body, and environment.
These comparisons underscore how AI researchers often draw upon ideas from cognitive science in their quest to build intelligent machines. They also highlight that both humans and AI rely on similar strategies—such as learning from feedback, updating predictions based on new data, and interacting with the environment—to navigate the world. Yet the mechanisms by which these strategies are implemented differ greatly, reflecting the fundamental differences between biological and artificial systems.
