Synthetic Cognition: Building Artificial Minds for Adaptive Learning

Abstract

Abstract: Synthetic cognition refers to the construction of artificial systems that replicate or simulate cognitive processes such as learning, reasoning, and adaptation. This paper investigates the computational frameworks and architectures for building artificial minds capable of adaptive learning, drawing on concepts from neuroscience, machine learning, and cognitive psychology. By evaluating neuro-symbolic architectures, reinforcement learning with episodic memory, and meta-learning models, Researcher demonstrated how these systems can generalize across tasks and environments. Using regression and predictive analyses on benchmark datasets (Omniglot, Mini-ImageNet, and MetaWorld),Researcher examined performance trends and cognitive scalability. Researcherr findings suggest that hybrid learning models equipped with symbolic reasoning and self-adaptation mechanisms outperform traditional neural networks in dynamic learning settings. Keywords: Synthetic cognition, adaptive learning, artificial minds, neuro-symbolic systems, meta-learning, reinforcement learning, cognitive architectures, deep learning, AI reasoning, generalization