nonsymbolic

Nonsymbolic is an adjective used to describe representations, processes, or reasoning that do not rely on discrete symbolic tokens or explicit rule-based manipulation of symbols. It stands in contrast to symbolic approaches that operate on predefined symbols and rules. The term is commonly used in discussions of artificial intelligence, cognitive science, and related fields.

In artificial intelligence, nonsymbolic AI refers to approaches that learn from data and experience rather than being programmed with explicit symbolic rules. This includes neural networks, deep learning, and other subsymbolic or embodied methods that represent information as patterns, vectors, or continuous states. Subsymbolic systems are typically contrasted with symbolic AI, which works with formal languages, logic, and explicit knowledge representations. Nonsymbolic methods are valued for handling perception, pattern recognition, and motor control, but can be less transparent and harder to interpret than symbolic systems.

In cognitive science and psychology, nonsymbolic knowledge denotes mental representations and processes that are not readily articulated in explicit rules or propositions. Examples include perceptual representations, procedural memory, motor planning, and sensorimotor integration. Researchers often study how nonsymbolic mechanisms interact with symbolic reasoning, seeking to understand how learned abilities can support or be complemented by explicit, rule-based thought.

Historically, the distinction between symbolic and nonsymbolic approaches has framed debates about how intelligence and cognition are organized. Hybrid and integrated models—such as neural-symbolic systems or differentiable programming—aim to combine the strengths of both paradigms, enabling learning from data while supporting explicit reasoning and interpretability.