subsymbolic

Subsymbolic refers to computational processes or cognitive representations that operate below the level of explicit symbols. In AI and cognitive science, it is used to describe learning systems and representations that are not easily interpretable as discrete symbolic tokens, in contrast to symbolic AI, which emphasizes explicit rules and schemas. Subsymbolic processing models information as distributed patterns across many units, enabling learning from raw sensory input and capturing statistical regularities in data.

Historically, the term arose in debates between connectionist (neural network) approaches and traditional rule-based systems. Subsymbolic models include neural networks, distributed representations, and other non-symbolic computation. With the rise of deep learning, subsymbolic methods have become dominant in perception, pattern recognition, and sequence modeling, while still being used in hybrid forms.

Key concepts include distributed representations, where meaning is carried by patterns across many units rather than by a single symbol; localist representations are an alternative; training occurs through gradient-based optimization and backpropagation; representations are often hierarchical, forming feature detectors at different levels.

Advantages of subsymbolic methods include robustness to noise, the ability to learn directly from raw data, and strong performance on perceptual tasks. Limitations include challenges in interpretability, difficulty in performing explicit compositional reasoning, and data requirements. Subsymbolic models are not inherently symbolic, but they can be integrated with symbolic systems in neuro-symbolic AI, to combine learned perception with rule-based reasoning.

See also: symbolic AI; connectionism; neural networks; distributed representation; deep learning; neuro-symbolic AI.