Sensescommon

Sensescommon is a term used in discussions of cognitive science and artificial intelligence to denote a conceptual layer that captures commonalities across perceptual modalities. The idea is to create a shared representation that aligns features from vision, audition, touch, and other senses to facilitate interpretation and grounding of sensory data.

Operationally, sensescommon may refer to a cross-modal latent space or a set of modality-agnostic attributes such as spatial relations, temporal patterns, intensity, texture, or affordances. Such a representation can be learned through self-supervised or supervised methods, often using contrastive objectives to align corresponding signals across modalities.

Applications of sensescommon include improving robustness in multimodal perception for robotics, grounding language in perception for AI assistants, and enabling cross-modal retrieval in multimedia systems. Proposals of sensescommon emphasize reducing fragmentation across modalities by providing a unified semantic substrate, which can help systems reason about objects, events, and actions across different sensory streams.

Limitations and debate: the term is not universally standardized, and approaches described as sensescommon vary in their concrete instantiation. Critics point to challenges in defining truly modality-agnostic primitives, handling modality-specific noise, and evaluating cross-modal grounding in real-world tasks. Proponents argue that a shared cross-modal representation can enhance generalization and transfer across tasks that involve multiple senses.

See also: multimodal learning, cross-modal representation, sensor fusion, grounding, latent space, contrastive learning.