Representaatioden

Representaatioden are representations used by a system to encode information about a domain. They encompass the mediums, structures, and rules that stand for aspects of the real or conceptual world within a model, algorithm, or theory. The term is used across disciplines such as cognitive science, information science, and computer science to emphasize the plurality and interdependence of representations within a framework.

Representaatioden can be categorized by form into symbolic, perceptual, and operational types. Symbolic representaatioden include formal languages, ontologies, and rule-based encodings. Perceptual representaatioden include images, audio features, and sensor-derived signals. Operational representaatioden include data structures, graphs, and process pipelines that organize computation and data flow.

The usefulness of representaatioden lies in abstraction and communication: they enable mapping between levels of description, facilitate reasoning, and support comparison across models. Key properties include fidelity (how well they reflect the target domain), granularity (level of detail), and invariance (robustness to irrelevant variation). Representaatioden are often designed to balance expressivity with efficiency, interpretability, and generalizability.

In practice, software engineering, AI, and cognitive science use multiple representaatioden to capture different aspects of a domain, and transitions between representations are a common topic of study. Critiques focus on bias, loss of context, and the risk of overfitting representations to specific tasks.

See also: representation, encoding, ontology, data schema.