Perceptualer

Perceptualer is a theoretical construct used in cognitive science and artificial intelligence to describe a unified account of how sensory data is transformed into coherent perception and guided action. Proponents describe it as an integrative framework that encompasses perception, multisensory integration, and perceptual decision making, linking sensory encoding with motor plans.

The term is not widely standardized and appears in some scholarly discussions as a provisional label for an approach that emphasizes continuous, probabilistic interpretation of sensory input. It is commonly framed within the broader traditions of predictive processing and Bayesian brain hypotheses.

Key ideas associated with Perceptualer include probabilistic representation of latent states, inference across sensory modalities, and active sampling of information. The framework often relies on predictions generated by internal models, with perception emerging from the interplay of sensory evidence and top-down expectations. It also addresses how context, prior experience, and action influence perceptual judgment.

Researchers discuss Perceptualer in relation to neuroscience experiments, computational modeling, and robotics. In AI, it informs architectures that combine perception with decision making and motor control. Critics argue that as a novel label it risks duplicating existing theories such as predictive coding and the Bayesian brain, and that clear operational definitions and empirical validation are needed for wider adoption.