Percepj

Percepj is a theoretical framework in cognitive science and artificial perception that treats perception and judgment as intertwined components of a single processing stream. Proponents argue that perceptual experience is shaped by evaluative processes such as goals, expectations, and value judgments, rather than by sensory data alone.

Origin and etymology: The term percepj combines 'perceive' and 'judgment' and was introduced in discussions of predictive processing in the late 2010s, to emphasize the inseparability of sensing and evaluating contextual meaning.

Framework and components: The model posits four interacting layers: a sensing layer that handles sensory input and feature extraction; an integration layer that aligns inputs with internal models; an evaluation layer that applies relevance, goals, risk, and value; and an action/output layer that generates interpretation, decisions, or behavior. The layers operate recursively with feedback loops.

Relation to other theories: Percepj builds on predictive processing and embodied cognition by explicitly including evaluative inference as part of perception. It contrasts with traditional feed-forward models that separate perception from decision making.

Applications and use: In artificial perception, percepj-inspired architectures aim for context-aware interpretation and more robust scene understanding. In neuroscience and psychology, it is used as a heuristic to study biases in perception and decision making. In user interfaces, it informs design to account for value-based interpretation of stimuli.

Criticism: Critics argue that percepj risks conflating perception with judgment and can be difficult to operationalize in experiments. Some propose clearer distinctions or alternative models that separate perceptual representation from evaluative response.

See also: perception, predictive processing, embodied cognition, cognitive architecture.