senseindirect

Senseindirect is a term used in cognitive science and artificial intelligence to describe a mode of perception in which an agent derives information about the state of the world from indirect evidence rather than direct sensory input. It involves inferring latent sensory states through priors, predictions, and contextual cues.

Core mechanisms include probabilistic inference, predictive processing, and causal reasoning. Agents maintain generative models that relate hidden states to observable cues; when direct data is weak or noisy, senseindirect reasoning relies on remaining information such as prior experience, multi-modal correlations, and causal structures to estimate the most likely state.

In practice, senseindirect can enhance robustness in perception systems, enabling estimation of occluded objects, uncertain measurements, or novel environments. Applications appear in robotics, where a robot may infer obstacle geometry from indirect cues like shading or motion, and in natural language processing, where context allows inference of sentiment or intent without explicit signals. It has also been discussed in human perception research as an account of how people translate indirect cues into perceptual judgments.

Terminology and scope vary; some researchers treat senseindirect as a subset of latent-state estimation, while others view it as a distinct processing regime motivated by predictive coding. The concept emphasizes the use of context, priors, and world models to compensate for incomplete direct sensing.

See also: predictive processing, Bayesian inference, latent variable models, sensor fusion, causal reasoning.