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This paper proposes the CogSense system, which is inspired by sense-makingcognition and perception in the mammalian brain to perform perception errordetection and perception parameter adaptation using probabilistic signaltemporal logic. As a specific application, a contrast-based perception adaptionmethod is presented and validated. The proposed method evaluates perceptionerrors using heterogeneous probe functions computed from the detected objectsand subsequently solves a contrast optimization problem to correct perceptionerrors. The CogSense probe functions utilize the characteristics of geometry,dynamics, and detected blob image quality of the objects to develop axioms in aprobabilistic signal temporal logic framework. By evaluating these axioms, wecan formally verify whether the detections are valid or erroneous. Further,using the CogSense axioms, we generate the probabilistic signal temporallogic-based constraints to finally solve the contrast-based optimizationproblem to reduce false positives and false negatives.

CogSense: A Cognitively Inspired Framework for Perception Adaptation