Human perception and memory have traditionally been described as sequential processesin which sensory input is encoded, consolidated, and later retrieved. However, emerging evidenceacross neuroscience, psychology, and computational modeling suggests that this classicalview fails to capture the dynamic, multisensory, and context-dependent nature of humancognition. This paper introduces the SENSE model—Sampling–Emotion–Networked–Schema-Based Encoding—a unified framework in which perception and memory arise frommultisensory pattern encoding weighted by emotional salience and structured by accumulatedknowledge. Sensory systems do not store raw data; instead, they generate patterns whosefidelity is determined by (a) sampling density, (b) emotional amplification, and (c) schemabasedknowledge structures. Memory is conceptualized not as a replay of stored sensory inputbut as a reconstruction generated from these weighted patterns. The SENSE model integratespredictive processing, emotional modulation, schema theory, and multisensory integration intoa single mechanistic account of how humans perceive, encode, and reconstruct the world.
Ramesh Lal Dharamdass (Sun,) studied this question.