PRISM: Predicting Complete Scene-Memory Report Distributions from Visual Geometry

Visual working memory preserves information about a scene after it is no longer visible. Yet it is usually modeled through accuracy or error magnitude, which discard which scene a person reports. Two equally distant errors can differ in visual content, making error identity a richer probe of remembered representations. We introduce PRISM, a framework for predicting complete distributions of scene reports from target–candidate visual similarity. PRISM-Eval isolates what frozen vision networks contribute beyond response-display structure and six fixed image descriptors. PRISM-Channel places visual evidence within a cognitive model of memory precision and guessing, while Cognitive-Channel Geometry Distillation (CCGD) learns a compact combination of frozen similarities. Across two continuous-report datasets, intermediate visual features improve held-out likelihood beyond all fixed controls. Combining three visual models improves cognitive-baseline predictions, with relative-weight gains under fixed regularization and positive adapted transfer to an independent memory task. Independent EEG data show that the behaviorally useful geometry captures neural structure beyond six fixed descriptors. PRISM thus turns memory error identity into a controlled bridge between natural-scene vision, working-memory theory, and neural representations.