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We present a unified signal detection theory (SDT) framework that applies to both, and use it to fit behavioral and computational data in matched paradigms. Both systems show logarithmic accuracy decline with association count (fan), but humans exhibit lower interference sensitivity ($\\alpha/\\sigma = 0.41$) than dense passage retrieval ($\\alpha/\\sigma = 0.67$), with cognitively-inspired HippoRAG falling between the two ($\\alpha/\\sigma = 0.44$). Behavioral experiments ($N = 112$) and simulations validate the framework; parameter recovery confirms identifiability ($r \\geq .93$) and model comparison favors the logarithmic specification over a power-law alternative ($\\Delta$BIC $> 15$). 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Behavioral experiments ($N = 112$) and simulations validate the framework; parameter recovery confirms identifiability ($r \\geq .93$) and model comparison favors the logarithmic specification over a power-law alternative ($\\Delta$BIC $> 15$). 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