Availability and Selection of Distributed Associations in Human Memory and Machine Learning

Human memory and learning reveal a recurring distinction between possessing information and selecting it appropriately. This article develops the hypothesis that learning history and current cues jointly constrain which distributed association guides a response and whether its use transfers to a changed task. It separates availability, cue-dependent access, and task-appropriate selection, drawing on established accounts of memory retrieval and self-directed learning. The central contribution is a comparative strategy: implement corresponding relational manipulations in people and learning machines while preserving the controls needed to interpret each implementation. A memory comparison crosses target exposure with competitor contingency, keeping marginal word counts constant and recording the identity of errors. This tests whether the same relational contrast predicts competitor substitutions across implementations. ACT-R and other retrieval models provide mechanistic comparisons, and a simplified activation model supplies a restricted baseline. A causal-learning comparison separates access to identifying evidence from personally choosing a test, distinguishing exact software replay from human yoked learning. A human subgroup without repeated belief reports addresses the possibility that measurement changes learning. Elementary probability examples separate information magnitude, predictive usefulness, and causal identification, and seven conditional predictions connect the theoretical distinctions to discriminating tests. All learning studies remain proposals. The research agenda specifies how convergent effects and implementation-specific differences could assess the scope of the hypothesis without assuming a common cognitive architecture.