Bridging associative and normative theories of animal learning, I show that an associative system can behave as if performing probabilistic inference by using the function f(V) = 1 - e-cV to transform associative strengths (V) into response probabilities. For example, using this function, an associative system can respond normatively to a compound stimulus AB, given previous separate experiences with the components A and B. The CR probability formulae that result from the proposed function have a normative interpretation in terms of statistical decision theory. The formulae also suggest a normative interpretation of stimulus generalization as a heuristic to infer whether different stimuli are likely to convey redundant or independent information about reinforcement. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

中文翻译：

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将关联强度映射到概率的响应函数。

桥接动物学习的关联理论和规范理论，我表明关联系统可以通过使用函数 f(V) = 1 - e-cV 将关联强度 (V) 转换为响应概率来执行概率推理。例如，使用此函数，联想系统可以规范地响应复合刺激 AB，给定先前对组件 A 和 B 的单独经验。从所提出的函数得出的 CR 概率公式在统计决策理论方面具有规范性解释. 这些公式还建议将刺激泛化作为一种​​启发式的规范解释，以推断不同的刺激是否可能传达关于强化的冗余或独立信息。（PsycInfo 数据库记录 (c) 2022 APA，保留所有权利）。