Formation of stimulus equivalence classes has been recently modeled through equivalence projective simulation (EPS), a modified version of a projective simulation (PS) learning agent. PS is endowed with an episodic memory that resembles the internal representation in the brain and the concept of cognitive maps. PS flexibility and interpretability enable the EPS model and, consequently the model we explore in this letter, to simulate a broad range of behaviors in matching-to-sample experiments. The episodic memory, the basis for agent decision making, is formed during the training phase. Derived relations in the EPS model that are not trained directly but can be established via the network's connections are computed on demand during the test phase trials by likelihood reasoning. In this letter, we investigate the formation of derived relations in the EPS model using network enhancement (NE), an iterative diffusion process, that yields an offline approach to the agent decision making at the testing phase. The NE process is applied after the training phase to denoise the memory network so that derived relations are formed in the memory network and retrieved during the testing phase. During the NE phase, indirect relations are enhanced, and the structure of episodic memory changes. This approach can also be interpreted as the agent's replay after the training phase, which is in line with recent findings in behavioral and neuroscience studies. In comparison with EPS, our model is able to model the formation of derived relations and other features such as the nodal effect in a more intrinsic manner. Decision making in the test phase is not an ad hoc computational method, but rather a retrieval and update process of the cached relations from the memory network based on the test trial. In order to study the role of parameters on agent performance, the proposed model is simulated and the results discussed through various experimental settings.

中文翻译：

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增强的等价投影模拟：刺激等价类形成的建模框架

刺激等价类的形成最近已通过等价投影模拟 (EPS) 进行建模，EPS 是投影模拟 (PS) 学习代理的修改版本。PS 被赋予类似于大脑内部表征和认知地图概念的情景记忆。PS 的灵活性和可解释性使 EPS 模型以及我们在这封信中探索的模型能够模拟匹配样本实验中的广泛行为。情景记忆是代理决策的基础，是在训练阶段形成的。EPS 模型中未直接训练但可以通过网络连接建立的派生关系在测试阶段试验期间通过似然推理按需计算。在这封信中，我们使用网络增强 (NE) 来研究 EPS 模型中派生关系的形成，这是一种迭代扩散过程，它在测试阶段为代理决策提供了一种离线方法。在训练阶段之后应用 NE 过程对记忆网络进行去噪，以便在记忆网络中形成派生关系并在测试阶段检索。在NE阶段，间接关系增强，情景记忆结构发生变化。这种方法也可以解释为智能体在训练阶段后的重放，这与行为和神经科学研究的最新发现一致。与 EPS 相比，我们的模型能够以更内在的方式对派生关系的形成和节点效应等其他特征进行建模。测试阶段的决策不是一种特殊的计算方法，而是基于测试试验从内存网络中检索和更新缓存关系的过程。为了研究参数对代理性能的作用，对所提出的模型进行了模拟，并通过各种实验设置讨论了结果。