The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model—an internal estimate of overall model fitness (“subjective fitness”). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

中文翻译：

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深刻感受到的情感：深度主动推理中效价的出现

情绪效价的正负轴长期以来一直被认为是适应性行为的基础，但其起源和潜在功能在很大程度上回避了正式的理论和计算模型。使用深度主动推理（一种基于反转感官数据生成方式的模型的分层推理方案），我们开发了一种原则性的情绪效价贝叶斯模型。该公式断言，智能体根据其行为模型的预期精度推断其价态——整体模型适应性的内部估计（“主观适应性”）。这种主观适应度指数可以在任何环境中进行估计，并利用二阶信念（关于信念的信念）的领域通用性。我们展示了维持内部价表示如何允许随后的情感主体先发制人地优化行动选择的信心。反过来，可以通过利用主观适应度的（贝叶斯最优）更新项来优化价表示，我们将其标记为情感电荷（AC）。AC 跟踪适应度估计的变化，并为预测和结果之间的无符号差异提供一个标志。我们通过将计算机情感代理置于需要上下文学习的 T 迷宫范例中，然后进行上下文反转来模拟由此产生的情感推理。这种情感推理的表述为情感、（心理）行为和内隐元认知之间的联系提供了原则性的解释。它描述了深层生物系统如何推断其情感状态并通过内部行动（即自上而下地调节保证信心的先验）来减少此类推断的不确定性。因此，我们证明了主动推理提供正式且计算上易于处理的情感解释的潜力。我们对该配方的表面有效性和潜在效用的证明代表了更大研究计划的第一步。接下来，可以利用该模型通过将模型拟合到行为和神经元反应来测试效价的假设作用。