In order to provide a behavior-analytic account of artificial intelligence (AI) operations and its predictive potential, we analyzed the extent to which a current version of a deep neural network (DNN) is able to model and forecast human learning. Human participants received individual automated training focusing on the relations among four 3-member stimulus classes where 2 of the 4 classes were composed of positive, algebraic, exponential expressions; 2 other classes were composed of negative exponential expressions. During the generalization test of novel stimulus relations, we assessed our 3 human participants in a series of 4 alternating contexts with 8 tests per context for a total of 32 tests of novel relations. When the DNN algorithm analyzed human training and generalization outcomes in terms of contextual control, clear resemblances between human and simulated participants became apparent. These findings are provocative in the sense that the simulated participants’ performances were predictive of the contextual control exhibited by humans during tests of novel relations. The degree to which these procedures might be adapted to enhance human potential is discussed. The outcomes from this study are related to several of the theoretical issues detailed within our separate conceptual AI study within this issue (Ninness & Ninness, 2020 ).

中文翻译：

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紧急虚拟分析：模拟衍生刺激关系的上下文控制

为了提供人工智能 (AI) 操作及其预测潜力的行为分析说明，我们分析了当前版本的深度神经网络 (DNN) 能够对人类学习进行建模和预测的程度。人类参与者接受了单独的自动化培训，重点是四个 3 人刺激类之间的关系，其中 4 个类中有 2 个由正数、代数、指数表达式组成；其他 2 个类由负指数表达式组成。在新刺激关系的泛化测试期间，我们在一系列 4 个交替环境中评估了我们的 3 名人类参与者，每个环境有 8 个测试，总共 32 个新关系测试。当 DNN 算法根据上下文控制分析人类训练和泛化结果时，人类和模拟参与者之间的明显相似之处变得明显。这些发现具有启发性，因为模拟参与者的表现可以预测人类在新关系测试期间表现出的情境控制。讨论了这些程序可以在多大程度上提高人类潜力。这项研究的结果与我们在本期单独的 AI 概念研究中详述的几个理论问题有关（Ninness & Ninness，2020 年）。讨论了这些程序可以在多大程度上提高人类潜力。这项研究的结果与我们在本期单独的 AI 概念研究中详述的几个理论问题有关（Ninness & Ninness，2020 年）。讨论了这些程序可以在多大程度上提高人类潜力。这项研究的结果与我们在本期单独的 AI 概念研究中详述的几个理论问题有关（Ninness & Ninness，2020 年）。