Exploration of the physical environment is an indispensable precursor to data acquisition and enables knowledge generation via analytical or direct trialing. Artificial Intelligence lacks the exploratory capabilities of even the most underdeveloped organisms, hindering its autonomy and adaptability. Supported by cognitive psychology, this works links human behavior and artificial agents to endorse self-development. In accordance with reported data, paradigms of epistemic and achievement emotion are embedded to machine-learning methodology contingent on their impact when decision making. A study is subsequently designed to mirror previous human trials, which artificial agents are made to undergo repeatedly towards convergence. Results demonstrate causality, learned by the vast majority of agents, between their internal states and exploration to match those reported for human counterparts. The ramifications of these findings are pondered for both research into human cognition and betterment of artificial intelligence.

翻译：物理环境的探索是数据获取不可或缺的前置条件，并能通过分析或直接尝试的方式生成知识。人工智能缺乏即使是最低等生物体所具备的探索能力，这制约了其自主性和适应性。在认知心理学的支持下，本研究将人类行为与人工主体相联结，以促进其自我发展。根据已有数据，知识性情绪与成就性情绪的模式被嵌入机器学习方法中，具体取决于它们对决策制定的影响。随后，我们设计了一项模仿先前人类试验的研究，使人工主体反复经历该过程直至收敛。结果表明，绝大多数主体在其内部状态与探索行为之间习得了因果关系，这与人类被试的报告结果相匹配。本文探讨了这些发现对理解人类认知及改进人工智能的双重启示。