Scientists, either working alone or in groups, require rich cognitive, social, cultural, and material environments to accomplish their epistemic aims. There is research in the cognitive sciences that examines intelligent behavior as a function of the environment (“environmental perspectives”), which can be used to examine how scientists integrate “cognitive-cultural” resources as they create environments for problem-solving. In this paper, I advance the position that an expanded framework of distributed cognition can provide conceptual, analytical, and methodological tools to investigate how scientists enhance natural cognitive capacities by creating specific kinds of environments to address their epistemic goals. In a case study of a pioneering neuroengineering lab seeking to understand learning in living networks of neurons, I examine how the researchers integrated conceptual, methodological, and material resources from engineering, neuroscience, and computational science to create different kinds of distributed problem-solving environments that enhanced their natural cognitive capacities, for instance, for reasoning, visualization, abstraction, imagination, and memory, to attain their epistemic aims.

科学家，无论是单独工作还是结成团队，都需要丰富的认知、社会、文化和物质环境来实现他们的认识目标。认知科学中有研究将智能行为作为环境的函数（“环境视角”）进行研究，这可用于研究科学家在创造解决问题的环境时如何整合“认知文化”资源。在本文中，我提出了一个立场，即分布式认知的扩展框架可以提供概念、分析和方法论工具，以研究科学家如何通过创造特定类型的环境来解决他们的认识目标来提高自然认知能力。在一个寻求了解活神经元网络学习的开创性神经工程实验室的案例研究中，我研究了研究人员如何整合来自工程、神经科学和计算科学的概念、方法和材料资源，以创建不同类型的分布式问题解决环境，从而增强他们的自然认知能力，例如，用于推理、可视化、抽象、想象力、 和记忆，以实现他们的认识目标。