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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.

AbstractBy focusing on episodes from a case study of healthcare design practice investigated in situ, the aim of this paper is to provide a better understanding of the nature and use of evidence in design. Our account portrays a practice where sources other than scientific research findings were also considered. Based on observations and interviews from the field, the paper first provides a brief account of sources and representations of evidence. The varieties of evidence within the observed practice fall into four major groups: precedents, scientific research, embodied knowledge, and anecdotes. We observed how the participants in the design process used each of these forms of evidence to formulate and explain their design ideas in terms of mechanistic models to form causal links. These mechanistic arguments, which follow a model of scientific thinking, were repositories of trans-disciplinary knowledge involving design and other disciplines.

A central challenge for science studies researchers in developing accounts of knowledge construction in science and engineering is to integrate the cognitive, social, cultural, and material dimensions of practice. Within science studies there is a perceived divide between cognitive practices, on the one hand, and cultural practices, on the other. Any such divide, though at times analytically useful, is artificial. Producing scientific knowledge requires the kind of sophisticated cognition that only rich social, cultural, and material environments can enable. This paper aims to move in the direction of an integrative account of these dimensions of practice. It discusses model-based reasoning practices in biomedical engineering research laboratories construed as ‘evolving cognitive-cultural systems’.

There is substantial evidence that traditional instructional methods have not been successful in helping students to ‘restructure’ their commonsense conceptions and learn the conceptual structures of scientific theories. This paper argues that the nature of the changes and the kinds of reasoning required in a major conceptual restructuring of a representation of a domain are fundamentally the same in the discovery and in the learning processes. Understanding conceptual change as it occurs in science and in learning science will require the development of a common cognitive model of conceptual change. The historical construction of an inertial representation of motion is examined and the potential instructional implications of the case are explored.

In his article, "Is Essentialism Unscientific?" (1988), Jarrett Leplin claims that I do not have sufficient grounds for rejecting the customary "philosophical method of discovery" that allows for the direct transfer of theories developed in the philosophy of language to science. While admitting that all attempts at transfer thus far have failed, he still maintains that method is sound. I argue that the wholesale failure of these attempts is reason enough to suspect the method and to try to devise one more suitable to fathoming how "meaning", "reference", and "meaning change" are to be understood for scientific theories. The method I have proposed in Nersessian (1984b), and subsequent work, demands that we learn how to incorporate the actual practices of meaning construction in science into our analyses. Leplin distorts my analysis and, thus, fails to understand the insights that study provides.

The article focuses on longitudinal case study regarding the significance of the use of chronicled reasoning and problem-solving process of engineering master's students. The study performed field observations, wrote field notes, and conduct informal and unstructured interviews. The study showed that engineers used external models to simulate possible scenarios, visualize the device output, and to tag device elements to determine the level to mixing.