Inspired by Ronald Giere’s cognitive approach to scientific models, Cognitive Structural Realism has presented a naturalist account of scientific representation. CSR characterises the structure of theories in terms of cognitive structures. These are informational structures embodied in the brains of scientists. CSR accounts for scientific representation in terms of the dynamical relationship between the organism and its environment. The proposal has been criticised on account of its negligence of social aspects of scientific practice. The present paper aims to chart out (...) a reply to the objection. It shows that cognitive structures do not need to be put inside the brains of single individuals. Cognitive structures are redefined as extended structures in distributed cognitive systems under Free Energy Principle. (shrink)

Studies of scientific practice demonstrate that the development of scientific models is an enactive and emergent process. Scientists make meaning through processes such as perspective taking, finding patterns, and following intuitions. In this paper, we focus on how a group of fourth grade learners and their teacher engaged in interpretation in ways that align with core ideas and practices in kinematics and computing. Cycles of measuring and modeling––including computer programming––helped to support classroom interactions that highlighted the interpretive nature of modeling (...) and participation in model construction as a knowledge-building process. We draw on literature from the history and philosophy of science in order to analyze the students’ interpretive actions as forms of epistemic and representational agency, constituting a construct we term disciplined interpretation. We demonstrate how students’ disciplined interpretative moves help to position them as owners of their own design decisions and their rights to interpret the phenomena they were modeling, data collected from those phenomena, and the scientific and computational models themselves. We present four extended episodes that characterize the nature of activity in the classroom and the development of students’ disciplined interpretations in terms of learning to recognize scientific patterns amid complex perceptual fields, and to represent them in ways that support sensemaking. (shrink)

We outline three challenges involved in designing external representations that promote sustainable use of natural resources. First, the task environment of sustainable resource-use is highly unstructured, and involves many uncoordinated and asynchronous actions. Following from this complex nature of the task environment, more task constraints and task interactions are involved in designing representations promoting sustainability, compared to representations that seek to make tasks easier in structured task environments, such as aircraft cockpits and control rooms. Second, external representations promoting sustainable resource-use (...) need to motivate people to make decisions that sustain resources, and persist with this behavior, even though alternate behaviors are easier and commonplace. Third, external representations promoting sustainability also need to lower the cognitive load involved in sustainability decisions. This three-tiered function (meeting complex task constraints, providing motivation, lowering cognitive load) makes such representations challenging to design. However, some early prototype designs promoting sustainable resource-use have appeared recently, primarily addressing electricity use. Analyzing these digital prototypes, we outline three design principles they share, and show how these seek to address the complexities of the sustainability problem-space (complexity of task environments, goals, and representations). We then argue that at least two further cognitive scaffolds are required for effectively promoting sustainable resource use (action scripts, deconstruction). We close with some of the limitations of this approach to promoting sustainability, and outline future work. (shrink)

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery, are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that (...) led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account. (shrink)

Manipulation of physical models such as tangrams and tiles is a popular approach to teaching early mathematics concepts. This pedagogical approach is extended by new computational media, where mathematical entities such as equations and vectors can be virtually manipulated. The cognitive and neural mechanisms supporting such manipulation-based learning—particularly how actions generate new internal structures that support problem-solving—are not understood. We develop a model of the way manipulations generate internal traces embedding actions, and how these action-traces recombine during problem-solving. This model (...) is based on a study of two groups of sixth-grade students solving area problems. Before problem-solving, one group manipulated a tangram, the other group answered a descriptive test. Eye-movement trajectories during problem-solving were different between the groups. A second study showed that this difference required the tangram's geometrical structure, just manipulation was not enough. We propose a theoretical model accounting for these results, and discuss its implications. (shrink)

This paper takes an embodied and extended cognition perspective to ER integration – a cognitive process through which a learner integrates external representations (ERs) in a domain, with her internal (mental) model, as she interacts with, uses, understands and transforms between those ERs. In the paper, I argue for a theoretical as well as empirical shift in future investigations of ER integration, by proposing a model of cognitive mechanisms underlying the process, based on recent advances in extended and embodied cognition. (...) I present this new model in contrast to the still dominant classical cognitivist (information processing) approaches to ER integration, and the educational technology intervention designs such approaches inspire. I then exemplify this distinction between the information processing model and the new model through a case of arithmetic problem solving. Corroborative neuroscience evidence presented in relation to this case provides empirical support for the new model by showing how bodily actions (sensorimotor mechanisms) are critical to ER integration and learning. Finally, as educational implications of the new model, I demonstrate the need for: (i) re-viewing the development of ER integration and expertise as fine-tuning of the learner's action or sensorimotor system, and (ii) a shift of focus in new-media intervention design principles based on this newer understanding of ER integration in science, technology, engineering and mathematics. (shrink)

Building computational models of engineered exemplars, or prototypes, is a common practice in the bioengineering sciences. Computational models in this domain are often built in a patchwork fashion, drawing on data and bits of theory from many different domains, and in tandem with actual physical models, as the key objective is to engineer these prototypes of natural phenomena. Interestingly, such patchy model building, often combined with visualizations, whose format is open to a wide range of choice, leads to the discovery (...) of new concepts and control structures. Two key questions are raised by this practice: how could discoveries arise from building external representations for which there is wide latitude in choice of the components from which they are built, and thus can be considered significantly arbitrary, and how could such discoveries allow engineering a real-world prototype system. To examine these questions, we present two case studies of discoveries that emerged from the building of such computational models in the bioengineering sciences. We then develop a process model that accounts for the discovery and transfer problems raised by both these cases, focusing on the process of building the model. Specifically, to account for the discovery problem, we propose that the process of building such models gradually leads to a close coupling between the modeler’s internal processes and the external dynamic model. To account for the transfer problem, we propose that the process of building the model leads to the creation of an enactive model that is generic, which closely enacts, and thus reveals, the way the system-level behavior of the engineered prototype emerges in time through the interaction of its parts. This enactive replication process leads to the model and the prototype forming a new class, which allows concepts and control structures developed for the computational model to be transfered to the real-world prototype. We argue that this account requires rethinking correspondence in engineering sciences as a plastic and enactive relation. A closer focus on the process of building models is required to develop a general account of this emerging approach to discovery. (shrink)

Humans continuously delegate and distribute cognitive functions to the environment to lessen their limits. They build models, representations, and other various mediating structures, that are thought to be good to think. The case of scientific innovation is particularly important: the main aim of this paper is to revise and criticize the concept of scientific innovation, reframing it in what I will call an eco-epistemic perspective, taking advantage of recent results coming from the area of distributed cognition (common coding) and abductive (...) cognition (manipulative). Taking advantage of this eco-cognitive perspective the article outlines how innovative scientific modeling activity can be better described taking advantage of the concept of “epistemic warfare”, which sees scientific enterprise as a complicated struggle for rational knowledge in which it is crucial to distinguish epistemic (for example scientific models) from non epistemic (for example fictions, falsities, propaganda) weapons. (shrink)