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Current models of analogical reasoning assume that representations of source examples and target problems occur in an amodal format--that is, a representation whose construction and processing are independent of activity in the perceptual and motor cortices of the brain. We examined the possible use of kinesthetic information--perceptual structures associated with the sensation of space and force--in the representation of source examples and target problems. Participants who recreated a source story while acting out the key elements were more likely to access the story when later working on the target problem than were participants who only verbally recreated the story or who verbally recreated it as well as sketched it. We argue that enactment made kinesthetic and spatial features more salient in participants' source story representations and that this aided performance. These results suggest that current models of analogical reasoning might be improved by including perceptual information as part of their representational schemes. [ABSTRACT FROM AUTHOR]

In this paper we aim to give an analysis and cognitive rationalization of a common practice or strategy of modeling in systems biology known as a middle-out modeling strategy. The strategy in the cases we look at is facilitated through the construction of what can be called mesoscopic models. Many models built in computational systems biology are mesoscopic (midsize) in scale. Such models lack the sufficient fidelity to serve as robust predictors of the behaviors of complex biological systems, one of the signature goals of the field. This puts some pressure on the field to provide reasons for why and how these practices are warranted despite not meeting the stated goals of the field. Using the results of ethnographic study of problem-solving practices in systems biology, we aim to examine the middle-out strategy and mesoscopic modeling in detail and to show that these practices are rational responses to complex problem solving tasks on cognitive grounds in particular. However making this claim requires us to update the standard notion of bounded rationality to take account of how human cognition is coupled to computation in these contexts. Our account fleshes out the idea that has been raised by some philosophers on the "hybrid" nature of computational modeling and simulation. What we call "coupling" both extends modelers' capacities to handle complex systems, but also produces various cognitive and computational constraints which need to be taken into account in any computational problem solving strategy seeking to maintain insight and control over the models produced., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations.

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), 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 (DC) 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., (Copyright © 2014 Cognitive Science Society, Inc.)

We begin our commentary by summarizing the commonalities and differences in cognitive phenomena across cultures, as found by the seven papers of this topic. We then assess the commonalities and differences in how our various authors have approached the study of cognitive diversity, and speculate on the need for, and potential of, cross-disciplinary collaboration., (Copyright © 2015 Cognitive Science Society, Inc.)

In this paper we draw upon rich ethnographic data of two systems biology labs to explore the roles of explanation and understanding in large-scale systems modeling. We illustrate practices that depart from the goal of dynamic mechanistic explanation for the sake of more limited modeling goals. These processes use abstract mathematical formulations of bio-molecular interactions and data fitting techniques which we call top-down abstraction to trade away accurate mechanistic accounts of large-scale systems for specific information about aspects of those systems. We characterize these practices as pragmatic responses to the constraints many modelers of large-scale systems face, which in turn generate more limited pragmatic non-mechanistic forms of understanding of systems. These forms aim at knowledge of how to predict system responses in order to manipulate and control some aspects of them. We propose that this analysis of understanding provides a way to interpret what many systems biologists are aiming for in practice when they talk about the objective of a "systems-level understanding.", (Copyright © 2014 Elsevier Ltd. All rights reserved.)

In this paper, we provide a novel analysis of the affordances and trade-offs of different strategies for integrating model-building and experimentation in integrative systems biology. Unimodal strategies rely on collaboration between experimenters and modelers in distinct laboratories. In a bimodal strategy modelers perform their own experiments. Each option has advantages and challenges. In the case of the labs we studied, the choice of strategy often depends on preferences held by lab directors as to the strategy that best achieves certain philosophical objectives concerning what they see as the aims of modeling in systems biology and the epistemic standards to apply to it. We identify an important connection between philosophical divisions in systems biology and the structure of research in systems biology. Better knowledge of these strategies and their philosophical motivations provide insight behind the diversifying structure of the field, and can help lab directors understand the challenges their researchers face, as well as the training and lab organization options available., (© 2014 Wiley Periodicals, Inc.)

The importation of computational methods into biology is generating novel methodological strategies for managing complexity which philosophers are only just starting to explore and elaborate. This paper aims to enrich our understanding of methodology in integrative systems biology, which is developing novel epistemic and cognitive strategies for managing complex problem-solving tasks. We illustrate this through developing a case study of a bimodal researcher from our ethnographic investigation of two systems biology research labs. The researcher constructed models of metabolic and cell-signaling pathways by conducting her own wet-lab experimentation while building simulation models. We show how this coupling of experiment and simulation enabled her to build and validate her models and also triangulate and localize errors and uncertainties in them. This method can be contrasted with the unimodal modeling strategy in systems biology which relies more on mathematical or algorithmic methods to reduce complexity. We discuss the relative affordances and limitations of these strategies, which represent distinct opinions in the field about how to handle the investigation of complex biological systems., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

This article presents a translational model of curricular design in which findings from investigating learning in university BME research laboratories (in vivo sites) are translated into design principles for educational laboratories (in vitro sites). Using these principles, an undergraduate systems physiology lab class was redesigned and then evaluated in a comparative study. Learning outcomes in a control section that utilized a technique-driven approach were compared to those found in an experimental class that embraced a problem-driven approach. Students in the experimental section demonstrated increased learning gains even when they were tasked with solving complex, ill structured problems on the bench top. The findings suggest the need for the development of new, more authentic models of learning that better approximate practices from industry and academia.

Designing, building, and experimenting with physical simulation models are central problem-solving practices in the engineering sciences. Model-based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher-artifact system. It draws on and further develops the framework of "distributed cognition" to interpret data collected in ethnographic and cognitive-historical studies of two biomedical engineering research laboratories, and articulates the notion of distributed model-based cognition to answer the question posed in the title., (Copyright © 2009 Cognitive Science Society, Inc.)