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)

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)

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.

Recently, several scholars have argued that scientists can accept scientific claims in a collective process, and that the capacity of scientific groups to form joint acceptances is linked to a functional division of labor between the group members. However, these accounts reveal little about how the cognitive content of the jointly accepted claim is formed, and how group members depend on each other in this process. In this paper, I shall therefore argue that we need to link analyses of joint (...) acceptance with analyses of distributed cognition. To sketch how this can be done, I shall present a detailed case study, and on the basis of the case, analyze the process through which a group of scientists jointly accept a new scientific claim and at a later stage jointly accept to revise previously accepted claims. I shall argue that joint acceptance in science can be established in situations where an overall conceptual structure is jointly accepted by a group of scientists while detailed parts of it are distributed among group members with different areas of expertise, a condition that I shall call a heterogeneous conceptual consensus. Finally, I shall show how a heterogeneous conceptual consensus can work as a constraint against scientific change and address the question how changes may nevertheless occur. (shrink)

A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering (...) scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Nersessian pushes the boundaries of the philosophy of science and cognitive science into areas not ventured before. She recounts a decades-long, wide-ranging, and richly detailed investigation of the innovative interdisciplinary modeling practices of bioengineering researchers in four university laboratories. She argues and demonstrates that the methods of cognitive ethnography and qualitative data analysis, placed in the framework of distributed cognition, provide the tools for a philosophical analysis of how scientific discoveries arise from complex systems in which the cognitive, social, material, and cultural dimensions of problem-solving are integrated into the epistemic practices of scientists. Specifically, she looks at how interdisciplinary environments shape problem-solving. Although Nersessian’s case material is drawn from the bioengineering sciences, her analytic framework and methodological approach are directly applicable to scientific research in a broader, more general sense, as well. (shrink)

We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: to identify the benefits of an ethnographic approach in philosophy of science and to structure an ethnographic approach for philosophical investigation best adapted to provide information relevant to philosophical interests and epistemic values. To this end, we advocate for a purpose-guided form of cognitive ethnography that mediates between the explanatory and normative interests of philosophy of science, while maintaining openness and (...) independence when framing such an investigation to achieve robust unbiased results. (shrink)

The important role of mathematical representations in scientific thinking has received little attention from cognitive scientists. This study argues that neglect of this issue is unwarranted, given existing cognitive theories and laws, together with promising results from the cognitive historical analysis of several important scientists. In particular, while the mathematical wizardry of James Clerk Maxwell differed dramatically from the experimental approaches favored by Michael Faraday, Maxwell himself recognized Faraday as “in reality a mathematician of a very high order,” and his (...) own work as in some respects a re‐representation of Faraday’s field theory in analytic terms. The implications of the similarities and differences between the two figures open new perspectives on the cognitive role of mathematics as a learned mode of representation in science. (shrink)

Recent work in epistemology and philosophy of science has argued that understanding is an important cognitive state that philosophers should seek to analyse. This paper offers a new perspective on understanding by looking to work in philosophy of mind and cognitive science. Understanding is normally taken to be inside the head. I argue that this view is mistaken. Often, understanding is a state that criss-crosses brain, body and world. To support this claim, I draw on extended cognition, a burgeoning framework (...) in cognitive science that stresses the crucial role played by tools, material representations and the wider environment in our cognitive processes. I defend an extended view of understanding against likely objections and argue that it has important consequences for questions concerning the nature of understanding and its relationship to explanation. (shrink)

Distributed cognition (d-cog) claims that many cognitive processes are distributed across groups and the surrounding material and cultural environment. Recently, Nancy Nersessian, Ronald Giere, and others have suggested that a d-cog approach might allow us to bring together cognitive and social theories of science. I explore this idea by focusing on the specific interpretation of d-cog found in Edwin Hutchins' canonical text Cognition in the wild. First, I examine the scope of a d-cog approach to science, showing that there are (...) important disputes between cognitive and social theorists on which d-cog remains silent. Second, I suggest that, where social explanations can be recast in d-cog terms, this reformulation will not be acceptable to all social theorists. Finally, I ask how we should make sense of the claim that, on a d-cog analysis, social factors are cognitive factors. (shrink)

This paper aims to contribute to the expanding discourse on inter- and transdisciplinarity. Referring to well-established distinctions in philosophy of science, the paper argues in favor of a plurality of four different dimensions: Interdisciplinarity with regard to objects, knowledge/theories, methods/practices, and further, problem perception/problem solving. Different philosophical thought traditions can be related to these distinguishable meanings. The philosophical framework of the four different dimensions will be illustrated by some of the most popular examples of research programs that are labeled interdisciplinary (...) : nanoresearch/nanoscience/nanotechnology, complex systems theory/chaos theory, biomimicry/bionics, and technology assessment/sustainability research. Thus, a minimal philosophy of science is required to understand and foster inter- and transdisciplinarity. (shrink)

Massive Open Online Courses (MOOCs) have, in recent years, become increasingly popular. An important challenge facing MOOCs is the ‘teacher bandwidth problem’: In the MOOC environment, where there are potentially hundreds of thousands of students, it is impossible for a few teachers to interact with individual students—there is not enough ‘teacher bandwidth’. According to Siemens and Downes’s theory of ‘connectivism’ (Siemens, 2004) one can make up for the lack of teacher bandwidth by relying on collaboration between students; philosophically speaking, however, (...) this theory is underdeveloped. In this paper, we consider the question of learner collaboration in online courses, and the theory of connectivism, from the perspective of social epistemology. We note the similarities between Siemens and Downes’s theory and virtue reliabilist theories of epistemic collaboration more broadly. Our paper has two main aims. First, we offer an illustration of how it is possible to conceptualise learner collaboration in online settings as analogous to collaboration between scientists. Second, we attempt to expand on and clarify what Siemens and Downes had in mind when they proposed the theory of connectivism. (shrink)

In Weaving the Web, Berners-Lee defines Social Machines as biotechnologically hybrid Web-processes on the basis of which, “high-level activities, which have occurred just within one human’s brain, will occur among even larger more interconnected groups of people acting as if the shared a larger intuitive brain”. The analysis and design of Social Machines has already started attracting considerable attention both within the industry and academia. Web science, however, is still missing a clear definition of what a Social Machine is, which (...) has in turn resulted in several calls for a “philosophical engineering” ; Halpin et al. 2010). This paper is a first attempt to respond to this call, by combining contemporary philosophy of mind and cognitive science with epistemology. The idea of philosophical engineering implies that a sufficiently good conception of Social Machines should be of both theoretical and practical advantage. To demonstrate how the present approach can satisfy both objectives it will be used in order to address one of Wikipedia’s most worrying concerns—i.e., the current and ongoing decline in the number of its active contributors. (shrink)

We historically and conceptually situate distributed cognition by drawing attention to important similarities in assumptions and methods with those of American ?functional psychology? as it emerged in contrast and complement to controlled laboratory study of the structural components and primitive ?elements? of consciousness. Functional psychology foregrounded the adaptive features of cognitive processes in environments, and adopted as a unit of analysis the overall situation of organism and environment. A methodological implication of this emphasis was, to the extent possible, the study (...) of cognitive and other processes in the natural (real world) contexts in which they occur. We therefore emphasize commonalities and differences between functional psychology and D-Cog. One purpose of the comparison is to consider the extent to which criticisms directed at functional psychology are relevant to D-Cog. We also examine the relation between functional psychology and philosophical pragmatism and conclude that D-Cog's conceptual framework would be strengthened through more explicit adoption of philosophical pragmatism, consistent with the eventual trajectory of functional psychology. (shrink)

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. (shrink)

The paper frames interdisciplinary research as creating complex, distributed cognitive-cultural systems. It introduces and elaborates on the method of cognitive ethnography as a primary means for investigating interdisciplinary cognitive and learning practices in situ. The analysis draws from findings of nearly 20 years of investigating such practices in research laboratories in pioneering bioengineering sciences. It examines goals and challenges of two quite different kinds of integrative problem-solving practices: biomedical engineering (hybridization) and integrative systems biology (collaborative interdependence). Practical lessons for facilitating (...) research and learning in these specific fields are discussed and a preliminary set of interdisciplinary epistemic virtues are proposed as candidates for cultivation in interdisciplinary practices of these kinds more widely. (shrink)

Researchers in the biological and biomedical sciences, particularly those working in laboratories, use a variety of artifacts to help them perform their cognitive tasks. This paper analyses the relationship between researchers and cognitive artifacts in terms of integration. It first distinguishes different categories of cognitive artifacts used in biological practice on the basis of their informational properties. This results in a novel classification of scientific instruments, conducive to an analysis of the cognitive interactions between researchers and artifacts. It then uses (...) a multidimensional framework in line with complementarity-based extended and distributed cognition theory to conceptualize how deeply instruments in different informational categories are integrated into the cognitive systems of their users. The paper concludes that the degree of integration depends on various factors, including the amount of informational malleability, the intensity and kind of information flow between agent and artifact, the trustworthiness of the information, the procedural and informational transparency, and the degree of individualisation. (shrink)

The complementary properties and functions of cognitive artifacts and other external resources are integrated into the human cognitive system to varying degrees. The goal of this paper is to develop some of the tools to conceptualize this complementary integration between agents and artifacts. It does so by proposing a multidimensional framework, including the dimensions of information flow, reliability, durability, trust, procedural transparency, informational transparency, individualization, and transformation. The proposed dimensions are all matters of degree and jointly they constitute a multidimensional (...) space in which situated cognitive systems can be located and have certain dimensional configurations. These dimensions provide a new perspective on the conditions for cognitive extension. They are, however, not meant to provide a set of necessary and sufficient conditions, but to provide a toolbox for investigating the degree and nature of the integration of agent and artifact into “new systemic wholes”. The higher a situated system scores on the proposed dimensions, the more functional integration occurs, and the more tightly coupled the system is. (shrink)

The goal of this paper is to develop a systematic taxonomy of cognitive artifacts, i.e., human-made, physical objects that functionally contribute to performing a cognitive task. First, I identify the target domain by conceptualizing the category of cognitive artifacts as a functional kind: a kind of artifact that is defined purely by its function. Next, on the basis of their informational properties, I develop a set of related subcategories in which cognitive artifacts with similar properties can be grouped. In this (...) taxonomy, I distinguish between three taxa, those of family, genus, and species. The family includes all cognitive artifacts without further specifying their informational properties. Two genera are then distinguished: representational and non-representational (or ecological) cognitive artifacts. These genera are further divided into species. In case of representational artifacts, these species are iconic, indexical, or symbolic. In case of ecological artifacts, these species are spatial or structural. Within species, token artifacts are identified. The proposed taxonomy is an important first step towards a better understanding of the range and variety of cognitive artifacts and is a helpful point of departure, both for conceptualizing how different artifacts augment or impair cognitive performance and how they transform and are integrated into our cognitive system and practices. (shrink)

The sciences use a wide range of visual devices, practices, and imaging technologies. This diversity points to an important repertoire of visual methods that scientists use to adapt representations to meet the varied demands that their work places on cognitive processes. This paper identifies key features of the use of visualization in a range of scientific domains and considers the implications of this repertoire for understanding scientists as cognitive agents.

Case studies of diverse scientific fields show how scientists use a range of resources to generate new interpretative models and to establish their plausibility as explanations of a domain. They accomplish this by manipulating imagistic representations in particular ways. I show that scientists in different domains use the same basic transformations. Common features of these transformations indicate that general cognitive strategies of interpretation, simplification, elaboration, and argumentation are at work. Social and historical studies of science emphasize the diversity of local (...) contexts of practice. However, the existence of common strategies shows that this diversity masks an important repertoire of cognitive strategies. Scientists use this repertoire to adapt their representations to meet the cognitive demands of different contexts of practice. This paper considers the implications of this finding for the notion of scientists as cognitive agents in distributed knowledge-producing systems. (shrink)

Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular (...) aspects of reality they are designed to investigate. Scientific experiments are constantly designed and re-designed in the context of implementation and use. They mediate between theoretical understanding and the practicalities of engaging with the empirical and social world. In order to model experiments we need to identify and represent features that all experiments have in common. We treat these features as parameters of a general model of experiment so that by varying these parameters different types of experiment can be modelled. (shrink)

Drawing on a range of literature, I introduce two new concepts for understanding and exploring distributed cognition: resilience engineering and degeneracy. By re-examining Ed Hutchins’ (1995) ethnographic study of the navigation team I show how a focus on the developmental acquisition of cognitive practices can draw out several crucial insights that have been overlooked. Firstly, that the way in which agents learn and acquire cognitive practices enables a form of resilience engineering: the process by which the system is able to (...) overcome and adapt to errors and the vagaries of nature. Secondly, that the best way to engineer a resilient system is through promoting degeneracy – how differing structures produce the same function – at the level of cognitive practices. These two features show that focusing on cognitive practices and developmental trajectories is important for both greater explanatory leverage; and is also useful in regard to the practicalities of designing real-world cognitive systems. (shrink)

This paper gives a characterization of distributed cognition (d-cog) and explores ways that the framework might be applied in studies of science. I argue that a system can only be given a d-cog description if it is thought of as performing a task. Turning our attention to science, we can try to give a global d-cog account of science or local d-cog accounts of particular scientific projects. Several accounts of science can be seen as global d-cog accounts: Robert Merton's sociology (...) of scientific norms, Philip Kitcher's 20th-century account of cognitive labor, and Kitcher's 21st-century notion of well-ordered science. Problems that arise for them arise just because of the way that they attribute a function to science. The paper concludes by considering local d-cog accounts. Here, too, the task is the crux of the matter. (shrink)

This paper argues that “distributed cognition” facilitates a framework for studying aspects of organizations as socio-technical systems. An approach studying tool use and workflows is laid out and utilized in an analysis of information processing at a post office. Finally, some implications are presented – for organizational as well as cognitive studies. Research on performative representations is called for and, consequently, a widening of the cognition-as-computation framework is suggested.

I resolve several pressing and recalcitrant problems in contemporary philosophy of science using resources from John Dewey's philosophy of science. I begin by looking at Dewey's epistemological and logical writings in their historical context, in order to understand better how Dewey's philosophy disappeared from the limelight, and I provide a reconstruction of his views. Then, I use that reconstruction to address problems of evidence, the social dimensions of science, and pluralism. Generally, mainstream philosophers of science with an interest in Dewey (...) pay little attention to the body of scholarship on Dewey and tend to misinterpret or miss important features of his work, while Dewey scholars generally do not connect his work to the nuanced problems of the contemporary scene (with some notable exceptions). My dissertation helps to fill this important gap and correct common interpretive mistakes by reconstructing and clarifying Dewey's philosophy of science and using it to resolve several contemporary problems. Though his is the road less traveled, Dewey's views provide a good starting place for addressing current concerns. He worked towards a model of science that is both fully naturalistic and fundamentally oriented towards human practice, demands that have been strongly argued for but poorly assimilated by most mainstream philosophers of science. He treats scientific practice, and human thinking generally, as not only embodied but also socially and technologically embedded, and thus can be used to open up a dialogue with much of the social studies of science. He has an anti- foundationalist but structured epistemology, and he offers a way to navigate the narrow paths between an immodest and simplistic realism and the pessimistic extremes of anti-realism and social constructivism, a pursuit of interest to many major philosophers of science at present. Philosophy of science took a different path in the twentieth century, beginning with the "received view'' of logical positivism that left many of the nuances of the original movement by the wayside. No aspect of that starting point has avoided disrepute in recent decades. I show that Dewey avoided the wrong turns of mid- century philosophy of science which are now blocking the way forward. (shrink)

What is cognition? Despite the existence of a science of cognition there is no clear agreement on what makes certain phenomena cognitive, and others not. Within cognitivism the issue was neglected. Human intelligence was used as a standard, and any process—natural or artificial—that fitted this standard sufficiently could be considered ‘cognitive’. For post-cognitivist psychology the situation is different. It cannot rely on the ‘human standard’ in the same way. One might even say that the need for a post-cognitivist psychology arose (...) because cognitivism began with this most complex of all cognitive systems without a good understanding and appreciation of more basic, biological cases. Embodied cognition approaches remedy this anthropocentric bias by addressing a more varied set of processes that are not strictly limited to humans. Under these circumstances the question what we take cognition to be is more urgent. Are phenomena like insect walking (Brooks) and goal-seeking missiles (O’Regan and Noë) examples of cognition or not? What criteria do we use to answer such questions? Given this problem, the notion of perception-action coupling (or sensorimotor contingencies) becomes an important and fairly obvious option to provide a foundation for the notion of cognition. However, and intriguingly, the same problem occurs again: What are perception-action couplings? What would make something an example of perception-action coupling? Where are we to draw a line, if anywhere? It is self-evident that O’Regan and Noë’s (2001) example of a goal-seeking missile is controversial, but why exactly? What is missing? Can we ever do more than making intuitive judgments here? A way out of this dilemma may be found by developing the claim that perception-action coupling must be grounded in a biological context (Keijzer, 2001), and following what Lyon (2005) calls a biogenic approach. This option raises a whole new field of issues and topics that is of central concern for a postcognitivist psychology. (shrink)

Large part of contemporary science is in fact technoscience, in the sense that it crucially depends on several technologies for the generation, collection, and analysis of data. This prompts a re-examination of the relations between science and technologies. In this essay, I advance the view that we’d better move beyond the ‘subordination view’ and the ‘instrumental’ view. The first aims to establish the primacy of science over technology, and the second uses technology instrumentally to support a realist position about theoretical (...) entities. I suggest that we should instead concentrate on how science and technology interact. This will reveal that technology has a poietic character, namely it actively partakes in the production of knowledge. But this poietic character can only be understood within the cognitive activity of scientific communities. Current research in molecular epidemiology, notably the projects funded within the ‘European exposome initiative’, serves as a motivation for such discussion and as an illustration of the claims made. (shrink)