- Structural Representations and the Explanatory Constraint Croatian Journal of Philosophy 13 (2):277-291, 2013.
My aim in this paper is to investigate what epistemic role, if any, do appeals to representations play in cognitive neuroscience. I suggest that while at present they seem to play something in between a minimal and a substantive explanatory role, there is reason to believe that representations have a substantial contribution to the construction of neuroscientic explanations of cognitive phenomena.
- What can polysemy tell us about theories of explanation? European Journal for Philosophy of Science 7 (1):41-56, 2017.
Philosophical accounts of scientific explanation are broadly divided into ontic and epistemic views. This paper explores the idea that the lexical ambiguity of the verb to explain and its nominalisation supports an ontic conception of explanation. I analyse one argument which challenges this strategy by criticising the claim that explanatory talk is lexically ambiguous, 375–394, 2012). I propose that the linguistic mechanism of transfer of meaning, 109–132, 1995) provides a better account of the lexical alternations that figure in the systematic polysemy of explanatory talk, and evaluate the implications of this proposal for the debate between ontic and epistemic conceptions of scientific explanation.
- The scope and limits of a mechanistic view of computational explanation Synthese 192 (10):3371-3396, 2015.
An increasing number of philosophers have promoted the idea that mechanism provides a fruitful framework for thinking about the explanatory contributions of computational approaches in cognitive neuroscience. For instance, Piccinini and Bahar :453–488, 2013) have recently argued that neural computation constitutes a sui generis category of physical computation which can play a genuine explanatory role in the context of investigating neural and cognitive processes. The core of their proposal is to conceive of computational explanations in cognitive neuroscience as a subspecies of mechanistic explanations. This paper identifies several challenges facing their mechanistic account and sketches an alternative way of thinking about the epistemic roles of computational approaches used in the study of brain and cognition. Drawing on examples from both low-level and systems-level computational neuroscience, I argue that at least some computational explanations of neural and cognitive processes are partially independent from mechanistic constraints.
- Network analyses in systems biology: new strategies for dealing with biological complexity (with S. Green, N. Jones, R. Scholl, I. Brigandt, and W. Bechtel) Synthese 2017.
The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity.
- Learning from large scale neural simulations In T. Mahfoud, S. McLean and N. Rose (eds) Vital Models: The making and use of models in the brain sciences. (forthcoming)
Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed in neuroscience. I distinguish two types of applications of the simulation methodology in neuroscientific research. Model-oriented applications aim to use the simulation outputs to derive new hypotheses about brain organization and functioning and thus to extend current theoretical knowledge and understanding in the field. Data-oriented applications of the simulation methodology target the collection and analysis of data relevant for neuroscientific research that is inaccessible via more traditional experimental methods. I argue for a two-stage evaluation schema which helps clarify the differences and similarities between three current large-scale simulation projects pursued in neuroscience.
- Turing patterns and biological explanation Disputatio (forthcoming)
Turing patterns are a class of minimal mathematical models that have been used to discover and conceptualize certain abstract features of early biological development. This paper examines a range of these minimal models in order to articulate and elaborate a philosophical analysis of their epistemic uses. It is argued that minimal mathematical models aid in structuring the epistemic practices of biology by providing precise descriptions of the quantitative relations between various features of the complex systems, generating novel predictions that can be compared with experimental data, promoting theory exploration, and acting as constitutive parts of empirically adequate explanations of naturally occurring phenomena, such as biological pattern formation. Focusing on the roles that minimal model explanations play in science motivates the adoption of a broader diachronic view of scientific explanation.
- Understanding the organization of cognitive approaches to translation In Routledge Companion to Philosophy and Translation (forthcoming)
Cognitive approaches to translation studies are driven by three interrelated aims: to understand the structure and organization of the capacities of cognitive agents involved in processes of translation, to build better theories and models of translation, and to develop more efficient methods and programs for translator training. Meeting the goals of such a broad agenda requires the fusion of different theoretical and experimental tools, from fields such as cognitive psychology, linguistics, and artificial intelligence. From exploratory studies that aimed to carve out the problem space for cognitive approaches to translation through methodologically refined studies based on triangulation and statistical analysis, to large scale projects that promise helpful technological innovations for translation studies, the current landscape of research programs that investigate the cognitive underpinnings of translation is both varied and constantly developing. This essay showcases some current research programs that reflect the fruitfulness of the interdisciplinary structure of translation studies. Instead of thinking about cognitive research on translation as being driven by a master cognitive theory, it is more descriptively adequate and more fruitful to understand it as a family of projects based on multiple theories that are relevant for studying different aspects of the translation process. This perspective allows us to extract the erotetic structure of these programs which are organized around specific problems or questions that have been shaped by previous research, by well-established cognitive hypotheses and by the current interests of the discipline of translation studies. Comparing different studies and models of translation will serve to illustrate how different theoretical and experimental approaches contribute to organizing and addressing specific problems on the agenda of a multidisciplinary field such as that of translation studies.
Why the Small Things in Life Matter: Philosophy of Biology From the Microbial Perspective Maureen A. O’Malley,Philosophy of Microbiology. Cambridge: Cambridge University Press, X+269 Pp., $30.39. (with Sara Green). Philosophy of Science 83 (1):152-158, 2016.
The Opacity of Mind: An Integrative Theory of Self-Knowledge. Philosophical Psychology 27 (6):934-938, 2014.