RESEARCH AREAS
Computation is central to many sciences, especially psychology and neuroscience. But what counts as a computer? The computational theory of cognition says that the brain performs computation and that the behavior of cognitive systems is causally explained by the computations they perform. But notice that this presupposes that the brain itself is a computer. Theories of physical computation aim to set out the conditions under which some physical system counts specifically as a computing system.
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Different theorists offer different accounts of how to understand the nature of physical computing systems. Often these different theories are taken to be incompatible. In my dissertation I develop a framework for understanding how these theories work together in virtue of targeting different aspects of physical computation. In my work I propose that no single theory can tell us everything that is needed to understand the nature of physical computation. Instead, what we need is a unified account that addresses that nature of computing mechanisms, how we should understand the implementation relation, and the role of semantic interpretation.
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My recent work in this area engages with the consequences of these theories across several debates. I am currently interested in how certain concepts are being used interchangeably when it comes to understanding the nature of a computational mind. In particular, I am interested in the differences between medium independence and multiple realizability and between abstraction and idealization. Some use these concepts interchangeably, while others don't. This mixed-use leads philosophers to talk past one another in ways that confuse and harm the debate. I maintain that each concept (MI/MR and Abstraction/Idealization) is distinct from the other and that the boundaries between them should be carefully accounted for within a theoretical framework.
Much of my recent work in physical computation involves engaging with modeling practices in neuroscience. My work on abstraction and idealization involves evaluating the roles that different models play in explanation. For example, how should we understand the difference between formal computational models and mechanistic models? How do these models work together to target different aspects of cognition? What role does abstraction play in mechanistic modeling and what role does idealization play in computational modeling? These differences are important when it comes to understanding how different explanations work together or how levels of explanations relate to each other.
I am also interested in how we should understand the role of computational models in our ontology. Should we be realists about computational models? If so, what kind of realist? Some of my current work looks at how we might go about being a realist in light of the extensive use of mathematization and idealization in computational modeling.
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And finally, some of my work links projects on physical computation with computational modeling in computational neuroscience. For example, how should we understand a theory of computational implementation when it comes to neuroscience? How should the relation be drawn? Can the relation be drawn?
I am interested in how the types of tools and experimental designs in the cognitive sciences contribute to theory development. In what ways are our theories a product of they kinds of tools that we have? What are the gaps between evidence we can collect from out various tools and methodologies and the theories that come from them? The relationship between tools and theory has consequences for debates regarding realism and instrumentalism, hypothesis formation, and explanation.
Often times this area of my research involves making clear how the machines work, what they measure, types of data they can gather, and how that data is transformed to inform theory.