I work mainly in philosophy of science, blending methods from philosophy and psychology to study scientific reasoning. Currently I spend most of my time thinking about cross-disciplinary theorizing between philosophy, neuroscience, and AI, and how the methods of all three fields help make complex systems, like brains and neural networks, intelligible. I also have related projects on the interpretation of large language models, the necessity of communication barriers in science, and the role of metaphysics in philosophy of science. In all these projects I take a pragmatic/deflationist approach, and one of my main goals is to elaborate and defend that approach by thinking about methodology in philosophy of science and the psychology of scientific explanation.
How computation explains (Mind & Language)
Cognitive science gives computational explanations of the brain. Philosophers have treated these explanations as if they simply claim that the brain computes. We have therefore assumed that to understand how and why computational explanation works, we must understand what it is to compute. In contrast, I argue that we can understand computational explanation by describing the resources it brings to bear on the study of the brain. Specifically, I argue that it introduces concepts and formalisms that complement cognitive science's modeling goals. This allows us to understand computational explanation without having to debate what it is to compute.
What is a theory of neural representation for? (Synthese)
Use and usability: Three levels of neural representation (Neurons, Behavior, Data, and Theory)
Scientific Concepts
- How computation explains (forthcoming in Mind & Language)
- What is a theory of neural representation for? (forthcoming in Synthese)
- Use and usability: Three levels of neural representation (forthcoming in Neurons, Behavior, Data, and Theory) (co-1st author, with Ben Baker, Richard Lange, Rosa Cao, Alessandro Achille, Odelia Schwartz, Niko Kriegeskorte, & Xaq Pitkow)
- Computational externalism (under review)
Methodology in Philosophy of Science
- Imposing vs finding unity: Mechanisms in embodied cognitive neuroscience (2024, Cognitive Neuroscience) (1st author, with Jonathan Bowen, Lucas Firas Kayssi, Kardelen Küçük, Varun Ravikumar, Yunus Şahin, and Michael Anderson)
- Commentary: Investigating the concept of representation in the neural and psychological sciences (2023, Frontiers in Psychology)
- What really lives in the swamp? Kinds and the illustration of scientific reasoning (under review)
- Integrating mechanistic findings into RECS (in preparation) (with Giovanni Rolla)
- Experimental philosophy of science: beyond taxonomy (in preparation)
In Draft
- Pragmatism and Deflationism in Philosophy of Cognitive Science
- Representation in explainable AI
- Concept clarification as scientific methodology
- Mysterianism, or the cosmic horror of the self
Concept clarification as scientific methodology
Scientists routinely have to interrogate central scientific concepts, especially where those concepts may be vague or ambiguous. This kind of work is an essential part of scientific research, but there is no agreed-upon methodology for it. In fact, there is little discussion of its methodology in the first place. The very idea of a methodology for concept clarification may sound strange — it is at least a very different thing than a methodology for, say, fMRI imaging, data analysis, or modeling practice. But there are more and less productive ways to clarify our concepts and different approaches serve different scientific goals. I distinguish three common approaches to the clarification of concepts: empirical, a priori, and intermediate methods. And I describe the rationale and significance of each approach, along with the distinctive role each can play in scientific inquiry.
Naturalism and Philosophy of Mind
Philosophers of mind tend to accept three claims. (1) Philosophy of mind should draw support and from cognitive science. (2) Philosophy of mind should deliver a metaphysics of mind: a definition of the mind, or an account of what it is to be minded. (3) The most promising approach in philosophy of mind is computational and representational. I argue that these claims are only consistent on a naïve view of cognitive science and the explanations it provides — specifically, an understanding of those explanations as metaphysically loaded. Starting from a more nuanced understanding of cognitive science, I bring out the inconsistency of the three claims and discuss how we can move forward by dropping one of them.
Computational, Representational, and Functional Explanation: A Case Study in the Antikythera Mechanism
The concepts of computation, representation, and function have central explanatory roles in many different sciences. Cognitive science in particular explains the brain as a sort of computer, whose parts have functions, with one of those functions being to represent environmental variables. But these forms of explanation are not fully understood. In philosophy, this problem is often approached through toy examples of computations, representations, and functions, or through real case studies from cognitive science. But toy examples are often too simplistic to illuminate scientific explanation, and it can be hard to distil general lessons from real and complicated case studies. A useful middle-ground can be found in the Antikythera mechanism — an ancient Greek astronomical device that is explained in computational, representational, and functional terms. I use the Antikythera mechanism to draw out the features of computational, representational, and functional explanation, and argue for a particular epistemic role and status for each.
Mysterianism, or the cosmic horror of the self
Mysterians argue that the nature of consciousness is fundamentally unknowable, and I argue that their sense of "unknowability" is precisely the same kind of unknowability that characterizes the objects of cosmic or Lovecraftian horror. I use this comparison to bring out some puzzles to do with the aesthetics of consciousness, especially concerning its relationship to philosophical and scientific theories of consciousness.
Gamification and Domain Transfer
I discuss the use of gamification in pedagogy, highlighting a lack of consensus on best practices and some difficulties we face trying to construct those best practices using empirical research. I then show that gamification is an example of domain transfer, and derive a tentative set of best practices based on a broader understanding of domain transfer in science, business, and other domains.
Scientists routinely have to interrogate central scientific concepts, especially where those concepts may be vague or ambiguous. This kind of work is an essential part of scientific research, but there is no agreed-upon methodology for it. In fact, there is little discussion of its methodology in the first place. The very idea of a methodology for concept clarification may sound strange — it is at least a very different thing than a methodology for, say, fMRI imaging, data analysis, or modeling practice. But there are more and less productive ways to clarify our concepts and different approaches serve different scientific goals. I distinguish three common approaches to the clarification of concepts: empirical, a priori, and intermediate methods. And I describe the rationale and significance of each approach, along with the distinctive role each can play in scientific inquiry.
Naturalism and Philosophy of Mind
Philosophers of mind tend to accept three claims. (1) Philosophy of mind should draw support and from cognitive science. (2) Philosophy of mind should deliver a metaphysics of mind: a definition of the mind, or an account of what it is to be minded. (3) The most promising approach in philosophy of mind is computational and representational. I argue that these claims are only consistent on a naïve view of cognitive science and the explanations it provides — specifically, an understanding of those explanations as metaphysically loaded. Starting from a more nuanced understanding of cognitive science, I bring out the inconsistency of the three claims and discuss how we can move forward by dropping one of them.
Computational, Representational, and Functional Explanation: A Case Study in the Antikythera Mechanism
The concepts of computation, representation, and function have central explanatory roles in many different sciences. Cognitive science in particular explains the brain as a sort of computer, whose parts have functions, with one of those functions being to represent environmental variables. But these forms of explanation are not fully understood. In philosophy, this problem is often approached through toy examples of computations, representations, and functions, or through real case studies from cognitive science. But toy examples are often too simplistic to illuminate scientific explanation, and it can be hard to distil general lessons from real and complicated case studies. A useful middle-ground can be found in the Antikythera mechanism — an ancient Greek astronomical device that is explained in computational, representational, and functional terms. I use the Antikythera mechanism to draw out the features of computational, representational, and functional explanation, and argue for a particular epistemic role and status for each.
Mysterianism, or the cosmic horror of the self
Mysterians argue that the nature of consciousness is fundamentally unknowable, and I argue that their sense of "unknowability" is precisely the same kind of unknowability that characterizes the objects of cosmic or Lovecraftian horror. I use this comparison to bring out some puzzles to do with the aesthetics of consciousness, especially concerning its relationship to philosophical and scientific theories of consciousness.
Gamification and Domain Transfer
I discuss the use of gamification in pedagogy, highlighting a lack of consensus on best practices and some difficulties we face trying to construct those best practices using empirical research. I then show that gamification is an example of domain transfer, and derive a tentative set of best practices based on a broader understanding of domain transfer in science, business, and other domains.
