About

Corey Maley is an associate professor of philosophy at Purdue University, with a research focus on the philosophy of science, neuroscience, cognitive science, and computation. He earned his Ph.D. in the Logic and Philosophy of Science program from Princeton University and previously worked at the University of Kansas. His academic background includes an undergraduate degree from the University of Nebraska, where he quadruple-majored in computer science, mathematics, philosophy, and psychology, earning both a B.S. and a B.A. Corey's primary research interests involve foundational issues in the philosophy of computation, particularly the incorporation of analog and other non-digital forms of computation into a unified conceptual framework. He investigates questions about the nature of computation, the distinctions between digital and analog computation, and how historical perspectives can inform current understanding. His work aims to clarify what constitutes computation in both artificial and natural systems, including the brain, and to explore how different types of computation can shed light on mental and neural processes. He is working on a book manuscript titled "The Analog Brain," supported by an NSF Scholar Award, which addresses these foundational questions. His research emphasizes the physical and conceptual differences between analog and digital computation, highlighting the importance of physical magnitudes in analog computation and its close ties to the medium of implementation. He seeks to demonstrate that understanding analog computation alongside digital computation is essential for a comprehensive understanding of what computation is, especially given the historical neglect of analog computers despite their theoretical significance. His work aims to contribute to the unification of cognitive and neural computations, providing a clearer account of how these processes can be understood as literal computations.

Research topics

• Computer Science
• Theoretical computer science
• Algorithm
• Epistemology
• Political Science
• Mathematics
• Artificial Intelligence
• History
• Law
• Pure mathematics
• Linguistics
• Psychology
• Philosophy
• Cognitive science
• Human–computer interaction

Selected publications

• Structural representation is analog representation

  Philosophy and the Mind Sciences · 2026-02-27

  articleOpen access1st authorCorresponding

  Recent years have seen an increasing amount of attention devoted to the subject of structural representation. Is there one type of structural representation or many? How do they differ from other types of representation? Are they really a genuine type of representation in the first place? All good questions, which I will address indirectly by arguing that structural representations are nothing more than analog representations. Understanding them as such provides some much needed theoretical clarity about this type of representation. Typical analog representations (e.g., liquid thermometers or analog clocks) are often "one-dimensional;" the corresponding one-dimensional characterization of these representations can be extended into multiple dimensions, which elucidates the structure of more complex analog representations, such as photographs, maps, or three-dimensional models. However, this analysis applies to structural representations without remainder. The upshot is that we can directly apply what we have learned about analog representation to our understanding of structural representation, which, if not directly answering these recent questions, greatly adds to our theoretical resources for doing so. The analog wheel has already been invented; we need not reinvent it for structural representation.

  PublisherOA PDFDOI

• CALM: Contextual Analog Logic with Multimodality

  ArXiv.org · 2025-06-17

  preprintOpen access

  In this work, we introduce Contextual Analog Logic with Multimodality (CALM). CALM unites symbolic reasoning with neural generation, enabling systems to make context-sensitive decisions grounded in real-world multi-modal data. Background: Classic bivalent logic systems cannot capture the nuance of human decision-making. They also require human grounding in multi-modal environments, which can be ad-hoc, rigid, and brittle. Neural networks are good at extracting rich contextual information from multi-modal data, but lack interpretable structures for reasoning. Objectives: CALM aims to bridge the gap between logic and neural perception, creating an analog logic that can reason over multi-modal inputs. Without this integration, AI systems remain either brittle or unstructured, unable to generalize robustly to real-world tasks. In CALM, symbolic predicates evaluate to analog truth values computed by neural networks and constrained search. Methods: CALM represents each predicate using a domain tree, which iteratively refines its analog truth value when the contextual groundings of its entities are determined. The iterative refinement is predicted by neural networks capable of capturing multi-modal information and is filtered through a symbolic reasoning module to ensure constraint satisfaction. Results: In fill-in-the-blank object placement tasks, CALM achieved 92.2% accuracy, outperforming classical logic (86.3%) and LLM (59.4%) baselines. It also demonstrated spatial heatmap generation aligned with logical constraints and delicate human preferences, as shown by a human study. Conclusions: CALM demonstrates the potential to reason with logic structure while aligning with preferences in multi-modal environments. It lays the foundation for next-gen AI systems that require the precision and interpretation of logic and the multimodal information processing of neural networks.

  PublisherOA PDFDOI

• Declaring independence from medium independence

  Mind & Language · 2025-06-23 · 1 citations

  articleOpen access1st authorCorresponding

  Computation is widely assumed to be necessarily medium independent, meaning that it is not defined in terms of any physical properties, but only by abstract automata (or something similar). I argue for two things. First, computation is not necessarily medium independent, because characterizing analog computation requires reference to physical properties. Second, insisting on the necessity of medium independence makes it impossible to characterize natural systems as legitimately computational (as opposed to being merely computationally describable). I conclude with some remarks on why concerns about medium independence and implementation in the philosophy of computation may be misguided in the first place.

  PublisherOA PDFDOI

• The mind-brain is a computer, but what is (neural) computation?

  2025-07-31

  book-chapter1st authorCorresponding

  Not long after the advent of mathematical models of computation—most famously Turing’s work on what we now call Turing machines (TMs)—researchers noted the utility of drawing connections between these models of computation and the operations of the mind. This was most clearly developed in the philosophy of mind under the guise of the computational theory of mind (CTM). In these early days, researchers also began drawing connections between computation and the operations of neural systems, although to a much lesser extent. The question of what, precisely, computation is was mostly answered by way of appealing to TMs: a physical system computes when it implements a TM (or other abstract computational specification). In recent decades, the connection between computation and neural operations—neural computation—has become a central focus in neuroscience, particularly computational neuroscience. Because neural computation seems fundamentally different from the type of computation at play in classical computational theory, the advent of neural computation has played a central role in rejuvenating the question of what computation is at the forefront of contemporary philosophical theories of computation. Taking seriously the idea that neural computation is a genuine species of computation (as we do) reveals at least two dogmas that must be interrogated. First, there is the logical dogma, according to which a strong connection exists between classical logic and computability and physical computation. Second, the architectural dogma supposes that the difference between computing and non-computing physical systems involves (at least partially) a distinct causal structure (e.g., discrete, digital, or stepwise). We show how neural computation, both as it arose in the guise of artificial neural networks (ANNs) in the 1980s and in more contemporary work in computational neuroscience, challenges these dogmas and thus requires a more nuanced approach to the question of what computation is. We conclude by showing how recent philosophical accounts of computation have begun to answer this question, thus shedding light on a more expansive, yet still principled, characterization of physical computation.

  PublisherDOI

• Computation for cognitive science: Analog versus digital

  Wiley Interdisciplinary Reviews Cognitive Science · 2024-04-24 · 7 citations

  articleOpen access1st authorCorresponding

  Cognitive science was founded on the idea that the mind/brain can be understood in computational terms. While computational modeling in science is ubiquitous, cognitive science takes the stronger stance that the mind/brain literally performs computations. Moreover, performing computations is crucial to explaining what the mind/brain does, qua mind/brain. Unfortunately, most scientists fail to consider analog computation as a legitimate and theoretically useful type of computation in addition to digital computation; to the extent that analog computation is acknowledged, it is mostly based on a simplistic and incomplete understanding. Taking computation to consist of only one type (i.e., digital) while ignoring another, interestingly distinct type (i.e., analog) leads to an impoverished understanding of what it could mean for minds/brains to compute. A full appreciation and understanding of analog computation-particularly in relation to digital computation-allows researchers to develop computational frameworks and hypotheses in new and exciting ways. Thus, somewhat counterintuitively, looking to the once-dominant computing paradigm of yesteryear can provide novel computational ways of thinking about the mind and brain. This article is categorized under: Philosophy > Foundations of Cognitive Science.

  PublisherOA PDFDOI

• Medium Independence and the Failure of the Mechanistic Account of Computation

  Ergo an Open Access Journal of Philosophy · 2023 · 19 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Political Science

  Current orthodoxy takes representation to be essential to computation. However, a philosophical account of computation that does not appeal to representation would be useful, given the difficulties involved in successfully theorizing representation. Piccinini's recent mechanistic account of computation proposes to do just that: it couches computation in terms of what certain mechanisms do without requiring the manipulation or processing of representations whatsoever (Piccinini 2015). Most crucially, mechanisms must process medium-independent vehicles. There are two ways to understand what "medium-independence" means on this account; however, on either understanding, the account fails. Either too many things end up being counted as computational, or purportedly natural computations (e.g., neural computations) cannot be counted at all. In the end, illustrating this failure sheds some light on the way to revise the orthodoxy in the hope of a better account of computation.

  PublisherOA PDFDOI

• Icons, Magnitudes, and Their Parts

  Crítica (México D F En línea) · 2023-05-12 · 5 citations

  articleOpen access1st authorCorresponding

  Analog representations come in different types. One distinction is between those representations that have parts that are themselves representations and those that do not (i.e., those for which the Parts Principle is true and those for which it is not). I offer a unified account of analog representation, showing what all types have in common. This account clarifies when the Parts Principle applies and when it does not, thereby illuminating why the Parts Principle is less interesting than one might have thought. Understanding analog representation instead requires understanding the kinds of magnitudes used in a particular representation, and the kinds of variation possible within a representational scheme.

  PublisherOA PDFDOI

• How (and why) to think that the brain is literally a computer

  Frontiers in Computer Science · 2022-09-09 · 3 citations

  articleOpen access1st authorCorresponding

  The relationship between brains and computers is often taken to be merely metaphorical. However, genuine computational systems can be implemented in virtually any media; thus, one can take seriously the view that brains literally compute. But without empirical criteria for what makes a physical system genuinely a computational one, computation remains a matter of perspective, especially for natural systems (e.g., brains) that were not explicitly designed and engineered to be computers. Considerations from real examples of physical computers—both analog and digital, contemporary and historical—make clear what those empirical criteria must be. Finally, applying those criteria to the brain shows how we can view the brain as a computer (probably an analog one at that), which, in turn, illuminates how that claim is both informative and falsifiable.

  PublisherOA PDFDOI

• Panpsychism and AI consciousness

  Synthese · 2022-05-31 · 4 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Analogue Computation and Representation

  The British Journal for the Philosophy of Science · 2021 · 53 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Theoretical computer science

  Relative to digital computation, analogue computation has been neglected in the philosophical literature. To the extent that attention has been paid to analogue computation, it has been misunderstood. The received view—that analogue computation has to do essentially with continuity—is simply wrong, as shown by careful attention to historical examples of discontinuous, discrete analogue computers. Instead of the received view, I develop an account of analogue computation in terms of a particular type of analogue representation that allows for discontinuity. This account thus characterizes all types of analogue computation, whether continuous or discrete. Furthermore, the structure of this account can be generalized to other types of computation: analogue computation essentially involves analogue representation, whereas digital computation essentially involves digital representation. Besides being a necessary component of a complete philosophical understanding of computation in general, understanding analogue computation is important for computational explanation in contemporary neuroscience and cognitive science.

  PublisherDOI

Recent grants

• Modeling Neural Processes as Analog Computations

  NSF · $155k · 2018–2019

Frequent coauthors

• Gualtiero Piccinini

  University of Missouri–St. Louis

  10 shared

• Khena M. Swallow

  Cornell University

  3 shared

• Derek Holder

  Washington University in St. Louis

  2 shared

• Jeffrey M. Zacks

  Washington University in St. Louis

  2 shared

• Zack Robinson

  University of Missouri–St. Louis

  1 shared

• Bradford Cokelet

  University of Kansas

  1 shared

• Nicole K. Speer

  University of Colorado Boulder

  1 shared

• Marcus Arvan

  University of Tampa

  1 shared

Education

• Ph.D., Philosophy

  Princeton University

  2015

• M.A., Philosophy

  Princeton University

  2010

• B.S., Computer Science, Mathematics, Psychology

  University of Nebraska-Lincoln

  2005

• B.A., Philosophy

  University of Nebraska-Lincoln

  2005

Awards & honors

• NSF Scholar Award