About

Cameron Buckner is a Professor and the Donald F. Cronin Chair in the Humanities at the University of Florida's Department of Philosophy. The page does not provide additional details about his research focus, background, or key contributions.

Research topics

• Computer Science
• Artificial Intelligence
• Philosophy
• Psychology
• Epistemology
• Cognitive science
• Sociology
• Social Science
• Mathematics
• Social psychology
• Mechanical engineering
• Geography
• Thermodynamics
• Engineering
• Law
• Physics
• Meteorology
• Data science

Selected publications

• “Captured” by centaur: Opaque predictions or process insights?

  Journal of Experimental Psychology Animal Learning and Cognition · 2025-09-29

  articleSenior author

  -a new computational model that "captures" human behavior better than alternatives-can help develop a new unified theory of cognition. In this commentary, we evaluate several of these roles in light of recent achievements and empirical data, recommending increasingly explicit scrutiny of the various modeling roles that Centaur might play in developing new explanatory theories of human cognition. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

  PublisherDOI

• Interventionist methods for interpreting deep neural networks

  2025-07-31 · 2 citations

  book-chapterSenior author

  Recent breakthroughs in artificial intelligence (AI) have primarily resulted from training deep neural networks (DNNs) with vast numbers of adjustable parameters on enormous datasets. Due to their complex internal structure, DNNs are frequently characterized as inscrutable “black boxes,” making it challenging to interpret the mechanisms underlying their impressive performance. This opacity creates difficulties for explanation, safety assurance, trustworthiness, and comparisons to human cognition, leading to divergent perspectives on these systems. This chapter examines recent developments in interpretability methods for DNNs, with a focus on interventionist approaches inspired by causal explanation in philosophy of science. We argue that these methods offer a promising avenue for understanding how DNNs process information compared to merely behavioral benchmarking and correlational probing. We review key interventionist methods and illustrate their application through practical case studies. These methods allow researchers to identify and manipulate specific computational components within DNNs, providing insights into their causal structure and internal representations. We situate these approaches within the broader framework of causal abstraction, which aims to align low-level neural computations with high-level interpretable models. While acknowledging current limitations, we contend that interventionist methods offer a path toward more rigorous and theoretically grounded interpretability research, potentially informing both AI development and computational cognitive neuroscience.

  PublisherDOI

• A Philosophical Introduction to Language Models - Part II: The Way Forward

  arXiv (Cornell University) · 2024-05-06 · 10 citations

  preprintOpen accessSenior author

  In this paper, the second of two companion pieces, we explore novel philosophical questions raised by recent progress in large language models (LLMs) that go beyond the classical debates covered in the first part. We focus particularly on issues related to interpretability, examining evidence from causal intervention methods about the nature of LLMs' internal representations and computations. We also discuss the implications of multimodal and modular extensions of LLMs, recent debates about whether such systems may meet minimal criteria for consciousness, and concerns about secrecy and reproducibility in LLM research. Finally, we discuss whether LLM-like systems may be relevant to modeling aspects of human cognition, if their architectural characteristics and learning scenario are adequately constrained.

  PublisherOA PDFDOI

• PSA volume 91 issue 2 Cover and Front matter

  Philosophy of Science · 2024-03-18

  articleOpen access
  PublisherOA PDFDOI

• Transitional gradation and the distinction between episodic and semantic memory

  Philosophical Transactions of the Royal Society B Biological Sciences · 2024-09-15 · 7 citations

  reviewOpen accessSenior author

  In this article, we explore various arguments against the traditional distinction between episodic and semantic memory based on the metaphysical phenomenon of transitional gradation. Transitional gradation occurs when two candidate kinds A and B grade into one another along a continuum according to their characteristic properties. We review two kinds of arguments-from the gradual semanticization of episodic memories as they are consolidated, and from the composition of episodic memories during storage and recall from semantic memories-that predict the proliferation of such transitional forms. We further explain why the distinction cannot be saved from the challenges of transitional gradation by appealing to distinct underlying memory structures and applying our perspective to the impasse over research into 'episodic-like' memory in non-human animals. On the whole, we recommend replacing the distinction with a dynamic life cycle of memory in which a variety of transitional forms will proliferate, and illustrate the utility of this perspective by tying together recent trends in animal episodic memory research and recommending productive future directions. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

  PublisherOA PDFDOI

• A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates

  arXiv (Cornell University) · 2024-01-08 · 23 citations

  preprintOpen accessSenior author

  Large language models like GPT-4 have achieved remarkable proficiency in a broad spectrum of language-based tasks, some of which are traditionally associated with hallmarks of human intelligence. This has prompted ongoing disagreements about the extent to which we can meaningfully ascribe any kind of linguistic or cognitive competence to language models. Such questions have deep philosophical roots, echoing longstanding debates about the status of artificial neural networks as cognitive models. This article -- the first part of two companion papers -- serves both as a primer on language models for philosophers, and as an opinionated survey of their significance in relation to classic debates in the philosophy cognitive science, artificial intelligence, and linguistics. We cover topics such as compositionality, language acquisition, semantic competence, grounding, world models, and the transmission of cultural knowledge. We argue that the success of language models challenges several long-held assumptions about artificial neural networks. However, we also highlight the need for further empirical investigation to better understand their internal mechanisms. This sets the stage for the companion paper (Part II), which turns to novel empirical methods for probing the inner workings of language models, and new philosophical questions prompted by their latest developments.

  PublisherOA PDFDOI

• What Is Deep Learning, and How Should We Evaluate Its Potential?

  2023-12-11

  book-chapter1st authorCorresponding

  Abstract This chapter explains the basic methodology of deep learning research and reviews its major achievements and criticisms. It reviews methodological considerations relevant to gauging the success of the DoGMA’s defense as an analysis of deep learning’s success. It motivates a choice to focus on rationality rather than intelligence, given concerns about the history of psychometric research from which many intelligence-based approaches descend. It considers biases that could distort the evaluation such as anthropomorphism and anthropocentrism. It particularly focuses on the way that nativist assumptions may be supported by a problematic bias, dubbed “anthropofabulation,” which combines anthropocentrism with confabulation about the superiority of average human performance.

  PublisherDOI

• Epilogue

  2023-12-11

  book-chapter1st authorCorresponding

  Extract Deep learning's skeptics hold that empiricists, in attempting to model the rational mind, are entitled to begin with only one or two learning rules, and their models must learn everything else from experience. Whatever the merits of this radical empiricist view, I hope to have illustrated that it does not capture the dominant perspective of researchers in deep learning. Instead, much of the most successful research in deep learning is motivated by and in turn bolsters a more moderate empiricist view—one shared by the majority of philosophical empiricists from the history of philosophy—which holds that abstract knowledge is derived through the cooperation and interaction of active, domain-general faculties. Like the historical empiricists, they eschew innate ideas, rather than innate faculties. Indeed, there are so many examples of this moderate empiricism alive and well in deep learning research today that one could fill an entire book with examples. I hope to also have illustrated that quite a bit more abstraction and rational decision-making can be captured by such faculty-inspired models than we might have supposed. Indeed, they can model some aspects of the loftiest heights of human rationality, and we should expect much more rapid progress in the years to come. However, most of the examples of faculty-inspired models reviewed in this book were one-offs—they only demonstrated the gains that can be obtained by the introduction of one or two faculty-like modules to an architecture. Rather than the next steps in artificial intelligence requiring more domain-specific innate knowledge, the next breakthroughs are likelier to be achieved by integrating more of these domain-general faculty modules into a coherent and cooperative faculty architecture of the sort envisioned by the historical empiricists. As we attempt to do so, we should expect that Control Problems will become more pressing, and engineers should begin preparing countermeasures now. I discussed some early ideas in this direction especially in Chapters 6 and 7, in the discussion of attention and social cognition.

  PublisherDOI

• PSA volume 90 issue 3 Cover and Front matter

  Philosophy of Science · 2023-07-01

  articleOpen access

  An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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• From Deep Learning to Rational Machines

  2023-12-11 · 44 citations

  book1st authorCorresponding

  Abstract This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning’s current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties—such as perception, memory, imagination, attention, and empathy—enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep-neural-network–based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke, David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to find the way to create more robust rational artificial agents, and philosophers can see how some of the historical empiricists’ most ambitious speculations can be realized in specific computational systems.

  PublisherDOI

Recent grants

• Understanding Deep Neural Networks

  NSF · $139k · 2020–2023

Frequent coauthors

• Colin Allen

  New York University Press

  255 shared

• Anna Alexandrova

  Cambridge University Press

  245 shared

• William Bechtel

  University of California, San Diego

  245 shared

• Robert W. Batterman

  University of California, San Diego

  245 shared

• Chris Haufe

  245 shared

• James Owen Weatherall

  University of Edinburgh

  245 shared

• Doreen Fraser

  245 shared

• Jutta Schickore

  Indiana University

  245 shared