Research

I am a philosopher of science specializing in the philosophical foundations of cognitive science. My research focuses on two sets of related issues. The first concerns the nature of scientific evidence and how cognitive scientists can develop high-quality evidence for cognitive models. Much of my work focuses on scientists using assumptions about the rationality of a system to provide evidence for cognitive models—specifically, in an approach known as resource rational analysis (Lieder and Griffiths, 2020). My work analyzes how this strategy has worked, how it should work, why it will work, and why it can work better with the conceptual foundations I develop. Toward this end, the second set of issues I am focused on concerns normativity and cognition. My research develops a framework of resource rationality to answer questions such as how to evaluate how well cognitive systems make use of limited resources, how cognitive limitations affect epistemic norms, the extent to which coming to know a system’s intentional states requires attributing rationality to that system, and related questions.

Resource Rationality

Resource rationality is rationality relative to constraints. The framework of resource rationality that I have developed involves a maximally broad notion of what can count as a constraint. This makes all systems trivially resource rational, as all systems can be understood to be doing their best relative to a sufficiently expansive set of constraints. Much of my research involves showing why this previously undefended and prima facie strange view is both well-motivated and provides a useful foundation for evaluating, prescribing, and studying psychological beings.  See my dissertation. 

A Topological Learning-Theoretic Analysis of Bounded Rational Analysis (forthcoming, Philosophy of Science)

This paper presents a topological learning-theoretic analysis of an approach in cognitive science called bounded rational analysis. In this approach, modelers begin by deriving an optimal cognitive model, then use discrepancies between idealized calculations and observed human behavior to identify psychological constraints (e.g., memory limits), and incorporate such constraints into a newly derived resource-optimal model in an iterative scientific process. I show that this de-idealization process exploits an epistemic asymmetry: models positing greater rationality are more falsifiable. A methodological preference for more rational models can therefore be epistemically justified via learning theory.

Isaac Newton, Interdisciplinarian (2026, Studies in History and Philosophy of Science)

This paper examines Newton’s evidential reasoning in his chronological studies.  I identify a pattern of evidential reasoning in his chronological works, in which Newton “exports” inductive risk to areas outside of chronology in order to arrive at more certain results in his historical studies. I look at Newton’s various sources for his chronological research, including primary and secondary historical documents, biology, social science, and astronomy, and analyze how Newton used these domains to turn historical data into evidence for his chronological scheme.  I argue that Newton employed what has been called demonstrative induction in chronology.  In demonstrative induction, the inductive risk is confined to the premises.  The strength of this kind of inference therefore depends on the strength of the premises.  I further argue that Newton placed restrictions on the kinds of premises that could enter into his demonstrative inductions. In particular, Newton required that premises needed to be supported by inductive generalization.  Further, Newton relied as much as possible on premises that were not hypotheses about chronology—e.g., matters of dating and ordering events in the past—but pertained to other domains of inquiry, such as textual interpretation and astronomy.  The result is an analysis that shows in what way Newton avoided hypotheses in chronology.

Metabolic Considerations Are Generic Biological Details Without Resource-Rational Analysis (forthcoming, Behavioral and Brain Sciences, commentary)

Haueis and Colaço (H&C) leave unanswered whether metabolic considerations are merely one neglected type of biological detail that constrains cognitive models, or a distinctive kind uniquely poised to do so. Their examples give little reason to think the latter. If the former, a methodology built around metabolic considerations specifically—rather than neuroscientific facts generally—is unjustified unless embedded within resource-rational analysis.

Meta-Reflection and Accountable AI (under review)

What capacities must an AI system possess to be held accountable for its actions? I argue that AI systems can be accountable agents when they possess sufficiently strong commitments to relevant norms (ethical, rational, conventional, etc.). This paper articulates empirically determinable necessary and sufficient conditions for possessing such commitments. Specifically, I argue that what I call meta-reflection toward a norm is both necessary and sufficient. Meta-reflection consists in maintaining resource-optimal performance by appropriately changing one’s cognitive strategy in response to changes in internal constraints. Importantly, meta-reflection can be established without identifying mental content in a system. Further, a commitment’s strength can be characterized by specifying the constraints under which a system fails to maintain resource-optimality. The path forward for a theory of AI responsibility requires articulating which qualitative internal system constraints are excusable, forgivable, or competence-undermining. Considerations of the mutability of constraints offer a partial way to delineate such classes. This framework bridges philosophical theories of responsibility to cognitive processes and provides a path toward identifying and engineering normative commitments in biological and artificial systems.

Alternatives to Hypothesis Testing: Demonstrative Induction in Cognitive Science (under review)

In this paper, I defend demonstrative induction (DI) against the criticism that it merely passes the buck of inductive risk to the premises. The virtues of structuring inferences deductively derive mainly from the ability to "isolate" inductive risk to specific areas of theory, as well as from the ability to "export" this risk to more certain domains than the one under investigation. I support this argument with examples from cognitive science, where I claim the current context of inquiry makes hypothesis testing particularly ineffective. Properly characterized bounded rationality assumptions can, however, help facilitate DI, allowing cognitive scientists to derive process models from environmental structure, export inductive risk outside the head, and isolate the remaining inductive risk to claims about cognitive limitations. I show how this strategy ameliorates the underdetermination problems that plague hypothesis testing.

Closing the Loop in Cognitive Science: The Diachronic Evidential Strategy of Bounded Rational Analysis (Under Review)

Why might a scientist want to establish a cognitive model as optimally suited to some particular environment?  In this paper, I suggest that an unexamined motivation for establishing models as optimal is to uncover systematic discrepancies between idealized human behavior and observed human behavior.  These discrepancies can lead to the discovery of previously unknown cognitive architecture details (e.g., resource constraints), which can then be incorporated into models and give rise to new idealized models that factor in these newly uncovered details.  Further discrepancies then arise, and the process repeats itself in an iterative fashion.  Most importantly, each step in this process provides evidence for descriptive claims about human cognition.  The point, then, of establishing optimal models is to facilitate a particular process for marshalling evidence about the human cognitive system.

Cognitive Limitations and Epistemic Norms (Draft)

Epistemology is often thought to come in “ideal” and “non-ideal” flavors. Ideal epistemology is thought to articulate pure epistemic norms, while non-ideal epistemology relativizes norms to cognitive limitations, such as bounded memory. I argue that this divide should be rejected. Ideal inductive norms apply only to agents with specific perceptual limitations. Perceptual limitations are not typically considered epistemic failures. In this paper, however, I present cases showing that distinguishing perceptual constraints from other subpersonal cognitive limitations that paradigmatically render norms non-ideal (such as memory constraints) leads to absurd consequences. The upshot is that no principled line can be drawn between ideal and non-ideal agents.

Intentionality and the Rationality Assumption (Draft)

This paper looks at intentionality and the rationality assumption—in particular, the view that attributing intentional states to a system requires attributing rationality to that system (e.g., Dennett, 1989; Davidson, 1995). I argue that the rationality assumption for intentionality should be understood in terms of my notion of resource rationality. Arguments against the rationality assumption, such as Stich’s (1985), do not defeat the reasons for thinking intentionality presupposes rationality—they just show that attributions of irrationality to intentional systems are possible. Thus intentionality does not presuppose ideal rationality. I argue that irrationality can be attributed by specifying constraints on rationality in the form of psychological details. However, the rationality presupposed by intentionality cannot be minimal rationality. I argue that until an agent's resource rationality is brought into view—that is, how they perform optimally relative to their constraints—it is not possible to fully specify the extent to which they possess intentional states. Thus, intentionality presupposes specifically resource rationality.

History of Mathematical Psychology Book Project (Draft)

I am currently writing a book with Colin Allen, Nuhu Osman Attah, Mara McGuire, Dzintra Ullis on the history of this research program that took formation in the 1950's and 1960's and continues today, in part in the form of the Society and the Journal of Mathematical Psychology. We have been performing interviews with important figures from mathematical psychology and reading historical materials. Watch our keynote address at MathPsych/ICCM 2021: Three Questions about Mathematical Psychology

ALIUS Research Group

I am one of three coordinators of ALIUS. ALIUS is an international and interdisciplinary research group dedicated to the investigation of all aspects of consciousness, with a specific focus on non-ordinary or understudied conscious states traditionally classified as altered states of consciousness. One of the main outputs of ALIUS Is the annual ALIUS Bulletin. See my interview with Daniel Dennett that I did with Daniel Friedman.