Gualtiero Piccinini is the Florence G. Kline Professor of Philosophy and Curators' Distinguished Professor at the University of Missouri, Columbia

He's known for his work on the nature of mind and computation as well as on how to integrate psychology and neuroscience. He also has studied the early history of the computational neuroscience of mind and brain, notably the work of Warren McCulloch and Walter Pitts.

According to the computational theory of cognition (CTC), cognitive capacities are explained by inner computations, which in biological organisms are realized in the brain. Computational explanation is so popular and entrenched that it’s common for scientists and philosophers to assume CTC without argument. But if we presuppose that neural processes are computations before investigating, we turn CTC into dogma. If, instead, our theory is to be genuinely empirical and explanatory, it needs to be empirically testable. To bring empirical evidence to bear on CTC, we need an appropriate notion of computation.

In order to ground an empirical theory of cognition, as CTC was designed to be, a satisfactory notion of computation should satisfy at least two requirements: it should employ a robust notion of computation, such that there is a fact of the matter as to which computations are performed by which systems, and it should not be empirically vacuous, as it would be if CTC could be established a priori. In order to satisfy these requirements, the computational theory of cognition should be grounded in a mechanistic account of computation. Once that is done, I evaluate the computational theory of cognition on empirical grounds in light of our best neuroscience. I reach two main conclusions: cognitive capacities are explained by the processing of spike trains by neuronal populations, and the processing of spike trains is a kind of computation that is interestingly different from both digital computation and analog computation.

Chapter 13, The Computational Theory of Cognition, in V. C. Müller (ed.), Fundamental Issues of Artificial Intelligence, Springer, 2016

Piccinini wrote an important history on the original idea of McCulloch and Pitts that the brain is doing logical calculations and solving problems in propositional logic, This was before a digital computer existed, and their ideas were used by John Von Neumann in his future design of computing architecture.

ABSTRACT. Despite its significance in neuroscience and computation, McCulloch and Pitts’s celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts’s paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts’s contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in computability theory), (ii) a technique that inspired the notion of logic design (a fundamental part of modern computer design), (iii) the first use of computation to address the mind–body problem, and (iv) the first modern computational theory of mind and brain.

The First Computational Theory of Mind and Brain: A Close Look at Mcculloch and Pitts's “Logical Calculus of Ideas Immanent in Nervous Activity"

Synthese 141: 175–215, 2004.

Piccinini has authored books on a mechanistic account of physical computation that he said is needed to identify processes in the brain that would qualify as doing computation. He has recently edited a volume on the neurocognitive foundations of mind. They include...

Physical Computation: A Mechanistic Account (2015)

Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. Physical Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as digital vs. analog, serial vs. parallel, neural network computation, program-controlled computation, and more. Piccinini argues that computation does not entail representation or information processing although information processing entails computation. Pancomputationalism, according to which every physical system is computational, is rejected. A modest version of the physical Church-Turing thesis, according to which any function that is physically computable is computable by Turing machines, is defended.

Neurocognitive Mechanisms: Explaining Biological Cognition (2020)

This book defends a neurocomputational theory of cognition grounded in a mechanistic, functionalist, egalitarian ontology. I argue that biological cognitive capacities are constitutively explained by multilevel neurocognitive mechanisms, which perform neural computations over neural representations. Providing a scientific explanation of cognition requires understanding how neurocognitive mechanisms work. Therefore, the science of cognition ought to include neuroscience to a degree that traditional cognitive science was not expected to. Scientists on the ground have been working on this for a while. Psychology is becoming more and more integrated with neuroscience.

The picture I defend stands opposite to both traditional reductionism (type identity theory) and anti-reductionism (autonomy of psychology). Contrary to traditional reductionism, neural computations and representations are not identical to their lower-level realizers. Contrary to traditional anti-reductionism, neural computations and representations are not entirely distinct from their realizers. Instead, neural computations and representations—both types and tokens—are aspects of their realizers.

The Physical Signature of Computation: A Robust Mapping Account (2024)

In The Physical Signature of Computation, Neal Anderson and Gualtiero Piccinini articulate and defend the robust mapping account--the most systematic, rigorous, and comprehensive account of computational implementation to date. Drawing in part from recent results in physical information theory, they argue that mapping accounts of implementation can be made adequate by incorporating appropriate physical constraints. According to the robust mapping account, the key constraint on mappings from physical to computational states--the key for establishing that a computation is physically implemented--is physical-computational equivalence: evolving physical states bear neither more nor less information about the evolving computation than do the computational states they map onto. When this highly nontrivial constraint is satisfied, among others that are spelled out as part of the account, a physical system can be said to implement a computation in a robust sense, which means that the system bears the physical signature of the computation. Anderson and Piccinini apply their robust mapping account to important questions in physical foundations of computation and cognitive science, including the alleged indeterminacy of computation, pancomputationalism, and the computational theory of mind. They show that physical computation is determinate, nontrivial versions of pancomputationalism fail, and cognition involves computation only insofar as neurocognitive systems bear the physical signature of specific computations. They also argue that both consciousness and physics outstrip computation.

Neurocognitive Foundations of Mind (2026)

The rise of cognitive neuroscience has prompted a rethinking of levels, computation, representation, psychological explanation, and the relation between psychology and neuroscience. Despite these advances, many philosophers and scientists of the mind continue to write as though cognitive neuroscience didn’t exist and psychology remains autonomous from neuroscience or, perhaps, they maintain that cognitive neuroscience has not deepened our understanding of the mind. The chapters in this volume showcase important ways in which cognitive neuroscience makes a profound difference to our understanding of the mind. The contributors address a wide range of topics, including explanation, computation, representation, inference, emotion, language, intention, and thought. Together, they demonstrate the ways in which cognitive neuroscience supersedes traditional cognitive science and supports a unified, integrated, multilevel, mechanistic, neurocomputational account of the mind.

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