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  A brief list of book suggestions:  






Piccinini's later work develops his mechanistic account of computation in terms of the brain: Neurocognitive mechanisms (2020). If you are interested in how a theory of physical computation can be applied to the brain, this book is a good option. 

A theory of physical computation goes beyond mathematical computation and asks what it means for a physical system to compute. There are different ways of giving a theory like this. Gualtiero Piccinini (2015) provides the most well-developed mechanistic account while Oron Shagrir (2021) gives the most well-developed semantic account. Anderson & Piccinini (forthcoming) promise a robust mapping view. 

Much of the work on physical computation characterizes these views as if they are in opposition to one another. In my dissertation, I argue that each view answers a different question about physical computation and that because each view addresses a different aspect, they are better understood as complementary.

While these views are related and relevant to computationalism, they are meant to address physical computation simpliciter. These views are meant to support the computationalism thesis. 


These books address computation when it comes to computationalism in both philosophy of mind and in cognitive science. The Routledge handbook has some important papers addressing physical computation, so it is also a good resource if you are interested in physical computation simpliciter. 

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The cybernetics movement plays an important role in the developments of cognitive science and artificial intelligence. Many of the people who are credited with the "birth" of CS or AI were part of the cybernetics movement starting in the 1940's. Understanding this history can help to make sense of a modern day science of the mind. There is also an important overlap with the cybernetics movement and topics in philosophy of technology. While philosophy of technology is often taken to be a "continental" philosophical enterprise and the philosophy that helped to grow cognitive science is typically associated with the "analytic" tradition, an important and insightful connection between the two ways of thinking about the mind/body connection can be found here.


Neuroscience and computation go hand-in-hand. These books present topics in neuroscience in a relatively accessible way. If you are interested in learning how neuroscientists build computational models, then these books are a very good resource. Understanding how the models are built, how data is used, what assumptions are made, etc. can be very helpful when it comes to different topics in philosophy of science, especially when it comes to understanding the role of mathematics in modeling. 

The Bechtel & Huang book is a good introduction into topics in philosophy of neuroscience if you're looking for a place to get started. The Oxford handbook is a great resource for a more in-depth look into philosophy of neuroscience. 

This Routledge handbook is a very good overview of contemporary issues in philosophy of psychology. Psychology often overlaps with neuroscience, so it is a helpful resource for those interested in phil neuro as well. 

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The Entangled Brain offers an in-depth argument against the modularity of the mind from the perspective of contemporary neuroscience. Topics in cognitive science regarding modularity are also relevant to some topics in philosophy of mind. For example, Jerry Fodor has weighed in considerably on the nature of computation and cognitive architecture, a debate that is still happening now (although it sometimes manifests in different ways).

The Principles of Neural Design is an excellent book if you are interested in thinking about the brain from an engineering perspective.


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There are many books on the philosophy of artificial intelligence depending on what you're interested in. Copeland's introduction to philosophy of AI presupposes some knowledge regarding the philosophy of computing, but provides an in-depth overview of the foundational issues in philosophy of AI (through the early 90's). You can also find a great collection of papers HERE

The Alignment Problem is a great resource if you are interested in contemporary issues with machine learning especially in light of the intense hype regarding LLM's and data. 

Buckner's (2023) book is a great resource for thinking about issues with deep learning. Many of the topics discussed in these books are relevant to philosophy of mind and cognitive science and can be used to inform our metaphysics, ontology, and ethics. 



Discovering Complexity is an important book if you are interested in explanation in science. This should not be confused with theories regarding the nature of explanation. Books like this (along with Craver & Darden's) book are often taken to be making claims about what  explanation is. This is not aways the case (especially with Craver's work). Instead, books on mechanistic explanation should be understood as one way of explicating a specific type of explanation in science. 

If you are interested in scientific modeling and how to understand their relationship with the material world, these books are a great place to start.

Idealized models are often described as "abstractions" but this is a mistake. Getting clear on the difference between abstraction and idealization can help to make sense of how different types of explanations relate to the physical world. This distinction has important consequences when it comes to a realism about scientific theories and the role of mathematics in modeling practices.


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           PHILOSOPHY OF MIND:  


Chalmers' philosophy of mind book is an excellent collection of papers that follows the development of philosophy of mind as we know it. It also provides readings from perspectives that have been historically left out of the philosophical cannon. I recommend this book to anyone who wants to get know philosophy of mind or for those who are looking to diversify their syllabus. 

Ramsey's book on representation is a great introduction to different ways of understanding representations in the cognitive sciences. 
Although it would benefit from an update (I'm sure according to Ramsey himself given his recent work), it is still a very nice accessible book on mental representations. 

Polger & Shapiro's book on multiple realization casts doubt on the multiple realization thesis as it's been typically understood in philosophy of mind. This book paired with the book on the identity theory of mind provides a lot of insight into how we should think about identity and multiple realization. There is reason to believe that the view of the Identity theory handed down by Putnam and then carried on through the literature gets the nature of the Identity theory wrong in many ways. A thorough investigation into the identity theory from the perspective of those it originates with can help to shed a more charitable light on the debate between multiple realization and the identity theory of mind. 

Naturally I've included Fodor's Language of Thought. LOT is an important part of philosophy of mind and the development of cognitive science. It is a rich view that is worth understanding in detail as many contemporary debates still touch on many of the issues undertaken in the LOT project. Whether you agree with LOT or not, these books are important foundational texts. 


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