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Plato's Camera: How the Physical Brain Captures a Landscape of Abstract Universals
In Plato's Camera, eminent philosopher Paul Churchland offers a novel account of how the brain constructs a representation--or 'takes a picture'--of the universe's timeless categorical and dynamical structure. This construction process, which begins at birth, yields the enduring background conceptual framework with which we will interpret our sensory experience for the rest of our lives. But, as even Plato knew, to make singular perceptual judgments requires that we possess an antecedent framework of abstract categories to which any perceived particular can be relevantly assimilated. How that background framework is assembled in the first place is the motivating mystery, and the primary target, of Churchland's book. His account draws on the best of the recent philosophical literature on semantic theory, and on the most recent results from cognitive neurobiology. The resulting story throws immediate light on issues that have been at the center of philosophy for a
289 pages, Hardcover
First published January 27, 2012
17 people are currently reading
400 people want to read
About the author
Paul M. Churchland
18 books64 followersPaul Churchland is a philosopher noted for his studies in neurophilosophy and the philosophy of mind. He is currently a Professor at the University of California, San Diego, where he holds the Valtz Chair of Philosophy. Churchland holds a joint appointment with the Cognitive Science Faculty and the Institute for Neural Computation. He earned his Ph.D. from the University of Pittsburgh in 1969 under the direction of Wilfrid Sellars. Churchland is the husband of philosopher Patricia Churchland, and the father of two children.
Churchland began his professional career as an instructor at the University of Pittsburgh in 1969; he also lectured at the University of Toronto from 1967-69. In 1969, Churchland took a position at the University of Manitoba, where he would teach for fifteen years: as an assistant professor (69 - 74) and associate professor (74 - 79), and then as a full professor from 1979 - 1984. Professor Churchland joined the Institute for Advanced Study at Princeton University in 1982, staying as a member until 1983. He joined the faculty at the University of California, San Diego in 1983, serving as Department Chair from 1986 - 1990.
Churchland has supervised a number of PhD students, including P.D. Magnus (now at the University at Albany) and Philip Brey (now at the University of Twente).
Along with his wife, Churchland is a major proponent of eliminative materialism, which claims that everyday mental concepts such as beliefs, feelings and desires are theoretical constructs without coherent definition; hence we should not expect such concepts to be a necessary part of a scientific understanding of the brain. Just as a modern understanding of science has no need for concepts such as luck or witchcraft to explain the world, Churchland argues that a future neuroscience is likely to have no need for "beliefs" or "feelings" to explain the mind. Instead, the use of objective phenomena such as neurons and their interaction should suffice. He points out that the history of science has seen many previous concepts discarded, such as phlogiston, caloric, the luminiferous ether, and vital forces.
Churchland began his professional career as an instructor at the University of Pittsburgh in 1969; he also lectured at the University of Toronto from 1967-69. In 1969, Churchland took a position at the University of Manitoba, where he would teach for fifteen years: as an assistant professor (69 - 74) and associate professor (74 - 79), and then as a full professor from 1979 - 1984. Professor Churchland joined the Institute for Advanced Study at Princeton University in 1982, staying as a member until 1983. He joined the faculty at the University of California, San Diego in 1983, serving as Department Chair from 1986 - 1990.
Churchland has supervised a number of PhD students, including P.D. Magnus (now at the University at Albany) and Philip Brey (now at the University of Twente).
Along with his wife, Churchland is a major proponent of eliminative materialism, which claims that everyday mental concepts such as beliefs, feelings and desires are theoretical constructs without coherent definition; hence we should not expect such concepts to be a necessary part of a scientific understanding of the brain. Just as a modern understanding of science has no need for concepts such as luck or witchcraft to explain the world, Churchland argues that a future neuroscience is likely to have no need for "beliefs" or "feelings" to explain the mind. Instead, the use of objective phenomena such as neurons and their interaction should suffice. He points out that the history of science has seen many previous concepts discarded, such as phlogiston, caloric, the luminiferous ether, and vital forces.
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March 28, 2022
Because I had read Paul Churchland’s earlier book, The Engine of Reason, the Seat of the Soul (1996), I was excited to see his newer book, Plato’s Camera (2013). The earlier book had tackled a number of philosophical problems regarding the mind and consciousness, using the insights gained from neuroscience. The latter book tackles a more specific philosophical problem, how to account for abstract concepts (Plato’s Forms) used in our everyday thinking, in terms of neurological processes. As a result of this investigation, Churchland reaches the remarkable conclusion that concepts are not linguaformal (a favorite word of his), but are rather multi-dimensional activational spaces in our neural networks, maps (so to speak) of the domain of our sensory experience on which we can pinpoint things.
This is rather a radical notion, namely that discursive (sentential, propositional) thought is not really the basis for the way we learn and conceptualize, but is actually a result of a neurological process that takes place in our brain prior to discursive thought. Churchland makes the powerful argument that animals and small children learn a great deal without or before acquiring language. Any theory of concepts should be prelinguistic, not the result of language.
How to get there is difficult to describe, although Churchland offers copious examples and discussion. Although his discussion concerns the human (or animal) nervous system, contemporary neuroscience benefits from the mutual influence between neurology and artificial intelligence. The latter seems to be a theoretical playground for the former. In either case, the idea of multi-dimensional arrays, or activational spaces, plays a major role. As sensory data is received, say as a visual array from optic nerves (similar to pixels on a TV screen), it is connected through a neural network to filtering layers of nerves, which are trained over time to discriminate patterns of increasing complexity. What one ends up with is a kind of map, say, of possible configurations (for example, of faces), on which one can subsequently identify specific faces.
The concept of mapping is a great analogy, except that one must remember that the neural maps described are not merely two-dimensional, but could have hundreds of dimensions. Thus the activational space which determines a particular concept might use many different situational criteria to determine whether and how a specific example belongs to that concept. Finding a point on a map is simple; finding something in a hundred-dimensional space is mind-boggling.
Churchland goes on to describe three levels of learning: the first level is the creation of maps in activational space as described above. The second is the dynamic process in which we modify our maps over time as a result of new experience or of comparing our maps with others. The third level is the cultural process in which conceptual maps get overthrown or subsumed by others. This is similar to Thomas Kuhn’s The Structure of Scientific Revolutions, in which paradigm shifts take place, such as the move from the Ptolemaic to the Copernican solar system, or from Newtonian to Einsteinian physics.
This is all a lot to process, although it is exciting. I think it is ironic that Churchland has to use the linguaformal format of a philosophy book to argue that concepts are not linguaformal. You would think that he could appeal directly to the activational spaces in our brains. Perhaps that is ultimately what he is doing. Churchland clearly thinks that he is marking out new territory; he argues specifically against those like Jerry Fodor who think we have a “language of thought.” He also sets his position against both Descartes’ notion of innate ideas and Locke’s notion of a blank slate.
I find the cross-fertilization between neuroscience and artificial intelligence to be exciting, especially as it relates to the nature of the mind and how we think. Paul Churchland certainly gives us a lot to think about, and a lot left to understand. This seems like a new direction for philosophy, beyond analytic and beyond post-modern.
This is rather a radical notion, namely that discursive (sentential, propositional) thought is not really the basis for the way we learn and conceptualize, but is actually a result of a neurological process that takes place in our brain prior to discursive thought. Churchland makes the powerful argument that animals and small children learn a great deal without or before acquiring language. Any theory of concepts should be prelinguistic, not the result of language.
How to get there is difficult to describe, although Churchland offers copious examples and discussion. Although his discussion concerns the human (or animal) nervous system, contemporary neuroscience benefits from the mutual influence between neurology and artificial intelligence. The latter seems to be a theoretical playground for the former. In either case, the idea of multi-dimensional arrays, or activational spaces, plays a major role. As sensory data is received, say as a visual array from optic nerves (similar to pixels on a TV screen), it is connected through a neural network to filtering layers of nerves, which are trained over time to discriminate patterns of increasing complexity. What one ends up with is a kind of map, say, of possible configurations (for example, of faces), on which one can subsequently identify specific faces.
The concept of mapping is a great analogy, except that one must remember that the neural maps described are not merely two-dimensional, but could have hundreds of dimensions. Thus the activational space which determines a particular concept might use many different situational criteria to determine whether and how a specific example belongs to that concept. Finding a point on a map is simple; finding something in a hundred-dimensional space is mind-boggling.
Churchland goes on to describe three levels of learning: the first level is the creation of maps in activational space as described above. The second is the dynamic process in which we modify our maps over time as a result of new experience or of comparing our maps with others. The third level is the cultural process in which conceptual maps get overthrown or subsumed by others. This is similar to Thomas Kuhn’s The Structure of Scientific Revolutions, in which paradigm shifts take place, such as the move from the Ptolemaic to the Copernican solar system, or from Newtonian to Einsteinian physics.
This is all a lot to process, although it is exciting. I think it is ironic that Churchland has to use the linguaformal format of a philosophy book to argue that concepts are not linguaformal. You would think that he could appeal directly to the activational spaces in our brains. Perhaps that is ultimately what he is doing. Churchland clearly thinks that he is marking out new territory; he argues specifically against those like Jerry Fodor who think we have a “language of thought.” He also sets his position against both Descartes’ notion of innate ideas and Locke’s notion of a blank slate.
I find the cross-fertilization between neuroscience and artificial intelligence to be exciting, especially as it relates to the nature of the mind and how we think. Paul Churchland certainly gives us a lot to think about, and a lot left to understand. This seems like a new direction for philosophy, beyond analytic and beyond post-modern.
July 16, 2017
Felsefenin 2500 yıllık problemlerinin artık günümüz bilimi sayesinde nasıl çözüldüğünü anlatıyor.
December 3, 2012
Ok, let's see if I can get this right.
In Plato's camera, Paul Churchland is trying to introduce a new
theory of how the human brain learns. His approach is pretty
remarkable because it is completely different from most other
theories like it. His basic concern is to show how the physical
processes in the brain interact with the environment and learn from
it. The first two-thirds of the book deal with neuronal networks in
the brain. These networks are made up of billions of neurons and
trillions of connections between those neurons. It is these
connections that enable the brain to learn from the reality in
which it lives. During the first 10 or so years of life the
connections between the neurons are slowly adjusted so they can
better register the similarities and differences between objects
encountered in reality. So our brains are able to distinguish
between, say, a dog and a cat because the neurons are being
activated so that they create representational maps of the
objective world.
There are a few really revolutionary ideas that Churchland pushes
in this book. The first is that the brain does not operate based on
language. Instead, the brain creates complex, high dimensional maps
of its environment that serve to locate the owner of the brain
within the spatio-temporal world through a process called
'indexing'. The brain uses whatever senses are available to place a
'you are here' sign on the map currently being deployed. Indexing
allows the creature with the brain to utilize a wide range of maps
for the many different situations that might be encountered in
objective reality. Each creature can have a variety of different
kinds of maps at its disposal and these maps can differ in accuracy
or detail depending on how often the creature has experienced that
sort of situation. Creatures will develop maps of the terrain, of
sounds, and things like facial recognition in order to survive in
the natural world. Churchland presents compelling evidence based on
years of research and experimentation with artificial neuronal
networks that this is in fact how the brain interacts with and
learns from the world.
During his discussion of maps, Churchland makes the surprising
claim that the brain recognizes causal processes before it
recognizes objects. What this means, as far as I understand it, is
that the brain pays attention to motion before it pays attention to
the actual structure of the object it is viewing. Churchland
introduces something called Hebbian Learning, that can be observed
in artificial neuronal networks. Hebbian Learning claims that a
neuron that is already activated (by an electro-chemical charge)
will be more likely to learn from subsequent electro-chemical
stimuli to that already activated neuron. This means that the basic
patterns to which brain is exposed are reinforced by the entire
network of neurons and their current activation levels through
recurrent pathways in the brain. These recurrent pathways make it
so that neurons that are higher up on the 'ladder' are able to send
electro-chemical impulses downward to further stimulate the sensory
neurons that initially encountered the world. For example, consider
an arrow in flight. Hebbian learning makes it so that a creature
will first recognize that the arrow is flying, rather than the
shape of the arrow. This idea is directly opposed to many well-
established philosophical ideas. Most of the time, we think that we
recognize an object (the arrow) first, and only afterward do we
recognize the fact that the arrow is in motion. If what Churchland
says is true (and there are 30 or so years of experimental data
that confirms that it is), this means that many philosophical ideas
will need to be reconsidered for validity.
Churchland also talks about the impact of social influences on
learning in the brain. He says that, even though the theory he is
presenting says that the brain at its most basic level is not
language-based, that language and society are means to a massive
accumulation of data and maps. These societal institutions allow
for a sort of cognitive activity outside of the individual and
could potentially mean that cognition can go on indefinitely. This
makes sense to me because that seems to be what is happening.
Society has now found an efficient way to store and catalogue all
the data and information that has come about through human
endeavors. Churchland also says that language serves a regulatory
purpose in that it allows humans to express ideas to the community
for verification or refutation. This is essential especially for
science because it provides the basis of peer-review. It allows us
to come to a consensus about whether an idea is factual or
fanciful. There is only one thing I am hesitant about. The language
the Churchland uses when talking about society could easily be
interpreted in a Hegelian manner. In fact, his idea about the
'cognitive organism' seems dangerously close to Hegel's idea of the
mind of the age (260). However, Churchland is adamant that this is
not what he has in mind, and in fact there are several places in
the book where he points out the failings of Hegel's theories. I
hope that these are kept in mind in the future so we don't fall
back into an idealist state of mind that does not stimulate
scientific research.
This book was really difficult, and I really had to stretch myself
to get through it. It is very much worth it, though. This review is
only a surface-level introduction, and there is a lot more that I
could have talked about. But for the sake of space and minimizing
the number of interpretational errors on my part, I have written a
short review. Churchland has presented solid evidence for his
claims, and I'm interested to see what happens in the field of
cognitive science after this book has been thoroughly digested.
In Plato's camera, Paul Churchland is trying to introduce a new
theory of how the human brain learns. His approach is pretty
remarkable because it is completely different from most other
theories like it. His basic concern is to show how the physical
processes in the brain interact with the environment and learn from
it. The first two-thirds of the book deal with neuronal networks in
the brain. These networks are made up of billions of neurons and
trillions of connections between those neurons. It is these
connections that enable the brain to learn from the reality in
which it lives. During the first 10 or so years of life the
connections between the neurons are slowly adjusted so they can
better register the similarities and differences between objects
encountered in reality. So our brains are able to distinguish
between, say, a dog and a cat because the neurons are being
activated so that they create representational maps of the
objective world.
There are a few really revolutionary ideas that Churchland pushes
in this book. The first is that the brain does not operate based on
language. Instead, the brain creates complex, high dimensional maps
of its environment that serve to locate the owner of the brain
within the spatio-temporal world through a process called
'indexing'. The brain uses whatever senses are available to place a
'you are here' sign on the map currently being deployed. Indexing
allows the creature with the brain to utilize a wide range of maps
for the many different situations that might be encountered in
objective reality. Each creature can have a variety of different
kinds of maps at its disposal and these maps can differ in accuracy
or detail depending on how often the creature has experienced that
sort of situation. Creatures will develop maps of the terrain, of
sounds, and things like facial recognition in order to survive in
the natural world. Churchland presents compelling evidence based on
years of research and experimentation with artificial neuronal
networks that this is in fact how the brain interacts with and
learns from the world.
During his discussion of maps, Churchland makes the surprising
claim that the brain recognizes causal processes before it
recognizes objects. What this means, as far as I understand it, is
that the brain pays attention to motion before it pays attention to
the actual structure of the object it is viewing. Churchland
introduces something called Hebbian Learning, that can be observed
in artificial neuronal networks. Hebbian Learning claims that a
neuron that is already activated (by an electro-chemical charge)
will be more likely to learn from subsequent electro-chemical
stimuli to that already activated neuron. This means that the basic
patterns to which brain is exposed are reinforced by the entire
network of neurons and their current activation levels through
recurrent pathways in the brain. These recurrent pathways make it
so that neurons that are higher up on the 'ladder' are able to send
electro-chemical impulses downward to further stimulate the sensory
neurons that initially encountered the world. For example, consider
an arrow in flight. Hebbian learning makes it so that a creature
will first recognize that the arrow is flying, rather than the
shape of the arrow. This idea is directly opposed to many well-
established philosophical ideas. Most of the time, we think that we
recognize an object (the arrow) first, and only afterward do we
recognize the fact that the arrow is in motion. If what Churchland
says is true (and there are 30 or so years of experimental data
that confirms that it is), this means that many philosophical ideas
will need to be reconsidered for validity.
Churchland also talks about the impact of social influences on
learning in the brain. He says that, even though the theory he is
presenting says that the brain at its most basic level is not
language-based, that language and society are means to a massive
accumulation of data and maps. These societal institutions allow
for a sort of cognitive activity outside of the individual and
could potentially mean that cognition can go on indefinitely. This
makes sense to me because that seems to be what is happening.
Society has now found an efficient way to store and catalogue all
the data and information that has come about through human
endeavors. Churchland also says that language serves a regulatory
purpose in that it allows humans to express ideas to the community
for verification or refutation. This is essential especially for
science because it provides the basis of peer-review. It allows us
to come to a consensus about whether an idea is factual or
fanciful. There is only one thing I am hesitant about. The language
the Churchland uses when talking about society could easily be
interpreted in a Hegelian manner. In fact, his idea about the
'cognitive organism' seems dangerously close to Hegel's idea of the
mind of the age (260). However, Churchland is adamant that this is
not what he has in mind, and in fact there are several places in
the book where he points out the failings of Hegel's theories. I
hope that these are kept in mind in the future so we don't fall
back into an idealist state of mind that does not stimulate
scientific research.
This book was really difficult, and I really had to stretch myself
to get through it. It is very much worth it, though. This review is
only a surface-level introduction, and there is a lot more that I
could have talked about. But for the sake of space and minimizing
the number of interpretational errors on my part, I have written a
short review. Churchland has presented solid evidence for his
claims, and I'm interested to see what happens in the field of
cognitive science after this book has been thoroughly digested.
December 13, 2012
Churchland's writing style leaves much to be desired, and I believe he could have said what he needed to say in about 15 pages. However, what he wanted to say was much more, that is, to take what amounted to cheap shots at folk psychology and linguistic theories of cognition. I'm not saying I agree with either or disagree with connectionism, but this book was interminable, and Churchland fails to address main issues like the role of affect, how memories are formed, and where procedural knowledge lies. I do think the concept of recurrent networks could be important, though.
February 18, 2021
I've seen from a few other reviews that this is considered by some to be a fairly difficult to follow book so I want to say upfront that I don't think this is the case. It is, however, touching on a few typically distinct subjects - from the sort of cognitive modelling seen in discussions on artificial intelligence and machine learning, to essays on epistemics, metaphysics, and philosophy of science.
For the latter group I'd say this has most currency - Churchland himself makes a few points about the dearth of (let's say for brevity) 'critical thinking' done by people studying the sciences. I think this may well be a difficult read for scientists who aren't well-versed in the big four of philosophy of science (Kuhn, Lakatos, Popper, Feyerabend) but Churchland is at pains to express arguments carefully and clearly.
In terms of the arguments he sets out - I'm not a cognitive scientist, so I can't comment too far on the sorts of issues he's addressing in that domain. From where I'm sat as a not-very-mathsy person, Churchland doesn't make the maths difficult to follow. While he doesn't quite fully articulate with cognitive models which are empirically true and which are considered metaphors (I'd need to read this again to decide if that were deliberate) but he is clear to articulate what he considers a gap in current thinking and what he considers to be a reasonable induction.
From my perspective as an ex-philosophy student, I think he nails a series of cognitive models well. The breadth of metaphysics here is perhaps uncomfortable to the non-philosophical reader - we've got everyone from Plato through to Heidegger here - but he's careful to succinctly compress arguments.
I'd probably not recommend this universally but I'd recommend to a fair few fields. Certainly philosophy of science, people interested in the intersection of cognitive science and metaphysics, people with an interest in AI / ML, people who want some idea of not just 'how the physical brain works' but the kinds of models and metaphors which inform science's understanding. Churchland is relatively critical of scientific orthodoxy so I'd say by-the-book scientists should read it but, frankly, those people don't show much interest in challenging their assumptions.
I picked up this because he was mentioned in an article about Paul Feyerabend, who I love, so a special shout out for the Feyerabendians out there.
For the latter group I'd say this has most currency - Churchland himself makes a few points about the dearth of (let's say for brevity) 'critical thinking' done by people studying the sciences. I think this may well be a difficult read for scientists who aren't well-versed in the big four of philosophy of science (Kuhn, Lakatos, Popper, Feyerabend) but Churchland is at pains to express arguments carefully and clearly.
In terms of the arguments he sets out - I'm not a cognitive scientist, so I can't comment too far on the sorts of issues he's addressing in that domain. From where I'm sat as a not-very-mathsy person, Churchland doesn't make the maths difficult to follow. While he doesn't quite fully articulate with cognitive models which are empirically true and which are considered metaphors (I'd need to read this again to decide if that were deliberate) but he is clear to articulate what he considers a gap in current thinking and what he considers to be a reasonable induction.
From my perspective as an ex-philosophy student, I think he nails a series of cognitive models well. The breadth of metaphysics here is perhaps uncomfortable to the non-philosophical reader - we've got everyone from Plato through to Heidegger here - but he's careful to succinctly compress arguments.
I'd probably not recommend this universally but I'd recommend to a fair few fields. Certainly philosophy of science, people interested in the intersection of cognitive science and metaphysics, people with an interest in AI / ML, people who want some idea of not just 'how the physical brain works' but the kinds of models and metaphors which inform science's understanding. Churchland is relatively critical of scientific orthodoxy so I'd say by-the-book scientists should read it but, frankly, those people don't show much interest in challenging their assumptions.
I picked up this because he was mentioned in an article about Paul Feyerabend, who I love, so a special shout out for the Feyerabendians out there.
March 26, 2018
It is a joke how it is written so bad , honestly i felt humiliated most of the time . He paraphrases himself on and on without saying anything new , i kept rereading lots of pages because i just couldn't believe all that repetition is for nothing , i though i missed something and then i felt humiliated again . It is obvious to me that he is trying to bloat the size of the book for a reason .
His way of introducing rival theories is very poor and unfair ,and then criticizing it as poor as being subjected ,very weak arguments indeed , in fact i do not think the counter theories are that dumb and deserve much more of a fair review .
2 stars because his theories is pretty interesting but represented in a drastically bad way.
His way of introducing rival theories is very poor and unfair ,and then criticizing it as poor as being subjected ,very weak arguments indeed , in fact i do not think the counter theories are that dumb and deserve much more of a fair review .
2 stars because his theories is pretty interesting but represented in a drastically bad way.
October 31, 2019
Badly written and badly argued.
June 21, 2012
Warning: This book is really not for folks who are interested in an introduction to cognitive science, philosophy of mind, or philosophy of science. Churchland writes with the baseline expectation of serious academic experience in all three of these areas, and isn't very generous in giving extrapolations of the fundamentals.
The second best book I've read this year, behind Action in Perception, Churchland's writing is wonderfully economical and his arguments are thorough. He does take the optics metaphor a little too far at times, and it really isn't clear what exactly he means when he compares people/brains to "eyes" or "cameras" because there is such a major differene between the classical and the contemporary conceptions of those two things.
Despite some lack of clarity in the introductory metaphor, Churchland's writing covers an enormous amount of ground in contemporary philosophy of mind. I haven't read any of Churchland's book-length work before this, but if it is similarly rigorous, I can definitely see myself reading a lot of it in graduate school. He packs the opening three chapters with a detailed account of neural mapping and concept formation based on vector-theories, and the work is really staggering.
There are a lot of philosophical problems, especially with the philosophy of mind, that Churchland is writing about. It seems to be very heavily neuro-centric, though I don't really think that is necessary for a lot of the discussions in the book. In that sense, Churchland's ideas are a little bit dated, but that's to be expected from a guy who has been a staple of the cognitive science field and philosophy of mind for the last 30 years.
The reason the book is so cool, though, is that the fourth chapter focuses on a broader philosophy of science informed by Churchland's account of concept formation. The philosophy of science that Churchland presents is a unique alternative to the sort of "Kant v. Hume v. James/Pierce" discussion that is so common, and I think that it may prove to be incredibly valuable for a number of up-and-coming philosophers of science, hopefully including myself.
For those interested in the cutting edge of epistemology, philosophy of mind, and philosophy of science, I strongly recommend this book. I often struggle to find extremely rigorous epistemology that is also in contact with good philosophy and history of science, and vice versa. Churchland's text sets a high bar of what that combination should look like in today's academy.
The second best book I've read this year, behind Action in Perception, Churchland's writing is wonderfully economical and his arguments are thorough. He does take the optics metaphor a little too far at times, and it really isn't clear what exactly he means when he compares people/brains to "eyes" or "cameras" because there is such a major differene between the classical and the contemporary conceptions of those two things.
Despite some lack of clarity in the introductory metaphor, Churchland's writing covers an enormous amount of ground in contemporary philosophy of mind. I haven't read any of Churchland's book-length work before this, but if it is similarly rigorous, I can definitely see myself reading a lot of it in graduate school. He packs the opening three chapters with a detailed account of neural mapping and concept formation based on vector-theories, and the work is really staggering.
There are a lot of philosophical problems, especially with the philosophy of mind, that Churchland is writing about. It seems to be very heavily neuro-centric, though I don't really think that is necessary for a lot of the discussions in the book. In that sense, Churchland's ideas are a little bit dated, but that's to be expected from a guy who has been a staple of the cognitive science field and philosophy of mind for the last 30 years.
The reason the book is so cool, though, is that the fourth chapter focuses on a broader philosophy of science informed by Churchland's account of concept formation. The philosophy of science that Churchland presents is a unique alternative to the sort of "Kant v. Hume v. James/Pierce" discussion that is so common, and I think that it may prove to be incredibly valuable for a number of up-and-coming philosophers of science, hopefully including myself.
For those interested in the cutting edge of epistemology, philosophy of mind, and philosophy of science, I strongly recommend this book. I often struggle to find extremely rigorous epistemology that is also in contact with good philosophy and history of science, and vice versa. Churchland's text sets a high bar of what that combination should look like in today's academy.
August 6, 2017
I said that Matter and Consciousness was the one to start with if you haven't read any Paul Churchland, but thinking of the question another way, if you think you are only going to read one book by Paul Churchland ever, then make that one book Plato's Camera.
(I think you'll find Plato's Camera a lot easier to understand, though, if you read M&C and some of this other earlier works first, the way I did. Maybe I'm wrong!)
(I think you'll find Plato's Camera a lot easier to understand, though, if you read M&C and some of this other earlier works first, the way I did. Maybe I'm wrong!)
August 5, 2016
highly interesting, even if not much in this book is new, it is a nice summary of the philosophical position of Paul Churchland
February 11, 2017
I quit because it was far too difficult for me, but I have all the intentions to go back to it somewhere in time.
Displaying 1 - 11 of 11 reviews