Preface
p.xi: "By now virtually everyone agrees that the scientific explanation for human language and cognition will be based on our bodies, brains, and experiences. The major exception is Naom Chomsky whose dominance of twentieth-century linguistics is unparalleled in any other academic field. I will later quote from Chomsky's 1993 book, Language and Thought, and he repeatedly stated the same idea in his 2003 Berkeley lectures: 'We don't know nearly enough about the brain for cognitive science to take it seriously.' Chomsky was focused on linguistic form; since this book deals with meaning, we won't encounter him again until chapter 22.
"How do our brains compute our minds?" [i.e. embodied cognition and embodied language processing vs. mind-body problem and Cartesian dualism]
" Information processing is my organizing theme. Language and thought are inherently about how information is acquired, used, and transmitted. Chapter 1 lays out some of the richness of language and its relation to experience. The central mechanism in my approach to the neural language problem is neural computation. Chapter 2 and chapter 3 provide a general introduction to neural computation. Chapter 4, chapter 5 and chapter 6 provide the minimal biologocal background on neurons, neural circuits, and how they develop. We focus on these properties of molecules, cells, and brain circuits that determine the character of our language.
" Chapter 7 and chapter 8 consider thought from the external perspective and look at the brain/mind as a behaving system. With all this background, chapter 9 introduces the technical tools that are used to model how various components of language and thought are realized in the brain. A fair amount of mechanism is required for my approach, which involves building computational models that actually exhibit the required behavior while remaining consistent with the findings from all disciplines. I refer to such systems as adequate computational models, which I believe are the only hope for scientifically linking brain and behavior. There is no guatentee that an adequate model is correct, but any correct model must be adequate in the sense defined here.
"The specific demonstrations begin with a study of how children learn their first words. [E.g. Development psychology, Neurophysiology, Behavioral neuroscience, ...?] This involves some general review ( chapter 10) and a more thorough study of conceptual structure ( chapter 11) needed for word learning. [E.g. Conceptual schema, Cultural frame, Conceptual metaphor, ...?} The first detailed model is presented in chapter 12, which describes Terry Regier's program that learns words for spatial relation concepts across languages. [Terry Regier is mentioned in Language acquisition] The theme of concrete word learning is then extended to cover words for simple actions in chapter 13 [ Language acquisition] and chapter 14, which describes David Bailey's demonstration system.
"The next section extends the discussion to words for abstract and metaphorical cpncepts. In chapter 15 and chapter 16, we look furthur at the structure of conceptual systems and how they arise through metaphorical mappings from direct experience. Chapter 17 takes the informal idea of understanding as imaginative simulation and shows how it can be made the basis for a concrete theory. This theory is shown in chapter 18 to be sufficiently rich to describe linguistic aspect—the shape of events. This is enought to capture the direct effects of hearing a sentence, but for the indirect consequences, we need one more computatonal abstraction of neural activity—belief networks, described in chapter 19. All fo these ideas are brought together in Srinivas Narayanan's program for understanding news stories, discussed in chapter 20.
Chapters 21 through 25 are about language form, that is, grammar--how grammar is learned and how gramatical processing works. Chapter 21 lays out the basic facts about the form of language that any theory must explain. Chapter 22 is partly a digression; it discusses the hotbutton issues surrounding how much of human grammar is innate. We see that classical questions become much different in and explicitly embodied theory of language and that such theories can be expressed in standard formalisms ( chapter 23).
" Chapter 24 shows how the formalized version of neural grammar can be used to build software systems for understanding natural language. The poster child for the entire theory is Nancy Chang's program ( Chapter 25) that models how children leanr their early grammar--as explicit mappings (constructions) relating linguistic form to meaning. Chapter 26 discusses two questions which are not currently answerable: the evolution of language and the nature of subjective experience. Finally, chapter 27 summarizes the book and suggests that further progress will require a broadly based unified cognitive science. But the scientific progress to date does support a range of practical and intellectual applications and should allow us to understand ourselves better.
Preface
p.xi: "By now virtually everyone agrees that the scientific explanation for human language and cognition will be based on our bodies, brains, and experiences. The major exception is Naom Chomsky whose dominance of twentieth-century linguistics is unparalleled in any other academic field. I will later quote from Chomsky's 1993 book, Language and Thought, and he repeatedly stated the same idea in his 2003 Berkeley lectures: 'We don't know nearly enough about the brain for cognitive science to take it seriously.' Chomsky was focused on linguistic form; since this book deals with meaning, we won't encounter him again until chapter 22.
"How do our brains compute our minds?" [i.e. embodied cognition and embodied language processing vs. mind-body problem and Cartesian dualism]
" Information processing is my organizing theme. Language and thought are inherently about how information is acquired, used, and transmitted. Chapter 1 lays out some of the richness of language and its relation to experience. The central mechanism in my approach to the neural language problem is neural computation. Chapter 2 and chapter 3 provide a general introduction to neural computation. Chapter 4, chapter 5 and chapter 6 provide the minimal biologocal background on neurons, neural circuits, and how they develop. We focus on these properties of molecules, cells, and brain circuits that determine the character of our language.
" Chapter 7 and chapter 8 consider thought from the external perspective and look at the brain/mind as a behaving system. With all this background, chapter 9 introduces the technical tools that are used to model how various components of language and thought are realized in the brain. A fair amount of mechanism is required for my approach, which involves building computational models that actually exhibit the required behavior while remaining consistent with the findings from all disciplines. I refer to such systems as adequate computational models, which I believe are the only hope for scientifically linking brain and behavior. There is no guatentee that an adequate model is correct, but any correct model must be adequate in the sense defined here.
"The specific demonstrations begin with a study of how children learn their first words. [E.g. Development psychology, Neurophysiology, Behavioral neuroscience, ...?] This involves some general review ( chapter 10) and a more thorough study of conceptual structure ( chapter 11) needed for word learning. [E.g. Conceptual schema, Cultural frame, Conceptual metaphor, ...?} The first detailed model is presented in chapter 12, which describes Terry Regier's program that learns words for spatial relation concepts across languages. [Terry Regier is mentioned in Language acquisition] The theme of concrete word learning is then extended to cover words for simple actions in chapter 13 [ Language acquisition] and chapter 14, which describes David Bailey's demonstration system.
"The next section extends the discussion to words for abstract and metaphorical cpncepts. In chapter 15 and chapter 16, we look furthur at the structure of conceptual systems and how they arise through metaphorical mappings from direct experience. Chapter 17 takes the informal idea of understanding as imaginative simulation and shows how it can be made the basis for a concrete theory. This theory is shown in chapter 18 to be sufficiently rich to describe linguistic aspect—the shape of events. This is enought to capture the direct effects of hearing a sentence, but for the indirect consequences, we need one more computatonal abstraction of neural activity—belief networks, described in chapter 19. All fo these ideas are brought together in Srinivas Narayanan's program for understanding news stories, discussed in chapter 20.
Chapters 21 through 25 are about language form, that is, grammar--how grammar is learned and how gramatical processing works. Chapter 21 lays out the basic facts about the form of language that any theory must explain. Chapter 22 is partly a digression; it discusses the hotbutton issues surrounding how much of human grammar is innate. We see that classical questions become much different in and explicitly embodied theory of language and that such theories can be expressed in standard formalisms ( chapter 23).
" Chapter 24 shows how the formalized version of neural grammar can be used to build software systems for understanding natural language. The poster child for the entire theory is Nancy Chang's program ( Chapter 25) that models how children leanr their early grammar--as explicit mappings (constructions) relating linguistic form to meaning. Chapter 26 discusses two questions which are not currently answerable: the evolution of language and the nature of subjective experience. Finally, chapter 27 summarizes the book and suggests that further progress will require a broadly based unified cognitive science. But the scientific progress to date does support a range of practical and intellectual applications and should allow us to understand ourselves better.