- Oxford Handbooks in Linguistics
- List of Abbreviations
- The Contributors
- Compositionality: Its Historic Context
- Composition A Lity In Montague Grammar
- The case for compositionality
- Compositionality Problems and how to Solve Them
- Direct Compositionality
- Semantic Monadicity with Conceptual Polyadicity
- Holism And Compositionality.
- Composition Ality, Flexibility, And Context Dependence
- Compositionality in Kaplan Style Semantics
- Formalizing the relationship between meaning and syntax
- Compositionality and The Context Principle
- Compositionality In Discourse From A Logical Perspective
- Lexical Decomposition In Grammar
- Lexical Decomposition in Modern Syntactic Theory
- Syntax in the Atom
- Co-composition Ality in Grammar
- Typicality and Composition a Lity: the Logic of Combining Vague Concepts
- Emergency!!!! Challenges to a Compositional Understanding of Noun–noun Combinations
- Can Prototype Representations Support Composition And Decomposition?
- Regaining Composure: A Defence Of Prototype Compositionality.
- Simple Heuristics For Concept Combination
- Compositionality and Beyond: Embodied Meaning in Language and Protolanguage
- Compositionality and Linguistic Evolution
- Communication And The complexity of semantics
- Prototypes and their Composition from an Evolutionary Point of View
- Connectionism, Dynamical Cognition, and Non-Classical Compositional Representation
- The Dual-Mechanism Debate
- Compositionality and Biologically Plausible Models
- Neuronal Assembly Models of Compositionality
- Non-Symbolic Compositional Representation and Its Neuronal Foundation: To wards An Emulative Semantics
- The Processing Consequences of Compositionality
Abstract and Keywords
Cognitive theories have expressed their components using an artificial symbolic language, such as first-order predicate logic, and the atoms in such representations are non-decomposable letter strings. A neural theory merely demonstrates how to implement a classical symbol system using neurons: this is actually an argument against the importance of the neural description. The fact that symbol systems are physically instantiated in neurons becomes a mere implementational detail, since there is a direct way to translate from the symbolic description to the more neurally plausible one. It might then be argued that, while the neural aspects of the theory identify how behavior arises, they are not fundamentally important for understanding that behavior. Classical symbol systems would continue to be seen as the right kinds of description for psychological processes.
Terrence C. Stewart is a Post-doctoral Fellow at the Centre for Theoretical Neuroscience at the University of Waterloo, where he is developing scalable methods for implementing high-level cognition using biologically realistic spiking neurons. He received his Ph.D. in Cognitive Science from Carleton University (specializing in the role of computational modelling within cognitive science) and his M.Phil. from the School of Cognitive and Computing Sciences (COGS) at the University of Sussex.
Chris Eliasmith is Canada Research Chair and Director of the Centre for Theoretical Neuroscience at the University of Waterloo. He is jointly appointed to the departments of Philosophy and Systems Design Engineering. His research interests include large‐scale, biologically realistic neural modeling, mental and neural representation, and neural dynamics. He is coauthor of the book Neural Engineering, which presents three principles for addressing such issues. He has published across a wide array of disciplines, with articles in journals including the Journal of Philosophy, Synthese, Cognitive Science, Neural Computation, Journal of Neuroscience, and Cerebral Cortex.
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