This book is about the interface between natural language and the sensorimotor system. It is obvious that there is an interface between language and sensorimotor cognition, because we can talk about what we see and do. The main proposal in the book is that the interface is more direct than is commonly assumed. To argue for this proposal I focus on a simple concrete episode—a man grabbing a cup—which can be reported in a simple transitive sentence (e.g. the English sentence The man grabbed a cup). In the first part of the book I present a detailed model of the sensorimotor processes involved in experiencing this episode, both as the agent bringing it about and as an observer watching it happen. The model draws on a large body of research in neuroscience and psychology. I also present a model of the syntactic structure of the associated transitive sentence, developed within the entirely separate discipline of theoretical linguistics. This latter model is a version of Chomsky’s ‘Minimalist ’ syntactic theory, which assumes that a sentence reporting the episode has the same underlying syntactic structure (called ‘logical form’) regardless of which language it is in. My main proposal is that these two independently motivated models are in fact closely

A natural language parser implemented entirely in simulated neurons is described. It produces a semantic representation based on frames. It parses solely using simulated fatiguing Leaky Integrate and Fire neurons, that are a relatively accurate biological model that is simulated efficiently. The model works on discrete cycles that simulate 10 ms. of biological time, so the parser has a simple mapping to psychological parsing time. Comparisons to human parsing studies show that the parser closely approximates this data. The parser makes use of Cell Assemblies and the semantics of lexical items is represented by overlapping hierarchical Cell Assemblies so that semantically related items share neurons. This semantic encoding is used to resolve prepositional phrase attachment ambiguities encountered during parsing. Consequently, the parser provides a neurally-based cognitive model of parsing.

ABSTRACT. Recent findings in neuroscience strongly suggest that an object’s features (e.g., its color, texture, shape, etc.) are represented in separate areas of the visual cortex. Although represented in separate neuronal areas, somehow the feature representations are brought together as a single, unified object of visual consciousness. This raises a question of binding: how do neural activities in separate areas of the visual cortex function to produce a feature-unified object of visual consciousness? Several prominent neuroscientists have adopted neural synchrony and attention-based approaches to explain object feature binding. I argue that although neural synchrony and/or attentional mechanisms might function to disambiguate an object’s features, it is difficult to see how either of these mechanisms could fully explain the unity of an object’s features at the level of visual consciousness. After presenting a detailed critique of neural synchrony and attention-based approaches to object feature binding, I propose interactive hierarchical structuralism (IHS). This view suggests that a unified percept (i.e., a feature-unified object

The special issue of The Linguistic Review on “The Role of Linguistics in Cognitive Science” presents a variety of viewpoints that complement or contrast with the perspective offered in Foundations of Language (Jackendoff 2002a). The present article is a response to the special issue. It discusses what it would mean to integrate linguistics into cognitive science, then shows how the parallel architecture proposed in Foundations seeks to accomplish this goal by altering certain fundamental assumptions of generative grammar. It defends this approach against criticisms both from mainstream generative grammar and from a variety of broader attacks on the generative enterprise, and it reflects on the nature of Universal Grammar. It then shows how the parallel architecture applies directly to processing and defends this construal against various critiques. Finally, it contrasts views in the special issue with that of Foundations with respect to what is unique about language among cognitive capacities, and it conjectures about the course of the evolution of the language faculty. I am honored that The Linguistic Review has suggested Foundations of Language (Jackendoff 2002a, henceforth FL) as a “unifying starting point ” for discussion of the role of

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ABSTRACT: A central issue in philosophy and neuroscience is the problem of unified visual consciousness. This problem has arisen because we now know that an object’s stimulus features (e.g., its color, texture, shape, etc.) generate activity in separate areas of the visual cortex (Felleman & Van Essen, 1991). For example, recent evidence indicates that there are very few, if any, neural connections between specific visual areas, such as those that correlate with color and motion (Bartels & Zeki, 2006; Zeki, 2003). So how do unified objects arise in visual consciousness? Some neuroscientists propose that neural synchrony is the mechanism that binds an object’s features into a unity (e.g., see Crick,

Variable binding is a difficult problem for neural networks. Two new mechanisms for binding by synaptic change are presented, and in both, bindings are erased and can be reused. The first is based on the commonly used learning mechanism of permanent change of synaptic weight, and the second on synaptic change which decays. Both are biologically motivated models. Simulations of binding on a paired association task are shown with the first mechanism succeeding with a 97.5 % F-Score, and the second performing perfectly. Further simulations show that binding by decaying synaptic change copes with cross talk, and can be used for compositional semantics. It can be inferred that binding by permanent change accounts for these, but it faces the stability plasticity dilemma. Two other existing binding mechanism, synchrony and active links, are compatible with these new mechanisms. All four mechanisms are compared and integrated in a Cell Assembly theory. 1 1

Simple recurrent networks (SRNs) in symbolic time series prediction (e. g. language processing models) are frequently trained with gradient descent based learning algorithms, notably with variants of backpropagation (BP). A major drawback for the cognitive plausibility of BP is that it is a supervised scheme in which a teacher has to provide a fully specified target answer. Yet, agents in natural environments often receive a summary feedback about the degree of success or failure only, a view adopted in reinforcement learning schemes. In this work we show that for SRNs in prediction tasks for which there is a probability interpretation of the network’s output vector, Elman BP can be reimplemented as a reinforcement learning (RL) scheme for which the expected weight updates agree with the ones from traditional Elman BP. Network simulations on formal languages corroborate this result and show that the learning behaviours of Elman backpropagation (BP) and its reinforcement variant are very similar also in online learning tasks.

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What is NeuroIT? And why does it need a roadmap? After all, there is something called “neuroinformatics, ” which has been around for a while and which is growing rapidly. The term neuroinformatics is often used to refer to the application of information technology (IT) to the “brain sciences. ” Almost all of the brain sciences have become considerably more complex, and recording and managing the results from experiments entails the use of ever-more complex and larger databases and analysis tools. Examples are the large heterogeneous data sets produced by fMRI machines, the complex electro-chemical mechanisms and genetic factors that determine neuron function, and so on. To understand these data, increasingly complex and time-consuming models are necessary, which run on ever-larger computers, which sometimes