Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff formulated four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard ’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. 2 Content

This research uses fMRI to understand the role of eight cortical regions in a relatively complex information-processing task. Modality of input (visual versus auditory) and modality of output (manual versus vocal) are manipulated. Two perceptual regions (auditory cortex and fusiform gyrus) only reflected perceptual encoding. Two motor regions were involved in information rehearsal as well as programming of overt actions. Two cortical regions (parietal and prefrontal) performed processing (retrieval and representational change) independent of input and output modality. The final two regions (anterior cingulate and caudate) were involved in control of cognition independent of modality of input or output and content of the material. An information-processing model, based on the ACT-R theory, is described that predicts the BOLD response in these regions. Different modules in the theory vary in the degree to which they are modality-specific and the degree to which they are involved in central versus peripheral cognitive processes.

Background: Theories of embodied knowledge argue that the representation and recruitment of motor processes may be important for deriving the meaning of many linguistic and perceptual elements. Aims: We examined the conditions under which gestural knowledge associated with manipulable objects is evoked. Methods & Procedures: A priming paradigm was used in which an object was presented in advance of a photograph of a hand gesture that participants were to mimic. On related trials, the target gesture was the same as the gesture typically used to interact with the object prime. On unrelated trials, the target gesture was not related to the object. In another set of experiments, a Stroop-like paradigm was used in which participants learned to produce manual responses to colour cues. After training, coloured photographs of manipulable objects were presented. The colour-cued gesture was either one typically used with the object or was unrelated to it. Outcomes & Results: In the priming experiments, response latencies were shorter in the related condition, but only when participants also made an identification response to

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

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In this paper we present models of how meaning is represented in the brain/mind, based upon the assumption that children develop meaning representations for words using two main sources of information: information derived from their concrete experience with objects and events in the world (which we refer to as attributional information) and information implicitly derived from exposure to language (which we refer to as distributional information). In the first part of the paper we present a model developed using self-organising maps (SOMs) starting from speaker-generated features (properties that speakers considered to be important in defining and describing the meaning of a word). This model captures meaning similarity between words based solely upon attributional information and has been shown to be successful in predicting a number of behavioural semantic effects. In the second part of the paper, we present a probabilistic model that goes beyond attributional information alone, integrating this information with distributional information derived from text corpora. The ability of this integrated model to learn semantic relationships is demonstrated with reference to comparable probabilistic models that use only attributional or distributional information. 1

Recent research has indicated that anteromedial temporal cortex (including the perirhinal cortex) may function as the endpoint of a hierarchically organized visual object--processing network providing the basis for fine-grained discrimination among objects. The present study examines whether the same system is involved in processing conceptual information when concepts, and their properties, are denoted by words. A lesion--behavior correlational study was conducted in which cortical damage in 21 braindamaged patients was correlated with behavioral scores in a verbally presented property verification task. Results indicated that the neural correlates of conceptual processing depend on the dynamic interaction between the content of a conceptual representation and the specific demands of the task and that the role of anteromedial temporal cortex in this process is not limited to the visual input modality. The results are consistent with the claim that anteromedial temporal cortex provides the neural structure necessary for the emergence of fine-grained conceptual knowledge about objects, although the region is strongly weighted toward processing of visually based object features.

Consider two individuals, John and Mary, who each possess a number of concepts. How can we determine that John and Mary both have a concept of, say, Horse? John and Mary may not have exactly the same knowledge of horses, but it is important to be able to place their horse concepts into correspondence with one another, if only so that we can say things like, “Mary’s concept of horse is much more sophisticated than John’s. ” Concepts should be public in the sense that they can be possessed by more than one person (Fodor, 1998; Fodor & Lepore, 1992), and for this to be the possible, we must be able to determine correspondences, or translations, between two individuals ’ concepts. There have been two major approaches in cognitive science to conceptual meaning that could potentially provide a solution to finding translations between conceptual systems. According to an “external grounding” account, concepts ’ meanings depend on their connection to the external world (this account is more thoroughly defined in the next section). By this account, the concept Horse means what it does because our perceptual apparatus can identify features that characterize horses. According to what we will call a “Conceptual web ” account, concepts ’ meanings depend on their connections to each other. By this account, Horse’s meaning depends on Gallop, Domesticated, and Quadruped, and in turn, these concepts depend on other concepts, including Horse (Quine & Ullian, 1970). In this chapter, we will first present a brief tour of some of the main proponents of conceptual web and external grounding accounts of conceptual meaning. Then, we will describe a computer algorithm that translates between conceptual systems. The initial goal of this computational work is to show how translating across systems is possible using only withinsystem relations, as is predicted by a conceptual web account. However, the subsequent goal is to show how the synthesis of external and internal information can dramatically improve translation. This work suggests that the external grounding and conceptual web accounts should not be

Running Head: The origin of epistemic structures Abstract: Organisms across species use the strategy of generating structures in their environment to lower cognitive complexity. Examples include pheromones, markers, colour codes, etc. Distributed Cognition theory has argued that studying such ‘epistemic structures ’ can provide insights into the development and nature of internal representations, and cognition itself. We develop this claim by providing a model of the origin of such structures, and present a simulation where organisms with only reactive behaviour learn, within their lifetime, to add such structures systematically to their world to lower cognitive load. This implementation is then extended to show that the same underlying process could generate traces of the world in an ‘internal environment ’ to lower cognitive load. We then examine two implications of this internal trace model. First, it provides a novel account of the origin of internal representations. Further, as both external and internal traces lower cognitive load and are generated using the same mechanism, the location of the structure becomes opportunistic, and a matter of utility. This supports the ‘extended mind ’ hypothesis. Second, the stored internal traces develop entirely out of actions. They thus encapsulate action components and could activate actions. This feature explains the origin of enactable and action-oriented mental content.

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