A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finite-state and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central (...) to the processing of non-adjacent dependencies as information has to be maintained across intervening material. One proposal is that an external memory device in the form of a limited push-down stack is used to process non-adjacent dependencies. We tested this hypothesis in an artificial grammar learning paradigm where subjects acquired non-adjacent dependencies implicitly. Generally, we found no qualitative differences between the acquisition of non-adjacent dependencies and adjacent dependencies. This suggests that although the acquisition of non-adjacent dependencies requires more exposure to the acquisition material, it utilizes the same mechanisms used for acquiring adjacent dependencies. We challenge the push-down stack model further by testing its processing predictions for nested and crossed multiple non-adjacent dependencies. The push-down stack model is partly supported by the results, and we suggest that stack-like properties are some among many natural properties characterizing the underlying neurophysiological mechanisms that implement the online memory resources used in language and structured sequence processing. (shrink)

In recent years, multivariate pattern analysis has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging, electroencephalography, and other neuroimaging methodologies. In a similar time frame, “deep learning” has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on (...) linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox intended to facilitate dMVPA for neuroscientists everywhere. (shrink)

Commercial applications of knowledge-based systems are changing from an embryonic to a growth business. Knowledge is classified by levels and types to differentiate various knowledge-based systems. Applications are categorized by size, generic types, and degree of intelligence to establish a framework for discussion of progress and implications. A few significant commercial applications are identified and perspectives and implications of these and other systems are discussed. Perspectives relate to development paths, delivery modes, types of integration, and resource requirements. Discussion includes organizational (...) opportunities and impacts, business perspectives, workplace issues, public effects, and educational needs. Common traps and modes of failures are mentioned.Major conclusions point to the great value of knowledge-based systems when development is pursued correctly, and to the existence of insufficient understanding of resource requirements, needs for integration, different talents, and incorporation into operational strategy to equitably exploit the significant economic and social costs associated with their creation and use. (shrink)

Plamondon & Alimi propose a universal account of trajectory formation and speed/accuracy trade-off in rapid movements but fail, because: (1) the kinematic model ignores the more fundamental dynamics of movement generation, and (2) it does not capture the essential space-time constraints of movement accuracy. Hence, the modeling lacks a biologically and behaviorally principled foundation and is driven by pragmatic function fitting.

A hallmark of skilled motor performance is behavioral flexibility – i.e., experts can not only produce a movement pattern to reliably achieve a given task goal, but also possess the ability to change that movement pattern to fit a new context. In this perspective article, we briefly highlight the factors that are critical to understanding behavioral flexibility, and its connection to movement variability, stability, and learning. We then address how practice strategies should be developed from a motor learning standpoint to (...) enhance behavioral flexibility. Finally, we highlight some important future avenues of work that are needed to advance our understanding of behavioral flexibility. We use examples from sport as a context to highlight these issues, especially in regard to elite performance and development. (shrink)

In the present study, using event-related functional magnetic resonance imaging, we investigated a group of participants on a grammaticality classification task after they had been exposed to well-formed consonant strings generated from an artificial regular grammar. We used an implicit acquisition paradigm in which the participants were exposed to positive examples. The objective of this studywas to investigate whether brain regions related to language processing overlap with the brain regions activated by the grammaticality classification task used in the present study. (...) Recent meta-analyses of functional neuroimaging studies indicate that syntactic processing is related to the left inferior frontal gyrus (Brodmann's areas 44 and 45) or Broca's region. In the present study, we observed that artificial grammaticality violations activated Broca's region in all participants. This observation lends some support to the suggestions that artificial grammar learning represents a model for investigating aspects of language learning in infants [TICS 4 (2000) 178] and adults [Proceedings of the National Academy of Sciences of United States of America 99 (2002) 529]. (shrink)

Governments, or at least the clever ones among them, are aware of the factors guiding business activities. In the course of adopting and enforcing economic legislation, they seek to attract business activities in order to increase national income, generate employment opportunities, and, very generally, please voters. Hence economic law may be considered an economic good, as suggested by the title of this book. That function, which most rules of economic law have in the competition of systems, was strengthened by the (...) worldwide liberalization of trade. Today, it is of greater significance than ever before. Lawyers, economists, academics, and practitioners, from inside and outside Germany, have taken a look at the facts and have discussed approaches to conceptualizing them. The resulting 30 essays, collected in this volume, contribute to the interpretation of existing, and the making of new, economic law. (shrink)