Modality-Independent Effects of Phonological Neighborhood Structure on Initial L2 Sign Language Learning

The goal of the present study was to characterize how neighborhood structure in sign language influences lexical sign acquisition in order to extend our understanding of how the lexicon influences lexical acquisition in both sign and spoken languages. A referent-matching lexical sign learning paradigm was administered to a group of 29 hearing sign language learners in order to create a sign lexicon. The lexicon was constructed based on exposures to signs that resided in either sparse or dense handshape and location neighborhoods. The results of the current study indicated that during the creation of the lexicon signs that resided in sparse neighborhoods were learned better than signs that resided in dense neighborhoods. This pattern of results is similar to what is seen in child first language acquisition of spoken language. Therefore, despite differences in child first language and adult second language acquisition, these results contribute to a growing body of literature that implicates the phonological features that structure of the lexicon is influential in initial stages of lexical acquisition for both spoken and sign languages. This is the first study that uses an innovated lexicon-construction methodology to explore interactions between phonology and the lexicon in L2 acquisition of sign language

Machine Learning Theory and Practice as a Source of Insight into Universal Grammar

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Computer Architectures to Close the Loop in Real-time Optimization

© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

A Meta-Learning Approach to One-Step Active Learning

We consider the problem of learning when obtaining the training labels is costly, which is usually tackled in the literature using active-learning techniques. These approaches provide strategies to choose the examples to label before or during training. These strategies are usually based on heuristics or even theoretical measures, but are not learned as they are directly used during training. We design a model which aims at \textit{learning active-learning strategies} using a meta-learning setting. More specifically, we consider a pool-based setting, where the system observes all the examples of the dataset of a problem and has to choose the subset of examples to label in a single shot. Experiments show encouraging results