A fundamental aspect of human cognition is the ability to parse our constantly unfolding experience into meaningful representations of dynamic events and to communicate about these events with others. How do we communicate about events we have experienced? Influential theories of language production assume that the formulation and articulation of a linguistic message is preceded by preverbal apprehension that captures core aspects of the event. Yet the nature of these preverbal event representations and the way they are mapped onto language (...) are currently not well understood. Here, we review recent evidence on the link between event conceptualization and language, focusing on two core aspects of event representation: event roles and event boundaries. Empirical evidence in both domains shows that the cognitive representation of events aligns with the way these aspects of events are encoded in language, providing support for the presence of deep homologies between linguistic and cognitive event structure. (shrink)

This paper proposes two parameter identification methods for a nonlinear membership function. An equation converted method is introduced to turn the nonlinear function into a concise model. Then a stochastic gradient algorithm and a gradient-based iterative algorithm are provided to estimate the unknown parameters of the nonlinear function. The numerical example shows that the proposed algorithms are effective.

In this study, two modified gradient descent algorithms are proposed for time-delayed models. To estimate the parameters and time-delay simultaneously, a redundant rule method is introduced, which turns the time-delayed model into an augmented model. Then, two GD algorithms can be used to identify the time-delayed model. Compared with the traditional GD algorithms, these two modified GD algorithms have the following advantages: avoid a high-order matrix eigenvalue calculation, thus, are more efficient for large-scale systems; have faster convergence rates, therefore, are (...) more practical in engineering practices. The convergence properties and simulation examples are presented to illustrate the efficiency of the two algorithms. (shrink)

The self-organizing model and expectation-maximization method are two traditional identification methods for switching models. They interactively update the parameters and model identities based on offline algorithms. In this paper, we propose a flexible recursive least squares algorithm which constructs the cost function based on two kinds of errors: the neighboring two-parameter estimation errors and the output estimation errors. Such an algorithm has several advantages over the two traditional identification algorithms: it can estimate the parameters of all the sub-models without prior (...) knowledge of the model identities; has less computational efforts; and can update the parameters with newly arrived data. The convergence properties and simulation examples are provided to illustrate the efficiency of the algorithm. (shrink)