186 research outputs found
Some theoretical and empirical background to Fodor’s systematicity arguments
This paper aims to clarify certain features of the systematicity arguments by a review of some of the largely underexamined background in Chomsky’s and Fodor’s early work on transformational grammar
Faculty Authors & Achievers Bibliography 2020
The office of the Vice President for Academic Affairs and the Wyndham Robertson Library congratulate all Hollins faculty who published or presented creative or scholarly works from Fall 2018 - Summer 2020. These pages include a list of those activities
A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System
Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI) and proportional integral differentiation (PID) cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance
What underlies rapid learning and systematic generalization in humans
Despite the groundbreaking successes of neural networks, contemporary models
require extensive training with massive datasets and exhibit poor out-of-sample
generalization. One proposed solution is to build systematicity and
domain-specific constraints into the model, echoing the tenets of classical,
symbolic cognitive architectures. In this paper, we consider the limitations of
this approach by examining human adults' ability to learn an abstract reasoning
task from a brief instructional tutorial and explanatory feedback for incorrect
responses, demonstrating that human learning dynamics and ability to generalize
outside the range of the training examples differ drastically from those of a
representative neural network model, and that the model is brittle to changes
in features not anticipated by its authors. We present further evidence from
human data that the ability to consistently solve the puzzles was associated
with education, particularly basic mathematics education, and with the ability
to provide a reliably identifiable, valid description of the strategy used. We
propose that rapid learning and systematic generalization in humans may depend
on a gradual, experience-dependent process of learning-to-learn using
instructions and explanations to guide the construction of explicit abstract
rules that support generalizable inferences.Comment: 22 pages, 48 references, 6 Figures, and one Table, plus S
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