The Full Transfer/Full Access model and L3 cognitive states

2020 ◽  
Author(s):  
Bonnie D. Schwartz ◽  
Rex A. Sprouse
Keyword(s):  
Transfer Model ◽  
Third Language ◽  
Cognitive States ◽  
Open Questions ◽  
Content Acquisition ◽  
Language L ◽  
Access Model ◽  
Transfer Models

Abstract This paper offers an overview of current models of third language (L3) acquisition, classifying each as a Wholesale Transfer model or as a Piecemeal Transfer model. We discuss what we consider to be some conceptual and empirical problems for the Piecemeal Transfer approaches and then discuss some advantages we see in Wholesale Transfer. Next, we home in on Wholesale Transfer models, arguing that one of them in particular seems to us to be the most promising, viz., the Typological Primacy Model (TPM – e.g., Rothman, 2011, 2015). Finally, we take up some open questions associated with the TPM and suggest some possible directions for future L3 research.

Transfer vs. dynamic cross-linguistic interactions

2020 ◽  
Author(s):  
Magdalena Wrembel
Keyword(s):  
Third Language ◽  
Cognitive States ◽  
L3 Acquisition ◽  
Keynote Paper ◽  
Data Support ◽  
Binary Approach ◽  
Property Transfer ◽  
Cross Language ◽  
Access Model ◽  
Transfer Models

Abstract In their keynote paper “The Full Transfer/Full Access model and L3 cognitive states” Schwartz & Sprouse (2021, henceforth S&S) present a thorough overview of third language (L3) acquisition transfer models by pinpointing their strong points as well as flaws of a conceptual and empirical nature. Their analysis is anchored in a principled distinction between models that are classified as representing wholesale transfer vs. those that are referred to as piecemeal or property-by-property transfer models. S&S side with the former, and level their criticism towards the latter approach. In this commentary, I would like to challenge this binary approach to debating the nature of transfer by taking an outsider’s position, i.e. that of a researcher working in a framework other than the generative one, in which the keynote paper is embedded. I will start by addressing some of the controversial points raised by S&S and then broaden the perspective by discussing the concept of transfer in L3 acquisition in the light of dynamic cross-language interactions and exploring alternative avenues in L3 research with data support from various linguistic domains.

Asymmetric transfer and development of temporal-aspectual sentence-final particles in English-Cantonese bilinguals’ L3 Mandarin grammars

2021 ◽  
Author(s):  
Yanyu Guo ◽  
Boping Yuan
Keyword(s):  
Structural Similarity ◽  
Bilingual Learners ◽  
Third Language ◽  
Proficiency Levels ◽  
Source Selection ◽  
Content Acquisition ◽  
Language L ◽  
English Speaking ◽  
Final Particles

Abstract Aiming to shed new light on the discussion on transfer at initial stages of third language (L3) acquisition and development at later stages, this article reports on an empirical study of L3 acquisition of Mandarin temporal-aspectual sentence-final particles (SFPs) le, ne and láizhe by English speaking and English-Cantonese bilingual learners, at both low and high proficiency levels. Cantonese is typologically and structurally closer to Mandarin than English is. Our findings show obvious facilitative effects on le by its Cantonese counterpart in English-Cantonese bilingual learners’ L3 Mandarin, which supports the L3 models that advocate the deterministic role of structural similarity in the transfer source selection. A transfer asymmetry is observed between the cases of le and láizhe. No transfer effects are found in the L3 Mandarin data of láizhe, even though it has an equivalent SFP in Cantonese. This discrepancy is argued to be attributable to input factors and misleading forms. Moreover, patterns observed over different proficiency levels indicate that the quality and quantity of input and the register property of a particular SFP can greatly affect initial transfer and later development of L3 acquisition.

Expanding the scope of l3 transfer study designs

2021 ◽  
Author(s):  
M. Juncal Gutierrez-Mangado
Keyword(s):  
Point Of View ◽  
Transfer Effects ◽  
Cognitive States ◽  
L3 Acquisition ◽  
Wh Questions ◽  
Study Designs ◽  
Transfer Study ◽  
Access Model ◽  
Transfer Models

Abstract This commentary discusses Schwartz and Sprouse’s (2020) (henceforth S&S) keynote article entitled “The Full Transfer/Full Access model and L3 cognitive states”, which presents an overview of transfer models in L3 acquisition. The present commentary focuses on the issue of transfer itself from the point of view of the language combinations that are argued in the keynote article are suitable for investigating transfer effects by drawing on data from the phenomenon known as medial wh-questions.

In defense of ‘copying and restructuring’

2020 ◽  
pp. 026765832097583
Author(s):  
Bonnie D Schwartz ◽  
Rex A Sprouse
Keyword(s):  
Second Language ◽  
Language Acquisition ◽  
Large Body ◽  
L2 Acquisition ◽  
Third Language ◽  
L3 Acquisition ◽  
Nonnative Language ◽  
Access Model

In her keynote article advocating the Linguistic Proximity Model for third language (L3) acquisition, Westergaard (2019) presents several arguments against ‘copying and restructuring’ in nonnative language acquisition, mechanisms central to Schwartz and Sprouse’s (1996) Full Transfer/Full Access model of second language (L2) acquisition. In this commentary, we seek to counter her arguments and also show that the results of a large body of studies on nonnative language acquisition are explained only by ‘copying and restructuring’.

scholarly journals Hippocampal theta bursting and waveform shape reflect CA1 spiking patterns

2018 ◽  
Author(s):  
Scott Cole ◽  
Bradley Voytek
Keyword(s):  
Local Field ◽  
Pyramidal Neurons ◽  
Field Potential ◽  
Brain Regions ◽  
Neuronal Firing ◽  
Detectable Effect ◽  
Field Recordings ◽  
Cognitive States ◽  
Open Questions ◽  
Hippocampal Theta

AbstractBrain rhythms are nearly always analyzed in the spectral domain in terms of their power, phase, and frequency. While this conventional approach has uncovered spike-field coupling, as well as correlations to normal behaviors and pathological states, emerging work has highlighted the physiological and behavioral importance of multiple novel oscillation features. Oscillatory bursts, for example, uniquely index a variety of cognitive states, and the nonsinusoidal shape of oscillations relate to physiological changes, including Parkinson’s disease. Open questions remain regarding how bursts and nonsinusoidal features relate to circuit-level processes, and how they interrelate. By analyzing unit and local field recordings in the rodent hippocampus, we uncover a number of significant relationships between oscillatory bursts, nonsinusoidal waveforms, and local inhibitory and excitatory spiking patterns. Bursts of theta oscillations are surprisingly related to a decrease in pyramidal neuron synchrony, and have no detectable effect on firing sequences, despite significant increases in neuronal firing rates during periods of theta bursting. Theta burst duration is predicted by the asymmetries of its first cycle, and cycle asymmetries relate to firing rate, synchrony, and sequences of pyramidal neurons and interneurons. These results provide compelling physiological evidence that time-domain features, of both nonsinusoidal hippocampal theta waveform and the theta bursting state, reflects local circuit properties. These results point to the possibility of inferring circuit states from local field potential features in the hippocampus and perhaps other brain regions with other rhythms.

COVID-19 Chest X-Ray Images Diagnosis: A Neutrosophic and Deep Transfer Learning Approach

2020 ◽  
Author(s):  
Nour Eldeen M. Khalifa ◽  
Florentin Smarandache ◽  
Mohamed Loey
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
World Health ◽  
Transfer Model ◽  
X Ray ◽  
Proposed Model ◽  
The World ◽  
Chest X Ray ◽  
Testing Accuracy ◽  
Transfer Models

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a neutrosophic with a deep learning model for the detection of COVID-19 from chest X-ray medical digital images is presented. The proposed model relies on neutrosophic theory by converting the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images and they are, the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. Using neutrosophic images has positively affected the accuracy of the proposed model. The dataset used in this research has been collected from different sources as there is no benchmark dataset for COVID-19 chest X-ray until the writing of this research. The dataset consists of four classes and they are COVID-19, Normal, Pneumonia bacterial, and Pneumonia virus. After the conversion to the neutrosophic domain, the images are fed into three different deep transfer models and they are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures and they have been used with related work. To test the performance of the conversion to the neutrosophic domain, four scenarios have been tested. The first scenario is training the deep transfer models with True (T) neutrosophic images only. The second one is training on Indeterminacy (I) neutrosophic images, while the third scenario is training the deep models over the Falsity (F) neutrosophic images. The fourth scenario is training over the combined (T, I, F) neutrosophic images. According to the experimental results, the combined (T, I, F) neutrosophic images achieved the highest accuracy possible for the validation, testing and all performance metrics such Precision, Recall and F1 Score using Resnet18 as a deep transfer model. The proposed model achieved a testing accuracy with 78.70%. Furthermore, the proposed model using neutrosophic and Resnet18 had achieved superior testing accuracy with a related work which achieved 52.80% with the same experimental environmental setup and the same deep learning hyperparameters.

Microvariation and transfer in L2 and L3 acquisition

2020 ◽  
pp. 026765832094106
Author(s):  
Alison Gabriele
Keyword(s):  
Second Language ◽  
First Language ◽  
Comprehensive Model ◽  
Third Language ◽  
L3 Acquisition ◽  
Open Questions ◽  
Language Research ◽  
Property Transfer

This commentary discusses Westergaard (2019), a keynote article in Second Language Research, which presents a comprehensive model of first language (L1), second language (L2), and third language (L3) acquisition. The commentary presents evidence from a previous study of L3 learners that provides support for Westergaard’s property-by-property transfer proposal. The commentary highlights strengths of the proposal, such as its focus on microvariation, and also outlines open questions, such as whether the model can predict in advance whether specific properties will be easier or harder to acquire.

scholarly journals Pairing Directional Solar Inputs From Ray Tracing to Solar Receiver/Reactor Heat Transfer Models on Unstructured Meshes: Development and Case Studies

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
H. Evan Bush ◽  
Andrew J. Schrader ◽  
Peter G. Loutzenhiser
Keyword(s):  
Heat Transfer ◽  
Monte Carlo ◽  
Case Studies ◽  
Ray Tracing ◽  
Three Dimensional ◽  
Transfer Model ◽  
Reactor Model ◽  
Ansys Fluent ◽  
Ray Tracing Model ◽  
Transfer Models

Abstract A novel method for pairing surface irradiation and volumetric absorption from Monte Carlo ray tracing to computational heat transfer models is presented. The method is well-suited to directionally and spatially complex concentrated radiative inputs (e.g., solar receivers and reactors). The method employs a generalized algorithm for directly mapping absorbed rays from a Monte Carlo ray tracing model to boundary or volumetric source terms in the computational mesh. The algorithm is compatible with unstructured, two and three-dimensional meshes with varying element shapes. Four case studies were performed on a directly irradiated, windowed solar thermochemical reactor model to validate the method. The method was shown to conserve energy and preserve spatial variation when mapping rays from a Monte Carlo ray tracing model to a computational heat transfer model in ansys fluent.

scholarly journals Pairing Directional Solar Inputs From Ray Tracing to Solar Receiver/Reactor Heat Transfer Models on Unstructured Meshes: Development and Case Studies

2020 ◽  
Author(s):  
H. Evan Bush ◽  
Andrew J. Schrader ◽  
Peter G. Loutzenhiser
Keyword(s):  
Heat Transfer ◽  
Case Studies ◽  
Ray Tracing ◽  
Three Dimensional ◽  
Transfer Model ◽  
Reactor Model ◽  
Ansys Fluent ◽  
Computational Mesh ◽  
Ray Tracing Model ◽  
Transfer Models

Abstract A novel method for pairing surface irradiation and volumetric absorption from ray tracing to computational heat transfer models is presented. The method is well-suited to directionally and spatially-complex concentrated radiative inputs, such as in solar receivers and reactors. The method employs a generalized algorithm for directly mapping absorbed rays from the ray tracing model to boundary or volumetric source terms in the computational mesh. The algorithm is compatible with unstructured, two and three-dimensional meshes with varying element shapes. To validate the method, four case studies were performed on a directly irradiated, windowed solar thermochemical reactor model. The method was shown to be energy conservative and to preserve spatial variation when mapping rays from a Monte Carlo ray tracing model to the computational heat transfer model in ANSYS Fluent.

Heat Transfer Analysis of Shape Memory Alloy Actuators

Aerospace ◽  
2006 ◽  
Author(s):  
Sarah Szykowny ◽  
Mohammad H. Elahinia
Keyword(s):  
Heat Transfer ◽  
Shape Memory ◽  
Smart Materials ◽  
Convection Heat Transfer ◽  
Heat Transfer Model ◽  
Heat Transfer Analysis ◽  
Transfer Model ◽  
Dead Weight ◽  
Actuator Dynamics ◽  
Transfer Models

In recent years, shape memory alloys (SMAs) have become widely used in engineering applications due to their simplicity in use and high power density. These smart materials are characterized by their unique temperature dependent phase transformations. An accurate heat transfer model is of the utmost importance for modeling the SMA actuator dynamics. This is due to the SMA constitutive and phase kinetic behaviors being directly dependent on the heat transfer model. The research shown in this paper was conducted to investigate the adequacy of the exiting heat transfer models for SMA wire actuators. Particularly, the convection heat transfer for SMA wires with diameter of a few hundred micrometers is studied. To this end, an SMA wire actuating a dead weight was used to experimentally determine how the heat transfer coefficient varies during transformation at varying levels of applied current. These results are then compared to several existing heat transfer models. In the experiments, the temperature of the wire was measured along with the strain and stress of the SMA wire. A detailed discussion of the theoretical models and experimental findings is presented in this paper.

Sign in / Sign up

Export Citation Format

Share Document