Sequential learning and the interaction between biological and linguistic adaptation in language evolution

Language Impairment (LI) is a developmental disorder that mainly manifests impaired language learning and processing. Evidence, largely from English-speaking population studies, has shown that children with LI compared to typically developing (TD) children have low scores in sequential learning tasks but similar performance in declarative learning tasks. According to the declarative/procedural model, LI children compensate for their deficiency in syntactic skills (i.e., deficits in the procedural memory system) by using the declarative memory system (indispensable for vocabulary acquisition). Although there are specific deficits in children with LI depending on the language they speak, it is assumed that this model can explain the shortcomings of such pathology regardless of the language spoken. In the current study, we compared the performance of fifteen school-aged Mexican Spanish-speaking children with LI and twenty TD children during sequential and declarative learning tasks and then analyzed the relationship between their performance in these tasks and their abilities in syntax and semantics. Children with LI displayed lower scores than normal children in the sequential learning task, but no differences were found in declarative learning performance with verbal or visual stimuli. No significant correlations were observed in children with LI between their performance in sequential learning and their abilities in semantics and no significant correlations were observed in TD children between their performance in sequential learning and their abilities in syntax. In contrast, for children with LI, a significant correlation between their performance in declarative learning and their abilities in semantics was observed and for the group of TD children a significant correlation between their performance in declarative learning and their abilities in syntax was observed. This study shows that Spanish-speaking children with LI display a pattern of learning impairment that supports the declarative/procedural model hypothesis. However, they display poor verbal declarative learning skills, probably due to low verbal working memory capacity.

Sequential Learning by Touch, Vision, and Audition Christopher M. Conway (cmc82@cornell.edu) Morten H. Christiansen (mhc27@cornell.edu) Department of Psychology Cornell University Ithaca, NY 14853, USA Abstract We investigated the extent to which touch, vision, and audition are similar in the ways they mediate the processing of statistical regularities within sequential input. While previous research has examined statistical/sequential learning in the visual and auditory domains, few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored in such research. Our data reveal commonalities between the ways in which these three modalities afford the learning of sequential information. However, the data also suggest that in terms of sequential learning, audition is superior to the other two senses. We discuss these findings in terms of whether statistical/sequential learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones. Introduction The acquisition of statistical/sequential information from the environment appears to be involved in many learning situations, ranging from speech segmentation (Saffran, Newport, & Aslin, 1996), to learning orthographic regularities of written words (Pacton, Perruchet, Fayol, & Cleeremans, 2001) to processing visual scenes (Fiser & Aslin, 2002). However, previous research, focusing exclusively on visual and auditory domains, has failed to investigate whether such learning can occur via touch. Perhaps more importantly, few studies have attempted directly to compare sequential learning as it occurs across the various sensory modalities. There are important reasons to pursue such avenues of study. First, a common assumption is that statistical/sequential learning is a broad, domain- general ability (e.g., Kirkham, Slemmer, & Johnson, 2002). But in order to adequately assess this hypothesis, systematic experimentation across the modalities is necessary. If differences exist between sequential learning in the various senses, it may reflect the operation of multiple mechanisms, rather than a single process. Second, in regards to the touch modality in particular, prior research has generally focused on low- level perception; discovering that the sense of touch can accommodate complex sequential learning may have important implications for tactile communication systems. This paper describes three experiments conducted with the aim to assess sequential learning in three sensory modalities: touch, vision, and audition. Experiment 1 provides the first direct evidence for a fairly complex tactile sequential learning capability. Experiment 2 provides a visual analogue of Experiment 1 and suggests commonalities between visual and tactile sequential learning. Finally, Experiment 3 assesses the auditory domain, revealing an auditory advantage for sequential processing. We conclude by discussing these results in relation to basic issues of cognitive and neural organization—namely, to what extent sequential learning consists of a single or multiple mechanisms. Sequential Learning We define sequential learning as an ability to encode and represent the order of discrete elements occurring in a sequence (Conway & Christiansen, 2001). Importantly, we consider a crucial aspect of sequential learning to be the acquisition of statistical regularities occurring among sequence elements. Artificial grammar learning (AGL; Reber, 1967) is a widely used paradigm for studying such sequential learning 1 . AGL experiments typically use finite-state grammars to generate the stimuli; in such grammars, a transition from one state to the next produces an element of the sequence. For example, in the grammar of Figure 1, the path begins at the left-most node, labeled S1. The next transition can lead to either S 2 or S3. Every time a number is encountered in the transition between states, it is added as the next element of the sequence, producing a sequence corresponding to the rules of the grammar. For example, by passing through the nodes S1, S2, S2, S4, S3, S5, the “legal” sequence 4-1-3-5-2 is generated. During a training phase, participants typically are exposed to a subset of legal sequences—often under the guise of a “memory experiment” or some other such task—with the intent that they will incidentally encode structural aspects of the stimuli. Next, they are tested on whether they can classify novel sequences as In the typical AGL task, the stimulus elements are presented simultaneously (e.g., letter strings)—rather than sequentially (i.e., one element at a time). We consider even the former case to be a sequential learning task because scanning strings of letters generally occurs in a left-to-right, sequential manner. However, our aim here is to create a truly sequential learning environment using temporally-distributed input.

We used event-related potentials (ERPs) to investigate the time course and distribution of brain activity while adults performed (1) a sequential learning task involving complex structured sequences and (2) a language processing task. The same positive ERP deflection, the P600 effect, typically linked to difficult or ungrammatical syntactic processing, was found for structural incongruencies in both sequential learning as well as natural language and with similar topographical distributions. Additionally, a left anterior negativity (LAN) was observed for language but not for sequential learning. These results are interpreted as an indication that the P600 provides an index of violations and the cost of integration of expectations for upcoming material when processing complex sequential structure. We conclude that the same neural mechanisms may be recruited for both syntactic processing of linguistic stimuli and sequential learning of structured sequence patterns more generally.

Artificial neural network, as a nonlinear mapping or adaptive power system made by linking neurons,which can effectively resolve problems such as gradient explosion in stock price prediction processing. Recurrent neural network (RNN) is a common model for processing stock time-series data and is suitable for stock data involving sequential machine learning tasks, but the prediction results are poor when using long time span or nonlinear data for prediction. To address the problems of low prediction accuracy of ordinary neural networks for stock data with poor linearity and the inability of a single LSTM model to show the recommendation level of a target stock, the paper proposes a deep learning factor integrated prediction model based on LSTM-K-Means.On this basis, a stock price prediction method based on long-term and short-term memory network namely LSTM and K-means clustering algorithm is proposed. The method is not only designed to model stock ups and downs at different levels of combinations, but also more intuitively identifies stocks with better ups through returns and volatilities. Through experimental verification, the stock price prediction method based on LSTM with K-Means proposed in this paper is effective.

Sequences assessed by declarative and procedural tests of memory in amnesic patients with hippocampal damage

Predicting which peptides can bind to a specific major histocompatibility complex (MHC) molecule could have great value for minimizing the number of peptides required to be synthesized and assayed. Artificial neural networks (ANNs)-based prediction method, which usually adopts backpropagation neural networks (BPNN) as a prediction model has high prediction accuracy, while its learning efficiency is low and its incremental learning cannot be realized. Sequential learning (SL) adds output neurons in such a way that a correct mapping between input and output patterns is guaranteed. We propose the SL-based prediction method, which chooses the modified sequential learning ahead masking (SLAM) model combined with incremental learning (FIL-SLAM) to predict MHC II-binding peptides. For the experimental data composed of 650 peptides to bind or not bind to HLA-DR4 (B1*0401), compared with BPNN, the proposed method shows a significant reduction in consuming time (95%) with only a slight reduction (1.3%) in average prediction accuracy.

This study aims to analyze the effect of motivation and work spirit towards employee performance because a high level of employee performance is needed in a company in order to achieve the goals set by the company. By having a high level of performance, employees will be able to overcome and solve problems that will be faced when carrying out their duties and obligations at work. And vice versa if the employee has a low level of performance then the employee will not be able to solve a problem or difficulty in carrying out his work. With high performance owned by employees, employees will be able to work optimally and help each other together in completing work that requires more energy and time. Basically, employee performance is the result of the process of the employee's personal self as well as the efforts of the company. The high performance of employees is an expectation for all companies or institutions that employ them.

PurposeThe purpose of this paper is to develop and test a model examining the key factors that emerge from the social customer relationship management (CRM) process. Specifically, this study aims to address the chain-of-effects that occurs as a result of the social CRM process, leading to superior levels of hotel performance.Design/methodology/approachData were collected using a quantitative approach. Using a mail and online questionnaire, data were gathered from 120 key informants in hotels in Ireland. The proposed model was tested using partial least squares structural equation modelling.FindingsResults demonstrate that the social CRM activities of hotels enhance hotel service innovation activities. This positively impacts the ability to develop a customer-linking capability, resulting in higher levels of customer performance. In turn, higher levels of customer performance leads to higher levels of financial performance.Practical implicationsService innovation and customer-linking capability are identified as critical outcomes of the social CRM process that lead to enhanced hotel performance.Originality/valueThis study explains the chain-of-effects through which the social CRM process results in higher levels of performance.

At present, machine learning techniques is frequently used to extraction and/or prediction some information from enormous collected data. Traditional machine learning is performed batch learning like a popular one, Artificial Neural Network (ANN). That means it can’t integrate new information into already trained model but it is retrained from scratch. Online Sequential Extreme Learning Machine (OS-ELM) is a one of online incremental machine learning techniques that can learn and update model from new receive data without to retrain the model. This paper shows the result of comparison experiment between ANN and OS-ELM and found that, the OS-ELM has acceptable performance in situation of few initial data for training and/or statistic properties of data is changing while it working.

Controlled and uncontrolled subject descriptions in the CF database: A comparison of optimal cluster-based retrieval results

One of the functions of cognitive control is to detect difficulties in information processing and adjust the level of performance. This article presents study on confl ict adaptation in which we investigated how detection of cognitive conflict influences behaviour on a subsequent trial. In two experiments we aimed to find out whether the adjustment can be carried out across two kinds of cognitive conflicts: between competing responses and requirements of two tasks. The modified version of the flanker task in switching paradigm was used so that participants had to switch between two tasks that shared the same stimulus set. The results showed that the two kinds of confl icts did not interact with each other. Furthermore, each conflict evoked conflict adaptation, but this adaptation did not occurred across conflicts. This suggests that conflict adaptation covers a set of mechanisms functioning uniquely in one domain.

Foreign Language AnnalsVolume 27, Issue 1 p. 77-83 The Case for a Sequential Second Language Learning Experience at the Middle Level Constance K. Knop, Constance K. Knop University of Wincosin-Madison Constance K. Knop (Ph.D., University of Wisconsin-Madison) is the Pickard-Bascom Professor of French and Professor of Curriculum and Instruction at the University of Wisconsin-Madison.Search for more papers by this authorPaul Sandrock, Paul Sandrock University of Wincosin-Madison Pad Sandrock (M.A., University of Wisconsin-Madison) is the state consultant for foreign language education at the Department of Public Instruction, Madison, WI.Search for more papers by this author Constance K. Knop, Constance K. Knop University of Wincosin-Madison Constance K. Knop (Ph.D., University of Wisconsin-Madison) is the Pickard-Bascom Professor of French and Professor of Curriculum and Instruction at the University of Wisconsin-Madison.Search for more papers by this authorPaul Sandrock, Paul Sandrock University of Wincosin-Madison Pad Sandrock (M.A., University of Wisconsin-Madison) is the state consultant for foreign language education at the Department of Public Instruction, Madison, WI.Search for more papers by this author First published: March 1994 https://doi.org/10.1111/j.1944-9720.1994.tb02311.xCitations: 3 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume27, Issue1March 1994Pages 77-83 This article also appears in:Content Standards, Articulation, and Curriculum RevisionTeaching and Learning Languages in the Elementary Grades RelatedInformation

We explore incremental assimilation of new knowledge by sequential learning. Of particular interest is how a network of many knowledge layers can be constructed in an on-line manner, such that the learned units represent building blocks of knowledge that serve to compress the overall representation and facilitate transfer. We motivate the need for many layers of knowledge, and we advocate sequential learning as an avenue for promoting the construction of layered knowledge structures. Finally, our novel STL algorithm demonstrates a method for simultaneously acquiring and organizing a collection of concepts and functions as a network from a stream of unstructured information.

Learning style is an individual's natural or habitual pattern of acquiring and processing information in learning situations. A core concept is that individuals differ in how they learn. This study focused on to look at a group of TESL undergraduates’ preference in learning styles. The finding showed that the students have different kind learning styles in language learning. Most of them seem to have moderate preference on the active, sensing and global learning, mild preference on the reflective, intuitive, verbal and sequential learning and quite strong preferences for visual learning style. The finding of this study might help both educator and students in developing the pedagogical process and at the same time enhancing the effective learning and finally to improve themselves in the future.

Introduction: Maintenance hemodialysis (HD) patients’ morbidity and mortality remain unacceptably high. It is important to identify risk factors affecting outcome and define their relative contribution. Methods: The data of 93 patients who started HD at the Sheffield Kidney Institute between January 1997 and June 1998 were analyzed retrospectively. Potential risk predictors of 5-year survival were evaluated both at baseline and at 1-year follow-up, including changes from baseline. Results: Patients median age was 60 years (range: 27-80), 75% were males and 23% were diabetics. According to the Index of Co-Existing Disease (ICED) score, 18% of patients were level 0-1 (low comorbidity), 56% level 2 and 26% level 3. The 5-year survival was 58%. The Cox models identified eight independent time-adjusted risk factors for 5-year survival, the importance of which as independent predictors was confirmed by logistic regression models. According to the artificial neural network (ANN) models, the relative importance of these factors was as follows: age (26.6%), baseline systolic blood pressure (24.1%), mean Kt/V during the first year (19.7%), baseline ICED score (14.8%), baseline diastolic blood pressure (8.6%), serum calcium change from baseline (2.8%), blood urea change from baseline (2.1%), serum creatinine change from baseline (1.3%). This ANN model had a high level of predictive performance as assessed by accuracy (93.3%) and by Receiver Operating Characteristic (ROC) curve analysis (AUC=0.92). Conclusion: Integrated use of regression analysis and probabilistic models allow computation of individual risk of mortality in HD. This may help in optimizing care and costs. Key Words: hemodialysis, risk factors, ICED, artificial neural network