Metaphor from the Derivational Perspective

Fachsprache ◽  
2019 ◽  
Vol 41 (S1) ◽  
pp. 4-22
Author(s):  
Larisa M. Alekseeva ◽  
Svetlana L. Mishlanova
Keyword(s):  
Network Model ◽  
Cognitive Process ◽  
Linear Models ◽  
Basic Feature ◽  
New Knowledge ◽  
Complicated Process ◽  
Traditional Approaches ◽  
Text Formation ◽  
Cognition And Knowledge ◽  
Knowledge Communication

Abstract The article focuses on the derivational perspective of metaphor studies. Derivation is regarded as a complex cognitive process, represented within speech activities. In this sense, derivation is viewed as a universal process of language units’ production according to the rules of text-formation. The basic feature of the derivational approach to the mechanism of metaphor is determined by the inner syntax, especially by the principle of contamination of two sentences – introductive and basic, which fulfill different functions. In this paper we shall present a theoretical account of metaphorisation as a universal derivational process controlled by means of such laws, as incorporation, contamination and compression. We take as basic the premise that metaphor is a more complicated process than it is described in traditional theories, since it is dependent on cognition and knowledge communication. In contrast to the traditional approaches, metaphor is regarded here as the result of combination of two pictures of the reality, referential and imaginative. We believe that derivatology generates a new knowledge about metaphor mechanism and metaphor modeling. Comparing to linear models of metaphor, the derivational model is considered to be a network model. The latest derivatological ideas about metaphor enrich the concept of metaphor taking into consideration that it has to be studied not in isolation, but within a broad frame of text, discourse, cognition and communication.

scholarly journals AN INDIVIDUAL'S COGNITIVE ACTIVITY PROBLEM IN HISTORY RETROSPECTIVE

2019 ◽  
pp. 188-194
Author(s):  
Інна Зайцева ◽  
Тетяна Пащенко ◽  
Петро Лузан
Keyword(s):  
Cognitive Process ◽  
Human Life ◽  
Cognitive Activity ◽  
Ancient Philosophy ◽  
Ancient India ◽  
New Knowledge ◽  
The World ◽  
Complicated Process ◽  
History Of ◽  
Systematic Knowledge

The article studies the aspects of cognitive activity and the activity in history retrospection. The cognition and its study is not something unchanged, once and for all given, but is something that develops under certain laws. It has long history with the sources in the most ancient philosophy. At every stage of its development the knowledge is the result of the history of knowledge, the essence of all human activity forms. Scientific knowledge has its historically altered morphology. Its historical path analysis makes it possible to argue its origin from ancient philosophical schools, philosophers of Ancient India, China, Egypt, the countries of Mesopotamia. The phenomenon of an individual’s cognitive activity is complex and multifaceted. Scientists argue that cognitive activity is a complicated process of transition from ignorance to knowledge, from inability to ability, from random observations to systematic knowledge of the material world, to mastering the scientific truths. At the same time, a man, mastering new knowledge, affects the world, which, in turn, changes human life. The article analyses the influence of historical conditions on developing scientific thought about cognition, cognitive activity. The effectiveness of studying students’ educational and cognitive activity depends on how thoroughly the cognitive process is studied and the way its laws and features are used. The authors emphasise that taking into account the cognition theory possibilities, its Klondike was set in the Ancient world era and filled with new content rising to a new level in each generation .

Model predictive control of a CSTR: A hybrid modeling approach

2010 ◽  
Vol 64 (3) ◽  
Author(s):  
Michal Kvasnica ◽  
Martin Herceg ◽  
Ľuboš Čirka ◽  
Miroslav Fikar
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Nonlinear Behavior ◽  
Continuous Stirred Tank Reactor ◽  
Modeling Framework ◽  
Future Evolution ◽  
Continuous Stirred Tank ◽  
Traditional Approaches

AbstractThis paper presents a case study of model predictive control (MPC) applied to a continuous stirred tank reactor (CSTR). It is proposed to approximate nonlinear behavior of a plant by several local linear models, enabling a piecewise affine (PWA) description of the model used to predict and optimize future evolution of the reactor behavior. Main advantage of the PWA model over traditional approaches based on single linearization is a significant increase of model accuracy which leads to a better control quality. It is also illustrated that, by adopting the PWA modeling framework, MPC strategy can be implemented using significantly less computational power compared to nonlinear MPC setups.

scholarly journals Reflections from challenges of working with HIV/Aids affected and infected orphans in Malawi

2010 ◽  
Vol 5 (2) ◽  
pp. 124-134
Author(s):  
Zainab Lognwe
Keyword(s):  
Professional Development ◽  
Social Work ◽  
Critical Incident ◽  
Cognitive Process ◽  
New Knowledge ◽  
Reflection In Action ◽  
Hiv Aids

Karen Healy uses the term reflection in action to refer to processes of refining knowledge in action so as to promote new ways of responding to the problems we encounter in practice. Thus, social work entails working with different people with different non routine challenges that needs reflective action to be dealt with effectively. The importance of reflection in social work cannot be overtly emphasised. After experiencing an emotional circumstance or situation, in reflecting, and through these experiences we can ably find valuable options for professional development. This process is very much in line with critical incident method. This method is described as both an emotional and cognitive process, proceeding from lower to higher levels of reflection, from analysing the experiences to conceptualizing new knowledge.

scholarly journals MAS architecture and knowledge model for vehicles data communication

2013 ◽  
Vol 1 (1) ◽  
pp. 23-31
Author(s):  
Emmanuel Adam ◽  
Emmanuelle Grislin-Le Strugeon ◽  
René Mandiau
Keyword(s):  
Mobile Robots ◽  
Traffic Flow ◽  
Mobile Agents ◽  
Autonomous Vehicles ◽  
Data Communication ◽  
Road Accident ◽  
Traffic Network ◽  
Knowledge Model ◽  
New Knowledge ◽  
Knowledge Communication

Completely autonomous vehicles in traffic should allow to decrease the number of road accident victims greatly, and should allow gains in terms of performance and economy. Modelling the vehicles interaction, and especially knowledge sharing, is one of the main challenges to optimize traffic flow with autonomous vehicles. We propose in this paper a model of knowledge communication between mobile agents on a traffic network. The model of knowledge and of interaction enables to propagate new knowledge without overloading the system with a too large number of communications. For that, only the new knowledge is communicated, and two agents communicate the same knowledge only once. Moreover, in order to allow agents to update their knowledge (perceived or created), a notion of degradation is used. A simulator has been built to evaluate the proposal, before to implement it in mobile robots. Some results of the simulator are proposed in this article.

Stock Price Trend Prediction and Recommendation using Cognitive Process

2017 ◽  
Vol 4 (2) ◽  
pp. 36-48 ◽  
Author(s):  
Vipul Bag ◽  
U. V. Kulkarni
Keyword(s):  
Stock Price ◽  
Cognitive Process ◽  
Recommendation System ◽  
Technical Quality ◽  
Trend Prediction ◽  
Price Trend ◽  
Weighted Index ◽  
New Knowledge ◽  
News Sentiment

The paper emphasizes on stock price trend prediction based on the online textual news. Cognitive process uses existing knowledge and generates new knowledge. Contextual features (CF) from news sites are extracted & recommendations based on the interpretations are generated. A Naïve bays classification algorithm is used to classify the news sentiments. A News Sentiment Index (NSI) is calculated and effect of the news on particular stock is calculated to predict the trend. Along with news sentiment index, technical quality of the same stock is calculated by various statistical technical indicators which are called as Stock Technical Index (STI). The weighted index of NSI and STI is used to predict the trend of stock price. In the previous recommendation systems, the context of the recommendation is not considered. However, it is shown in this research that if the authors consider the news context while recommendation, the performance of the recommendation system will drastically improve. The results are compared with traditional systems and it shows significant improvement.

scholarly journals Outcome expectations drive learned behaviour in larval Drosophila

2006 ◽  
Vol 273 (1604) ◽  
pp. 2965-2968 ◽  
Author(s):  
Bertram Gerber ◽  
Thomas Hendel
Keyword(s):  
Cognitive Process ◽  
Memory Trace ◽  
Outcome Expectations ◽  
Basic Feature ◽  
Positive Outcome ◽  
Current Situation ◽  
Processing Step ◽  
Expected Outcome ◽  
Expected Outcomes

Why does Pavlov's dog salivate? In response to the tone, or in expectation of food? While in vertebrates behaviour can be driven by expected outcomes, it is unknown whether this is true for non-vertebrates as well. We find that, in the Drosophila larva, odour memories are expressed behaviourally only if animals can expect a positive outcome from doing so. The expected outcome of tracking down an odour is determined by comparing the value of the current situation with the value of the memory for that odour. Memory is expressed behaviourally only if the expected outcome is positive. This uncovers a hitherto unrecognized evaluative processing step between an activated memory trace and behaviour control, and argues that learned behaviour reflects the pursuit of its expected outcome. Shown in a system with a simple brain, an apparently cognitive process like representing the expected outcome of behaviour seems to be a basic feature of behaviour control.

scholarly journals Application of Digital Cognitive Biomarkers for Alzheimer’s Disease: Identifying Cognitive Process Changes and Impending Cognitive Decline

2020 ◽  
pp. 1-4
Author(s):  
J.R. Bock ◽  
J. Hara ◽  
D. Fortier ◽  
M.D. Lee ◽  
R.C. Petersen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Item Response ◽  
Cognitive Process ◽  
Bayes Factor ◽  
Group Differences ◽  
Cognitively Normal ◽  
Response Data ◽  
Traditional Approaches

Background: Recent Alzheimer’s disease (AD) trials have faced significant challenges to enroll pre-symptomatic or early stage AD subjects with biomarker positivity, minimal or no cognitive impairment, and likelihood to decline cognitively during a short trial period. Our previous study showed that digital cognitive biomarkers (DCB), generated by a hierarchical Bayesian cognitive process (HBCP) model, were able to distinguish groups of cognitively normal individuals with impending cognitive decline from those without. We generated DCBs using only baseline Auditory Verbal Learning Test’s wordlist memory (WLM) item response data from the Mayo Clinic Alzheimer’s Disease Patient Registry. Objectives: To replicate our previous findings, using baseline ADAS-Cog WLM item response data from the Alzheimer’s Disease Neuroimaging Initiative, and compare DCBs to traditional approaches for scoring word-list memory tests. Design: Classified decliner subjects (n = 61) as those who developed amnestic MCI or AD dementia within 3 years of normal baseline assessment and non-decliner (n = 442) as those who did not. Measures: Evaluated the relative value of DCBs compared to traditional measures, using three analytic approaches to group differences: 1) logistic regression of summary scores per ADAS-Cog WLM task; 2) Bayesian modeling of summary scores; and 3) HBCP modeling to generate DCBs from item-level responses. Results: The HBCP model produced posterior distributions of group differences, of which Bayes factor assessment identified three DCBs with notable group differences: Immediate Retrieval from Durable Storage, (BFds = 11.8, strong evidence); One-Shot Learning, (BFds = 4.5, moderate evidence); and Partial Learning (BFds = 2.9, weak evidence). In contrast, logistic regression of summary scores did not significantly discriminate between groups, and the Bayes factor assessment of modeled summary scores provided moderate evidence that the groups were equivalent (BFsd = 3.4, 3.1, 2.9, and 1.4, respectively). Conclusions: This study demonstrated DCBs’ ability to distinguish , at baseline, between impending cognitive decline and non-decline groups where individuals in both groups were classified as cognitively normal. This validated findings from our previous study, demonstrating DCBs’ advantages over traditional approaches. This study warrants further refinement of the HBCP DCBs to predict impending cognitive decline in individuals and other factors associated with AD, such as physical biomarker load.

scholarly journals Interpreting interaction effects in generalized linear models of nonlinear probabilities and counts

2020 ◽  
Author(s):  
Connor McCabe ◽  
Max Andrew Halvorson ◽  
Kevin Michael King ◽  
Xiaolin Cao ◽  
Dale Sim Kim
Keyword(s):  
Generalized Linear Models ◽  
Logistic Model ◽  
Linear Models ◽  
Interaction Effects ◽  
Predictor Variables ◽  
Model Parameters ◽  
Marginal Effect ◽  
Psychology Research ◽  
Product Term ◽  
Traditional Approaches

Psychology research frequently involves the study of probabilities and counts. These are typically analyzed using generalized linear models (GLMs), which can produce these quantities via nonlinear transformation of model parameters. Interactions are central within many research applications of these models. To date, typical practice in evaluating interactions for probabilities or counts extends directly from linear approaches, in which evidence of an interaction effect is supported by using the product term coefficient between variables of interest. However, unlike linear models, interaction effects in GLMs describing probabilities and counts are not equal to product terms between predictor variables. Instead, interactions may be functions of the predictors of a model, requiring non-traditional approaches for interpreting these effects accurately. Here, we define interactions as change in a marginal effect of one variable as a function of change in another variable, and describe the use of partial derivatives and discrete differences for quantifying these effects. Using guidelines and simulated examples, we then use these approaches to describe how interaction effects should be estimated and interpreted for GLMs on probability and count scales. We conclude with an example using the Adolescent Brain Cognitive Development Study demonstrating how to correctly evaluate interaction effects in a logistic model.

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