scholarly journals Context-Sensitive Control of Adaptation: Self-Modeling Networks for Human Mental Processes Using Mental Models

2021 ◽  
Author(s):  
Raj Bhalwankar ◽  
Laila van Ments ◽  
Jan Treur
Keyword(s):  
Machine Learning ◽  
Mental Models ◽  
Mental Model ◽  
Mental Processes ◽  
Computational Approaches ◽  
Context Sensitive ◽  
Computational Architecture ◽  
The World ◽  
Learning And Adaptation ◽  
Adaptation Processes

Within their mental and social processes, humans often learn, adapt and apply specific mental models of processes in the world or other persons, as a kind of blueprints. In this paper, it is discussed how analysis of this provides useful inspiration for the development of new computational approaches from a Machine Learning and Network-Oriented Modeling perspective. Three main elements are: applying the mental model by internal simulation, developing and revising a mental model by some form of adaptation, and exerting control over this adaptation in a context-sensitive manner. This concept of controlled adaptation relates to the Plasticity Versus Stability Conundrum from neuroscience. The presented analysis has led to a three-level computational architecture for controlled adaptation. It is discussed and illustrated by examples of applications how this three-level computational architecture can be specified based on a self-modeling network and used to model controlled learning and adaptation processes based on mental models in a context-sensitive manner.

Dance? I Can’t Dance! Developing an Understanding of Mental Models Through Line Dancing

2016 ◽  
Vol 1 (4) ◽  
pp. 226-241
Author(s):  
Tim O. Peterson ◽  
Claudette M. Peterson ◽  
Cynthia L. Krom ◽  
Brian A. Griffith
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Self Concept ◽  
Business Courses ◽  
The World

Mental models guide our attitudes and direct our actions. Senge defined a mental model as a deeply ingrained set of assumptions, generalizations, or images that influence how we understand and respond to the world around us. Line dancing is an example of a process that includes many types of mental models. In fact, each dance is a mental model of its own. In addition, we each come to dancing with our own mental models about dancing and our self-concept. In this article, we share experiences of using line dances in classes as a way to help students be engaged while learning about a complex topic. Three sets of supplemental materials are included: (a) music suggestions, video links, and steps to help teach each dance; (b) suggestions to apply line dancing in various business courses; and (c) reflections on dance and mental models by the instructors.

Emotions, Simulation, and Abstract Art

2021 ◽  
pp. 1-33
Author(s):  
P. N. Johnson-Laird ◽  
Keith Oatley
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Simulation Theory ◽  
Abstract Art ◽  
Emotional States ◽  
Mental Processes ◽  
Basic Emotions ◽  
Complex Emotions ◽  
Access To Knowledge ◽  
Do So

Abstract Some people feel emotions when they look at abstract art. This article presents a ‘simulation’ theory that predicts which emotions they will experience, including those based on their aesthetic reactions. It also explains the mental processes underlying these emotions. This new theory embodies two precursors: an account of how mental models represent perceptions, descriptions, and self-reflections, and an account of the communicative nature of emotions, which distinguishes between basic emotions that can be experienced without knowledge of their objects or causes, and complex emotions that are founded on basic ones, but that include propositional contents. The resulting simulation theory predicts that abstract paintings can evoke the basic emotions of happiness, sadness, anger, and anxiety, and that they do so in several ways. In mimesis, models simulate the actions and gestures of people in emotional states, elicited from cues in the surface of paintings, and that in turn evoke basic emotions. Other basic emotions depend on synaesthesia, and both association and projection can yield complex emotions. Underlying viewers’ awareness of looking at a painting is a mental model of themselves in that relation with the painting. This self-reflective model has access to knowledge, enabling people to evaluate the work, and to experience an aesthetic emotion, such as awe or revulsion. The comments of artists and critics, and experimental results support the theory.

scholarly journals Intersentential coreference expectations reflect mental models of events

2017 ◽  
Author(s):  
Theres Grüter ◽  
Aya Takeda ◽  
Hannah Rohde ◽  
Amy J. Schafer
Keyword(s):  
Mental Models ◽  
Language Processing ◽  
Mental Model ◽  
Temporal Dynamics ◽  
Discourse Processing ◽  
Mental Event ◽  
Event Representation ◽  
The World ◽  
Incoming Information ◽  
New Evidence

Comprehenders’ perception of the world is mediated by the mental models they construct. During discourse processing, incoming information allows comprehenders to update their model of the events being described. At the same time, comprehenders use these models to generate expectations about who or what will be mentioned next. The temporal dynamics of this interdependence between language processing and mental event representation has been difficult to disentangle. The present visual world eye-tracking experiment measures listeners’ coreference expectations during an intersentential pause between a sentence about a transfer-of-possession event and a continuation mentioning either its Source or Goal. We found a temporally dispersed but sustained preference for fixating the Goal that was significantly greater when the event was described as completed rather than incomplete (passed versus was passing). This aligns with reported offline sensitivity to event structure, as conveyed via verb aspect, and provides new evidence that our mental model of an event leads to early and, crucially, proactive expectations about subsequent mention in the upcoming discourse.

A pragmatist philosophy of psychological science and its implications for replication

2018 ◽  
Vol 41 ◽  
Author(s):  
Ana Gantman ◽  
Robin Gomila ◽  
Joel E. Martinez ◽  
J. Nathan Matias ◽  
Elizabeth Levy Paluck ◽  
...  
Keyword(s):  
William James ◽  
Mental Processes ◽  
Psychological Science ◽  
Behavioral Measurement ◽  
The World ◽  
Pragmatist Philosophy

AbstractA pragmatist philosophy of psychological science offers to the direct replication debate concrete recommendations and novel benefits that are not discussed in Zwaan et al. This philosophy guides our work as field experimentalists interested in behavioral measurement. Furthermore, all psychologists can relate to its ultimate aim set out by William James: to study mental processes that provide explanations for why people behave as they do in the world.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

CIKM 2020 conference report

2021 ◽  
pp. 1-4
Author(s):  
Mathieu D'Aquin ◽  
Stefan Dietze
Keyword(s):  
United States ◽  
Machine Learning ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Conference Report ◽  
The United States ◽  
Knowledge Based ◽  
The World ◽  
Science Conference ◽  
Information And Knowledge Management

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

scholarly journals COVID-19 Misinformation Online and Health Literacy: A Brief Overview

2021 ◽  
Vol 18 (15) ◽  
pp. 8091
Author(s):  
Salman Bin Naeem ◽  
Maged N. Kamel Boulos
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Health Literacy ◽  
Information Content ◽  
Digital Health ◽  
Negative Effects ◽  
The World ◽  
Synergistic Approach ◽  
Viral Nature

Low digital health literacy affects large percentages of populations around the world and is a direct contributor to the spread of COVID-19-related online misinformation (together with bots). The ease and ‘viral’ nature of social media sharing further complicate the situation. This paper provides a quick overview of the magnitude of the problem of COVID-19 misinformation on social media, its devastating effects, and its intricate relation to digital health literacy. The main strategies, methods and services that can be used to detect and prevent the spread of COVID-19 misinformation, including machine learning-based approaches, health literacy guidelines, checklists, mythbusters and fact-checkers, are then briefly reviewed. Given the complexity of the COVID-19 infodemic, it is very unlikely that any of these approaches or tools will be fully effective alone in stopping the spread of COVID-19 misinformation. Instead, a mixed, synergistic approach, combining the best of these strategies, methods, and services together, is highly recommended in tackling online health misinformation, and mitigating its negative effects in COVID-19 and future pandemics. Furthermore, techniques and tools should ideally focus on evaluating both the message (information content) and the messenger (information author/source) and not just rely on assessing the latter as a quick and easy proxy for the trustworthiness and truthfulness of the former. Surveying and improving population digital health literacy levels are also essential for future infodemic preparedness.

scholarly journals HOW INDUSTRY 4.0 RESHAPES THE WORLD: RECOMMENDATIONS BASED ON COMPLEX GRAPH NETWORK ANALYSIS

2021 ◽  
Vol 1 ◽  
pp. 1755-1764
Author(s):  
Rongyan Zhou ◽  
Julie Stal-Le Cardinal
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Network Models ◽  
Global Supply Chain ◽  
Great Opportunity ◽  
Strategic Research ◽  
Complex Graph ◽  
The World ◽  
Tremendous Challenge

Abstract Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed.

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