description::
· the-science about the-mind.
name::
* McsEngl.McsEdu000009.last.html//dirEdu//dirMcs!⇒sciMind,
* McsEngl.dirEdu/McsEdu000009.last.html!⇒sciMind,
* McsEngl.mind-science!⇒sciMind,
* McsEngl.mindology!⇒sciMind,
* McsEngl.science.mind!⇒sciMind,
* McsEngl.sciMind,
* McsEngl.sciMind!=McsEdu000009,
* McsEngl.sciMind!=science.mind.human,
====== langoGreek:
* McsElln.επιστήμη-του-νου!η!=sciMind,
description::
* mind,
** dngMinding,
** fctSensing,
** fctThinking,
** fctEmoting,
** fctMemoring,
** fctCognizing (sensing and thinking),
** fctFeeling (sensing and emoting),
** actCommunicating,
** concept,
** worldview,
name::
* McsEngl.evoluting-of-sciMind,
* McsEngl.sciMind'evoluting,
{2023-08-28}::
=== McsHitp-creation:
· creation of current concept.
name::
* McsEngl.sciMind'parent-child-tree,
* McsEngl.sciMind'child-parent-tree,
name::
* McsEngl.sciMind'part-whole-tree,
* McsEngl.sciMind'whole-part-tree,
whole-tree-of-sciMind::
*
* ... Sympan.
generic-tree-of-sciMind::
* science,
* ... entity.
* McsEngl.sciMind'generic-tree,
specific-tree-of-sciMind::
* cognitive-science,
* epistemology,
* linguistics,
* semiotics,
* McsEngl.sciMind'specific-tree,
description::
"data science overview:
Data science is a multidisciplinary field that combines various techniques, algorithms, processes, and systems to extract valuable insights and knowledge from structured and unstructured data. It encompasses a wide range of skills and tools to analyze, interpret, and visualize data for informed decision-making. Here's an overview of key aspects of data science:
1. Data Collection: Data scientists collect data from various sources, including databases, web scraping, sensors, and more. This data can be in the form of structured data (tables) or unstructured data (text, images, videos).
2. Data Cleaning and Preprocessing: Raw data often contains errors, missing values, and inconsistencies. Data scientists clean and preprocess the data to ensure it's accurate and suitable for analysis. This may involve imputing missing values, handling outliers, and transforming data.
3. Exploratory Data Analysis (EDA): EDA involves the use of statistical and visualization techniques to understand the characteristics and patterns within the data. It helps identify trends, correlations, and potential insights.
4. Feature Engineering: Feature engineering is the process of creating new features or transforming existing ones to improve the performance of machine learning models. It involves selecting relevant features and encoding categorical variables.
5. Machine Learning: Machine learning is a core component of data science. Data scientists use various algorithms and models to build predictive and descriptive models. Common machine learning tasks include classification, regression, clustering, and recommendation.
6. Model Evaluation and Validation: Data scientists assess the performance of machine learning models using metrics like accuracy, precision, recall, and F1 score. They also use techniques like cross-validation to ensure model generalizability.
7. Data Visualization: Data visualization tools and libraries (e.g., Matplotlib, Seaborn, D3.js) are used to create charts, graphs, and interactive visualizations to communicate insights effectively.
8. Big Data Technologies: With the growth of large datasets, data scientists often work with big data technologies like Hadoop, Spark, and NoSQL databases to process and analyze data at scale.
9. Deep Learning: Deep learning, a subset of machine learning, focuses on neural networks with many layers (deep neural networks). It is used for tasks like image recognition, natural language processing, and speech recognition.
10. Natural Language Processing (NLP): NLP techniques are used to process and analyze human language data, enabling applications such as sentiment analysis, chatbots, and language translation.
11. Time Series Analysis: Data scientists analyze time-ordered data to make predictions and detect patterns. This is crucial in applications like stock market forecasting and demand forecasting.
12. AI Ethics and Bias Mitigation: Data scientists must consider ethical implications and potential biases in their models. Efforts are made to ensure fairness, transparency, and accountability in AI and data science applications.
13. Deployment and Productionization: After building and testing models, data scientists work on deploying them into production environments, often using platforms like Docker and Kubernetes.
14. Continuous Learning: Data science is a rapidly evolving field, and data scientists need to stay updated with the latest techniques, tools, and best practices.
15. Interdisciplinary Collaboration: Data scientists often work closely with domain experts, software engineers, and business analysts to address real-world problems and create data-driven solutions.
Data science plays a critical role in various industries, including healthcare, finance, e-commerce, marketing, and more. It helps organizations make data-driven decisions, improve processes, and gain a competitive advantage in today's data-rich world."
[{2023-10-09 retrieved} https://chat.openai.com/c/ae3830ba-9bbd-4d91-9d64-7f3a9b927abc]
name::
* McsEngl.data-science,
* McsEngl.sciMind.009-data-science,
* McsEngl.sciMind.data-science,
* McsEngl.sciMindData,
description::
"overview of dataset:
A dataset is a structured collection of data that is organized and stored in a way that makes it accessible and useful for various purposes, such as analysis, research, machine learning, and more. Datasets come in various forms and serve different applications. Here is an overview of datasets, including their types and uses:
1. **Structured Datasets**:
- Structured datasets are organized in a tabular format, often resembling a spreadsheet with rows and columns.
- They are commonly used in databases and are suitable for applications like data analysis, business intelligence, and traditional machine learning tasks.
- Examples include customer databases, financial records, and government census data.
2. **Text Datasets**:
- Text datasets consist of unstructured text documents, such as articles, books, emails, and social media posts.
- These datasets are used in natural language processing (NLP) tasks, such as sentiment analysis, text classification, and language modeling.
- Examples include the Wikipedia text corpus and social media comments.
3. **Image Datasets**:
- Image datasets contain collections of images, often labeled or annotated.
- They are essential for computer vision tasks, including image classification, object detection, and image generation.
- Examples include the MNIST dataset for handwritten digit recognition and the ImageNet dataset.
4. **Audio Datasets**:
- Audio datasets consist of audio recordings, such as speech, music, and environmental sounds.
- They are used in applications like speech recognition, music classification, and sound analysis.
- Examples include the Common Voice dataset for speech recognition and the Million Song Dataset for music analysis.
5. **Time Series Datasets**:
- Time series datasets capture data points at regular intervals over time.
- They are essential for forecasting, trend analysis, and anomaly detection.
- Examples include financial market data, weather measurements, and IoT sensor data.
6. **Graph Datasets**:
- Graph datasets represent data as networks of nodes and edges.
- They are used in graph analytics, network analysis, and social network research.
- Examples include social networks like Facebook and academic citation networks.
7. **Geospatial Datasets**:
- Geospatial datasets contain geographic and spatial information.
- They are used in applications such as geographic information systems (GIS), map generation, and location-based services.
- Examples include GPS data, satellite imagery, and census data with geographic coordinates.
8. **Biological Datasets**:
- Biological datasets include genetic, genomic, and proteomic data.
- They are used in bioinformatics for tasks like DNA sequencing, protein structure prediction, and drug discovery.
- Examples include the Human Genome Project dataset and protein structure databases like the Protein Data Bank.
9. **Custom Datasets**:
- Custom datasets are created for specific research or application needs.
- They can include data from various sources and formats, such as sensor data, surveys, and experiments.
- Examples can range from medical research datasets to datasets for testing autonomous vehicles.
Datasets are crucial for training and testing machine learning models, conducting research, and making data-driven decisions. Their quality, size, and relevance play a significant role in the success of data-driven projects. Additionally, data privacy and ethical considerations are essential when working with datasets, especially those containing sensitive information.
[{2023-10-25 retrieved} https://chat.openai.com/c/7e4c64d3-f397-4522-b956-b960cbf731ba]
description::
"evolution of data science:
The field of data science has evolved significantly over the years, driven by advances in technology, the availability of large and diverse datasets, and the growing demand for data-driven insights in various industries. Here is a brief overview of the evolution of data science:
1. Early Roots (1960s-1980s):
- The early roots of data science can be traced back to statistics, computer science, and operations research.
- Researchers and analysts used traditional statistical methods and manual data processing to extract insights from data.
- The focus was primarily on descriptive statistics and hypothesis testing.
2. Emergence of Data Mining (1990s):
- Data mining emerged as a subfield of data science in the 1990s.
- It involved the use of algorithms and computational techniques to discover patterns, trends, and associations in large datasets.
- Data mining techniques, such as decision trees, clustering, and association rule mining, became popular.
3. Big Data Era (2000s-2010s):
- The proliferation of the internet and digital technologies led to the generation of vast amounts of data, giving rise to the "Big Data" era.
- Technologies like Hadoop and Spark were developed to handle and process large datasets.
- Data storage and processing capabilities expanded, enabling organizations to extract more value from their data.
4. Machine Learning and AI Integration:
- Machine learning (ML) and artificial intelligence (AI) became integral to data science.
- ML algorithms, including deep learning, natural language processing, and reinforcement learning, were applied to solve a wide range of problems.
- The use of neural networks and deep learning models led to breakthroughs in image recognition, speech recognition, and more.
5. Data Science as a Profession (2010s-Present):
- Data science became a recognized profession, and universities started offering formal data science programs.
- Data scientists were in high demand as organizations recognized the potential of data for decision-making and competitive advantage.
- The field saw the emergence of specialized roles, such as data engineers, data analysts, and machine learning engineers.
6. Ethical Considerations and Data Privacy:
- With the increased use of data, ethical concerns and data privacy issues became more prominent.
- Data science practitioners and organizations started to focus on responsible data handling, fairness in AI, and transparency in algorithms.
7. Integration with Business Strategy:
- Data science shifted from a purely technical endeavor to an integral part of business strategy.
- Companies leveraged data science to make informed decisions, improve customer experiences, and gain a competitive edge.
8. Advancements in Tools and Platforms:
- Data science tools and platforms, such as Python, R, Jupyter, and cloud-based services, continued to evolve, making it easier for practitioners to work with data.
- Automated machine learning (AutoML) tools also gained popularity, simplifying the model building process.
9. Interdisciplinary Nature:
- Data science became increasingly interdisciplinary, involving expertise from fields like domain knowledge, psychology, and ethics.
- Collaboration between data scientists and subject matter experts became essential for solving complex problems.
The evolution of data science is ongoing, and it continues to adapt to new technologies, methodologies, and challenges. As the world generates even more data, data science will likely play an even more critical role in shaping our understanding of the world and making data-driven decisions."
[{2023-10-09 retrieved} https://chat.openai.com/c/206229f5-f849-43b7-9436-2e95d6767e3e]
name::
* McsEngl.evoluting-of-sciMindData,
* McsEngl.sciMindData'evoluting,
description::
× generic: science,
× whole: sciMind,
"cognitive-science definition
Cognitive science is the interdisciplinary study of the mind and its processes. It draws on methods and theories from psychology, linguistics, neuroscience, philosophy, computer science, anthropology, and artificial intelligence. Cognitive scientists study intelligence and behavior, with a focus on how nervous systems represent, process, and transform information.
Some of the key areas of study in cognitive science include:
* Perception: How we take in information from the world around us, such as sight, hearing, touch, taste, and smell.
* Attention: How we focus on and select relevant information from the environment.
* Memory: How we store and retrieve information over time.
* Language: How we understand and produce spoken and written language.
* Reasoning: How we make decisions and solve problems.
* Learning: How we acquire new knowledge and skills.
* Emotion: How we experience and express emotions.
* Consciousness: What it means to be aware of ourselves and the world around us.
Cognitive science is a rapidly growing field with many potential applications. For example, cognitive scientists are working to develop new treatments for cognitive disorders such as Alzheimer's disease and autism. They are also developing new technologies that can help us learn and think more effectively.
Here are some examples of cognitive science:
* Studying how the brain processes language.
* Developing artificial intelligence systems that can understand and respond to natural language.
* Designing educational games that help children learn new concepts.
* Creating virtual reality environments that can be used to simulate real-world experiences.
* Investigating the relationship between emotions and cognition.
Cognitive science is a fascinating and interdisciplinary field that is constantly evolving. It is a field that is making significant contributions to our understanding of the mind and its workings."
[{2023-08-28 retrieved} https://bard.google.com/]
name::
* McsEngl.cognitive-science!⇒sciCognitive,
* McsEngl.sciCognitive,
* McsEngl.sciCognitive!=cognitive-science,
* McsEngl.sciMind.001-cognitive-science!⇒sciCognitive,
* McsEngl.sciMind.cognitive-science!⇒sciCognitive,
====== langoGreek:
* McsElln.γνωστική-επιστήμη!=sciCognitive, ChatGPT,
* McsElln.νοητική-επιστήμη!=sciCognitive, Bard,
descriptionLong::
"cognitive-science definition
Cognitive science is an interdisciplinary field of study that focuses on understanding the nature of human cognition, which refers to the mental processes and activities related to acquiring, processing, storing, and using information. This field seeks to uncover the mechanisms and principles underlying various aspects of human intelligence, such as perception, attention, memory, language, problem-solving, decision-making, and learning.
Cognitive science draws upon insights and methodologies from a range of disciplines, including psychology, neuroscience, linguistics, philosophy, computer science, anthropology, and education. Researchers in cognitive science aim to develop theories and models that explain how the mind works, often using computational and experimental approaches to test and refine these theories.
Overall, cognitive science seeks to provide a comprehensive understanding of how humans think, reason, and interact with their environment, and how these cognitive processes are realized in the brain."
[{2023-08-28 retrieved} https://chat.openai.com/?model=text-davinci-002-render-sha]
Cognitive science is the interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does and how it works. It includes research on intelligence and behavior, especially focusing on how information is represented, processed, and transformed (in faculties such as perception, language, memory, reasoning, and emotion) within nervous systems (human or other animal) and machines (e.g. computers). Cognitive science consists of multiple research disciplines, including psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology.[1] It spans many levels of analysis, from low-level learning and decision mechanisms to high-level logic and planning; from neural circuitry to modular brain organization. The fundamental concept of cognitive science is "that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."[1]
[http://en.wikipedia.org/wiki/Cognitive_science]
Cognitive science is most simply defined as the scientific study either of mind or of intelligence (e.g. Luger 1994).
[http://en.wikipedia.org/wiki/Cognitive_science]
Cognitive science refers to the interdisciplinary study of the acquisition and use of knowledge. It includes as contributing disciplines: artificial intelligence, psychology, linguistics, philosophy, anthropology, neuroscience, and education. The cognitive science movement is far reaching and diverse, containing within it several viewpoints.
Cognitive science grew out of three developments: the invention of computers and the attempts to design programs that could do the kinds of tasks that humans do; the development of information processing psychology where the goal was to specify the internal processing involved in perception, language, memory, and thought; and the development of the theory of generative grammar and related offshoots in linguistics. Cognitive science was a synthesis concerned with the kinds of knowledge that underlie human cognition, the details of human cognitive processing, and the computational modeling of those processes.
There are five major topic areas in cognitive science: knowledge representation, language, learning, thinking, and perception.
Eysenck, M.W. ed. (1990). The Blackwell Dictionary of Cognitive Psychology. Cambridge, Massachusetts: Basil Blackwell Ltd.
[U of A Cog Sci Dictionary]
The main concern of cognitive science is the construction of models of human thinking - models which detail the processes intervening between environmental stimuli and behaviour. A shared assumption is that the mind is representational in nature and that cognitive processes can be described in terms of their function, without reference to a neural substrate. During the 1960's and 70's, cognitive science emerged from the neighbouring fields of linguistics, psychology, computer science, philosophy, neuroscience and anthropology, as a multidisciplinary effort centred around a number of common problems and the computer as a research tool. In cognitive science, the computer has been used in order to simulate cognitive processes. Today, the area still borrows from its neighbouring disciplines, but generally takes a broader perspective on cognition, and uses a wider range of methodologies.
[Lund University Congitive Science (Sweden)]
"the study of intelligence and intelligent systems, with particular reference to intelligent behaviour as computation" (Simon & Kaplan, 1989)
Simon, H. A. & C. A. Kaplan, "Foundations of cognitive science", in Posner, M.I. (ed.) 1989, Foundations of Cognitive Science, MIT Press, Cambridge MA.
[U of A Cog Sci Dictionary(CognSci)]
Cognitive science includes the study of thought processes such as learning and remembering and the perception of objects and events in one's environment. Current research areas include neural mechanisms of vision, visual perception, cognitive neuroscience, attention, memory, categorization, conceptual and perceptual development, spatial processing, attitudes, conceptual and perceptual development, decision making, and negotiation. Many members of the cognitive area employ neural and computation modeling in their research.
[http://www.vanderbilt.edu/AnS/psychology/cogsci/CogHomePage.html] 1997.10
description::
* intelligence,
* sensing,
* thinking,
* memoring,
* emoting,
* communicating,
* learning,
* teaching,
* planning,
===
In any way, the essential questions of cognitive science seem to be: What is intelligence? and How is it possible to model it computationally?.
[http://en.wikipedia.org/wiki/Cognitive_science]
The problem of constructing a robot is a good test of progress in cognitive science. A robot needs
- perception,
- memory,
- knowledge,
- learning,
- planning and
- communicative abilities,
that is, exactly those capacities that cognitive science aims at understanding.
[Peter Gardenfors. Cognitive science: from computers to anthills as models of human thought]
Below are some of the main topics that cognitive science is concerned with.
Artificial intelligence
Attention (is the selection of important information)
Language processing
Learning and development
Memory
Perception and action
description::
Critique of Cognitive Science
The claim that human minds work by representation and computation is an empirical conjecture and might be wrong. Although the computational-representational approach to cognitive science has been successful in explaining many aspects of human problem solving, learning, and language use, some philosophical critics such as Hubert Dreyfus (1992) and John Searle (1992) have claimed that this approach is fundamentally mistaken. Critics of cognitive science have offered such challenges as:
1. The emotion challenge: Cognitive science neglects the important role of emotions in human thinking.
2. The consciousness challenge: Cognitive science ignores the importance of consciousness in human thinking.
3. The world challenge: Cognitive science disregards the significant role of physical environments in human thinking.
4. The body challenge: Cognitive science neglects the contribution of the body to human thought and action.
5. The social challenge: Human thought is inherently social in ways that cognitive science ignores.
6. The dynamical systems challenge: The mind is a dynamical system, not a computational system.
7. The mathematics challenge: Mathematical results show that human thinking cannot be computational in the standard sense, so the brain must operate differently, perhaps as a quantum computer.
Thagard (2005) argues that all these challenges can best be met by expanding and supplementing the computational-representational approach, not by abandoning it.
[http://plato.stanford.edu/entries/cognitive-science/]
description::
"Mark Turner is a cognitive scientist, linguist, and author. He is Institute Professor and Professor and Chair of Cognitive Science at Case Western Reserve University, where he was for two years Dean of the College of Arts and Sciences. He was previously Distinguished University Professor at the University of Maryland and Associate Director of the Center for Advanced Study in the Behavioral Sciences. Turner has been a fellow of the Institute for Advanced Study, the John Simon Guggenheim Memorial Foundation, the Center for Advanced Study in the Behavioral Sciences, the National Humanities Center, and the National Endowment for the Humanities. The French Academy awarded him the Prix du Rayonnement de la langue et de la litte'rature franc,aises in 1996. He was born in 1954.
The work of Gilles Fauconnier and Turner founded the theory of conceptual blending.
His wife is the award-winning children's author Megan Whalen Turner.
× Webaddress: https://www.markturner.org/,
[http://en.wikipedia.org/wiki/Mark_Turner_%28cognitive_scientist%29]
name::
* McsEngl.human.1954.Turner.Mark,
* McsEngl.human.Turner.Mark.1954,
* McsEngl.wrkrSciCognitive.Turner.Mark.1954,
description::
European Institute of Cognitive Sciences and Engineering
http://www-eurisco.onecert.fr/
University of California, Los Angeles (UCLA)
http://www.lifesci.ucla.edu/cogsci/
Cognitive Science, as a discipline, is concerned with learning how animals (and machines) acquire knowledge, represent that knowledge, and how they manipulate those representations.
University of California, San Diego
http://cogsci.ucsd.edu/
University of Exeter
http://www.exeter.ac.uk/CogSci/
description::
* Eysenck, M.W. ed. (1990). The Blackwell Dictionary of Cognitive Psychology. Cambridge, Massachusetts: Basil Blackwell Ltd.
name::
* McsEngl.sciCognitive'Infrsc,
description::
1990s:
** 1995: Cognition in the Wild, * Hutchins, E (1995), * Cambridge, Mass: MIT Press
** 1993: Language comprehension in ape and child, * Savage-Rumbaugh, E. Sue (1993), * Monographs of the Society for Research in Child Development (serial no.233), vol 58, nos. 3-4.
** 1991: Consciousness Explained, * Dennett, D. C. (1991), * Boston, MA: Little, Brown and Co.
** 1990: Unified Theories of Cognition, * Newell, A. (1990), * Cambridge, MA: Harvard University Press.
1980s:
** 1989: Mental rotation of the neuronal population vector, * Georgopoulos, Apostolos P., Lurito, Joseph T., Petrides, Michael., Schwartz, Andrew B., et al. (1989), * Science. Vol 243(4888), Jan 1989, 234-236.
** 1988: Segregation of form, color, movement and depth: Anatomy, physiology and perception, * Livingstone M. & Hubel D. (1988), * Science, 240(4853):740-9, 1988
** 1987: The Intentional Stance, * Dennett, D. C. (1987), * MIT Press.
** 1987: Recognition-by-components: a theory of human image understanding, * Biederman, I. (1987), * Psychological Review, 94, 115-147.
** 1987: Neural Darwinism: The Theory of Neuronal Group Selection, * Edelman, G. M. (1987), * New York: Basic Books
** 1987: Circuitry of primate prefrontal cortex and the regulation of behavior by representational memory, * Goldman-Rakic, P.S. (1987), * Handbook of Physiology, vol5(1), American Physiological Society, Bethesda, MD: 373-417
** 1986: Working Memory, * Baddeley, A. (1986), * Oxford University Press
** 1986: Parallel distributed processing: Explorations in the microstructure of cognition, * Rumelhart, D. E., McClelland, J. L. (1986), * MIT Press: Cambrige, Mass
** 1983: The modularity of mind: An essay on faculty psychology, * Fodor, J. (1983), * Cambridge, MA: MIT Press
** 1983: The Architecture of Cognition, * Anderson, J.R. (1983), * Cambridge, MA: Harvard University Press
** 1983: Mental models: Towards a cognitive science of language, inference, and consciousness, * Johnson-Laird, P. M. (1983), * Cambridge MA: Harvard University Press
** 1982: Vision: a computational investigation into the human representation and processing of visual information, * Marr, D. (1982), * San Francisco: W. H. Freeman.
** 1982: Two cortical visual systems, * Ungerleider, L.G. and Mishkin, M. (1982), * In D.J. Ingle, M.A. Goodale, and R.J.W. Mansfield (Eds.), Analysis of visual behavior. Cambridge, MA: MIT Press
** 1982: Acquisition of cognitive skill, * Anderson, J. R. (1982), * Psychological Review, 89, 369-406
** 1980: Minds, brains and programs, * Searle J.R. (1980), * The Behavioral and Brain Sciences, 3.
** 1980: A feature integration theory of attention, * Treisman, A. and Gelade, G. (1980), * Cognitive Psychology, 12, 97-136
1970s:
** 1979: The interpretation of structure from motion, * Ullman, S. (1979), * Proceedings of the Royal Society of London B., 203, 405-426
** 1979: The Hippocampus as a Cognitive Map, * O'Keefe, J., and Nadel, L. (1979), * Behavioral and Brain Sciences, vol 2(4).
** 1979: The Ecological Approach to Visual Perception, * Gibson, J. J. (1979), *
** 1978: Toward a model of text comprehension and production, * Kintsch, W. and van Dijk, T. A. (1978), * Psychological Review, 85, 363-394.
** 1978: Chronometric Explorations of Mind, * Posner, M. I. (1978), * Hillsdale, N.J.: Lawerence Erlbaum Associates
** 1977: Scripts, plans, goals, and understanding, * Schank, R. C. and Abelson, R. P. (1977), * Hillsdale, NJ: Erlbaum.
** 1977: Ferrier lecture: Functional architecture of macaque monkey visual cortex, * Hubel, D.H. and Wiesel, T.N. (1977), * Proceedings of the Royal Society of London B, 198, 1-59.
** 1975: The language of thought, * Fodor, J. A. (1975), * New York: Crowell
** 1975: Psychophysics: Introduction to its perceptual, neural, and social prospects, * Stevens, S. S. (1975), * New York, NY: John Wiley and Sons. 1975, v, 329.
** 1975: Posterior parietal association cortex of the monkey: command functions for operations within extrapersonal space, * Mountcastle VB, Lynch JC, Georgopoulos A, Sakata H, Acuna C (1975), * J Neurophysiol 38:871-908
** 1975: A framework for representing knowledge, * Minsky, M. (1975), * In P. H. Winston (Ed.), The Psychology of Computer Vision. New York: McGraw-Hill, pp. 211-277.
** 1974: Working memory, * Baddeley, A.D., and Hitch, A.D. (1974), * In G. Bower (Ed.), The psychology of learning and motivation, Vol. 8; pp 47-89). New York: Academic Press
** 1974: Judgments under uncertainty: Heuristics and biases, * Tversky, A., & Kahneman, D. (1974), * Science, 185, 1124-1131.
** 1973: Visual Perception of biological motion and a model for its analysis, * Johansson G. (1973), * Perception & Psychophysics, Vol. 14 (2); 201-211
** 1973: The Working Brain: An Introduction to Neuropsychology, * Luria, A. R. (1973), * New York: Basic Books.
** 1973: On the psychology of prediction, * Kahneman, D., & Tversky, A. (1973), * Psychological Review, 80, 237-251
** 1973: Natural Categories, * Rosch, E. (1973), * Cognitive Psychology, 7,573-605
** 1973: Encoding specificity and retrieval processes in episodic memory, * Tulving, E. and Thompson, D. (1973), * Psychological Review, 80, 352-373.
** 1972: What Computers Can't Do, * Dreyfus, H. L. (1972), * MIT Press
** 1972: Single units and sensation: A neuron doctrine for perceptual psychology?, * Barlow, H. (1972), * Perception, 1, 371-394
** 1972: Levels of Processing: A framework for memory research, * Craik, F.I. and Lockhart, R. S. (1972), * Journal of Verbal Learning and Verbal Behavior, 12, 599-607.
** 1972: Human problem solving, * Newell, A., & Simon, H. A. (1972), * Englewood Cliffs, NJ: Prentice-Hall
** 1971: Speech perception in infants, * Eimas, P.D., Siqueland, E.R., Jusczyk, P., & Vigorito, J. (1971), * Science Vol 171 (3968), 303-306
** 1971: Mental rotation of three-dimensional objects, * Shepard, R.N., and Metzler, J. (1971), * Science, 171, 701-703
** 1971: Lightness and retinex theory, * Land, E. H. and McCann, J. J. (1971), * Journal of the Optical Society of America, 61 (1), 1-11.
** 1971: Foundations of Cyclopean Perception, * Julesz, B. (1971), * Chicago, IL: The University of Chicago Press.
** 1970: The Bisected Brain, * Gazzaniga, M. (1970), * New York: Appleton-Century-Crofts
1960s:
** 1969: The Sciences of the Artificial, * Simon, H. A. (1969), * MIT Press, 1969
** 1969: Principles of Perceptual Learning and Development, * Gibson, E.J. (1969), * Appleton, Century and Croft
** 1969: Perceptrons, * Minsky, M., & Papert, S. (1969), * (1969) Cambridge: MIT Press.
** 1969: On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images, * Blakemore, C., & Campbell, F.W. (1969), * Journal of Physiology, 203, 203-260
** 1968: Human memory: A proposed system and its control processes, * Atkinson, R. C. and Shiffrin, R. M. (1968), * In K. W. Spence and J. T. Spence (Eds.), The Psychology of learning and motivation: Advances in research and theory (vol. 2). New York: Academic Press.
** 1968: Hemisphere deconnnection and unity in conscious awareness, * Sperry, R. W. (1968), * American Psychologist 23 (10), 723-733.
** 1968: Application of Fourier Analysis to the Visibility of Gratings, * Campbell, F.W., and Robson, J.G. (1968), * Journal of Physiology. 197: 551-566.
** 1967: The coordination and regulation of movements, * Bernstein, N.A. (1967), * Oxford, New York: Pergamon Press
** 1967: Cognitive Psychology, * Neisser, U. (1967), * Cognitive Psychology. Englewood Cliffs, N.J.:Prentice-Hall.
** 1967: Biological foundations of language, * Lenneberg, E. H. (1967), * New York: Wiley
** 1966: Theory of self-reproducing automata, * Von Neumann, J., & Burks, A. W. (1966), * Urbana,: University of Illinois Press.
** 1966: The senses considered as perceptual systems, * Gibson, J. J. (1966), * Boston, MA: Hougton Mifflin.
** 1966: Signal detection theory and psychophysics, * Green, D.M., and Swets, J.A. (1966), * New York: Wiley
** 1966: High-speed memory scanning in human memory, * Sternberg, S. (1966), * Science, 153, 652-654.
** 1965: Aspects of the theory of syntax, * Chomsky, N. (1965), * Cambridge, MA: MIT Press.
** 1964: Theory of games and economic behavior, * Von Neumann, J., & Morgenstern, O. (1964), * ([3d ] ed.). New York,: Science Editions J. Wiley.
** 1962: The analysis of proximities: Multidimensional scaling with an unknown distance function, * Shepard, R. N. (1962), * Psychometrika, 27(2), 125-140
** 1962: The Structure of Scientific Revolutions, * Kuhn, T. S. (1962), * Chicago: University of Chicago Press
** 1960: The information available in brief visual presentations, * Sperling, G. (1960), * Psychological Monographs, 74 (Whole number 498)
** 1960: Plans and the Structure of Behavior, * Miller, G.A., Galanter, E, and Pribram, K.H. (1960), * New York, Holt, Rinehart and Winston, Inc.
** 1960: Design for a Brain: The Origins of Adaptive Behavior, * Ashby, W. Ross (1960), * New York: Willey
1950s:
** 1959: What the Frog's Eye Tells the Frog's Brain, * Lettvin, J.Y., Maturana, H.R., McCulloch, W.S., & Pitts, W.H. (1959), * Proceedings of the IRE, Vol. 47, No. 11, pp. 1940-51.
** 1959: Sensory mechanisms, the reduction of redundancy, and intelligence, * Barlow, H. B. (1959), * Proceedings of the symposium on the mechanization of thought processes. National Physical Laboratory. HMSO, London,
** 1959: A review of Skinner's "Verbal Behavior", * Chomsky, N. (1959), * Language, 35, 26-58
** 1958: Perception and Communication, * Broadbent, D. (1958), * New York: Pergamon Press
** 1957: Syntactic Structures, * Chomsky, N. (1957), * The Hague: Mouton
** 1957: Loss of recent memory after bilateral hippocampal lesions, * Scoville, W. B., and Milner, B. (1957), * Journal of Neurology, Neurosurgery, and Psychiatry, 20,
** 1956: The magical number seven, plus or minus two: Some limits on our capacity for processing information, * Miller, G. A. (1956), * Psychological Review, 63, 81-97
** 1956: Language, Thought and Reality; selected writings, * Whorf, B. L. (ed. by J. Carroll) (1956), * Boston, MA: MIT Press.
** 1956: A study of thinking, * Bruner, J. R., Goodnow, J. J., & Austin, G. A. (1956), * New York: Wiley
** 1954: The Construction of Reality in the Child, * Piaget, J. (1954), * NY: Ballantine Books
** 1953: Philosophical investigations, * Wittgenstein, Ludwig (1953), * New York: Macmillan, c1953
** 1952: The Conceptual Framework of Psychology, * Brunswik, E. (1952), * Chicago: University of Chicago Press.
** 1951: The problem of serial order in behavior, * Lashley, K. S. (1951), * In Cerebral Mechanisms in Behavior, Wiley, pp. 112-136.
** 1950: The perception of the Visual World, * Gibson, J. J. (1950), * Westport, Conn.: Greenwood Press
** 1950: Computing machinery and intelligence, * Turing, A. M. (1950), * Mind, 59, 433-460.
1940s:
** 1949 ** The organization of behavior; a neuropsychological theory, * Hebb, D.O. (1949), * Wiley-Interscience, New York
** 1949: The formation of learning sets, * Harlow, H. F. (1949), * Psychological Review, 1949, 56, 51-65
** 1948: Cybernetics, or Control and Communication in the Animal and the Machine, * Wiener, N. (1948), * New York: Wiley
** 1948: Cognitive maps in rats and men, * Tolman, E.C. (1948), * Psychological Review 55, 189-208
** 1948: A mathematical theory of communication, * Shannon C. E. (1948), * Bell System Technical Journal, 27, 379-423
** 1945: The Operational Analysis of Psychological Terms, * Skinner, B. F. (1945), * Psychological Review, 52, 270-277
** 1943: The Nature of Explanation, * Craik, K. (1943), * Cambridge, England: Cambridge University Press.
** 1943: A logical calculus of the ideas immanent in nervous activity, * McCulloch, W. S., & Pitts, W. (1943), * Mathematical Biophysics, 5:115-133 (pp. 18-27).
1930s:
** 1936: On computable numbers: With an application to the Entscheidungsproblem, * Turing, A.M. (1936), * Proceedings of the London Mathematical Society, ser. 2, 42,
** 1935: Studies of interference in serial verbal reactions, * Stroop, J. (1935), * Journal of Experimental Psychology, 18, 643-661
** 1935: Principles of Gestalt Psychology, * Koffka, K. (1935), * New York: Harcourt, Brace and Co.
** 1934: Thought and Language (Myshlenie i rech'.), * Vygotsky, Lev (1964 (original work 1934)), * (E.Hanfmann and G. Vakar, Trans.). Cambridge, M.I.T Press.
** 1932: Remembering: A study in experimental and social psychology, * Bartlett, F. C. (1932), * Cambridge, England: The University Press
** 1930: The child's conception of the world, * Piaget, J. (1930), * New York: Harcourt, Brace, and World.
1920s:
** 1927: The interpretation of dreams, * Freud, S. (1927), * (1st English Edition) London: George Allen and Unwin Ltd.
** 1925: The Mentality of Apes, * Koehler, W. (1925), * New York: Harcourt and Brace
1900s:
** 1906: The Integrative Action of the Nervous System, * Sherrington, Charles S., Sir (1906), * New York: Scribner's Sons
[https://web.mnstate.edu/schwartz/cogsci100.htm]
description::
"In philosophy of mind, the computational theory of mind (CTM), also known as computationalism, is a family of views that hold that the human mind is an information processing system and that cognition and consciousness together are a form of computation. Warren McCulloch and Walter Pitts (1943) were the first to suggest that neural activity is computational. They argued that neural computations explain cognition.[1] The theory was proposed in its modern form by Hilary Putnam in 1967, and developed by his PhD student, philosopher, and cognitive scientist Jerry Fodor in the 1960s, 1970s, and 1980s.[2][3] It was vigorously disputed in analytic philosophy in the 1990s due to work by Putnam himself, John Searle, and others.
The computational theory of mind holds that the mind is a computational system that is realized (i.e. physically implemented) by neural activity in the brain. The theory can be elaborated in many ways and varies largely based on how the term computation is understood. Computation is commonly understood in terms of Turing machines which manipulate symbols according to a rule, in combination with the internal state of the machine. The critical aspect of such a computational model is that we can abstract away from particular physical details of the machine that is implementing the computation.[3] For example, the appropriate computation could be implemented either by silicon chips or biological neural networks, so long as there is a series of outputs based on manipulations of inputs and internal states, performed according to a rule. CTM therefore holds that the mind is not simply analogous to a computer program, but that it is literally a computational system.[3]
Computational theories of mind are often said to require mental representation because 'input' into a computation comes in the form of symbols or representations of other objects. A computer cannot compute an actual object but must interpret and represent the object in some form and then compute the representation. The computational theory of mind is related to the representational theory of mind in that they both require that mental states are representations. However, the representational theory of mind shifts the focus to the symbols being manipulated. This approach better accounts for systematicity and productivity.[3] In Fodor's original views, the computational theory of mind is also related to the language of thought. The language of thought theory allows the mind to process more complex representations with the help of semantics. (See below in semantics of mental states).
Recent work has suggested that we make a distinction between the mind and cognition. Building from the tradition of McCulloch and Pitts, the computational theory of cognition (CTC) states that neural computations explain cognition.[1] The computational theory of mind asserts that not only cognition, but also phenomenal consciousness or qualia, are computational. That is to say, CTM entails CTC. While phenomenal consciousness could fulfill some other functional role, computational theory of cognition leaves open the possibility that some aspects of the mind could be non-computational. CTC, therefore, provides an important explanatory framework for understanding neural networks, while avoiding counter-arguments that center around phenomenal consciousness."
[{2023-09-28 retrieved} https://en.wikipedia.org/wiki/Computational_theory_of_mind]
name::
* McsEngl.computational-theory-of-mind,
* McsEngl.computationalism,
* McsEngl.sciMind.008-computational-theory-of-mind,
* McsEngl.sciMind.computational-theory-of-mind,
description::
· the-science of science.
name::
* McsEngl.epistemology,
* McsEngl.sciMind.002-epistemology,
* McsEngl.sciMind.epistemology,
* McsEngl.sciSci,
descriptionLong::
"Epistemology (/ɪˌpɪstəˈmɒlədʒi/ (listeni); from Ancient Greek ἐπιστήμη (epistḗmē) 'knowledge', and -logy) is the branch of philosophy concerned with knowledge, and is considered a major subfield of philosophy, along with other major subfields such as ethics, logic, and metaphysics.[1] Epistemologists study the nature, origin, and scope of knowledge, epistemic justification, the rationality of belief, and various related issues. Debates in epistemology are generally clustered around four core areas:[2][3][4]
The philosophical analysis of the nature of knowledge and the conditions required for a belief to constitute knowledge, such as truth and justification
Potential sources of knowledge and justified belief, such as perception, reason, memory, and testimony
The structure of a body of knowledge or justified belief, including whether all justified beliefs must be derived from justified foundational beliefs or whether justification requires only a coherent set of beliefs
Philosophical skepticism, which questions the possibility of knowledge, and related problems, such as whether skepticism poses a threat to our ordinary knowledge claims and whether it is possible to refute skeptical arguments
In these debates and others, epistemology aims to answer questions such as "What do people know?", "What does it mean to say that people know something?", "What makes justified beliefs justified?", and "How do people know that they know?"[1][2][5][6] Specialties in epistemology ask questions such as "How can people create formal models about issues related to knowledge?" (in formal epistemology), "What are the historical conditions of changes in different kinds of knowledge?" (in historical epistemology), "What are the methods, aims, and subject matter of epistemological inquiry?" (in metaepistemology), and "How do people know together?" (in social epistemology)."
[{2023-08-31 retrieved} https://en.wikipedia.org/wiki/Epistemology]
description::
"Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or logical truths. It studies how conclusions follow from premises due to the structure of arguments alone, independent of their topic and content. Informal logic is associated with informal fallacies, critical thinking, and argumentation theory. It examines arguments expressed in natural language while formal logic uses formal language. When used as a countable noun, the term "a logic" refers to a logical formal system that articulates a proof system. Logic plays a central role in many fields, such as philosophy, mathematics, computer science, and linguistics.
Logic studies arguments, which consist of a set of premises together with a conclusion. An example is the argument from the premises "it's Sunday" and "if it's Sunday then I don't have to work" to the conclusion "I don't have to work".[1] Premises and conclusions express propositions or claims that can be true or false. An important feature of propositions is their internal structure. For example, complex propositions are made up of simpler propositions linked by logical vocabulary like ∧ (and) or → (if...then). Simple propositions also have parts, like "Sunday" or "work" in the example. The truth of a proposition usually depends on the meanings of all of its parts. However, this is not the case for logically true propositions. They are true only because of their logical structure independent of the specific meanings of the individual parts.
Arguments can be either correct or incorrect. An argument is correct if its premises support its conclusion. Deductive arguments have the strongest form of support: if their premises are true then their conclusion must also be true. This is not the case for ampliative arguments, which arrive at genuinely new information not found in the premises. Many arguments in everyday discourse and the sciences are ampliative arguments. They are divided into inductive and abductive arguments. Inductive arguments are statistical generalizations, like inferring that all ravens are black based on many individual observations of black ravens.[2] Abductive arguments are inferences to the best explanation, for example, when a doctor concludes that a patient has a certain disease which explains the symptoms they suffer.[3] Arguments that fall short of the standards of correct reasoning often embody fallacies. Systems of logic are theoretical frameworks for assessing the correctness of arguments.
Logic has been studied since antiquity. Early approaches include Aristotelian logic, Stoic logic, Nyaya, and Mohism. Aristotelian logic focuses on reasoning in the form of syllogisms. It was considered the main system of logic in the Western world until it was replaced by modern formal logic, which has its roots in the work of late 19th-century mathematicians such as Gottlob Frege. Today, the most used system is classical logic. It consists of propositional logic and first-order logic. Propositional logic only considers logical relations between full propositions. First-order logic also takes the internal parts of propositions into account, like predicates and quantifiers. Extended logics accept the basic intuitions behind classical logic and extend it to other fields, such as metaphysics, ethics, and epistemology. Deviant logics, on the other hand, reject certain classical intuitions and provide alternative explanations of the basic laws of logic."
[{2023-08-30 retrieved} https://en.wikipedia.org/wiki/Logic]
name::
* McsEngl.logic!⇒sciLogic,
* McsEngl.logic//sciMind!⇒sciLogic,
* McsEngl.sciLogic,
* McsEngl.sciLogic!=science.mind.logic,
* McsEngl.sciMind.005-logic!⇒sciLogic,
====== langoGreek:
* McsElln.επιστήμη-της-Λογικής!=sciLogic,
descriptionLong::
"logic definition
Logic is a systematic and structured way of reasoning and drawing conclusions based on a set of principles, rules, and established relationships between propositions or statements. It provides a framework for evaluating arguments, identifying valid reasoning, and distinguishing between sound and unsound conclusions.
In logic, propositions are statements that can be either true or false. Logical operations, such as conjunction (AND), disjunction (OR), negation (NOT), implication (IF...THEN), and equivalence (IF AND ONLY IF), allow for the manipulation and combination of propositions to form more complex statements.
The main branches of logic include propositional logic (dealing with propositions and their relationships) and predicate logic (which extends to include variables, quantifiers, and more complex statements involving objects and properties). Logic plays a crucial role in various fields, including mathematics, philosophy, computer science, linguistics, and artificial intelligence, by providing a formalized structure for reasoning and inference."
[{2023-08-30 retrieved} https://chat.openai.com/?model=text-davinci-002-render-sha]
"logic definition
Logic is the study of correct reasoning. It is the science of how to reason well and to avoid fallacies. Logic is divided into two main branches: formal logic and informal logic.
* Formal logic is the study of the logical structures of arguments. It is concerned with the form of arguments, rather than their content. Formal logic is used to study the validity of arguments, which means whether the conclusion of an argument follows logically from its premises.
* Informal logic is the study of the informal fallacies that people make in their reasoning. It is concerned with the content of arguments, rather than their form. Informal logic is used to help people to identify and avoid fallacies in their own reasoning, and to evaluate the reasoning of others.
Here is an example of a formal logic argument:
Premise 1: All humans are mortal.
Premise 2: Socrates is a human.
Conclusion: Therefore, Socrates is mortal.
This argument is valid because the conclusion follows logically from the premises. The premises state that all humans are mortal and that Socrates is a human. The conclusion states that Socrates is mortal. This is a logical consequence of the premises, because if all humans are mortal and Socrates is a human, then Socrates must be mortal.
Here is an example of an informal logic fallacy:
Premise: My uncle is a doctor, so he must be right about everything.
Conclusion: Therefore, I should get a COVID-19 vaccine because my uncle says it's safe.
This argument is informal because it commits the ad hominem fallacy. The ad hominem fallacy is a fallacy that attacks the person making an argument, rather than the argument itself. In this case, the argument is attacking the uncle's credibility, rather than the argument that he is making about COVID-19 vaccines.
Logic is an important tool for critical thinking. It can help us to identify and avoid fallacies, and to reason more effectively. Logic is also used in many other fields, such as mathematics, computer science, and philosophy."
[{2023-08-30 retrieved} https://bard.google.com/]
description::
"the Law of Excluded Middle (LEM) states that for any proposition, either the proposition or its negation is true, and there are no other possibilities."
name::
* McsEngl.LEM!=law-of-excluded-middle@sciLogic,
* McsEngl.law-of-excluded-middle@sciLogic,
* McsEngl.sciLogic'law-of-excluded-middle,
description::
"mathematical logic vs logic
"Logic" is a broad term that encompasses various branches and systems of reasoning, while "mathematical logic" is a specific subfield of logic that applies mathematical techniques and formalism to the study of logical systems. Here's a comparison between the two:
1. Scope and Generality:
- Logic: Logic, in its most general sense, refers to the study of reasoning, argumentation, and inference. It includes informal reasoning, everyday reasoning, and various formal systems.
- Mathematical Logic: Mathematical logic is a specialized branch of logic that focuses on formal, mathematical representations of logical systems. It deals with the development of formal languages, axioms, and rigorous proofs.
2. Formalism and Symbolism:
- Logic: Logic can be studied informally, using natural language, or formally, using symbolic languages. Informal logic is often used in everyday discourse and argumentation.
- Mathematical Logic: Mathematical logic emphasizes the use of symbolic languages and precise mathematical notation. It employs formal systems, including propositional logic, first-order logic, and higher-order logics, to analyze and prove properties of logical systems.
3. Rigor and Proof:
- Logic: While informal logic focuses on practical reasoning and argument evaluation, it may not always provide a rigorous framework for formal proofs.
- Mathematical Logic: Mathematical logic places a strong emphasis on rigorous proof techniques. It uses formal proof systems, such as axiomatic systems and deduction rules, to establish the validity of logical statements.
4. Applications:
- Logic: Logic has broad applications in various fields, including philosophy, linguistics, computer science, and artificial intelligence. It is used for analyzing arguments, constructing valid reasoning, and making decisions.
- Mathematical Logic: Mathematical logic is primarily concerned with the foundations of mathematics, the study of formal languages, and the development of logical systems. It is often used as a tool to analyze and understand the structure of mathematical reasoning.
5. Subfields:
- Logic: Logic encompasses various subfields, including philosophical logic, modal logic, non-classical logic, and many others.
- Mathematical Logic: Mathematical logic is a specific subfield within logic that deals with the mathematical aspects of logical systems. It includes areas like model theory, proof theory, set theory, and computability theory.
In summary, while "logic" is a broad field that covers various forms of reasoning, "mathematical logic" is a specialized branch that applies mathematical rigor and formalism to the study of logical systems, with a particular focus on the foundations of mathematics and the development of formal languages and proofs. Mathematical logic is a subset of logic that uses mathematics as a tool to analyze and understand logic itself."
[{2023-10-08 retrieved} https://chat.openai.com/c/8b6e5141-94b7-476d-ab45-e00fab292d12]
name::
* McsEngl.mathLogic'relation-to-sciLogic,
* McsEngl.sciLogic'relation-to-mathLogic,
description::
"The evolution of logic is a complex and fascinating history that spans thousands of years. Logic is the systematic study of valid inference and reasoning, and its development has been influenced by various cultures and thinkers throughout history. Here's a brief overview of the key milestones in the evolution of logic:
1. Ancient Mesopotamia and Egypt (circa 2000-1000 BCE):
- The earliest recorded use of logic can be traced back to the ancient civilizations of Mesopotamia and Egypt. These cultures developed basic forms of reasoning and argumentation, often in the context of legal and administrative texts.
2. Ancient Greece (circa 6th-4th centuries BCE):
- The ancient Greeks made significant contributions to the development of logic. Pre-Socratic philosophers, such as Parmenides and Heraclitus, engaged in philosophical debates that involved logical reasoning.
- However, it was Aristotle (384-322 BCE) who laid the foundation for Western logic. His works, particularly "Organon," introduced formal systems of deduction, including syllogisms, which are still studied in logic today.
3. Hellenistic and Roman Periods (circa 3rd century BCE - 5th century CE):
- The Stoics, a school of Hellenistic philosophy, developed propositional logic and introduced the idea of valid inference based on conditional statements.
- Roman philosophers like Cicero furthered the study of rhetoric and argumentation, contributing to the practical use of logic in persuasion and debate.
4. Medieval Period (5th century - 15th century CE):
- Logic became an essential part of scholastic philosophy during the Middle Ages. Scholars like Boethius and Thomas Aquinas integrated Aristotelian logic into Christian theology.
- The development of modal logic, which deals with necessity and possibility, was a significant achievement during this period.
5. Renaissance and Early Modern Period (15th century - 18th century CE):
- The Renaissance witnessed a resurgence of interest in classical texts, including Aristotle's works on logic.
- In the 17th century, philosophers like René Descartes, Baruch Spinoza, and Gottfried Wilhelm Leibniz contributed to the development of formal logic, mathematical logic, and the philosophy of mind.
6. 19th and 20th Centuries:
- The 19th century saw the emergence of symbolic logic, pioneered by George Boole and Augustus De Morgan. Boole's algebraic approach laid the groundwork for Boolean logic, which is fundamental in computer science.
- In the late 19th and early 20th centuries, logicians like Bertrand Russell and Alfred North Whitehead attempted to create a comprehensive logical foundation for mathematics in their "Principia Mathematica."
- The development of predicate logic and mathematical logic, along with contributions from Kurt Gödel, Alan Turing, and others, led to significant advances in the understanding of formal systems and computability.
7. Contemporary Logic:
- Contemporary logic includes a wide range of specialized branches, including modal logic, non-classical logic (e.g., fuzzy logic and paraconsistent logic), and many-valued logic.
- Computer science and artificial intelligence have also significantly influenced the development and application of logic in areas like automated reasoning and theorem proving.
The evolution of logic continues today with ongoing research in various branches of formal and informal logic. Logic remains a fundamental tool in mathematics, philosophy, computer science, and many other fields, playing a crucial role in shaping our understanding of reasoning and inference."
[{2023-10-08 retrieved} https://chat.openai.com/c/eeb84e42-1830-4bae-a865-29bf7bcd70c8]
name::
* McsEngl.evoluting-of-sciLogic,
* McsEngl.sciLogic'evoluting,
generic-tree-of-sciLogic::
* sciMind,
* ... entity,
* McsEngl.sciLogic'generic-tree,
specific-tree-of-sciLogic::
* formal-logic,
* fuzzy-logic,
* infinite-valued-logic,
* informal-logic,
* many-valued-logic,
* McsEngl.sciLogic.specific-tree,
description::
"overview of formal-logic:
Formal logic is the study of the rules of reasoning. It is a branch of philosophy that deals with the principles of valid inference. Formal logic is abstract in that it does not deal with the content of statements, but only with their logical form. This means that formal logic can be used to evaluate the validity of arguments regardless of their subject matter.
Formal logic is based on the idea that there are certain rules that govern how we can move from one statement to another. These rules are designed to ensure that our conclusions follow logically from our premises.
There are two main types of formal logic: deductive logic and inductive logic. Deductive logic is concerned with arguments that are logically valid, meaning that if the premises are true, then the conclusion must also be true. Inductive logic is concerned with arguments that are probable, meaning that if the premises are true, then the conclusion is likely to be true, but not necessarily.
Some of the key concepts in formal logic include:
* Propositions: Statements that are either true or false.
* Arguments: Groups of propositions that are used to support or refute a claim.
* Validity: The property of an argument that guarantees that the conclusion is true if the premises are true.
* Soundness: The property of an argument that is both valid and has true premises.
* Fallacies: Errors in reasoning that can lead to false conclusions.
Formal logic is a powerful tool that can be used to improve our thinking and to identify errors in reasoning. It is a valuable skill for anyone who wants to be able to think critically and to make rational decisions.
Here are some examples of how formal logic can be used:
* To evaluate the validity of arguments: We can use formal logic to determine whether or not an argument is valid, regardless of the content of the argument. This can be useful for evaluating the arguments of politicians, advertisers, and other people who are trying to persuade us to believe something.
* To identify fallacies: Formal logic can help us to identify fallacies, which are errors in reasoning that can lead to false conclusions. This can be useful for avoiding making mistakes in our own thinking and for identifying the mistakes of others.
* To develop new theories: Formal logic can be used to develop new theories in a variety of fields, including philosophy, mathematics, and computer science. For example, formal logic is used to develop computer programs and to verify their correctness.
Formal logic is a complex and challenging subject, but it is also a very rewarding one. It can help us to think more clearly and to reason more effectively."
[{2023-10-12 retrieved} https://bard.google.com/chat/4291ca3c09aa3d45]
name::
* McsEngl.formal-logic!⇒logicFormal,
* McsEngl.logicFormal,
* McsEngl.sciLogic.formal!⇒logicFormal,
* McsEngl.sciLogicFormal!⇒logicFormal,
* McsEngl.sciLogicFormal!=formal-logic,
* McsEngl.sciMind.formal-logic!⇒logicFormal,
description::
"To define the logic it is next necessary to determine the axioms of the logic. The axioms are those sentences which are to be considered true without proof."
[{2023-10-17 retrieved} https://www.rbjones.com/rbjpub/logic/log001.htm]
description::
"Next the inference rules must be defined. Inference rules determine how new theorems can be derived from one or more previously proven theorems."
[{2023-10-17 retrieved} https://www.rbjones.com/rbjpub/logic/log001.htm]
description::
"If the language has a semantics this will usually determine a subset of the sentences of the language which are "true", these are the sentences which should be provable in the logic."
[{2023-10-17 retrieved} https://www.rbjones.com/rbjpub/logic/log001.htm]
name::
* McsEngl.logicFormal'truth,
* McsEngl.truth@logicFormal,
description::
"If all the sentences which can be proven using the logic are true then the logic is sound."
[{2023-10-17 retrieved} https://www.rbjones.com/rbjpub/logic/log001.htm]
description::
"If all the true sentences are provable then the logic is complete."
[{2023-10-17 retrieved} https://www.rbjones.com/rbjpub/logic/log001.htm]
description::
"An inference rule is a method of deriving conclusions from premises. When inference rules for a formal language are codified it becomes a formal logic."
[{2023-10-17 retrieved} https://www.rbjones.com/rbjpub/logic/log001.htm]
description::
"formal-logic vs mathematical-logic:
Formal logic is the study of the principles of reasoning. It is concerned with the rules of inference that allow us to draw valid conclusions from given premises. Mathematical logic is a branch of mathematics that uses formal logic to study mathematical concepts, arguments, and theories.
One key difference between formal logic and mathematical logic is that formal logic is more general, while mathematical logic is more specific. Formal logic can be used to analyze any type of argument, regardless of its subject matter. Mathematical logic, on the other hand, is specifically designed to analyze mathematical arguments.
Another difference between formal logic and mathematical logic is that mathematical logic is more rigorous. Formal logic arguments can be expressed in natural language, but mathematical logic arguments must be expressed in a formal language. This makes mathematical logic arguments easier to analyze and less prone to ambiguity.
Here is a table that summarizes the key differences between formal logic and mathematical logic:
Characteristic | Formal Logic | Mathematical Logic
Generality | More general | More specific
Rigor | Less rigorous | More rigorous
Language | Can be expressed in natural language | Must be expressed in a formal language
Applications | Can be used to analyze any type of argument | Specifically designed to analyze mathematical arguments
Some examples of formal logic include:
* Boolean logic
* Propositional logic
* First-order logic
* Modal logic
* Epistemic logic
Some examples of mathematical logic include:
* Set theory
* Model theory
* Proof theory *Recursion theory
Formal logic and mathematical logic are both important fields of study. Formal logic is essential for critical thinking and clear communication. Mathematical logic is essential for the development and study of mathematics.
In practice, the distinction between formal logic and mathematical logic is often blurred. Many philosophers use mathematical logic to analyze philosophical arguments, and many mathematicians use formal logic to reason about mathematical problems."
[{2023-10-12 retrieved} https://bard.google.com/chat/4fb63244d30d5caa]
name::
* McsEngl.logicFormal'relation-to-mathematical-logic,
* McsEngl.mathLogic'relation-to-formal-logic,
description::
"formal vs informal logic
Formal logic and informal logic are two branches of logic that deal with the study of reasoning and argumentation, but they differ in their focus, methods, and applications.
Formal Logic:
Formal logic is a rigorous and mathematical approach to reasoning that focuses on the structure and validity of arguments. It uses symbols and formal languages to represent propositions, logical connectives, and inference rules. Formal logic is concerned with determining whether an argument is valid based on the rules of deductive reasoning. Validity in formal logic means that the conclusion follows necessarily from the premises, regardless of the actual content of the premises or conclusion.
Key features of formal logic:
* Symbolic representation: Formal logic uses symbols to represent statements, making it abstract and precise.
* Formal languages: Logical formulas are constructed using predefined rules of syntax.
* Deductive reasoning: Formal logic is primarily concerned with deductive validity, where the conclusion is guaranteed to be true if the premises are true.
* Notable systems: Propositional logic and predicate logic (first-order logic) are common formal logical systems.
Informal Logic:
Informal logic, also known as critical thinking, is concerned with the practical evaluation of arguments found in everyday language. It focuses on understanding the content and context of arguments rather than just their structure. Informal logic is used to assess the strength of arguments in real-life situations, taking into account factors like language ambiguity, context, relevance, and the use of rhetorical devices.
Key features of informal logic:
* Natural language: Informal logic deals with arguments presented in ordinary language, rather than symbolic notation.
* Contextual analysis: It considers the context, background, and real-world implications of arguments.
* Inductive reasoning: Informal logic often deals with inductive reasoning, where the conclusion is likely but not necessarily true based on the premises.
* Practical applications: Informal logic is used to evaluate arguments in debates, advertisements, legal cases, and everyday conversations.
In summary, formal logic emphasizes the mathematical and abstract study of valid deductive reasoning using symbolic notation, while informal logic focuses on assessing the content and context of arguments presented in natural language, often involving inductive reasoning and practical applications. Both approaches are valuable for different purposes; formal logic is essential for analyzing the validity of arguments with precision, while informal logic is crucial for evaluating arguments encountered in everyday life."
[{2023-08-30 retrieved} https://chat.openai.com/?model=text-davinci-002-render-sha]
"formal vs informal logic
Formal logic and informal logic are two different approaches to the study of logic.
Formal logic is the study of the logical form of arguments. It uses a precise, abstract language to represent arguments, and it develops a set of rules for determining whether or not an argument is valid. Valid arguments are those in which the conclusion must be true if the premises are true.
Informal logic is the study of the reasoning used in everyday arguments. It does not use a precise, abstract language, and it does not develop a set of rules for determining the validity of arguments. Instead, it focuses on the identification and evaluation of common fallacies, which are errors in reasoning.
Here is a table summarizing the key differences between formal logic and informal logic:
Feature | Formal logic | Informal logic
Language | Precise, abstract language | Natural language
Rules | Set of rules for determining the validity of arguments | No set of rules
Focus | Logical form of arguments | Reasoning used in everyday arguments
Purpose | To study the logical structure of arguments | To identify and evaluate common fallacies
Here are some examples of formal logic:
All men are mortal. Socrates is a man. Therefore, Socrates is mortal.
If it is raining, then the ground is wet. It is raining. Therefore, the ground is wet.
Here are some examples of informal logic:
The Bible says that God exists, so God must exist.
You can't trust politicians. They're all liars.
Just because you're different doesn't mean you're wrong.
Formal logic is a powerful tool for analyzing arguments, but it is not always the best tool for evaluating arguments in everyday life. Informal logic is more flexible and can be used to analyze arguments that are not expressed in a precise, abstract language.
Both formal logic and informal logic are important tools for understanding and evaluating arguments. The best approach to use will depend on the specific argument being analyzed."
[{2023-08-30 retrieved} https://bard.google.com/]
name::
* McsEngl.sciLogicFormal'relation-to--informal-logic,
* McsEngl.sciLogicFormalNo'relation-to--formal-logic,
description::
"overview of symbolic-logic
Symbolic logic, also known as formal logic or mathematical logic, is a branch of philosophy and mathematics that deals with the use of symbols and formal systems to represent, analyze, and manipulate logical statements and arguments. It provides a precise and structured framework for studying and understanding the principles of reasoning. Here is an overview of symbolic logic:
1. Notational System:
- Symbolic logic uses a specialized notational system where symbols represent various logical components, such as propositions, logical operators, variables, and quantifiers.
- Common symbols include ∧ (conjunction), ∨ (disjunction), ¬ (negation), → (implication), ↔ (biconditional), ∀ (universal quantifier), and ∃ (existential quantifier).
2. Propositional Logic:
- Propositional logic deals with the relationships between simple propositions (statements that are either true or false) using logical operators.
- It focuses on logical connectives (AND, OR, NOT, etc.) and truth tables to evaluate compound propositions.
3. Predicate Logic (First-Order Logic):
- Predicate logic extends propositional logic by introducing variables, predicates, and quantifiers.
- Predicates are functions that take one or more arguments and return a truth value, while quantifiers (universal and existential) are used to express statements about sets of objects.
4. Rules of Inference:
- Symbolic logic provides a set of rules of inference that formalize the process of deducing new statements from given premises.
- Common rules include modus ponens, modus tollens, and universal instantiation.
5. Formal Proofs:
- Symbolic logic allows for the construction of formal proofs, which are step-by-step demonstrations of the validity of arguments or theorems.
- Proof theory is a branch of symbolic logic that studies formal systems for constructing valid proofs.
6. Mathematical Foundations:
- Symbolic logic is closely linked to the foundations of mathematics. It provides the tools and methods to analyze and define mathematical structures, such as sets, functions, and relations.
- Gödel's incompleteness theorems, a famous result in mathematical logic, have had a profound impact on our understanding of the limits of mathematical formalism.
7. Applications:
- Symbolic logic is applied in various fields, including philosophy, linguistics, computer science, artificial intelligence, and philosophy.
- In computer science, it is used for designing algorithms, programming languages, and formal verification of software.
8. Philosophical Significance:
- In philosophy, symbolic logic has played a crucial role in understanding and formalizing arguments and has contributed to the development of the philosophy of language and the philosophy of mathematics.
9. Non-Classical Logics:
- While classical logic is the most well-known, symbolic logic also encompasses non-classical logics, like modal logic, fuzzy logic, and many-valued logic, which have different sets of rules and semantics.
Symbolic logic provides a powerful framework for studying and understanding the principles of reasoning and has far-reaching implications across a wide range of academic disciplines and practical applications. It has greatly influenced the way we structure and analyze arguments, make decisions, and express mathematical and logical concepts."
[{2023-10-12 retrieved} https://chat.openai.com/c/f0875d4c-6e4a-4fc0-b868-d94b58d61dd3]
name::
* McsEngl.logicSymbolic,
* McsEngl.sciLogic.symbolic,
* McsEngl.symbolic-logic,
description::
"overview of informal-logic:
Informal logic, also known as non-formal logic or everyday logic, is the branch of logic that deals with arguments and reasoning outside of the strict formalism found in symbolic or mathematical logic. It focuses on the way people naturally engage in reasoning and argumentation in everyday life, rather than adhering to rigid rules and symbols.
Here is an overview of informal logic:
1. Argument Analysis: Informal logic is primarily concerned with analyzing arguments. An argument consists of a set of statements where one statement (the conclusion) is claimed to be supported by others (the premises). Informal logicians examine the structure and content of arguments to assess their validity and soundness.
2. Fallacies: One of the central aspects of informal logic is the study of fallacies. Fallacies are common errors in reasoning that can make arguments appear persuasive but are, in fact, logically flawed. Examples of fallacies include ad hominem attacks, slippery slope arguments, and appeal to emotions.
3. Critical Thinking: Informal logic promotes critical thinking skills, encouraging individuals to evaluate arguments and evidence critically. It involves recognizing biases, assumptions, and hidden premises in arguments and questioning the validity and relevance of these elements.
4. Rhetoric: Informal logic is closely related to the study of rhetoric, which focuses on persuasive communication. Rhetorical devices and strategies are often employed in everyday arguments and discussions, and informal logicians examine how these techniques can influence reasoning.
5. Language and Ambiguity: Informal logic acknowledges the importance of language and the potential for ambiguity and vagueness in communication. It deals with issues related to semantics, definitions, and linguistic clarity, as misunderstandings in language can lead to flawed reasoning.
6. Real-World Application: Informal logic is highly practical and applicable to everyday life. It is used in various contexts, including legal arguments, political debates, advertising, and critical analysis of news and media. Informal logic helps people become more discerning consumers of information and better decision-makers.
7. Informal Fallacies: Informal logic classifies fallacies into categories, such as fallacies of relevance (e.g., red herring, ad populum), fallacies of presumption (e.g., begging the question, false dilemma), and fallacies of ambiguity (e.g., equivocation, amphiboly).
8. Informal vs. Formal Logic: Informal logic differs from formal logic, which uses mathematical symbols and strict rules of inference. While formal logic is more abstract and precise, informal logic is concerned with the less structured and more natural way people engage in argumentation.
Overall, informal logic is a valuable tool for enhancing critical thinking skills, identifying errors in everyday reasoning, and engaging in more productive discussions and debates. It equips individuals with the ability to think critically, recognize fallacies, and communicate effectively in various personal and professional contexts."
[{2023-10-12 retrieved} https://chat.openai.com/c/7cdcdac2-b79e-464e-9356-12201ec61fa4]
name::
* McsEngl.informal-logic!⇒sciLogicFormalNo,
* McsEngl.sciLogic.informal!⇒sciLogicFormalNo,
* McsEngl.sciLogicFormalNo,
* McsEngl.sciLogicFormalNo!=informal-logic,
description::
"Classical logic (or standard logic[1][2] or Frege-Russell logic[3]) is the intensively studied and most widely used class of deductive logic.[4] Classical logic has had much influence on analytic philosophy.
Characteristics
* Each logical system in this class shares characteristic properties:[5]
* Law of excluded middle and double negation elimination
* Law of noncontradiction, and the principle of explosion
* Monotonicity of entailment and idempotency of entailment
* Commutativity of conjunction
* De Morgan duality: every logical operator is dual to another
While not entailed by the preceding conditions, contemporary discussions of classical logic normally only include propositional and first-order logics.[4][6] In other words, the overwhelming majority of time spent studying classical logic has been spent studying specifically propositional and first-order logic, as opposed to the other forms of classical logic.
Most semantics of classical logic are bivalent, meaning all of the possible denotations of propositions can be categorized as either true or false."
[{2023-10-12 retrieved} https://en.wikipedia.org/wiki/Classical_logic]
name::
* McsEngl.classical-logic,
* McsEngl.logicClassical,
* McsEngl.sciLogic.classical,
description::
"In classical logic, the validity of a formula depends only on its form, not on its meaning."
[{2023-10-13 retrieved} https://en.wikipedia.org/wiki/Computability_logic]
description::
"overview of non-classical-logic:
Non-classical logics are formal systems that differ in a significant way from standard logical systems such as propositional and predicate logic. There are several ways in which this is done, including by way of extensions, deviations, and variations. The aim of these departures is to make it possible to construct different models of logical consequence and logical truth.
One of the most important distinctions between classical and non-classical logics is their treatment of truth. Classical logic is based on the principle of bivalence, which states that every proposition is either true or false. Non-classical logics, on the other hand, may reject bivalence in favor of a many-valued logic, in which propositions can take on a range of truth values, or they may develop alternative notions of truth altogether.
Another important distinction is their treatment of contradiction. Classical logic adheres to the law of non-contradiction, which states that no proposition can be both true and false at the same time. Non-classical logics, on the other hand, may reject the law of non-contradiction, or they may develop alternative ways of dealing with contradiction.
There are many different kinds of non-classical logic, each with its own unique features. Some examples include:
* Modal logic: Modal logic is a family of logics that are used to reason about possibility and necessity. It is often used in philosophy and artificial intelligence.
* Intuitionistic logic: Intuitionistic logic is a logic that is based on the idea of constructive proof. It is often used in mathematics and computer science.
* Paraconsistent logic: Paraconsistent logics are logics that can tolerate contradiction. They are often used to reason about inconsistent knowledge bases.
* Many-valued logic: Many-valued logics are logics that allow for more than two truth values. They are often used to reason about uncertainty and ambiguity.
* Fuzzy logic: Fuzzy logic is a logic that is based on the idea of degrees of truth. It is often used in artificial intelligence and control systems.
Non-classical logics have a wide range of applications in philosophy, mathematics, computer science, and other fields. For example, modal logic is used to reason about the meaning of language, intuitionistic logic is used to prove mathematical theorems, paraconsistent logic is used to reason about inconsistent legal codes, many-valued logic is used to reason about uncertainty in medical diagnosis, and fuzzy logic is used to control robots.
Here are some examples of how non-classical logics can be used:
* Modal logic can be used to reason about the meaning of the sentence "It is possible that I will win the lottery." Classical logic cannot reason about possibility and necessity, so it cannot distinguish between the sentence "It is possible that I will win the lottery" and the sentence "I will win the lottery." Modal logic, on the other hand, can make this distinction.
* Intuitionistic logic can be used to prove the mathematical theorem known as the "fan theorem." The fan theorem is a theorem about the geometry of triangles. It is difficult to prove the fan theorem using classical logic, but it can be easily proven using intuitionistic logic.
* Paraconsistent logic can be used to reason about inconsistent legal codes. Legal codes often contain contradictions, but they still need to be interpreted and applied. Paraconsistent logic can be used to reason about inconsistent legal codes in a way that is consistent with the law.
* Many-valued logic can be used to reason about uncertainty in medical diagnosis. Medical diagnosis often involves reasoning about incomplete or conflicting information. Many-valued logic can be used to reason about uncertainty in medical diagnosis in a way that is more realistic than classical logic.
* Fuzzy logic can be used to control robots. Fuzzy logic can be used to control robots in a way that is more robust to noise and uncertainty than classical logic.
Non-classical logic is a rapidly developing field of research with a wide range of applications. It is an essential tool for anyone who wants to understand the nature of reasoning and the foundations of knowledge."
[{2023-10-12 retrieved} https://bard.google.com/chat/2dbfea1c0f6ded89]
name::
* McsEngl.logicClassicalNo,
* McsEngl.non-classical-logic,
* McsEngl.sciLogic.non-classical,
name::
* McsEngl.finite-value-logic,
* McsEngl.sciLogic.finite-value,
name::
* McsEngl.binary-value-logic,
* McsEngl.sciLogic.binary-value,
name::
* McsEngl.sciLogic.ternary-value,
* McsEngl.ternary-value-logic,
name::
* McsEngl.many-value-logic,
* McsEngl.sciLogic.many-value,
description::
"In logic, an infinite-valued logic (or real-valued logic or infinitely-many-valued logic) is a many-valued logic in which truth values comprise a continuous range. Traditionally, in Aristotle's logic, logic other than bivalent logic was abnormal, as the law of the excluded middle precluded more than two possible values (i.e., "true" and "false") for any proposition.[1] Modern three-valued logic (ternary logic) allows for an additional possible truth value (i.e., "undecided")[2] and is an example of finite-valued logic in which truth values are discrete, rather than continuous. Infinite-valued logic comprises continuous fuzzy logic, though fuzzy logic in some of its forms can further encompass finite-valued logic. For example, finite-valued logic can be applied in Boolean-valued modeling,[3][4] description logics,[5] and defuzzification[6][7] of fuzzy logic."
[{2023-10-13 retrieved} https://en.wikipedia.org/wiki/Infinite-valued_logic]
name::
* McsEngl.infinite-value-logic,
* McsEngl.sciLogic.infinite-value,
descriptionLong::
"overview of infinite-value-logic:
Infinite-Value Logic, also known as Multi-Valued Logic or Many-Valued Logic, is a type of mathematical and philosophical framework for dealing with logical reasoning that extends beyond the traditional binary (true/false) or ternary (true/false/unknown) logic systems. In Infinite-Value Logic, there are more than two truth values, often an infinite number of them, allowing for a more nuanced and flexible representation of information and reasoning. Here's an overview of some key aspects of Infinite-Value Logic:
1. Many Truth Values: Infinite-Value Logic goes beyond the two classical truth values of "true" and "false." It includes various intermediate truth values, which can represent degrees of truth, uncertainty, or relevance.
2. Fuzzy Logic: One of the most well-known systems of Infinite-Value Logic is fuzzy logic, developed by Lotfi Zadeh in the 1960s. Fuzzy logic allows for degrees of truth between 0 (completely false) and 1 (completely true) and is particularly useful for dealing with vague or imprecise information.
3. Applications: Infinite-Value Logic is applied in various fields, including artificial intelligence, control systems, decision-making, and linguistics. Fuzzy logic, in particular, has found applications in fields like fuzzy control systems and fuzzy expert systems.
4. Representation of Uncertainty: Infinite-Value Logic is useful for modeling and managing uncertainty in information. It can handle situations where a statement can be partially true, partially false, or somewhere in between, making it a valuable tool for real-world problem-solving.
5. Algebra and Operators: Infinite-Value Logic often employs different algebraic structures and operators compared to classical logic. It uses functions and operators to combine and manipulate truth values in a more granular way. For example, conjunction and disjunction operations can be generalizations of classical "AND" and "OR" operators.
6. Mathematical Framework: Infinite-Value Logic is a mathematical framework with formal rules and principles that allow for precise reasoning and inference. This framework can include propositional and predicate calculus adapted to handle multiple truth values.
7. Fuzzy Sets: In fuzzy logic, the concept of fuzzy sets plays a central role. A fuzzy set is a generalization of a classical set, where elements have degrees of membership, allowing for a more nuanced representation of relationships.
8. Rule-Based Systems: Many applications of Infinite-Value Logic involve rule-based systems, where rules express conditions and conclusions in a fuzzy or multi-valued logic format. These systems are particularly useful for decision support and expert systems.
9. Handling Complex Systems: Infinite-Value Logic is well-suited for dealing with complex systems where simple binary true/false decisions may not suffice. It provides a framework for managing and processing information in a more natural and adaptable manner.
Infinite-Value Logic, particularly in the form of fuzzy logic, has proven to be a valuable tool for handling uncertainty and imprecision in various real-world applications. It allows for a more nuanced and flexible approach to reasoning and decision-making, making it a key component in the field of artificial intelligence, control systems, and beyond."
[{2023-10-13 retrieved} https://chat.openai.com/c/dd611348-f01b-45b0-81eb-fdf401aa4e3d]
description::
"overview of Aristotelian-logic:
Aristotelian logic is a system of logic developed by the ancient Greek philosopher Aristotle. It is the earliest known formal system of logic, and it has had a profound influence on Western thought. Aristotelian logic is based on the syllogism, which is a three-part argument that consists of two premises and a conclusion. The premises are statements that are assumed to be true, and the conclusion is a statement that follows logically from the premises.
There are many different types of syllogisms, but they all share the same basic structure. The first premise states a relationship between two classes of things, such as "All men are mortal." The second premise states that a particular thing belongs to one of the classes, such as "Socrates is a man." The conclusion then states that the particular thing must also belong to the other class, such as "Therefore, Socrates is mortal."
Aristotelian logic also includes a number of other concepts, such as the law of non-contradiction, the law of excluded middle, and the square of opposition. The law of non-contradiction states that it is impossible for something to be both true and false at the same time. The law of excluded middle states that everything must either be true or false, there is no third option. The square of opposition is a diagram that shows the relationships between different types of propositions.
Aristotelian logic has been used for centuries to study and debate a wide range of topics, including philosophy, mathematics, and law. It is still taught in many schools and universities today, and it remains an important tool for critical thinking and reasoning.
Here is an example of a simple syllogism:
* Premise 1: All men are mortal.
* Premise 2: Socrates is a man.
* Conclusion: Therefore, Socrates is mortal.
This syllogism is valid, meaning that the conclusion follows logically from the premises. If the premises are true, then the conclusion must also be true.
Aristotelian logic is a powerful tool for reasoning and critical thinking. It can help us to identify logical fallacies, evaluate the strength of arguments, and form our own well-reasoned opinions.
Here are some of the benefits of studying Aristotelian logic:
* It can help you to think more clearly and rationally.
* It can help you to identify logical fallacies in your own thinking and in the thinking of others.
* It can help you to evaluate the strength of arguments and to form your own well-reasoned opinions.
* It can help you to communicate your ideas more effectively.
Aristotelian logic is a valuable skill for anyone who wants to be able to think critically and reason effectively."
[{2023-10-12 retrieved} https://bard.google.com/chat/67839da50bcddd4f]
name::
* McsEngl.Aristotelian-logic,
* McsEngl.logicAristotelian,
* McsEngl.sciLogic.Aristotelian,
* McsEngl.term-logic,
* McsEngl.traditional-logic,
descriptionLong::
"overview of term-logic:
Term logic, also known as traditional logic or Aristotelian logic, is a formal system of logic that was developed by the ancient Greek philosopher Aristotle. It is one of the foundational systems of classical logic and is distinct from modern predicate logic, which is more widely used in contemporary mathematics and philosophy.
Here's an overview of term logic:
1. Propositions: Term logic primarily deals with propositions, which are statements that can be either true or false. These propositions are formed by combining terms, which are words or phrases that represent objects, individuals, or concepts.
2. Categorical Syllogisms: Term logic is particularly concerned with categorical syllogisms. A categorical syllogism is a deductive argument consisting of three propositions: two premises and a conclusion. These propositions are categorized into one of four standard forms, each of which has specific rules for validity. The four forms are based on the quantity and quality of terms (universal or particular, affirmative or negative).
3. Four Categorical Propositions: In term logic, propositions are categorized into four basic forms, represented by letters:
- A propositions: Universal affirmative statements (e.g., "All humans are mortal").
- E propositions: Universal negative statements (e.g., "No birds are mammals").
- I propositions: Particular affirmative statements (e.g., "Some dogs are friendly").
- O propositions: Particular negative statements (e.g., "Some cars are not electric").
4. Square of Opposition: The square of opposition is a diagram that illustrates the relationships between the four basic categorical propositions. It shows how the truth values of one proposition relate to the truth values of other propositions (contradiction, contrariety, subcontrariety, and subalternation).
5. Syllogistic Rules: Term logic includes a set of rules and principles for determining the validity of categorical syllogisms. These rules include the laws of conversion, obversion, and contraposition, which are used to transform and evaluate syllogisms.
6. Limited Expressive Power: One of the limitations of term logic is its relatively limited expressive power compared to modern predicate logic. It is primarily designed to analyze categorical relationships, and it doesn't provide the flexibility to represent complex relationships or quantifiers used in modern mathematical and logical reasoning.
7. Historical Significance: Term logic was the dominant form of logic for centuries, with Aristotle's works serving as a foundation for Western philosophical and logical thought. It was later supplanted by predicate logic, developed by logicians like George Boole and Augustus De Morgan, which is more versatile and capable of representing more complex relationships.
Despite its historical importance, term logic is mostly of interest to historians of philosophy and classical logic. Contemporary formal logic relies more on predicate logic and its mathematical foundations for reasoning and problem-solving."
[{2023-10-14 retrieved} https://chat.openai.com/c/5b4e936e-785b-4f3a-9e6d-3d0784b7306b]
description::
"overview of deductive-logic:
Deductive logic is a system of reasoning that allows us to draw conclusions from premises that are assumed to be true. It is a top-down approach to reasoning, where we start with general statements and work toward specific conclusions.
Deductive arguments are typically represented in the following form:
* Premise 1: All humans are mortal.
* Premise 2: Socrates is a human.
* Conclusion: Therefore, Socrates is mortal.
In this example, the premises are two general statements that are assumed to be true. The conclusion is a more specific statement that is derived from the premises.
If all of the premises in a deductive argument are true, and the argument is valid (i.e., the conclusion follows logically from the premises), then the conclusion must also be true. This is known as the principle of validity.
Deductive logic is used in many different fields, including mathematics, philosophy, law, and science. It is a powerful tool for thinking critically and making sound decisions.
Here are some examples of deductive logic in everyday life:
* If it is raining, then the ground is wet. The ground is wet. Therefore, it is raining.
* If I study hard, then I will pass the exam. I studied hard. Therefore, I will pass the exam.
* All dogs are mammals. My pet is a dog. Therefore, my pet is a mammal.
Deductive logic can also be used to identify fallacies in arguments. A fallacy is an error in reasoning that can lead to false conclusions. Some common fallacies include:
* Ad hominem: Attacking the person making the argument instead of the argument itself.
* Straw man: Misrepresenting the other person's argument in order to make it easier to attack.
* Begging the question: Assuming the conclusion of the argument in the premises.
By understanding deductive logic, we can better evaluate the arguments that we encounter in everyday life and avoid being misled by fallacies.
Here are some of the types of deductive logic:
* Syllogism: A syllogism is a deductive argument that consists of two premises and a conclusion. The most common form of syllogism is the categorical syllogism, which is made up of three categorical propositions (e.g., all A are B, all C are A, therefore, all C are B).
* Modus ponens: Modus ponens is a rule of inference that allows us to derive a conclusion from two premises. The premises must be in the following form: if P, then Q; and P is true. The conclusion is then Q.
* Modus tollens: Modus tollens is another rule of inference that allows us to derive a conclusion from two premises. The premises must be in the following form: if P, then Q; and Q is false. The conclusion is then not P.
Deductive logic is a powerful tool for thinking critically and making sound decisions. By understanding the different types of deductive logic, we can better evaluate the arguments that we encounter in everyday life."
[{2023-10-13 retrieved} https://bard.google.com/chat/95347a88b1428e6f]
name::
* McsEngl.deductive-logic,
* McsEngl.logicDeductive,
* McsEngl.sciLogic.deductive,
description::
"dialectical logic overview:
Dialectical logic is a system of thought that emphasizes the interconnectedness and dynamic nature of reality. It was developed by the German philosopher Georg Wilhelm Friedrich Hegel (1770-1831) and later adapted by Karl Marx and other thinkers.
Dialectical logic is based on the following key principles:
* Change is constant. Reality is not static, but is constantly changing and evolving.
* Everything is interconnected. Nothing exists in isolation, but rather is interconnected with everything else.
* Opposites are interdependent. Opposites are not mutually exclusive, but rather are complementary and necessary for each other's existence.
* Negation of the negation. Development occurs through a process of negation, in which an existing thesis is negated by its opposite (antithesis), and this negation is then negated itself, resulting in a new synthesis that incorporates elements of both the thesis and antithesis.
Dialectical logic has been used to analyze a wide range of phenomena, including history, society, nature, and human thought. It has been particularly influential in the fields of Marxism, critical theory, and feminist theory.
Here is an example of how dialectical logic can be used to analyze a real-world phenomenon:
Thesis: Capitalism is the best economic system.
Antithesis: Capitalism is exploitative and unsustainable.
Synthesis: A new economic system that incorporates the positive aspects of capitalism (e.g., efficiency and innovation) while eliminating its negative aspects (e.g., inequality and environmental destruction).
This is just a simple example, and there are many different ways to apply dialectical logic to different situations. However, it gives a basic idea of how dialectical logic can be used to understand the complex and ever-changing world around us.
Dialectical logic vs. formal logic
Dialectical logic is often contrasted with formal logic, which is a system of thought that emphasizes the logical consistency of arguments. Formal logic is based on a set of fixed rules and principles, and it is used to evaluate the validity of arguments.
Dialectical logic, on the other hand, is more concerned with the process of developing and refining ideas. It is not bound by the same rigid rules as formal logic, and it is often used to explore new and challenging ideas.
Dialectical logic is not intended to replace formal logic, but rather to supplement it. Formal logic is essential for ensuring the logical consistency of arguments, but dialectical logic is necessary for developing and refining ideas.
Conclusion
Dialectical logic is a powerful tool for understanding the complex and ever-changing world around us. It is based on the principles that change is constant, everything is interconnected, opposites are interdependent, and development occurs through a process of negation of the negation.
Dialectical logic has been used to analyze a wide range of phenomena, including history, society, nature, and human thought. It has been particularly influential in the fields of Marxism, critical theory, and feminist theory.
Dialectical logic is often contrasted with formal logic, but it is not intended to replace it. Rather, dialectical logic is meant to supplement formal logic by providing a framework for developing and refining ideas."
[{2023-10-11 retrieved} https://bard.google.com/chat/5afbf9325369fbaf]
[{2023-10-11 retrieved} https://bard.google.com/chat/5afbf9325369fbaf]
name::
* McsEngl.dialectical-logic,
* McsEngl.sciLogic.dialectical,
descriptionLong::
"overview of dialectical logic:
Dialectical logic, also known as dialectical reasoning or dialectics, is a method of argumentation and philosophical reasoning that has its roots in ancient Greek philosophy, particularly in the works of Plato and Aristotle. It's a form of discourse that seeks to explore and understand the complexities and contradictions within a given subject or issue. Dialectical logic is often associated with the philosophical tradition of Hegelian dialectics, which was developed by Georg Wilhelm Friedrich Hegel in the 19th century. Here is an overview of dialectical logic:
1. Thesis, Antithesis, and Synthesis: The core of dialectical logic is the triadic structure of thesis, antithesis, and synthesis. The process begins with a thesis, which represents a particular point of view or idea. This thesis then encounters its opposite, the antithesis, resulting in a clash of ideas and contradictions. Through this conflict, a synthesis emerges, which is a higher-level concept that reconciles the opposing viewpoints, preserving elements of truth from both the thesis and antithesis.
2. Change and Development: Dialectical logic emphasizes change and development. It sees reality as dynamic and in a constant state of flux, with contradictions and conflicts being essential drivers of progress and growth. The synthesis of opposing ideas leads to a new thesis, creating a continuous cycle of development.
3. Negation of the Negation: Another key concept in dialectical logic is the "negation of the negation." This idea suggests that the resolution of a contradiction not only brings about a synthesis but also negates the initial contradiction itself. In other words, it implies a process of overcoming and transcending contradictions.
4. Historical and Societal Application: Hegel's dialectical method was often applied to historical and societal development. According to his philosophy, human history unfolds through a dialectical process where each stage of society and culture gives rise to its own contradictions and eventually leads to a new stage.
5. Critique and Analysis: Dialectical logic encourages critical thinking and analysis. It seeks to uncover underlying contradictions and tensions within concepts, ideas, or social structures, thereby offering a more profound understanding of complex issues.
6. Applicability: Dialectical logic has found applications in various fields, including philosophy, social sciences, and political theory. It has been used to analyze societal and historical changes, understand the evolution of ideas and philosophies, and explore contradictions in ethical and moral frameworks.
7. Criticism and Variations: Dialectical logic is not without criticism. Some argue that it can be overly abstract or prone to subjective interpretation. Additionally, various philosophers and thinkers have developed their own variations and interpretations of dialectical reasoning, adapting it to their specific philosophical or analytical frameworks.
In summary, dialectical logic is a method of philosophical reasoning that emphasizes the dynamic and evolving nature of reality, the resolution of contradictions, and the development of new ideas and concepts through a triadic process of thesis-antithesis-synthesis. It has had a significant impact on philosophy, social sciences, and various other fields of thought."
[{2023-10-11 retrieved} https://chat.openai.com/c/ccb50342-59f2-45b6-8dd1-34bd3c36c251]
description::
"Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.[1] By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.
The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Iranian Azerbaijani mathematician Lotfi Zadeh.[2][3] Fuzzy logic had, however, been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski.[4]
Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or fuzzy sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack certainty.[5][6]
Fuzzy logic has been applied to many fields, from control theory to artificial intelligence."
[{2023-08-31 retrieved} https://en.wikipedia.org/wiki/Fuzzy_logic]
name::
* McsEngl.fuzzy-logic,
* McsEngl.sciLogic.fuzzy,
* McsEngl.sciLogicFuzzy,
description::
"Modal logic is a kind of logic used to represent statements about necessity and possibility. It plays a major role in philosophy and related fields as a tool for understanding concepts such as knowledge, obligation, and causation. For instance, in epistemic modal logic, the formula ◻P can be used to represent the statement that P is known. In deontic modal logic, that same formula can represent that P is a moral obligation. Modal logic considers the inferences that modal statements give rise to. For instance, most epistemic logics treat the formula ◻P → P as a tautology, representing the principle that only true statements can count as knowledge.
Modal logics are formal systems that include unary operators such as ◊ and ◻, representing possibility and necessity respectively. For instance the modal formula ◊P can be read as "possibly P" while ◻P can be read as "necessarily P". In the standard relational semantics for modal logic, formulas are assigned truth values relative to a possible world. A formula's truth value at one possible world can depend on the truth values of other formulas at other accessible possible worlds. In particular, ◊P is true at a world if P is true at some accessible possible world, while ◻P is true at a world if P is true at every accessible possible world. A variety of proof systems exist which are sound and complete with respect to the semantics one gets by restricting the accessibility relation. For instance, the deontic modal logic D is sound and complete if one requires the accessibility relation to be serial.
While the intuition behind modal logic dates back to antiquity, the first modal axiomatic systems were developed by C. I. Lewis in 1912. The now-standard relational semantics emerged in the mid twentieth century from work by Arthur Prior, Jaakko Hintikka, and Saul Kripke. Recent developments include alternative topological semantics such as neighborhood semantics as well as applications of the relational semantics beyond its original philosophical motivation.[1] Such applications include game theory,[2] moral and legal theory,[2] web design,[2] multiverse-based set theory,[3] and social epistemology.[4]"
[{2023-09-20 retrieved} https://en.wikipedia.org/wiki/Modal_logic]
name::
* McsEngl.modal-logic,
* McsEngl.sciLogic.modal,
* McsEngl.sciLogicModal,
description::
"overview of philosophy-of-logic:
The philosophy of logic is a branch of philosophy that examines the nature, scope, and principles of logic. Logic is the study of valid reasoning and inference, and it plays a foundational role in various fields, including mathematics, science, linguistics, and philosophy itself. The philosophy of logic seeks to understand the underlying concepts and principles of logic and to address fundamental questions about the nature of reality, knowledge, language, and reasoning. Here is an overview of some key topics within the philosophy of logic:
1. **Nature of Logic:** Philosophers of logic examine what logic is, its foundations, and its relationship to other disciplines. They debate whether logic is a product of human thought or a fundamental aspect of reality.
2. **Truth and Validity:** Central to logic is the distinction between truth and validity. Philosophers explore the relationship between true statements and valid arguments, as well as the nature of truth and falsity itself.
3. **Logical Systems:** Different logical systems, such as classical logic, modal logic, and non-classical logics, are studied. Philosophers analyze the strengths and limitations of these systems and investigate their applicability in different contexts.
4. **Logical Paradoxes:** The philosophy of logic delves into paradoxes like the liar paradox, Russell's paradox, and Gödel's incompleteness theorems. These paradoxes challenge our understanding of truth, reference, and the limits of formal systems.
5. **Formal and Informal Logic:** Formal logic uses symbolic language to analyze arguments, while informal logic focuses on everyday reasoning. Philosophers discuss the relationship between these two approaches and their respective advantages and limitations.
6. **Epistemology of Logic:** This subfield explores questions related to the nature of knowledge and belief in logic. What justifies our confidence in the principles of logic, and how do we come to know logical truths?
7. **Semantics and Pragmatics:** Philosophers examine the relationship between the meaning of logical expressions and their use in natural language. This includes debates about the connection between formal logic and natural language.
8. **Philosophy of Mathematics:** Logic is closely connected to mathematics. Philosophers explore the philosophical foundations of mathematics, including debates about the nature of mathematical objects, the existence of mathematical truths, and the relationship between logic and mathematics.
9. **Philosophy of Language:** Language and logic are intertwined. This area of study investigates the role of language in logical reasoning and the relationship between language, thought, and reality.
10. **Meta-logic:** Meta-logic deals with the study of logic itself. It explores questions about the structure and properties of logical systems, the nature of logical consequence, and the criteria for evaluating logical theories.
11. **History of Logic:** The history of logic is an important aspect of the philosophy of logic. It examines the development of logical thought from ancient times to the present, including the contributions of notable philosophers like Aristotle, Leibniz, Frege, and Tarski.
In summary, the philosophy of logic is a multifaceted field that addresses fundamental questions about the nature of reasoning, truth, and the relationship between language and reality. It is an essential discipline for anyone interested in understanding the foundations of rational thought and the role of logic in various academic and practical domains."
[{2023-10-14 retrieved} https://chat.openai.com/c/06410892-7f60-418a-b9d6-554cdf52c86f]
name::
* McsEngl.logicPhilosophy,
* McsEngl.philosophy-of-logic,
* McsEngl.sciLogic.philosophy,
description::
"overview of quantum logic:
Quantum logic is a mathematical formalism that is used to describe the propositions about quantum systems. It is a non-classical logic that is based on the structure of projection operators in a Hilbert space.
Classical logic is based on the Boolean algebra, which has two truth values: true and false. Quantum logic, on the other hand, is based on the orthomodular lattice, which has a richer structure. An orthomodular lattice is a partially ordered set with two operations: conjunction and negation. However, unlike the Boolean algebra, the conjunction operation in an orthomodular lattice is not distributive over the negation operation.
This difference in structure is necessary to account for the non-classical features of quantum mechanics, such as superposition and entanglement. For example, in classical mechanics, it is always possible to simultaneously determine the position and momentum of a particle. However, in quantum mechanics, this is not possible due to the uncertainty principle.
Quantum logic can be used to reason about the relationships between different quantum observables. For example, it can be used to show that certain pairs of observables, such as position and momentum, are incompatible. This means that it is impossible to simultaneously determine the values of both observables with certainty.
Quantum logic is also used in the design and implementation of quantum computers. Quantum computers rely on the ability to manipulate quantum superpositions of states. Quantum logic can be used to verify that quantum algorithms are correct and to design efficient implementations of these algorithms.
Here is an overview of some of the key concepts in quantum logic:
* Orthomodular lattice: A partially ordered set with two operations: conjunction and negation. The conjunction operation is not distributive over the negation operation.
* Projection operator: A linear operator on a Hilbert space that projects the state of the system onto a subspace of the Hilbert space.
* Quantum observable: A physical quantity that can be measured on a quantum system. Quantum observables are represented by projection operators.
* State of a quantum system: A vector in the Hilbert space of the system.
Quantum logic can be used to reason about the relationships between different quantum observables and states. For example, it can be used to show that certain pairs of observables are incompatible, or that certain states are orthogonal. Quantum logic can also be used to define new quantum observables and to design and implement quantum algorithms.
Quantum logic is a complex and challenging subject, but it is also a very powerful one. It provides a deep understanding of the foundations of quantum mechanics and has the potential to revolutionize many fields, including computing, cryptography, and communication."
[{2023-10-12 retrieved} https://bard.google.com/chat/75be085360bdce8]
name::
* McsEngl.logicQuantum,
* McsEngl.quantum-logic!=logicQuantum,
* McsEngl.sciLogic.quantum!=logicQuantum,
description::
"A mental representation (or cognitive representation), in philosophy of mind, cognitive psychology, neuroscience, and cognitive science, is a hypothetical internal cognitive symbol that represents external reality or its abstractions.[1][2]
Mental representation is the mental imagery of things that are not actually present to the senses.[3] In contemporary philosophy, specifically in fields of metaphysics such as philosophy of mind and ontology, a mental representation is one of the prevailing ways of explaining and describing the nature of ideas and concepts.
Mental representations (or mental imagery) enable representing things that have never been experienced as well as things that do not exist.[4] Think of yourself traveling to a place you have never visited before, or having a third arm. These things have either never happened or are impossible and do not exist, yet our brain and mental imagery allows us to imagine them. Although visual imagery is more likely to be recalled, mental imagery may involve representations in any of the sensory modalities, such as hearing, smell, or taste. Stephen Kosslyn proposes that images are used to help solve certain types of problems. We are able to visualize the objects in question and mentally represent the images to solve it.[4]
Mental representations also allow people to experience things right in front of them—however, the process of how the brain interprets and stores the representational content is debated.[5]"
[{2023-09-28 retrieved} https://en.wikipedia.org/wiki/Mental_representation]
name::
* McsEngl.cognitive-representation,
* McsEngl.mental-representation,
* McsEngl.sciMind.007-mental-representation,
* McsEngl.sciMind.mental-representation,
description::
"Philosophy of mind is a branch of philosophy that studies the ontology and nature of the mind and its relationship with the body. The mind–body problem is a paradigmatic issue in philosophy of mind, although a number of other issues are addressed, such as the hard problem of consciousness and the nature of particular mental states.[1][2][3] Aspects of the mind that are studied include mental events, mental functions, mental properties, consciousness and its neural correlates, the ontology of the mind, the nature of cognition and of thought, and the relationship of the mind to the body.
Dualism and monism are the two central schools of thought on the mind–body problem, although nuanced views have arisen that do not fit one or the other category neatly.
* Dualism finds its entry into Western philosophy thanks to René Descartes in the 17th century.[4] Substance dualists like Descartes argue that the mind is an independently existing substance, whereas property dualists maintain that the mind is a group of independent properties that emerge from and cannot be reduced to the brain, but that it is not a distinct substance.[5]
* Monism is the position that mind and body are ontologically indiscernible entities, not dependent substances. This view was espoused by the 17th-century rationalist Baruch Spinoza.[6] Physicalists argue that only entities postulated by physical theory exist, and that mental processes will eventually be explained in terms of these entities as physical theory continues to evolve. Physicalists maintain various positions on the prospects of reducing mental properties to physical properties (many of whom adopt compatible forms of property dualism),[7][8][9][10][11][12] and the ontological status of such mental properties remains unclear.[11][13][14] Idealists maintain that the mind is all that exists and that the external world is either mental itself, or an illusion created by the mind. Neutral monists such as Ernst Mach and William James argue that events in the world can be thought of as either mental (psychological) or physical depending on the network of relationships into which they enter, and dual-aspect monists such as Spinoza adhere to the position that there is some other, neutral substance, and that both matter and mind are properties of this unknown substance. The most common monisms in the 20th and 21st centuries have all been variations of physicalism; these positions include behaviorism, the type identity theory, anomalous monism and functionalism.[15]
Most modern philosophers of mind adopt either a reductive physicalist or non-reductive physicalist position, maintaining in their different ways that the mind is not something separate from the body.[15] These approaches have been particularly influential in the sciences, especially in the fields of sociobiology, computer science (specifically, artificial intelligence), evolutionary psychology and the various neurosciences.[16][17][18][19] Reductive physicalists assert that all mental states and properties will eventually be explained by scientific accounts of physiological processes and states.[20][21][22] Non-reductive physicalists argue that although the mind is not a separate substance, mental properties supervene on physical properties, or that the predicates and vocabulary used in mental descriptions and explanations are indispensable, and cannot be reduced to the language and lower-level explanations of physical science.[23][24] Continued neuroscientific progress has helped to clarify some of these issues; however, they are far from being resolved. Modern philosophers of mind continue to ask how the subjective qualities and the intentionality of mental states and properties can be explained in naturalistic terms.[25][26]
However, a number of issues have been recognized with non-reductive physicalism. First, it is irreconcilable with self-identity over time. Secondly, intentional states of consciousness do not make sense on non-reductive physicalism. Thirdly, free will is impossible to reconcile with either reductive or non-reductive physicalism. Fourthly, it fails to properly explain the phenomenon of mental causation.[27]"
[{2023-09-28 retrieved} https://en.wikipedia.org/wiki/Philosophy_of_mind]
name::
* McsEngl.philosophy-of-mind,
* McsEngl.sciMind.006-philosophy-of-mind,
* McsEngl.sciMind.philosophy-of-mind,
description::
"Semiotics (also called semiotic studies) is the systematic study of sign processes (semiosis) and meaning-making. Semiosis is any activity, conduct, or process that involves signs, where a sign is defined as anything that communicates something, usually called a meaning, to the sign's interpreter. The meaning can be intentional, such as a word uttered with a specific meaning; or unintentional, such as a symptom being a sign of a particular medical condition. Signs can also communicate feelings (which are usually not considered meanings) and may communicate internally (through thought itself) or through any of the senses: visual, auditory, tactile, olfactory, or gustatory (taste). Contemporary semiotics is a branch of science that studies meaning-making and various types of knowledge.[1]
The semiotic tradition explores the study of signs and symbols as a significant part of communications. Unlike linguistics, semiotics also studies non-linguistic sign systems. Semiotics includes the study of signs and sign processes, indication, designation, likeness, analogy, allegory, metonymy, metaphor, symbolism, signification, and communication.
Semiotics is frequently seen as having important anthropological and sociological dimensions; for example the Italian semiotician and novelist Umberto Eco proposed that every cultural phenomenon may be studied as communication.[2] Some semioticians focus on the logical dimensions of the science, however. They examine areas also belonging to the life sciences—such as how organisms make predictions about, and adapt to, their semiotic niche in the world (see semiosis). Fundamental semiotic theories take signs or sign systems as their object of study; applied semiotics analyzes cultures and cultural artifacts according to the ways they construct meaning through their being signs. The communication of information in living organisms is covered in biosemiotics (including zoosemiotics and phytosemiotics).
Semiotics is not to be confused with the Saussurean tradition called semiology, which is a subset of semiotics.[3][4]"
[{2023-08-30 retrieved} https://en.wikipedia.org/wiki/Semiotics]
name::
* McsEngl.sciMind.004-semiotics!⇒sciSemiotics,
* McsEngl.sciMind.semiotics!⇒sciSemiotics,
* McsEngl.sciSemiotics,
* McsEngl.semiology!⇒sciSemiotics,
* McsEngl.semiotic-studies!⇒sciSemiotics,
* McsEngl.semiotics!⇒sciSemiotics,
description::
"A SIGN is a material object (phenomenon, event) used to take the place of some other object, property or relation and is employed to obtain, store, process and transmit communications (information, knowledge)"
[{1989} Getmanova, Logic, p25]
In semiotics, a sign is "something that stands for something else, to someone in some capacity". (Marcel Danesi and Paul Perron, "Analyzing Cultures".) It may be understood as a discrete unit of meaning, and includes words, images, gestures, scents, tastes, textures, sounds — essentially all of the ways in which information can be communicated as a message by any sentient, reasoning mind to another.
[http://en.wikipedia.org/wiki/Sign_%28semiotics%29]
name::
* McsEngl.sciSemiotics'sign,
* McsEngl.sign//sciSemiotics,
description::
"semantics vs semiotics
Semantics and semiotics are both branches of linguistics that study meaning. However, they do so in different ways.
Semantics is the study of the meaning of words and phrases in a language. It is concerned with the relationship between linguistic expressions and their referents, or the things they refer to. For example, the word "dog" refers to a four-legged mammal that barks.
Semiotics is the study of signs and symbols. It is a broader field than semantics, and it includes the study of meaning in all kinds of communication systems, including language, art, music, and gesture. A sign is anything that stands for something else. For example, a traffic light is a sign that tells us whether to stop or go.
In other words, semantics is the study of meaning in language, while semiotics is the study of meaning in all kinds of communication systems.
Here is a table that summarizes the key differences between semantics and semiotics:
Semantics | Semiotics
The study of meaning in language | The study of signs and symbols
Concerned with the relationship between linguistic expressions and their referents | Concerned with the relationship between signs and their signifieds
A narrower field of study | A broader field of study
Focuses on language | Can be applied to any communication system
Here are some examples of how semiotics can be used to study meaning in different communication systems:
* In art, semiotics can be used to analyze the meaning of paintings, sculptures, and other works of art. For example, a semiotician might analyze the symbols used in a painting to understand the artist's message.
* In music, semiotics can be used to analyze the meaning of songs, operas, and other musical pieces. For example, a semiotician might analyze the lyrics of a song to understand the singer's message.
* In gesture, semiotics can be used to analyze the meaning of body language. For example, a semiotician might analyze the way people nod their heads to understand what they are trying to communicate.
Semiotics is a complex and interdisciplinary field of study that has applications in many different areas. It is a valuable tool for understanding how meaning is created and conveyed in different communication systems."
[{2023-08-30 retrieved} https://bard.google.com/]
name::
* McsEngl.sciSemantics'relation-to-semiotics,
* McsEngl.sciSemiotics'relation-to-sematics,
description::
Chandler.Daniel, Semiotics: The Basics, Routledge, London, UK, 2002. ISBN 0-415-36375-6
Jakobson.Roman, "Language in Relation to Other Communication Systems", pp. 570-579 in Selected Writings, Volume 2, Mouton, The Hague, 1971. ISBN 9-027-93178-X
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