Cognition

Empiricism John Locke, a leading philosopher of British empiricism Empiricism is a theory which states that knowledge comes only or primarily from sensory experience.[1] One of several views of epistemology, the study of human knowledge, along with rationalism and skepticism, empiricism emphasizes the role of experience and evidence, especially sensory experience, in the formation of ideas, over the notion of innate ideas or traditions;[2] empiricists may argue however that traditions (or customs) arise due to relations of previous sense experiences.[3] Empiricism, often used by natural scientists, says that "knowledge is based on experience" and that "knowledge is tentative and probabilistic, subject to continued revision and falsification. Etymology[edit] The English term "empirical" derives from the Greek word ἐμπειρία, which is cognate with and translates to the Latin experientia, from which we derive the word "experience" and the related "experiment". History[edit] Background[edit] See also[edit]

Cognitive bias Systematic pattern of deviation from norm or rationality in judgment A continually evolving list of cognitive biases has been identified over the last six decades of research on human judgment and decision-making in cognitive science, social psychology, and behavioral economics. The study of cognitive biases has practical implications for areas including clinical judgment, entrepreneurship, finance, and management.[10][11] The notion of cognitive biases was introduced by Amos Tversky and Daniel Kahneman in 1972[12] and grew out of their experience of people's innumeracy, or inability to reason intuitively with the greater orders of magnitude. The "Linda Problem" illustrates the representativeness heuristic (Tversky & Kahneman, 1983[14]). Critics of Kahneman and Tversky, such as Gerd Gigerenzer, alternatively argued that heuristics should not lead us to conceive of human thinking as riddled with irrational cognitive biases. Biases can be distinguished on a number of dimensions. [edit]

Artificial intelligence AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. History[edit] Research[edit] Goals[edit] Planning[edit] Logic-based

Knowledge Knowledge acquisition involves complex cognitive processes: perception, communication, and reasoning; while knowledge is also said to be related to the capacity of acknowledgment in human beings.[2] Theories of knowledge[edit] In contrast to this approach, Wittgenstein observed, following Moore's paradox, that one can say "He believes it, but it isn't so," but not "He knows it, but it isn't so." [5] He goes on to argue that these do not correspond to distinct mental states, but rather to distinct ways of talking about conviction. What is different here is not the mental state of the speaker, but the activity in which they are engaged. For example, on this account, to know that the kettle is boiling is not to be in a particular state of mind, but to perform a particular task with the statement that the kettle is boiling. Wittgenstein sought to bypass the difficulty of definition by looking to the way "knowledge" is used in natural languages. Communicating knowledge[edit]

Color Psychology by David Johnson Like death and taxes, there is no escaping color. It is ubiquitous. Yet what does it all mean? Why are people more relaxed in green rooms? Colors often have different meanings in various cultures. Black Black is the color of authority and power. White Brides wear white to symbolize innocence and purity. Red The most emotionally intense color, red stimulates a faster heartbeat and breathing. The most romantic color, pink, is more tranquilizing. Blue The color of the sky and the ocean, blue is one of the most popular colors. Green Currently the most popular decorating color, green symbolizes nature. Yellow Cheerful sunny yellow is an attention getter. Purple The color of royalty, purple connotes luxury, wealth, and sophistication. Brown Solid, reliable brown is the color of earth and is abundant in nature. Colors of the Flag In the U.S. flag, white stands for purity and innocence. Food for Thought While blue is one of the most popular colors it is one of the least appetizing.

The Birth of Behavioral Psychology - Author: Dave Grossman "Behavioral Psychology" The Birth of Behavioral Psychology Around the turn of the century, Edward Thorndike attempted to develop an objective experimental method for testing the mechanical problem solving ability of cats and dogs. Thorndike devised a number of wooden crates which required various combinations of latches, levers, strings, and treadles to open them. Thorndike's initial aim was to show that the anecdotal achievement of cats and dogs could be replicated in controlled, standardized circumstances. Thorndike was particularly interested in discovering whether his animals could learn their tasks through imitation or observation. By 1910 Thorndike had formalized this notion into the "Law of Effect," which essentially states that responses that are accompanied or followed by satisfaction (i.e., a reward, or what was later to be termed a reinforcement) will be more likely to reoccur, and those which are accompanied by discomfort (i.e., a punishment) will be less likely to reoccur.

Neuroscience, free will and determinism: 'I'm just a machine' What does this mean in terms of free will? "We don't have free will, in the spiritual sense. What you're seeing is the last output stage of a machine. There are lots of things that happen before this stage – plans, goals, learning – and those are the reasons we do more interesting things than just waggle fingers. The conclusions are shocking: if we are part of the universe, and obey its laws, it's hard to see where free will comes into it. "If you see a light go green, it may mean press the accelerator; but there are lots of situations where it doesn't mean that: if the car in front hasn't moved, for example. Slowly, however, we are learning more about the details of that complexity. "What happens if someone commits a crime, and it turns out that there's a lesion in that brain area? This runs shockingly contrary to the sense of freedom that we feel in terms of controlling our actions, on which we base our whole sense of self and system of morality. Prof Haggard is dismissive.

Semantic Web The Semantic Web is a collaborative movement led by international standards body the World Wide Web Consortium (W3C).[1] The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a "web of data". The Semantic Web stack builds on the W3C's Resource Description Framework (RDF).[2] According to the W3C, "The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries".[2] The term was coined by Tim Berners-Lee for a web of data that can be processed by machines.[3] While its critics have questioned its feasibility, proponents argue that applications in industry, biology and human sciences research have already proven the validity of the original concept. History[edit] Purpose[edit] Limitations of HTML[edit] Semantic Web solutions[edit]

Belief Mental state of holding a proposition or premise to be true Beliefs are the subject of various important philosophical debates. Notable examples include: "What is the rational way to revise one's beliefs when presented with various sorts of evidence?" Various conceptions of the essential features of beliefs have been proposed, but there is no consensus as to which is the right one. Representationalism [edit] Representationalism characterizes beliefs in terms of mental representations. There are different ways of conceiving how mental representations are realized in the mind. Interpretationism can be combined with eliminativism and instrumentalism about beliefs. Biologist Lewis Wolpert discusses the importance of causal beliefs and associates the making and use of tools with the origin of human beliefs.[21] Beliefs can be categorized into various types depending on their ontological status, their degree, their object or their semantic properties. Occurrent and dispositional Rules.

The Street Work of Banksy: British Graffiti Artist | Design Cookbook Banksy is the pseudonym of a British graffiti artist, political activist and painter, whose identity is unconfirmed. Using a unique stenciling technique Banksy’s artistic works of political and social commentary have been featured on streets, walls, and bridges of cities throughout the world. Here we’ll show you some of the best work from this international street artist Banksy. Banksy.co.ukOnline Shop Sponsored Links © 2011 Design Cookbook. Behaviorism Behaviorism (or behaviourism), is the science of behavior that focuses on observable behavior only,[1] it is also an approach to psychology that combines elements of philosophy, methodology, and theory.[2] It emerged in the early twentieth century as a reaction to "mentalistic" psychology, which often had difficulty making predictions that could be tested using rigorous experimental methods. The primary tenet of behaviorism, as expressed in the writings of John B. Watson, B. From early psychology in the 19th century, the behaviorist school of thought ran concurrently and shared commonalities with the psychoanalytic and Gestalt movements in psychology into the 20th century; but also differed from the mental philosophy of the Gestalt psychologists in critical ways.[5] Its main influences were Ivan Pavlov, who investigated classical conditioning although he did not necessarily agree with behaviorism or behaviorists, Edward Lee Thorndike, John B. Versions[edit] Two subtypes are:

Embodied Cognition  Embodied Cognition is a growing research program in cognitive science that emphasizes the formative role the environment plays in the development of cognitive processes. The general theory contends that cognitive processes develop when a tightly coupled system emerges from real-time, goal-directed interactions between organisms and their environment; the nature of these interactions influences the formation and further specifies the nature of the developing cognitive capacities. Since embodied accounts of cognition have been formulated in a variety of different ways in each of the sub-fields comprising cognitive science (that is, developmental psychology, artificial life/robotics, linguistics, and philosophy of mind), a rich interdisciplinary research program continues to emerge. Table of Contents 1. 2. To say that cognition is embodied means that it arises from bodily interactions with the world. a. i. Hannah’s problem was different from Gabriel’s, but it was also the same. ii.

Business semantics management Business Semantics Management [1] [2] (BSM) encompasses the technology, methodology, organization, and culture that brings business stakeholders together to collaboratively realize the reconciliation of their heterogeneous metadata; and consequently the application of the derived business semantics patterns to establish semantic alignment between the underlying data structures. Accordingly, BSM is established by two complementary cycles each grouping a number of activities. The first cycle is the semantic reconciliation cycle, and the second cycle is the semantic application cycle. Semantic Reconciliation[edit] Semantic reconciliation is a process cycle constituted of four subsequent activities: scope, create, refine, and articulate. Pragmatic Perspective Unification[edit] During unification, a new proposal for the next version of the upper common ontology is produced, aligning relevant parts from the common and divergent stakeholder perspectives. Semantic Application[edit] Application[edit]