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ἐπιστήμη ( Episteme ), personification of knowledge in the Celsus Library in Ephesus , Turkey

As knowledge is an available for individuals or groups of constituents is usually facts , theories and rules understood that through the highest possible degree of certainty distinguished so that their validity or truth is understood. Knowledge or its storage is considered a cultural asset .

Paradoxically , descriptions of facts declared as knowledge can be true or false, complete or incomplete. In epistemology , knowledge is traditionally defined as a true and justified view ( English justified true belief ), the problems of this determination are discussed up to the present. Since, in the direct knowledge of the world, the present facts are filtered and interpreted by the biological perception apparatus , it is a challenge to a theory of knowledge whether and how the reproduction of reality can be more than a hypothetical model .

In constructivist and falsificationist approaches, individual facts can only be considered reliable knowledge relative to others, with which they represent the world for the cognizant , but the question of the ultimate justification can always be asked . Individual modern positions, such as pragmatism or evolutionary epistemology, replace this justification with validation in a social context or with evolutionary suitability: In pragmatism, knowledge is recognized by a reference group as knowledge that enables the individual and group interests to be pursued successfully, in the evolutionary Epistemology, the criteria for knowledge are biologically pre-programmed and are subject to mutation and selection.


The expression 'knowledge' comes from the Old High German wiȥȥan or the Indo-European perfect form * woida , I have seen ', thus also' I know '. From the Indo-European root * u̯e (i) d (behold, see) or * graze , the Latin videre , see 'and Sanskrit veda , knowledge' are derived.


The definition as true and justified opinion enables the distinction between the concept of knowledge and related concepts such as conviction , belief and general opinion . It also largely corresponds to the everyday understanding of knowledge as “having knowledge of something”. Nevertheless, there is no agreement in philosophy about the correct definition of the concept of knowledge. Mostly it is assumed that “true, justified opinion” is not sufficient for knowledge. In addition, an alternative language usage has established itself in which knowledge is understood as networked information . According to this definition, information becomes knowledge content if it is placed in a context that enables an appropriate use of information. A corresponding use of the term has established itself not only in computer science , but also in psychology , education and the social sciences .

As a fundamental epistemological term, knowledge is at the center of numerous philosophical debates. In the context of the philosophical concept analysis , the question of the exact definition of the concept of knowledge is asked. In addition, philosophy addresses the question of how and to what extent people can obtain knowledge. It is also discussed to what extent the possibilities for knowledge in individual subject areas are limited. The skepticism doubts the human capacity for knowledge completely or partially.

Finally, an important theme of 20th century philosophy is the social character of knowledge. It is pointed out that people only acquire knowledge in social and historical contexts. Among other things, this raises the question of whether a given content of knowledge is always to be understood as an expression of a certain cultural context, or whether knowledge is fundamentally linked to a cross-cultural claim to validity.

In empirical research, knowledge is equally a topic in the natural and social sciences. Psychology examines the way in which knowledge is stored and networked in people. In the last few decades, this research has been supplemented by approaches from cognitive neuroscience , which describe information processing at the brain level . The subject of knowledge representation also plays a central role in artificial intelligence , with the aim being to make various forms of knowledge available in an effective way in artificial systems. In education and the social sciences, research is carried out into how knowledge is conveyed, acquired and made available. It is discussed on the level of learning psychology how individuals get to new knowledge and how knowledge can be conveyed in a meaningful way. In a broader context, the questions of what meanings different forms of knowledge have in a society and how access to knowledge is regulated socially, culturally and economically are discussed.

Philosophical concept analysis

The analysis of our concept of knowledge is one of the central problems of today's epistemology. Already Plato discussed Theatetus various attempts of a definition of knowledge. Such an analysis only became a central theme with the advent of analytical philosophy , according to which the analysis of our language is the core area of ​​philosophy.

Know-how and know-that

A common distinction going back to Gilbert Ryle separates so-called knowledge-how (or also “practical knowledge”) from knowledge-that (or “ propositional knowledge”). Ryle understands knowledge-how to be an ability or disposition, such as the ability to ride a bicycle or play the piano. Linguistically, we express such knowledge in sentences like “Tina knows how to ride a bicycle” or “Paul knows how to play the piano”. Such knowledge usually does not relate to facts and often cannot easily be represented in language. For example, a virtuoso pianist cannot convey his knowledge-how to a layperson by mere explanation. Ryle himself opposes the “intellectualistic” view that knowledge-how can ultimately be reduced to a (possibly complex) set of known propositions. This thesis continues to be discussed in epistemology.

In contrast to knowing-how, knowing-that relates directly to propositions, i.e. to statements that can be expressed verbally. For example, we talk about knowledge - that in sentences like “Ilse knows that whales are mammals” or “Frank knows that there is no highest prime number.” However, the known proposition does not always have to be directly embedded in the attribution of knowledge. Sentences like "Lisa knows how many planets the solar system has" or "Karl knows what Sarah gets for Christmas" express knowledge-that, namely because there is a proposition that Lisa or Karl alludes to. Knowledge-that relates to facts, which is why the epistemological debates about skepticism, for example, are usually limited to knowledge-that.

Definitions of knowledge

According to a thesis held in analytical philosophy , propositional knowledge is a true, justified belief. The sentence “S knows p” is then true if (1) p is true (S can only consider false things to be true, but then he is wrong) (truth condition ); (2) S is convinced that p is true (belief condition); (3) S can give a reason / a justification for the fact that p is true (justification condition).

To what extent this analysis was discussed by Plato is controversial. In the Theaetetos , among other things, the thesis is raised that knowledge is true opinion with understanding, but this is then rejected.

See also Gettier problem / historical classification

The idea of ​​definition

First of all, you can only know something if you have a corresponding opinion: The sentence "I know that it is raining, but I do not think that it is raining" would be a contradiction in terms. However, an opinion is not enough for knowledge. For example, one can have wrong opinions, but not wrong knowledge. So knowledge can only exist if one has a true opinion. However, not every true opinion is knowledge. Thus, a person can have a true opinion about the next lottery numbers, but they can hardly know what the next lottery numbers will be.

Many philosophers now argue that a true opinion must be justified if it is to represent knowledge. For example, one can have knowledge of lottery numbers that have already been drawn, but justifications are possible here. In the case of future lottery numbers, this is not possible, which is why even a true opinion cannot represent knowledge here. Such a definition of knowledge also allows a distinction to be made between “knowing” and “just thinking” or “believing”.

The Gettier problem

In 1963 the American philosopher Edmund Gettier published an essay in which he claimed to show that even a true, justified opinion does not always represent knowledge. In the Gettier problem, situations are designed in which there is true, justified opinions but no knowledge. Among other things, Gettier discusses the following case: Suppose Smith and Jones applied for a job. Smith has the justified opinion that Jones will get the job because the employer has made such suggestions. Smith also has the justified opinion that Jones has ten coins in his pocket. From these two justified opinions follows the equally justified opinion:

(1) The man who will get the job has ten coins in his pocket.

Now, however, Smith and not Jones gets the job - without Smith knowing. In addition, without realizing it, Smith also has ten coins in his pocket. So not only does Smith have the justified opinion that (1) is true, the proposition is actually true. So Smith has a true, justifiable opinion that (1) is true. However, of course, he does not know that (1) is true, because he has no idea how many coins are in his own pocket.

This example seems quite constructed, but it is only about the basic point that situations can be thought in which a true, justified opinion does not represent knowledge. This is enough to show that “knowledge” cannot be defined accordingly.

The "Gettier Debate"

An extensive debate followed Gettier's essay. It was generally accepted that the Gettier problem shows that knowledge cannot be defined as true, justified opinion . However, it remained controversial how to deal with the problem raised by Gettier. David Armstrong argued, for example, that one only has to add a fourth condition in order to arrive at a definition of knowledge. He suggested that true, justified opinions should count as knowledge only if the opinion itself is not derived from false assumptions. In the example discussed by Gettier, one would not speak of knowledge because Smith's opinion was based on the false assumption that Jones would get the job. In the 1960s and 1970s, numerous similar proposals were made about a fourth condition of knowledge.

Numerous other counterexamples against proposals for the definition of knowledge were also put forward within this debate. A particularly well-known thought experiment comes from Alvin Goldman: Imagine a region in which the residents set up deceptively real barn dummies on the roadside, so that visitors passing through, similar to Potemkin villages, have the impression of seeing real barn doors. Now assume that a visitor stops in front of the region's only real barn by chance. This visitor has the opinion that he is in front of a barn. This opinion is also true and justified by the visual impression. Still, you wouldn't want to say he knows he's in front of a real barn. It was just by accident that he didn't land in front of one of the countless dummies. This example is, among other things, a problem for Armstrong's definition, since the visitor does not seem to base his opinion on false assumptions, and therefore Armstrong's definition would also attribute knowledge to him.

Goldman himself wanted to support an alternative approach with this example: he advocated the thesis that the justification condition must be replaced by a causal reliability condition. It does not matter that a person can rationally justify their opinion, rather the true opinion must be caused in a reliable way. This is not the case in the barn example mentioned above: the visitor can only recognize barns unreliably, since in the current environment he would often be deceived by barn dummy. Goldman's theory fits into a number of approaches which, in various ways, require a reliable method. These approaches are known as reliabilistic . A key problem for these approaches is the so-called “generality problem”: It is possible that the same person has a reliable method on a general level, but on a more specific level it is unreliable. For example, in the barn example, the visitor still has reliable perception, but his barn perception is very unreliable. On an even more specific level, namely the perception of the very specific barn in front of which the visitor is, his perception would again be reliable. If a representative of reliability now wants to commit to a level of generality, on the one hand the problem arises of precisely defining this level, on the other hand there is a threat of further counter-examples in which another level represents the more natural perspective.

Is knowledge definable?

The problems presented result from the claim to give an exact definition, consequently a single, constructed counterexample is enough to refute a definition. In view of this situation, one can ask oneself whether a definition of “knowledge” is necessary or even possible. In terms of Ludwig Wittgenstein 's late philosophy, one can argue, for example, that “knowledge” is an everyday language without sharp boundaries and that the various uses of “knowledge” are only held together by family similarities. Such an analysis would exclude a general definition of “knowledge”, but would not have to lead to a problematization of the concept of knowledge. One would only have to give up the idea of ​​being able to define “knowledge” exactly.

Timothy Williamson's thesis that knowledge cannot be explained with the help of other terms, but should rather be viewed as the starting point for other epistemological efforts is particularly influential . This Williamson thesis is the core of the currently widespread “Knowledge First” theory of knowledge. But even outside of this trend, the attempt to define knowledge is increasingly being rejected, for example by Ansgar Beckermann , who suggests truth as a better target concept for epistemology.

Semantics and pragmatics of knowledge ascriptions

In the more recent epistemological debate, attempts at definition have faded into the background. Instead, it is discussed in detail how the semantics and pragmatics of sentences of the form "S knows that P" (so-called knowledge attributions) interact and what influence the context exerts. The following table sets out the five main positions in this debate:

position Importance of knowledge attribution Key representatives
Contextualism Depends on the context of the expression of the attribution of knowledge Fred Dretske , Robert Nozick , Stewart Cohen , Keith DeRose , David Kellogg Lewis
Subject-Sensitive Invariantism Depends on the context of the subject (the "knower") Jeremy Fantl , Matthew MacGrath , Jason Stanley
relativism Depends on the context of the observation John MacFarlane
Infallibilistic Pragmatic Invariantism Requires absolute certainty Peter K. Unger , Jonathan Schaffer
Fallibilistic Pragmatic Invariantism Requires less than absolute certainty Patrick Rysiew

From the perspective of contextualism, the semantic truth depends directly on certain properties of the context in which the knowledge ascription was made. For example, a court hearing would produce higher standards of knowledge than a bar talk. In contrast, subject-sensitive invariantists are of the opinion that only the context of the subject about whom the knowledge ascription is concerned influences the truth of this knowledge ascription. Relativists, on the other hand, believe that the truth of these attributions of knowledge depends on the context in which they are viewed.

All three positions mentioned have in common that they allow the context to influence the semantics. In contrast, pragmatic invariantists reject such influence. They argue that only because of pragmatic effects the impression arises that the truth conditions of knowledge ascriptions are subject to fluctuations. A distinction is commonly made between fallibilistic and infallibilistic forms of this position. Infallibilists are of the opinion that knowledge requires absolute certainty. As a result, many knowledge attributions turn out to be semantically incorrect, which is why this position is also referred to as skeptical. On the other hand, falibilists take the view that the truth conditions of knowledge ascriptions are less strict. This avoids skepticism, but completely different pragmatic effects must be asserted.

Forms of knowledge

Knowledge encompasses a large number of different phenomena, which is why classifications have been established that differentiate between different forms of knowledge. Such classifications can be made on the basis of numerous criteria: For example, knowledge can relate to different subject areas, it can be associated with different degrees of certainty , and it can be acquired, justified and presented in different ways or be available in different ways.

Exact and empirical knowledge

Exact knowledge

Edmund Husserl defines "the mathematical form of treatment in all strictly developed theories (in the true sense of the word) as the only scientific one, the only one that offers systematic cohesion and perfection, which offers insight into all possible questions and the possible forms of their solution", while "mathematics is the science whose only object is the structure of the human mind itself". David Hilbert specifies: "Everything that can be an object of scientific thought, as soon as it is ripe for the formation of a theory, falls under the axiomatic method and thus indirectly to mathematics". According to the definition, a theory can be axiomatized if it can be represented in a recursively enumerable language.

However, as Gödel's incompleteness theorem has shown, this term is too broad here. For in this case neither the completeness nor the fundamental condition of the consistency of the axiom system is assured. For this it is necessary and sufficient that the theory is formulated in a recursive language or essentially equivalent to a Chomsky-1 grammar (recursive languages ​​are a little more general than Chomsky-1 languages; such extensions are based on diagonalization and have no further ones practical significance). But understanding is a further condition necessary for exact knowledge. For example, the idea of ​​the “ether” in electrodynamics blocked the correct interpretation of the experiments for years (e.g. Lorentz's contraction). The situation in quantum mechanics was particularly dramatic. Max Born , Werner Heisenberg and Pascual Jordan had worked out a basic theory in their “three-man work”, which explained all observable phenomena without contradiction, but the results could not be interpreted.

Thus it is necessary and sufficient for exact knowledge:

1. The knowledge of a structure that is based on a complete and consistent system of axioms or is formulated in a recursive language or practically equivalent in a Chomsky-1 grammar.

2. Understanding the theory.

Both conditions can only be met by mathematics and theoretical physics. And for physics, too, Heisenberg mentions only four complete theories: classical mechanics , electrodynamics in combination with the special theory of relativity , the statistical theory of heat and quantum mechanics .

Empirical knowledge

While theoretical knowledge goes back to Plato , empirical knowledge has its foundation in Aristotle . This is based on the following postulates: 1. There is an outside world independent of the subject. Or the objects are at least independent of the examining subject. 2. The data required for the theory are obtained through the five senses. 3. Every result of the theory has to be justified by experience.

For the procedure of knowledge, Galileo Galilei then identified the following moments as ideal:

1. The hypothesis regarding an experience content.

2. The review of the same.

For the test, Mendel recognized statistics as the basic set of instruments, which was what enabled empiricism to develop into a science in the first place.

It was already clear to Aristotle that no reliable knowledge could be achieved in this way. Arkesilaos and Karneades classified empirical knowledge as 1. Believable, 2. Believable and undisputed, and 3. Believable, undisputed and universally tested knowledge. In the modern era, Hume then exposed the problem in all sharpness and showed that any consistent empiricism leads to total skepticism. Also, the logical positivism and critical rationalism had to oppose this "Hume's challenge" nothing, "We do not know, we advise" ( Karl Popper ).

René Descartes , the founder of modern rationalism , already strictly separated empiricism, the res extensa , from the spiritual, the res cogitans . In fact, as Hume has shown, logical principles cannot be justified empirically. Conversely, empirical statements cannot be logically justified either, the "Cartesian juxtaposition of a res cogitans, the human being, and a res extensa, the world around him, is incurable". In fact, "it is strictly demonstrable that, from the standpoint of purely theoretical reflection, matter and form of knowledge, a priori law of reason and empirical givens never merge, but instead keep falling apart as we progress". “Why can we use mathematics to describe nature without describing the mechanism behind it? Nobody knows".

The extent to which empirical knowledge can be assigned to one of the classifications of Arkesilaos and Karneades or, in modern terms, what degree of probability this knowledge may claim, will therefore crucially depend on the following criteria:

1. To what extent does the theory on which the hypothesis is based fulfills the condition of exactness? Is the hypothesis even formulated in this theory?

2. To what extent does the test method used meet the statistical requirements? Next: To what extent are the conditions of objectivity , reliability and validity fulfilled here?

It is clear that according to the current state of knowledge, only the natural sciences meet these conditions.

Differentiation according to the origin of knowledge

Noam Chomsky (2004)

Other classification systems divide knowledge not according to the form of availability, but according to the origin of the knowledge. The distinction between congenital and acquired knowledge is by Noam Chomsky become theory of innate linguistic knowledge to a central theme of cognitive science research. Chomsky argues that children's language acquisition can only be explained if one assumes that people already have an innate grammatical knowledge. Some cognitive scientists transfer the thesis of innate knowledge to other areas. The most extensive thesis is represented by evolutionary psychologists , who assume that many forms of knowledge were already established evolutionarily in the Stone Age and are therefore universal, innate features of the human psyche. Not only is the extent of such innate knowledge disputed, it is also not clear whether innate cognitive mechanisms can appropriately be called knowledge .

The philosophical distinction between a priori and a posteriori knowledge, which goes back to Immanuel Kant, must be distinguished from the question of innate knowledge. Knowledge is considered empirical precisely when it arises from experience , i.e. when it is based on everyday observations or scientific experiments . In contrast, knowledge is considered a priori if its validity can be checked independently of experience. A classic candidate for a priori knowledge is analytical meaning knowledge . The truth of the sentence All bachelors are unmarried results from the meaning of the words alone, one does not have to empirically check whether all bachelors are actually unmarried. Mathematical knowledge is also often viewed as a priori. There is no agreement in philosophy about the scope of a priori knowledge, and the existence of a priori knowledge is also generally denied in some cases.

Explicit and implicit knowledge

The distinction between explicit knowledge and implicit knowledge is important for many disciplines. It was introduced by Michael Polanyi in 1966 . As explicitly knowledge content apply, on a subject consciously have which can express verbally if necessary. Implicit content, on the other hand, is characterized by the fact that it is not available in such a way. The implicit dimension of knowledge is playing an increasing role in research, as it has been shown that much of the central knowledge content is not explicitly available. Examples:

  • Doctors can often make diagnoses or scientists analyze experiments with great reliability without being able to explicitly state all the rules according to which they proceed in diagnosis or analysis.
  • Linguistic knowledge is largely only available implicitly (see feeling for language ), since even competent speakers can only specify a fraction of the semantic , syntactic and pragmatic rules of a language.
  • In research on artificial intelligence , implicit knowledge poses a significant challenge because it has been shown that complex explicit knowledge is often much easier to implement than seemingly uncomplicated implicit knowledge. So it is easier to create an artificial system that proves theorems than to teach a system to move accident-free through an everyday environment.

Matthias Claudius wrote: “You often know the most when you can't quite say why.” The difference between explicit and implicit knowledge is also taken up in the concept of skill level development .

Declarative and procedural knowledge

In psychology, a distinction can also be made between different types of knowledge with reference to current classifications in memory research: Much knowledge content is only available for a short time and is not stored in long-term memory. Examples of this are knowledge of a telephone number and the exact formulation of a sentence. In contrast, other content than long-term knowledge can be available for decades or for the rest of your life. Within long-term knowledge, a distinction is made between declarative and procedural knowledge. Contents are considered declarative if they relate to facts and can be described linguistically in the form of statements. A distinction must be made between this and procedural knowledge, which is related to the course of action and which often resists linguistic formulation. Typical examples of procedural knowledge are cycling, dancing or swimming. For example, many people can ride a bicycle without being aware of the individual motor actions that are necessary for this activity.

Finally, in declarative knowledge, a distinction is made between semantic and episodic knowledge . Semantic knowledge is abstract world knowledge (example: "Paris is the capital of France."). Episodic knowledge, on the other hand, is linked to the memory of a specific situation. (An example: "I was on vacation in Paris last summer.")

Organizational approach

Knowledge pyramid

In knowledge management and knowledge logistics, knowledge is, for the time being, a true state variable and a self-referential process. It is already changing its definition, since this itself becomes part of knowledge. A prerequisite for knowledge is an alert and self-reflective state of consciousness that is dualistic. Knowledge is information saturated with the context of experience . Information is a data component which caused a difference in the observer due to observer-dependent relevance. Data is something that can, but does not have to be, perceived. This definition is in line with the DIKW model. The latter represents data, information, knowledge in an ascending pyramid and leads to organizational memory systems, the main goal of which is to deliver the right information to the right person at the right time so that they can choose the most suitable solution. In this way, knowledge is linked with its use, which is an essential basis for action in information systems. Knowledge therefore describes in a larger context the entirety of all organized information and its mutual relations , on the basis of which a rational system can act. Knowledge allows such a system - before its knowledge horizon and with the aim of self-preservation - to react sensibly and consciously to stimuli .

Knowledge representation

“Knowledge representation” is a central term in many cognitive science disciplines such as psychology, artificial intelligence , linguistics and cognitive neuroscience . The use of the concept of knowledge differs from the philosophical and everyday use. Robert Solso , for example, defines knowledge as “the storage, integration and organization of information in memory. [...] Knowledge is organized information, it is part of a system or network of structured information. ”In a similar way, the neuroscience lexicon defines:“ Information is the raw material for knowledge. [...] In order for information to become knowledge, people must select, compare, evaluate, draw conclusions, link, negotiate and exchange ideas with others. "

A concept of knowledge understood in this way is independent of the truth of the stored information and also of the consciousness of the knowing system. A computer could have knowledge in the sense of this definition just like a human or any other animal. Knowledge stands out from simple information because it is networked with additional information. The sentence mice are mammals initially only expresses information. The information becomes knowledge when it is linked to further information about "mouse" or "mammal". With a concept of knowledge understood in this way, different research projects are carried out in the empirical sciences. The cognitive psychology developed models for the organization of knowledge in humans, cognitive neuroscience describes information processing in the brain and the artificial intelligence developed knowledge-based systems that organize information and networking.

Semantic networks

Hypothetical semantic network according to Collins and Quillian

The organization of information into knowledge is often explained in psychology with the help of semantic networks . It is believed that humans have simple information such as canaries are birds or birds have feathers . If such information is linked with one another, they result in a semantic network and allow conclusions to be drawn about other facts such as canaries have feathers . A complex semantic network is an economic form of knowledge storage: features that apply to birds in general do not have to be stored anew for each bird species; the same applies to features that apply to animals in general.

Collins and Quillian developed a model (see figure) of semantic networks, which they also subjected to an experimental test. Collins and Quillian assumed that the journey between the nodes of the semantic network would take time. Judging sets of the species birds have feathers. should therefore be measurably faster than the assessment of sentences of the species birds breathe. In fact, subjects took an average of 1310 milliseconds to evaluate sentences of the first type, while sentences of the second type took 1380 milliseconds to complete. If the information was two nodes away in the semantic network, 1470 milliseconds were required. However, there are irregularities: Frequently used information such as apples are edible was retrieved very quickly, even if the information “edible” can be assigned to a more general node such as “food”. Collins and Quillian built this knowledge into their model by assuming that frequently used information is stored directly at a corresponding node, so that no time-consuming journey in the semantic network is necessary. The model also has the advantage that it can work with exceptions. A typical feature of birds such as “can fly” can be stored at the corresponding node, even if not all birds can fly. The exceptions are saved at nodes like "Strauss".

Knowledge representation in artificial intelligence

The concept of semantic networks is also used in artificial intelligence for knowledge modeling , as it enables knowledge to be organized efficiently. For example, following the example of Collins and Quillian, a knowledge-based system can be constructed that answers questions about the characteristics of living beings. A non-graphic description of the semantic network is possible by defining two relations .

  1. isa: A is a subset of B.
  2. hasprop: A has property B.

With the help of these relations, the knowledge represented in the semantic network can be represented as follows: (canary isa bird), (ostrich isa bird), (bird isa animal) ... (canary hasprop sing), (ostrich hasprop does not fly), (ostrich hasprop large), (bird hasprop fly) ... Further facts can easily be derived from such a knowledge base , so that only a small part of the knowledge has to be saved explicitly. For example: (canary isa bird) and (bird hasprop fly) → (canary hasprop fly).

Not all approaches to knowledge representation are based on semantic networks; an alternative approach is based on the concept of the schema . In a scheme, relevant characteristics for a defined quantity are first specified. For example, the following characteristics could be singled out for the number of birds: body coverage, locomotion, housing, number of offspring. In the following, a standard scheme is defined in which the prototype properties are defined. For Vogel the scheme could look something like this:

Standard scheme: bird

Body covering: feathers
Locomotion: flying, running
Housing: nest
Number of offspring: 1 to 6

For subsets such as canary or ostrich , this standard scheme can be changed to a more specific scheme if necessary. This would be necessary in the case of exceptions (an ostrich cannot fly) or more specific information (such as the number of offspring).

A typical application of knowledge representation is the construction of expert systems that store and make available large amounts of specialist knowledge. Such systems are used in subject areas in which the human memory is overwhelmed with the amount of facts, for example in medical diagnostics or data interpretation. A very early expert system was developed in 1972 mycin , which for the diagnosis and treatment of infectious diseases with antibiotics should be used. There are now numerous expert systems that are also used commercially.

Another field of application is dialogue systems that are used in human-computer interaction and are intended to enable a person to communicate with a computer using natural language . For example, ELIZA, programmed by Joseph Weizenbaum in 1966, simulated a conversation with a psychotherapist . The program responded to statements of the type “I have a problem with my father” with the sentence “Tell me more about your family.” Such a reaction was made possible by the semantic linking of terms such as “father” and “family”. In the meantime, programs are also being written that aim to enable general, context-independent communication. The idea of ​​such a program goes back to the Turing test , which was formulated by Alan Turing in 1950 . According to Turing, one should speak of “thinking machines” precisely when computers cannot be distinguished from humans in communication. Really existing dialogue systems are far from such a goal and thus make the problems of the applied knowledge representation clear. For one thing, humans have such a large and varied amount of knowledge that a complete knowledge database in a computer does not seem to be possible. On the other hand, many forms of knowledge oppose a simple and efficient representation, for example in a semantic network. An example of this is the human knowledge of humor and irony - dialogue systems are not able to adequately explain jokes.

Knowledge-based systems are implemented in very different ways; in addition to semantic networks and schemes, various logic-based systems, scripts and complex if-then control systems are used. In modern, knowledge-based systems, hybrid architectures are often used that combine different knowledge representation techniques. In the last few decades, knowledge representations based on artificial neural networks have also become popular.

Connectionism and Neuroscience

Simplified representation of an artificial neural network

The question arises as to the interplay between the structure of memory and cognitive processes in order to obtain information about the representation of knowledge. From the psychology of knowledge and the above explanations one can infer that knowledge is not explicitly defined in cognitive science, but rather is understood as a memory content and as a cognitive phenomenon. Knowledge is implicitly defined by being closely tied to the concepts of information and representation.

Classical approaches to knowledge representation in psychology and computer science are oriented towards symbolic language; they postulate and network units that are each defined by their symbolic content. In the semantic network mentioned, quantities and properties are symbolically represented and linked by two types of relations. In connectionism or in Parallel Distributed Processing (PDP), however, knowledge is represented by linking simple units ( artificial neurons ). In a neural network (see figure for a simple example), one input leads to a spread of activity in the network and can lead to different outputs depending on the processing . A typical example of how such neural networks work is pattern recognition : The aim of the neural network is to “recognize” certain patterns, that is, to indicate the presence or absence of the pattern for a given input. A corresponding network could, for example, have two output units, with one unit always being activated when the pattern is present and the other unit being activated when the pattern is not present.

If such a network is to lead to the desired results, it must be capable of learning. Basic learning in neural networks is implemented by Hebb's learning rule , which was formulated in 1949 by the psychologist Donald Olding Hebb . Learning in neural networks is realized by weighting the connections between the units and thus leading to different levels of activity. If, for example, two connections from one unit A to the units B and C depend on the weighting of the connections, how strongly the activation of A is transferred to the activations of B and C. Learning is now achieved by changing the weightings. In the case of pattern recognition, a network would be trained in such a way that when a pattern is presented, the connections to one output are strengthened, while when a non-pattern is presented, the connections to the other output are strengthened. Through this process, the network learns to react to different variants of the pattern with the correct output and then to independently "recognize" new, previously unknown variants of the pattern.

Artificial neural networks differ from symbol-language approaches in particular in that no individual unit represents knowledge, but rather the knowledge (e.g. via patterns) is implemented in the system in a distributed manner. Connectionist and symbolic approaches have different strengths and weaknesses. While connectionist systems are often used for pattern or speech recognition , classic methods are suitable for the representation of explicit, semantic knowledge, for example.

Furthermore, connectionist systems are more similar to the way the brain processes them, in which individual neurons are also not viewed as representations of knowledge. Rather, a stimulus such as a visual stimulus leads to a complex spread of activity in the brain, which is why knowledge processing and storage in the brain is also explained by the model of distributed representation. In cognitive neuroscience , corresponding activity patterns are researched with the help of imaging methods such as magnetic resonance tomography . One goal is the search for neural correlates of states of consciousness and knowledge. If a person perceives a color or an edge visually, he acquires knowledge about the world and at the same time certain activities are caused in the brain. Cognitive neuroscientists are now trying to find out which brain activities are associated with corresponding states of perception and knowledge.

The social character of knowledge

Social Epistemology

The philosophical debate about the concept of knowledge and cognitive science research on the representation of knowledge is predominantly individualistic , as it deals with the knowledge of a single agent. In contrast, it is undisputed that knowledge is created, conveyed and checked in social contexts. This fact has led to the development of a social epistemology , which in turn can be divided into classical and non-classical approaches.

Classical approaches are based on the definition of “knowledge” as justified or reliable, true opinion, but emphasize the intersubjective context in which knowledge is acquired. Alvin Goldman , for example, examines everyday and scientific practices with reference to the question of whether they are useful for generating true opinions. The practices Goldman examined include organizing research, recognizing scientific authorities, judicial processes, and press opinion-forming. Another approach comes from Philip Kitcher , who studies the effects of the cognitive division of labor on truth-finding. The progress of science is based on Kitcher on a heterogeneous scientific community in which different interests and methodological working beliefs.

In non-classical approaches of social epistemology, however, the influence of social practices on truth, justification or reliability is not examined. Rather, it describes sociologically, historically or ethnologically how opinion-forming practices are de facto organized.

Sociology of Science and History of Science

Non-classical approaches to social epistemology are often closely linked to research in the sociology and history of science. In these disciplines, the focus is on the empirical description of opinion-forming practices and not on their evaluation according to epistemological criteria. In accordance with this goal, factors are examined that lead to the acceptance of opinions as “knowledge”. These factors can be far from the proposed in the classical theory of science criteria such verification , verification by Falsifikationsversuche and consistency vary.

There are numerous sociological and historical case studies that describe how opinions are established as “knowledge” in societies. For example, said Paul Feyerabend 1975 that the enforcement of the heliocentric world view was not based on new discoveries, but a skillful propaganda strategy of Galileo Galilei . According to Feyerabend, the representatives of the geocentric world view “did not recognize the propaganda value of predictions and dramatic shows, nor did they make use of the intellectual and social power of the newly created classes. They lost because they did not take advantage of existing opportunities. "

Michel Foucault explained in 1983 in The Will to Know that the increasing knowledge about human sexuality was tied to political power mechanisms: “Around the 18th century there was a political, economic and technical incentive to talk about sex. And not so much in the form of a general theory of sexuality, but in the form of analysis, bookkeeping, classification and specification, in the form of quantitative and causal studies. ” Bruno Latour provides sociological studies on current research processes . According to Latour (1987), the acceptance of a scientific opinion as knowledge essentially depends on the formation of alliances in the responsible scientific community .

Constructivism and Relativism

Even if many case studies in the sociology and history of science are controversial, it is generally recognized that the acceptance of scientific opinions often depends on factors such as political and rhetorical constellations, the formation of alliances and the interests of the research company.

These results from the sociological and scientific point of view allow various interpretations. Proponents of a classically oriented epistemology can point out that some of the factors mentioned can be suitable for generating true opinions in the scientific community. For example, the formation of alliances described by Latour means that researchers have to refer to the judgment and competence of other scientists. In addition, such case studies showed that the scientific community is occasionally misdirected by political and rhetorical influence. Such an interpretation is based on the conviction that a sharp distinction must be made between “knowledge” and “accepted as knowledge in a context”.

Such a distinction between “knowledge” and “accepted as knowledge in a context” is rejected in relativistic constructivism . Such positions explain that “there are no context-free or cross-cultural standards for rationality.” Without these standards, however, “knowledge” can only be defined relative to cultural beliefs, the distinction between “knowledge” and “accepted as knowledge in a context” consequently collapses. Such a rejection of the traditional concept of knowledge presupposes the rejection of the idea of ​​a theory and interest- independent reality : as long as one understands facts as independent of theories and interests, one can reject opinions independently of context by declaring that they do not correspond to the facts. The relativist constructivist Nelson Goodman therefore explains:

“The physicist considers his world to be the real one by ascribing the deletions, additions, irregularities, and stresses of other [world] versions to imperfections of perception, urgencies to practice, or to poetic freedom. The phenomenalist regards the world of perception as fundamental, while the cuts, abstractions, simplifications and distortions of other versions are the result of scientific, practical or artistic interests. For the man on the street, most versions of science, art, and perception differ in some ways from the familiar and subservient world. [...] Not only movement, derivation, weighting and order are relative, but also reality "

- Nelson Goodman

Not all constructivist positions, however, amount to a relativist constructivism in the sense of Goodman. Non-relativistic constructivisms explain with Goodman that descriptions, weightings and orders are actually relative to contexts. In this sense, many central scientific terms such as “type”, “gender”, “disease” or “quark” are shaped by the cultural context and interests. Nevertheless, such context-dependent terms would refer to context-independent facts in reality.

Limits of knowledge

The human capacity for knowledge can be questioned from different perspectives. On the one hand, human knowledge is generally disputed, on the other hand, individual subject areas are described as cognitively inaccessible. A general criticism of the capacity for knowledge can be found among relativistic and skeptical philosophers. If relativists reject the concept of truth as an illusion, the idea of ​​knowledge as a specifically true opinion also collapses. The sophist Protagoras is already given the opinion that one cannot distinguish between simple opinion (dóxa) and knowledge (episteme) . In contrast, skeptics accept the idea of objective facts and thus also the concept of knowledge. However, they doubt the human ability to gain knowledge of these facts.

Area-specific limits are to be distinguished from such general doubts about human cognitive abilities. On the one hand, metaphysical limits of knowledge can be assumed. This is the case, for example, when it is argued that humans cannot acquire knowledge about the existence of God , free will, or the nature of consciousness . For reasons of principle, these topics should elude empirical testing and should not be researchable through rational speculation . On the other hand, empirical knowledge limits can also be postulated that result from the cognitive or technical limitations of humans. For example, some dynamics could be so complex that humans cannot model or predict them. This is discussed, for example, in relation to economics and climate research .


René Descartes in a portrait by Frans Hals , 1648

Skepticism begins with the observation that opinions can only be labeled as knowledge if they can be checked. An opinion, about the truth of which one basically cannot say anything, cannot represent knowledge. In a second step, general doubts about the verifiability of opinions are raised. The best-known skeptical strategy is methodological doubt , as it is developed in the first meditation of René Descartes ' Meditationes de prima philosophia . Descartes begins by stating that the apparent knowledge of facts in the world is mediated by the senses and is also known that the senses can deceive . Descartes now recognizes that there are situations in which hallucinations seem to be excluded, such as the perception of a stove that one is sitting in front of and that one can clearly see. But here, too, doubts could be aroused, as one would also have similar experiences in dreams and provided that apparently obvious perceptions could always be deceived by dreaming. Finally Descartes creates the scenario of a god who deceives people in their apparent knowledge of actual reality. Descartes' point is not that such thought experiments are probable or even plausible. Rather, it should be demonstrated that such scenarios cannot be refuted and therefore cannot be ruled out. However, this enables a skeptic to argue that we cannot show any of our opinions to be truthful and thus we cannot obtain any certain knowledge at all.

Skeptical scenarios are constructed in such a way that they cannot be empirically refuted. Any evidence cited against the general deception can in turn be rejected from the skeptic's perspective as part of the deception. Nevertheless, various objections to skepticism have been developed. One strategy is to deny relevance to the skeptical hypotheses. The skeptical scenarios may not be refutable, but they turned out to be irrelevant as they made no difference to humans. One problem with this objection is that it does not seem to defend the concept of knowledge. Even if the truth of the skeptical hypotheses made no pragmatic difference, the possibility of knowledge would remain doubtful, since the skeptical hypotheses cannot be ruled out.

Other strategies consist in refuting the skeptic, for example by showing that skepticism cannot be formulated without contradictions . A well-known rebuttal strategy is Hilary Putnam's brain-in-tank argument . Putnam argues that the meanings of thoughts and concepts are essentially dependent on the causal relationships through which they are caused : If a person were to live permanently in a dream world, his thoughts and concepts would relate to this dream world. “There is a tree here.” Would refer to the trees in the dream world and would therefore be true. The same applies to us, according to Putnam, our thoughts and concepts relate to what causes them and are mostly true. The skeptical scenario cannot therefore be formulated without contradictions.

Metaphysical limits

Metaphysical theories are characterized by the fact that they cannot be verified empirically. If, for example, the question of the existence of God is called metaphysical, this means that the empirical sciences can neither confirm nor refute the existence of God. However, this does not imply that one cannot gain knowledge of metaphysical subjects. In addition to empirical investigations, metaphysical arguments such as proofs of God can lead to a decision. If metaphysical theories are to represent a limit to knowledge, one has to claim that they cannot be decided empirically or metaphysically. The best-known variant of such a position can be found in Immanuel Kant's Critique of Pure Reason .

“Human reason has a special fate in one species of its knowledge: that it is troubled by questions that it cannot reject; for they are given to it by the nature of reason itself, which it cannot answer either; for they exceed all faculties of human reason. […] [Reason plunges] into obscurity and contradictions, from which it can indeed deduce that there must be hidden errors somewhere at the bottom, but which it cannot discover because the principles which it uses because it is about the Go beyond the limit of all experience, no longer acknowledge any touchstone of experience. The battleground for these endless disputes is now called metaphysics . "

- Immanuel Kant

According to Kant, the discussion of metaphysical theses leads to antinomies : equally convincing arguments can be put forward for the approval and rejection of metaphysical theses, so the discussion ends in a contradiction. In the Transcendental Dialectic , Kant discusses four questions and contrasts “thesis” and “antithesis”:

The antinomies of pure reason
Thesis Antithesis
1. "The world has a beginning in time, and according to space it is also enclosed within limits." "The world has no beginning and no limits in space, but is, both in terms of time and space, infinite."
2. "Every compound substance in the world consists of simple parts, and everywhere there is nothing but the simple, or that which is composed of it." “No compound thing in the world consists of simple parts, and nothing simple exists in the same.” (Infinite divisibility)
3. “Causality according to the laws of nature is not the only one from which the phenomena of the world as a whole can be derived. It is still necessary to assume a causality through freedom to explain it. " "It is not freedom, everything in the world just happens according to the laws of nature."
4th "Something belongs to the world which, either as its part or its cause, is an absolutely necessary being." "There is no absolutely necessary being everywhere, neither in the world nor outside the world, as its cause."

According to Kant, the thesis and antithesis can be "proven" with the help of metaphysical arguments. However, since they contradict each other, metaphysics does not lead to knowledge or cognition, but to a systematic self-overtaxing of people. Nevertheless, according to Kant, people cannot ignore the metaphysical questions; they have to take a stand on them. However, this is not possible with the help of rational arguments and knowledge, but only through postulates.

Not all philosophers accept the thesis that metaphysics represents a fundamental limit of knowledge, whereby a distinction must be made between two types of objections. On the one hand, one can accept that metaphysical questions cannot be decided, and at the same time claim that this shows the futility or irrelevance of metaphysical questions. On the other hand, one can argue that metaphysical questions can be decided on a rational level.

The first strategy is linked to the linguistic-philosophical tradition of verificationism , according to which a sentence is meaningless if it cannot be checked or verified in principle . This thesis can be explained using fantasy sentences such as There is a hottmück : If you find out in which situations a hottmück can be identified, you can understand the meaning of "hottmück". However , if it applies to every situation it is unclear whether there is a hottmück , the term seems to be completely indefinite and therefore without semantic content. The verificationist idea was applied to all metaphysics by the representatives of the Vienna Circle : If metaphysical theses cannot be verified in principle, then they are pointless. So one cannot actually find answers to metaphysical questions, but this does not limit the space of knowledge, since metaphysical questions are incomprehensible and meaningless. A central problem of verificationism is that the claim that non-verifiable sentences are meaningless is itself non-verifiable. So if one applies the verificationist thesis to verificationism, verificationism itself seems to be meaningless.

Such problems have led to positions critical of metaphysics being formulated more as attitudes than as philosophical positions in the present. In naturalistic philosophers like Willard Van Orman Quine , the proposal is to limit themselves to generate insights on the empirical sciences. Quine does not want to prove that "philosophical speculation" is pointless, rather he suggests simply contenting oneself with empirical questions.

These metaphysical-critical tendencies are contrasted by a “return of metaphysics” in contemporary analytical philosophy . Modern metaphysicians claim with Kant that metaphysical questions are understandable and meaningful. Against Kant, however, it is asserted that there is no reason to assume that metaphysical problems are generally unsolvable. Metaphysical knowledge is thus possible.

Empirical limits

Limits to knowledge do not have to result from metaphysical problems, but can also be based on the inaccessibility of empirical data. An uncontroversial example is history in which many facts can no longer be reconstructed. Often it is no longer possible to find out what a historical figure did on a particular day because there is no evidence. However, empirical limits do not have to be based on the lack of data, but can also result from the complexity of the data. For example, the project of precise and long-term weather forecasts is reaching the limits of human modeling abilities .

Such empirical limits become an epistemological problem when they threaten to collide with the explanatory claims of entire scientific disciplines. A typical example of the lack of availability of data is astrobiology , which among other things deals with the existence of life beyond the earth . To the extent that astrobiology is concerned with planets beyond the solar system , it has little reliable data at its disposal. Astrobiologists try to counter this problem with indirect evidence, probability estimates and analogy arguments, the best known example of which is the Drake equation .

The lack of data also plays a crucial role in the evolutionary psychology debate . Evolutionary psychologists try to explain the way people think and feel as adaptations to Stone Age environmental conditions. Critics like John Dupré accuse evolutionary psychology of being unable to substantiate their hypotheses, since the relevant data on the Stone Age living conditions and the cognitive evolution of humans are simply not available. Evolutionary psychological hypotheses were therefore more like “fantasy stories” than knowledge.

The question of the limits of empirical knowledge also arises in connection with complex dynamics and scientific prognoses . As early as 1928, for example, the economist and game theorist Oskar Morgenstern argued that economic forecasts were fundamentally not possible. Predictions are only possible if there are discoverable laws. However, since economic development is based on the irregular behavior of individual actors, one cannot obtain any knowledge about the development of the economy. In addition, economic development is largely shaped by factors such as economic structural change, political and natural events. Such factors are often decisive for economic trend changes, but cannot be adequately integrated into forecast models. Consequently, one should give up the illusion of being able to generate knowledge with prognoses:

“You [the economic institutes] should give up the forecast. That is the one lesson that can be drawn with all clarity. These dilettantisms, which are inevitably doomed to failure and which dress themselves in the much sought-after cloak of science, discredit science and, in their interest, the communis opinio of those with economic theory should take the wind out of the sails of these institutes - as long as they stick to the prognosis. "

- Oskar Morgenstern

Representatives and critics of the above-mentioned scientific disciplines agree that the research projects are pervaded by uncertainties and that no absolute certainty can be achieved. On the one hand, however, the degree of uncertainty and, on the other hand, the question of how much uncertainty is acceptable in the scientific community is controversial. The debate is made more difficult by the general acceptance that absolute certainty cannot be the goal of empirical science. Fallibilist positions argue that there can be no certainty in the empirical sciences either. Since empirical theories cannot be justified by compelling logical evidence , there is always room for error, regardless of how well an empirical theory agrees with the available data. Such fallibilism excludes certainty, but not knowledge. Despite the potential for error in principle, most scientific opinions can be true and justified. However, fallibilistic considerations raise the question of how great uncertainties may be in the context of knowledge.

However, far-reaching doubts about the scope of empirical knowledge are formulated in the context of pessimistic induction , according to which most current scientific theories are wrong and therefore do not represent knowledge. The argument of the pessimistic induction is based on the historical scientific observation that numerous theories in the past agreed well with the data and nonetheless turned out to be false. Examples of this can be the ether theory , geological Neptunism , phlogiston theory or humoral pathology . Consequently, one cannot infer their probable truth from the explanatory successes of current theories. On the contrary, the failure of most past approaches in the present inductively suggests the failure of most current theories in the future. This problem can be responded to in different ways: One can try to show that current scientific theories differ qualitatively from the examples of the history of science. It is also argued that science is not about a true description of the facts, but about successful models with good predictive and explanatory power.

Knowledge and society

Knowledge society

In the social sciences, with reference to the concept of the knowledge society, the thesis is often represented that the social and economic role of knowledge has fundamentally changed in the 20th century. Meinhard Miegel, for example, explains that the development towards a knowledge society should be viewed as the “third huge paradigm shift in the history of mankind”. After the development from agricultural to industrial societies, the transition from industrial to knowledge societies can now be observed.

Such a transformation is initially noticeable in the economic and working world, as Sigrid Nolda describes, “that the concept of the knowledge society is generally based on the growing importance of knowledge as a resource and basis for social action. Since the 1970s, work has essentially been characterized by its cognitive value, i.e. knowledge. ”In addition to the economic and social importance of knowledge, however, the availability of knowledge is also changing due to new information and communication technologies and a changed educational policy .

Such a definition remains vague, since the social and economic meaning of knowledge is not an exclusive characteristic of knowledge societies. Basically, every job requires different forms of knowledge, and the distribution of knowledge is an essential characteristic of social differences even in ancient societies. In this sense, the UNESCO World Report Towards Knowledge Societies declares that every society should be viewed as a knowledge society.

Knowledge distribution and freedom of knowledge

To the extent that the distribution and availability of knowledge is of social and economic importance, access to knowledge is also discussed as a problem of justice . The importance of knowledge in contemporary societies is discussed as both a problem and an opportunity. On the one hand, it is argued that due to the central role of knowledge in society, a poor level of knowledge and access to it lead to far-reaching social disadvantage. In addition to classic topics such as income or work distribution, the distribution of knowledge is now a central issue of justice.

A more detailed analysis often makes use of the connection between knowledge and power , as already expressed by Francis Bacon in the slogan scientia potestas est (“knowledge is power”). In this context, Michel Foucault's works are particularly influential , according to which social power has essentially been realized through knowledge systems since the 18th century. Traditionally, the sovereign's power was determined by his ability to kill: “He reveals his power over life only through death, which he is able to demand. The so-called "right of life and death" is in reality the right to make death and to let life . His symbol was the sword. ”In modern societies, however, power over people manifests itself in a different way than positive knowledge, for example about mental and physical health and illness, reproduction, birth and death rates or the level of health. This knowledge becomes an instrument of power in biopolitics , not only through direct political intervention in the legal system, health and education policy, but also through influencing scientific and public discourse . Following Foucault, the connection between knowledge and power is often described as reciprocal: Not only does knowledge implicit power, but, conversely, knowledge would be guided by power mechanisms. Which knowledge is considered relevant is determined, for example, by the promotion of science, the creation of pedagogical curricula or the setting of priorities in the media.

However, the importance of knowledge in modern societies is not only critically examined in relation to questions of justice and power. Rather, the knowledge society is often also seen as a positive development that can at least potentially give all citizens general access to knowledge. As a positive ideal, this idea is formulated as freedom of knowledge, according to which every citizen has the right to free access to knowledge. For example, the UNESCO World Report explains “The current distribution of new technologies and the development of the Internet as a public network seem to offer new possibilities for a public knowledge forum. Do we now have the means to achieve equal and universal access to knowledge? This should be the cornerstone of true knowledge societies. ”At the same time, however, it is emphasized that contemporary societies are quite far from this ideal and that numerous cultural, political and economic realities stand in the way of general freedom of knowledge. The open access and open content movement, which strives for free access and the free re-usability of knowledge, is responding to such limits on freedom of knowledge.

Acquisition and transfer of knowledge

The acquisition and transfer of knowledge is researched in learning psychology and pedagogy . As a rule, a very broad concept of knowledge is used, which is also intended to do justice to pedagogical practice and consequently includes implicit and explicit knowledge and knowledge of very different types. Learning psychology can be traced back to Hermann Ebbinghaus and Wilhelm Wundt at least into the 19th century . In 1885, Ebbinghaus introduced the first learning curves in psychology, which describe the relationship between learning effort and learning output. Such attempts to quantify the acquisition of knowledge in humans were supplemented in the 20th century by various learning theories that attempt to explain knowledge acquisition on a broad theoretical level. A classic model is conditioning , according to which living beings show a certain reaction to a certain stimulus. In conditioning, the desired reaction is trained by repeatedly presenting combined stimuli. While behaviorism tried to explain the acquisition of knowledge completely through stimulus-reaction mechanisms, one began in the 1960s to postulate internal psychological states which, as knowledge representations, should explain the learning success. In the last few decades, learning theories have also been added that describe the acquisition of knowledge with the help of neural networks and neuroscientific findings (see the section on knowledge representation ).

In research in the field of learning psychology, on the one hand, attempts are made to understand how people acquire knowledge on a general, theoretical level. On the other hand, however, specific knowledge acquisition strategies are described and explained, which can vary greatly depending on the knowledge topic, age level, individual cognitive profiles and cultural context. As educational psychology, such research provides a basis for the development of educational knowledge transfer strategies.

Overall, pedagogy is to be understood as the science of imparting knowledge, whereby a distinction can be made between general pedagogy and differential or application-related pedagogy :

  • The General Education is seen as a basic science that describes the basal mechanisms of knowledge transfer theory. There have been repeated doubts about the possibility of general pedagogy as a basic discipline, because learning and teaching take place (and interact) in different contexts with very different learning and teaching strategies.
  • In addition, different approaches are used to examine the transfer of knowledge with reference to specific groups; examples of sub-disciplines are pre-school education , special education , university education and adult education .
  • In various application subjects, questions are also asked about the requirements for imparting knowledge in certain subject areas, for example in intercultural education , theater education or sex education .

Even if all sub-areas of pedagogy can be understood as approaches to imparting knowledge, "knowledge" has developed into a new basic term in some pedagogical theories under the influence of learning psychological constructivism , information theory, new media and the debate about the knowledge society . It is pointed out that knowledge is an essentially social phenomenon and therefore cannot be reduced to a student-teacher interaction. Knowledge is "socially constructed" in collaborative work with the help of various media and an appropriate pedagogical theory and practice must address these characteristics of knowledge generation. A well-known approach is the knowledge building theory of Carl Bereiter and Marlene Scardamalia . On the basis of the knowledge society concept, Bereiter and Scardamalia assume that knowledge transfer and generation in contemporary societies can only be achieved to a small extent through classic approaches such as teaching methods and curricula : “The new challenge is to lead young people into a culture that pushes the boundaries of knowledge on all sides. It's about helping to find a constructive and personally satisfying role in this culture. "


The acquisition of knowledge requires basic skills that are acquired during kindergarten and primary school and are to be developed by attending secondary schools: reading skills (the ability to read individual words, sentences and entire texts fluently and understand them in context ), writing skills and / or arithmetic . Reading skills are the central element.

Every school leaver has a certain information and media skills . These two have become a basic skill:

  • the company is changing rapidly;
  • many people (especially students and working people) are exposed to a growing information overload (see also information overload ).

Information literacy and media literacy are prerequisites for the self-organized development of knowledge, the development of new and the expansion of existing skills and the coping with problems . You have an important role in the concept of lifelong learning (it should enable you to learn independently throughout your life span).

Knowledge in the learning objective taxonomy according to Bloom

Bloom's taxonomy of learning objectives is widespread in education . The factual knowledge only has the first, preparatory rank:

  1. Knowledge
  2. Understanding (comprehension)
  3. Apply (Application)
  4. Analysis
  5. Synthesis
  6. Evaluation


The History of Knowledge Center , a joint scientific competence center of the University of Zurich and the ETH Zurich, founded in 2005, has committed itself to promoting and coordinating cultural, historical and philosophical research and teaching on modern knowledge systems and knowledge societies.



Classical positions in the history of philosophy
Definition of the concept of knowledge
The social character of knowledge
Limits of knowledge

Cognitive and human sciences

  • Niels Birbaumer , Dieter Frey, Julius Kuhl, Friedhart Klix (eds.): Encyclopedia of Psychology / Series 2: Encyclopedia of Psychology. Volume 6: Knowledge. Hogrefe-Verlag, 1998, ISBN 3-8017-0531-5 .
  • Noam Chomsky : Knowledge of Language: Its Nature, Origin, and Use. Praeger Publishers, 1985, ISBN 0-275-90025-8 .
  • Frank van Harmelen, Vladimir Lifschitz, Bruce Porter (Eds.): Handbook of Knowledge Representation. Elsevier Science, ISBN 0-444-52211-5 .
  • Hermann Helbig : Knowledge Representation and the Semantics of Natural Language. Springer, 2005, ISBN 3-540-24461-1 .
  • Hartmut Krech: How much knowledge is there in the world? Cognitive researchers dare a quantitative answer. In: Die Zeit , September 5, 1998, , accessed on January 15, 2020.
  • Guy R. Lefrancois, Silke Lissek: Psychology of learning. Springer, Berlin 2006, ISBN 3-540-32857-2 .
  • Rainer Schützeichel (Hrsg.): Handbuch Wissenssoziologie und Wissensforschung. Constance 2007.
  • John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations: Logical, Philosophical, and Computational Foundations. Course Technology, 1999, ISBN 0-534-94965-7 .
  • Wolfgang G. Stock, Mechtild Stock: Knowledge representation: Evaluation and provision of information. Oldenbourg, 2008, ISBN 978-3-486-58439-4 .

Society and Education

Web links

Wiktionary: Knowledge  - explanations of meanings, word origins, synonyms, translations

Individual evidence

  1. Alois Walde: Latin etymological dictionary. 3. Edition. Heidelberg 1938, II, p. 784f.
  2. Julius Pokorny: Indo-European Etymological Dictionary. Bern / Vienna 1859 (revised version: 2007, p. 1125)
  3. ^ The dictionary of origin (=  Der Duden in twelve volumes . Volume 7 ). 2nd Edition. Dudenverlag, Mannheim 1989, p. 816 . See also DWDS ( “to know” ) and Friedrich Kluge : Etymological dictionary of the German language . 7th edition. Trübner, Strasbourg 1910 ( p. 497 ).
  4. ^ Matthias Steup: Epistemology. In Edward N. Zalta (ed.): The Stanford Encyclopedia of Philosophy (Winter 2018 Edition), < >
  5. ^ Gilbert Ryle: The Concept of Mind . The University of Chicago Press 1949, pp. 25-61.
  6. Jeremy Fantl: Knowledge How. In Edward N. Zalta (ed.): The Stanford Encyclopedia of Philosophy (Fall 2017 Edition), < >
  7. Gettier himself names Roderick Chisholm (Perceiving: A Philosophical Study. Cornell University Press 1957, p. 16) and AJ Ayer (The Problem of Knowledge. Macmillan 1956, p. 34) as references for this position.
  8. See also: Alexander Becker: Wrong opinion and knowledge in the theater. Archive for the History of Philosophy 88 (2006), 296-313.
  9. Plato , Theaetetus 201d-206b .
  10. ^ Edmund Gettier : Is Justified True Belief Knowledge? In: Analysis. Volume 23, 1963, pp. 121-123.
  11. David Malet Armstrong : Belief, Truth, and Knowledge. Cambridge University Press, Cambridge 1973, ISBN 0-521-09737-1 .
  12. Keith Lehrer, Thomas Paxson: Knowledge: Undefeated Justified True Belief. In: The Journal of Philosophy. 1969.
  13. ^ Alvin Goldman: Discrimination and Perceptual Knowledge. In: The Journal of Philosophy. 1976.
  14. Alvin Goldman, Alvin and Bob Beddor: Reliabilist Epistemology. In Edward N. Zalta (ed.): The Stanford Encyclopedia of Philosophy (Winter 2016 Edition), < >
  15. ^ Richard Feldman: Reliability and Justification . The Monist 68 (1985): 159-174.
  16. cf. Ludwig Wittgenstein: Philosophical Investigations . §67
  17. Timothy Williamson: Knowledge and Its Limits . Oxford University Press 2000.
  18. Ansgar Beckermann : On the incoherence and irrelevance of the concept of knowledge. Plea for a new agenda in epistemology. In: Journal for Philosophical Research. 2001.
  19. ^ Fred Dretske: Epistemic Operators. The Journal of Philosophy 67 (1970): 1007-1023.
  20. ^ Robert Nozick: Philosophical Explanations . Belknap Press 1981.
  21. ^ Stewart Cohen: Knowledge and Context. The Journal of Philosophy 83 (1986): 574-583.
  22. ^ Keith DeRose: Contextualism and Knowledge Attributions. Philosophy and Phenomenological Research 52 (1992): 913-929.
  23. David Lewis: Elusive Knowledge. Australasian Journal of Philosophy 74 (1996): 549-567.
  24. Jeremy Fantl & Matthew McGrath: Evidence, Pragmatics, and Justification. Philosophical Review 111 (2002): 67-94.
  25. ^ Jason Stanley: Knowledge and Practical Interest . Oxford University Press 2005.
  26. ^ John MacFarlane: The Assessment Sensitivity of Knowledge Attributions. Oxford Studies in Epistemology 1 (2005), 197-233.
  27. Peter Unger: Ignorance. A case for skepticism . Oxford University Press 1975.
  28. Jonathan Schaffer: Skepticism, Contextualism, and Discrimination. Philosophy and Phenomenological Research 69 (2004), 138-155
  29. Patrick Rysiew: The Context Sensitivity of Knowledge Attributions. Noûs 35 (2001): 477-514.
  30. Edmund Husserl: Phenomenology of Mathematics. Kluwer Academic Publishers, Dordrecht 1989.
  31. ^ Hannah Arendt: Vita Activa. Piper, Munich 1967.
  32. ^ Hermann Weyl: Philosophy of Mathematics and Natural Science. R.Oldenbourg, Munich 1976.
  33. cf. Peter Schreiber: Fundamentals of Mathematics. VEB Verlag der Wissenschaften, Berlin 1977.
  34. Kurt Gödel : About formally undecidable sentences of the Principia Mathematica and related systems. In: Monthly booklet for mathematics and physics. No. 38, 1931, pp. 173-198.
  35. ^ JE Hopcroft, JD Ullman: Introduction to Automata Theory, Formal Languages ​​and Complexity Theory. Addison-Wesley, Bonn / New York / Amsterdam 1988.
  36. a b c d cf. Wolfgang Schlageter: Knowledge in the sense of the sciences - exact knowledge, empirical knowledge, limits of knowledge. August von Goethe Verlag, Frankfurt am Main 2013.
  37. cf. Werner Heisenberg: Steps across borders. Piper Munich 1973.
  38. cf. Ernst Hoffmann: The ancient philosophy from Aristotle to the end of antiquity. In: Max Dessoir: Textbook of Philosophy. Berlin 1925.
  39. cf. Ernst Mach: Mechanics and their development. Leipzig 1933.
  40. cf. Francois Jacob: The logic of the living. Fischer, Frankfurt 1972.
  41. cf. Ernst Cassirer: The problem of knowledge in philosophy and science in modern times. Volume 1, Darmstadt 1974.
  42. cf. Ernst Hofmann: The ancient philosophy from Aristotle to the end of antiquity. In: Max Dessoir: Textbook of Philosophy. Berlin 1925.
  43. cf. Johannes Hirschberger: History of Philosophy. Volume 2, Freiburg 1953.
  44. Wolfgang Stegmüller: The problem of induction: Hume's challenge and modern answers. Darmstadt 1975.
  45. cf. Ernst von Aster: The History of Modern Philosophy. In: Max Dessoir: History of Philosophy. Fourier, Wiesbaden 1975.
  46. ^ Hannah Arendt: Vita Activa. Piper Verlag, Munich 1981.
  47. ^ Ernst Cassirer: The problem of knowledge in philosophy and science in modern times. Volume 4, Darmstadt 1974.
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  114. BS Bloom: Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain. David McKay, New York 1956 and DR Krathwohl, BS Bloom, BB Masia: Taxonomy of Educational Objectives, the Classification of Educational Goals. Handbook II: Affective Domain. David McKay, New York 1973.