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Knowledge Representation and Hemispheric Distribution/Specialisation


The Latin word for “to know” is “cognoscere” and after Princeton's WordNet a possible synonym for “knowledge” is “cognition”. This close etymological affinity between "knowledge" and "cognition" is an indicator for the importance of “knowledge” for cognitive psychology and cognitive science. Most human cognitive abilities rely on or interact with what we call knowledge. How do people navigate through the world? How do they solve problems, how do they comprehend their surroundings, on which basis do people make decisions and draw inferences? For all these questions, knowledge and mental representations of the world without doubt are part of the answer.

But what is knowledge? According to Merriam-Webster-OnLine-Dictionary knowledge is “the range of one’s information and understanding” and “the circumstance or condition of apprehending truth or fact through reasoning”. Thus, knowledge is a structured collection of information, that can be acquired through learning, perception or reasoning.

In this chapter we deal with what structures humans build to represent their knowledge about the world in their brain. First, we will introduce the idea of concepts and categories as a model for storing and sorting information to you , and then talk about semantic networks, which are similar to neural networks and also try to explain the way humans store and handle information. Apart from the human aspect, we are also going to talk about knowledge representation in artificial systems, which can be helpful tools to store and access knowledge and draw quick inferences.

After having looked at how knowledge is stored and made available in the human brain and artificial systems, we will talk about a quite different topic, namely hemispheric specialisation. This topic also connects to many other chapters of this book. Where, for example, is memory located, and which parts of the brain are relevant for emotions and motivation? Although these are interesting questions, in this chapter we focus on the general differences between right and left hemisphere. We consider the question whether they differ in what or in how they process and give an overview about experiments with which knowledge was gained in this field.

Historical and Philosophical Aspects

Is explaining the activity within our two hemispheres enough to explain our conscious selves? Is our true self really that spatially limited to this soft stuff in our skull? It seems obvious that this cannot be the case and that the brain rather is in constant interaction with our body and thereby with the environment we live in. Perceiving our environment is a human ability that is usually done unconsciously. Somatic marker are an good example for unconscious perception and the influence it has on our behavior. This knowledge is qualitative as well as quantitative. Hence, our decisions in each
circumstance is based on more information than we are aware of.

There have been arguments going on for a long period of time how to value the information of sensory data in comparison to our ‘mind’. Is just everything we are pure receiving and modeling of physical information and talking about a mind just a waste of time? Or is instead the body just a hindrance in gaining knowledge, as already Socrates claimed, because it does not “afford men any truth”?

Theories on Knowledge Representation in the Brain

Concepts and Categories


For many cognitive functions concepts are important. Concepts are mental representations and are used for the use and understanding of language, for reasoning and memory. One function of concepts is the categorization which has been studied most intensively.In the following we will deal with this particular function of a concept.

Categories in our Life

Imagine to wake up every single morning and start wondering about all the things you have never seen before. Think about how you would feel if an unknown car parked in front of your house. You have seen thousands of cars but since you have never seen this specific car in this particular position, you would not be able to provide yourself with any explanation. But since we are able to find an explanation, the questions we need to ask ourselves are: How are we able to abstract from prior knowledge and why do we not start all over again if we are confronted with a slightly new situation? The answer is easy: We categorize knowledge. Categorization is the process of abstracting and assigning objects to categories. Categories are so called “pointers of knowledge”. You can imagine a category as a box, inside which similar objects are grouped and which is labeled with common properties and other general information about the category. Our brain does not only memorize specific examples of members of a category, but also stores general information that all members have in common and which therefore defines the category. Coming back to the car-example, this means that our brain does not only store how your car, your neighbors and your friends car look like, but it also provides us with the general information that most cars have four wheels, need to be fueled and so on.

Because categorization allows us to quickly get a general picture of a scene by allowing us to recognize new objects as members of a category, it saves us much time and energy that we otherwise would have to spend in investigating new objects. It helps us to focus on the important details in our environment, and enables us to quickly draw the correct inferences. To make this obvious, imagine yourself standing at the side of a road, wanting to traverse it. A car approaches from the left. Now, the only thing you need to know about this car is the general information provided by the category, that it will run you over if you don't wait until it has passed. You don't need to care about the car's color, number of doors and so on. If you were not able to immediately assign the car to the category "car", and infer the necessity to step back, you would get hit because you'd still be busy with examining the details of that specific and unknown car. Therefore categorization has proved itself as being very helpful for surviving during evolution and allows us to quickly and efficiently navigate through our environment.

Definitional Approach

Take a look at the following picture, you will see three different kinds of cars. They differ in shape, color and other features save that they are all cars.

What makes us so convinced about the identity of these objects? Maybe we can try to find a definition which describes all the cars. All cars have four wheels? Really? There are some which have only three. All cars drive with petrol. That’s not true for all cars either. Apparently we will fail to come up with a definition. The reason for this failure is that we have to generalize to make a definition. But because not all members of one category share identical features definitions are problematic especially concerning natural or human-made objects.

Wittgenstein (1953) was one of the people who dealt with this problem of the definitional approach. Wittgenstein claimed to have found a solution. He developed the idea of family resemblance. That means that members of a category resemble each other in several ways. For example cars differ in shape, color and many other properties but every car resembles somehow other cars.

Prototype Approach

The prototype approach was proposed by Rosch in 1973. A prototype is an average case of all members in a particular category, but it is not an actual, really existent member of the category. Even extreme various features of members within one category can be explained by this approach. Different degrees of prototypicality represent differences among category-members. Members which resemble the prototype in a very strongly are high-prototypical. Low-prototypicality is the opposite namely members which differ in a lot of ways from the prototype. There seem to be connections to the idea of family resemblance and indeed some experiments showed that high prototypicality and high family resemblance are strongly connected.

The typicality effect describes the fact that high-prototypical members are faster recognized as a member of a category. For example participants had to decide whether statements like “A penguin is a bird”, “A sparrow is bird” are true. Their decisions were much faster concerning prototypical members as for the “sparrow” than for atypical members as “penguin”. Participants also tend to prefer prototypical members of a category when asked to list objects of a category. Concerning the birds-example, they rather list “sparrow” than “penguin”, which is a quite intuitive result. In addition prototypical objects are strongly affected by priming.

Exemplar Approach

The typicality effect can also be explained by the third approach which is concerned with exemplars. Similar to a prototype, an exemplar is very typical of the category. The difference between exemplars and prototypes is that exemplars are actually existent members of a category that a person has encountered in the past. It is proposed that objects which are similar to the majority of exemplars are classified faster. A sparrow would be classified very fast because it looks similar to many other
birds. Therefore an atypical bird like a penguin would not be classified as fast because it doesn’t share it appearance with many other exemplars of birds.

Prototype vs. Exemplar Approach

For both prototype and exemplar approach there are experiments whose results support either one approach. Some people claim that the exemplar approach has less problems with variable categories and with atypical cases within categories. The reason for that could be that “real” category-members are used and all information of the individual exemplars, which can be useful when encountering other members later, are stored. Another point where the approaches can be compared is how well they work for different sized categories. The exemplar approach seems to work better for smaller categories and prototypes do better for larger categories.

In the end one quote gives a good sense of how useless and even impossible it is to decide which approach is the “right” one: “We know generally what cats are (the prototype), but we know specifically our own cat the best (an exemplar).” (Minda & Smith, 2001)

Hierarchical Organization of Categories

Now that we know about the different approaches of how we go about forming categories and negotiating between their members, let us look at the structure of a category and the relationship between categories.

The basic idea is that larger categories can be split up into more specific and smaller categories. Rosch stated that by this process three levels of categorization are created:

It is interesting that the decrease of information from basic to superordinate is really high but that the increase of information from basic down to subordinate is rather low. Scientists wanted to find out if among these levels one is preferred over the others. They asked participants to name presented objects as quickly as possible. The result was that the subjects tended to use the basic-level name, which includes the optimal amount of stored information. So a picture of a retriever would mainly be named “dog” rather then “animal” or “retriever”. It is important to note that the levels are different for each person depending on factors such as expertise and culture.

Affecting Factors on Categorization

One factor which influences our categorization is knowledge itself. Experts pay more attention to specific features of objects in their area than non-experts would do. For example after presenting some pictures of birds experts of birds tend to say the subordinate name (blackbird, sparrow) while nonexperts just say "bird". The basic level in the area of interest of an expert is therefore lower than the basic level of a layperson. Therefore knowledge and experience of people affect categorization.

Another factor is culture. If something is of great importance in a culture people of that culture are like experts and have more knowledge about this topic than another people in whose culture this area does not play such an important role. A person coming from a Western culture is more likely to have more knowledge about computers than a person coming from an African tribe.

Representation of Categories in the Brain

There is evidence that some areas in the brain are selective for different categories. But it is not very probably that there is a corresponding brain area for each category. One prove for a selectivity of brain areas is the results of neurophysiological research. There seems to be a kind of double dissociation for living and non-living things. It has been proven by fMRI studies that non-living and living things are indeed represented in different brain areas. It is important to denote that nevertheless there is much overlap between the activation of different brain areas by categories. Moreover when going one step closer into the physical area there is a connection to mental categories, too. There seem to exist neurons which respond better to objects of a particular category, namely so called “category-specific neurons”. These neurons fire not only as a response to one object but to many objects within one category. This leads to the idea that probably many neurons fire if a person recognizes a particular object and that maybe these combined patterns of the firing neurons represent the object.

Semantic Networks

The "Semantic Network approach" proposes that concepts of the mind are arranged into networks, in other words, in a functional storage-system for the `meanings' of words. Of course, the concept of a semantic net is very flexible. In a graphical illustration of such a semantic net, concepts of our mental dictionary are represented by nodes, which in this way represent a piece of knowledge about our world.

The properties of a concept could be placed, or "stored", next to a node representing that concept. Links between the nodes indicate the relationship between the objects. But the links can not only show, that there is a relationship. They can indicate the kind of relation by their length, for example.

Semantic Network according to Collins and Quillian with Every concept in the net is in a dynamical nodes, links, concept names and properties (GFDL -

correlation with other concepts, which maybe have Lbartels) protoypically similar characteristics or functions.

Collins and Quillian's Model

One of the first ones, who thought about such structure models of human memory that could be run on a computer, was Ross Quillian (1967). Together with Allan Collins, he developed the Semantic Network with related categories and with a hierarchical organization.

In the picture on the right hand side, Collins and Quillians network with added properties at each node is shown. As already mentioned, the skeleton-nodes are interconnected by links. At the nodes, concept names are added. Like in paragraph "Hierarchical Organisation of Categories", general concepts are on the top and more particular ones at the bottom. By looking at the concept "car", one gets the information, that a car has 4 wheels, has an engine, has windows, and furthermore moves
around, needs fuel, is manmade. The properties of the higher concepts in line are inherited to the lower ones. This is called Cognitive Economy.

Cognitive Economy

In order not to produce redundancies, Collins and Quillian thought of this information inheritance principle. Information, that is shared by several concepts, is stored in the highest parent node, containing the information. So all son-nodes, that are below the information bearer , also can access the information about the properties.

But there are exceptions. Sometimes a special car has not four wheels, but three. This specific property is stored in the son-node.

Correlation between Distance of Concepts and Information Retrieval

The logic structure of the network is convincing. Since it can show, that the time of retrieving a concept and the distances in the network correlate. The correlation is proven by the sentence-verification technique. In experiments probands had to answer statements about concepts with "yes" or "no". It took actually longer to say "yes", if the concept bearing nodes were further apart.

Spreading Activation Correlation of distance of concepts and retrieval time for

information (GFDL - Lbartels

The phenomenon, that adjacent concepts are activated is called Spreading activation. These concepts are far more easily accessed by memory, they are ,—N

"primed". S--'

"*"♦ primary search

As seen in the picture at the right, the starting point of that search activation is truck, going to car, as the participant thinks from truck to car. Spreading activation, coming from car, primes all surrounding concepts, namely sports car, van and vehicle.


spreading activation

J primed

This was studied and backed by David Meyer and Roger Schaneveldt (1971) with a lexical-decision task. Probands had to decide, if word pairs were words or ng activation

non-words. They were faster at finding real word pairs,*W/" (GFDL - Lbartels

if the concepts of the two words were close-by in the intended network .


While having the ability to explain many questions, the model has flaws.

The Typicality Effect is one of them. It is known that "reaction times for more typical members of a category are faster than for less typical members". (MITECS) This contradicts with the assumptions of Collins and Quillian's Model, that the distance in the net is responsible for reaction time.

Also Cognitive Economy is questioned. Since by means of experiments, it was found out, that some properties are stored at the specific node.

Furthermore, there are examples of faster concept retrieval, although the distances in the network are longer.

All this led to another version of the Semantic Network approach.

Interpersonal different link length and layout of concepts (GFDL - Lbartels

Collins and Loftus Model - A Developed C&Q-Model

Collins and Loftus (1975) tried to abandon these problems by using shorter or longer links depending on the relatedness and interconnections between formerly not directly linked concepts. Also the former hierarchic structure was substituted by a more individual structure of a person. Only to name a few of the extensions. Like shown in the picture on the right, the new model represents interpersonal differences, such as acquired during a humans lifespan. They manifest themselves in the layout and the various lengths of the links of the same concepts.

But after all these enhancements, the model is so omnipotent, that some researches scarced it for being too flexible. In their opinion, the model is no longer a scientific theory, because it is not


Collins and Loftus Model (GFDL - Lbartels

Connectionist Approach

As written above, every concept in a semantic net is in a dynamical correlation with other concepts, which maybe have protoypically similar characteristics or functions. In the brain neural networks are organised similarly.

Furthermore, it is useful to include the Parallel distributed processing (PDP) network features of "spreading activation" and "parallel A para lsd distributed processing (PDP) network (GFDL distributed activity" in a concept of such a semantic net, to explain the complexity of the very sophisticated environment.

The connectionists did this by modeling their networks after neural networks in the nervous system. Instead of nodes, the units of the networks are divided into subgroups like Input-, Output- and Hidden nodes, as shown on the picture on the left hand side.

Representation of Concepts in Networks

Excitatory and inhibitory connections between units just like in synapses in the brain allow 'input' to be analyzed and evaluated. For computing the outcome of such systems, it is useful to attach a certain 'weight' to the input of the connectionists system, that mimics the strength of a stimulus of the human nervous system.

Different inputs are represented in different output patterns. As shown in the picture on the right hand side, "truck" has the output pattern +7,+5,+1,+3. A sports car has another pattern, for example

+4+9+1+8. So it is not possible to monitor only one output unit to get the whole information needed to specify the kind of car, or object.

Learning in a PDP Network, with backpropagation

& B E3

■ Correct pattern for "truck"

Basic Principles of Connectionism

It has to be emphasised, that connectionist networks are no models of how the nervous system works. It is a hypothetical approach to represent categories in network patterns. Another name for the connectionist approach is Parallel Distributed 1 Processing approach, for short PDP, since processing takes place in parallel lines and the output is distributed across many units.

Operation of Connectionist Networks

The picture on the right shows a, learning process in a PDP. Learning is' necessary, since the patterns, for" example for "truck", are not build-in.

First the stimulus, that stands for truck is presented to the input units. Then the links pass on the signal to the hidden units, that distribute the signal to the output units via further links.

■ Output unit response

• Input units


error signals

'/ *------Back


0 0 0 0

Correct pattern and output match, (no error signal)

After many trials, the

* weights are

'calibrated, the network corresponds correctly to "truck"


In the first trial, the output units shows a wrong pattern, an error signal, that does not represent "truck". But after many repetitions, the pattern is correct. This is achieved by back propagation. The error signals are send back to the hidden units and the

signals are reprocessed. During theseProcessing steps of learning in a PDP (GFDL - Lbartels repetive trials, the "weights" of the signal are gradually calibrated on behalf of the error signals in order to get a right output pattern at last.

After having achieved a correct pattern for "truck", the system is ready to learn a new concept. This new concept will then have a new and different output pattern in comparison to "truck". Yet, by learning the new concept, the old can be "forgotten" and if forgotten, needs to be relearned.

Evaluating Connectionism

The PDP approach is important for KR studies. It is far from perfect, but on the move to get there.

Some supporting arguments are the following: The process of learning enables the system to make generalizations, because similar concepts create similar patterns. After knowing one car, the system can recognize similar patterns as other cars, or may even predict how other cars look like. Furthermore, the system is protected against total wreckage. A damage to single units will not cause the system's total breakdown, but will delete only some patterns, which use those units. This is called graceful degradation and is often found at patients with brain lessions. These two arguments lead to the third. The PDP is organised similarly to the human brain. And some effective computer programmes have been developed on this basis, that were able to predict the consequences of human brain damage.

On the other hand, problems of the connectionist approach are: Formerly learned concepts can be superposed by new concept. In addition PDP can not explain more complex processes than learning concepts. Neither, can it explain the phenomenon of rapid learning. It is assumed, that rapid learning takes place in the hippocampus, and that conceptual and gradual learning is located in the cortex.

In conclusion, the PDP approach can explain some, but not everything. So, a combination of different approaches is most goal oriented.

Mental Representation

There are different theories on how living beings, especially humans encode information to knowledge. We may think of diverse mental representations of the same object. When reading the written word “car”, we call this a discrete symbol. It matches with all imaginable cars and is therefore not bound to a special vehicle. It is an abstract, or amodal, representation. Else wise if seeing a picture of a car. It might be a red sport wagon. Now we speak of a non-discrete symbol, an imaginable picture that appears in front of our inner eye and that fits only to some similar cars.

Propositional Approach

The Propositional Approach is one possible way to model mental representation in the human brain. It works with discrete symbols which are strongly connected among each other. The usage of discrete symbols necessitates clear definitions of each symbol, as well as information about the syntactic rules and the context dependencies in which the symbols may be used. The symbol “car” is only comprehensible for people how do understand English and have seen a car before and therefore know what a “car” is about. The Propositional Approach in an explicit way to explain mental representation.


Definitions of propositions differ in the different fields of research and are still in discussion. One possibility is the following:

"Traditionally in philosophy a distinction is made between sentences and the ideas underlying those sentences, called propositions. A single proposition may be expressed by an almost unlimited number of

134 | Cognitive Psychology and Neuroscience


sentences. Propositions are not atomic, however; they may be broken down into atomic concepts called "Concepts".

Mental Propositions

In addition Mental Propositions deal with the storage, retrieval and interconnection of information as knowledge in the human brain.

There is a big discussion, if the brain really works with propositions or if the brain processes its information to and from knowledge in another way or perhaps in more ways.

Imagery Approach

One possible alternative to the Propositional Approach, is the Imagery Approach. Since here the representation of knowledge is understood as the storage of images as we see them, it is also called analogical or perceptual approach. In contrast to the Propositional Approach it works with non-discrete symbols and is modality specific. It is an implicit approach to mental representation. The picture of the sport wagon includes implicitly seats of any kind. If additionally mentioned that they are off-white, the image changes to a more specific one. How two non-discrete symbols are combined is not as predetermined as it is for discrete symbols. The picture of the off-white seats may exist without the red car around, as well as the red car did before without the off-white seats.

The Imagery and the Propositional Approaches are also discussed in chapter 8.

Knowledge Representation (KR) in Computational Models of Cognition

Almost all these theories evolved in symbiosis with the developing computer sciences. Computer processing was seen as a model of human brain processing.

Computer Science wants to understand the human brain by developing artificial models of its functioning and on the other hand uses the human brain as an inspiration for computational systems.

Knowledge Representation (KR), one of the crucial parts of Artificial Intelligence (AI), deals with information encoding, storing and usage for "computational models of cognition" (MITECS). KR has many connections to other fields concerning (human) information processing like logic, linguistics, reasoning, and the philosophical aspects of these fields, to name only a few. There are two main interest of KR: First, the kind of information, that is encoded. This is called knowledge engineering. Second, the formalisms of information representations. That means, how information is stored. Most KR applications are particularly developed for a specific use, for example a digital map for robot navigation or a graph like account of events for visualizing stories. Yet, the overall aim is to find an artificial way to draw conclusions with preferably short derivations. Humans can draw conclusions very well. But AI applications, like robots or programs are still very bad at that issue. Drawing conclusions is one of the most difficult tasks for artificial intelligence. Furthermore, there is a need for a uniform kind of knowledge representation, since there are many different ways of incompatible representations. For further information, read the passage about intertranslation between KR formalisms. In order to achieve these goals, it is necessary to create a "conceptual framework" for a common terminology.

Knowledge Engineering

Knowledge engineering deals with finding the right type of conceptual vocabulary. That means that each special knowledge type is expressed best by its own specific conceptual vocabulary. Different kinds of knowledge are, for example: Rules of games, attributes of objects and their relations to each other, temporal relations, chronologies, world knowledge...


Related conceptual vocabularies, that are able to describe objects and their relationships are called ontologies. These conceptual vocabularies are highly formal and able to express meaning in a specific fields of knowledge. They are used for queries and assertions to knowledge bases and make knowledge sharing possible. In order to represent different kinds of knowledge, that means several ontologies, in one framework, Jerry Hobbs (1985) proposed the principle of ontological promiscuity. This means, to mix several ontologies together to cover a range of different knowledge types, as many as possible.

A query to such a system could be for example: “Take a cube from a table”.

First, since we live in a temporal world, the action must be a progressive processing, that can be broken down in successive steps/states. Temporal information is often represented as functions on states of the world. E.g.: to_grow(child,adult). After applying the function to_grow to child, the child becomes an adult.

Second, we make general statements about and rules for our system/world. Axioms on the rules of the environment are stated, like gravitational forces.

Third, we try out the chain of tasks, that have to be done to take a cube from a table. 1) Reach out for the cube with the hand, 2) grab it, 3) raise the hand with the cube, etc. Logical Reasoning is the perfect tool for this task, because the system can also recognize if the task is possible.

Frame Problem

But there is a problem with the procedure described above. It is called Frame Problem. The system in the example deals with changing states or in other words: the steps of actions that take place, change the environment. That is, the cube changes its place. Yet, the system does not make any propositions about the table so far. We need to make sure, that after picking up the cube from the table, the table does not change its state. It should not disappear or break down. This could happen, since the table is no longer needed. The systems tells that the cube is in the hand and omits any information about the table. In order to work out the Frame Problem there have to be stated some special axioms or similar things. The Frame Problem has not been solved completely. There are different approaches to a resolution. Some add object spatial and temporal boundaries to the system/world (Hayes 1985). Others try more direct modeling. They do transformations on state descriptions. For example: Before the transformation the cube is on the table, after transformation , the table still exists, but independent from the cube.

Knowledge Representation Formalisms

All KR formalisms need a strict syntax, semantics and inferring procedures in order to be clear and computable. Furthermore most formalisms have the following attributes to be able to express information more detailed: the Semantic Network Approach, concept hierarchies (Vehicle -> car -> truck) and property inheritance (A truck has four wheels, like a car). There are attributes that provide the possibility to add new information to the system without creating any inconsistencies, and the possibility to create a "closed-world" assumption. For example if the information that we have gravitation on earth is omitted, the closed-world assumption must be false for our earth/world.

Different Types of Formal Languages

There are symbolic, functional, logical, and object oriented notations, which formulate KR systems. These different kinds of encoding are inspired by Imagery, Semantic Networks, First-order logic, Second-order logic, Bayesian probabilistic inference, fuzzy reasoning, and others like nonmonotonic reasoning, that can displace old with more detailed information.

Expressive Power of Formalisms vs. Deductive Complexity

A problem for KR formalisms is the mutually exclusiveness of expressive power and deductive reasoning in one formalism. If a formalism has a big expressive power, it is able to describe a wide range of (different) information, but is not able to do brilliant inferring from (given) data. Propositional logic is restricted to Horn clauses. A Horn clause is a disjunction of literals with at most one positive literal. It has a very good decision procedure(inferring), but can not express generalizations. An example is given in the logical programming language Prolog. If a formalism has a big deductive complexity, it is able to do brilliant inferring, i.e. make conclusions, but has a poor range of what it can describe. An example is second-order logic. So, the formalism has to be tailored to the application of the KR system. This is reached by compromises between expressiveness and deductive complexity. In order to get a greater deductive power, expressiveness is sacrificed and vice versa.

Application of KR – Databases

KR can be and is used as extension of Database Technology. It is mostly not used as a computational model of cognition, but as a pool of information. In these cases general rules and models are not needed. With growing storage media, one is capable to create simple knowledge bases stating all specific facts. The information is stored in the form of sentential knowledge, that is knowledge saved in form of sentences comparable to propositions, or program code.

Intertranslation between KR Formalisms

With the growth of the field of knowledge bases, there have been developed many different standards. They all have different syntactic restrictions. To allow intertranslation, there have been created different "interchange" formalisms. For example the Knowledge Interchange Format, First-Order Set Theory plus LISP(Genesereth et al. 1992) or a Graph Notation (Peirce).

Gap between Human and Artificial KR

There is still a long way for researchers till they will be able to formalize all human kinds of knowledge. Intuitive, temporal and spatial knowledge defy themselves from control and can not be formalized today. Also the understanding of physical coherences and story comprehension is not framed properly. Whereas the complex strategies of chess playing are already formalized. The chess computer Deep Blue beat the world chess champion Kasparow in 1997.

KR in AI

As seen above KR is a key to process given unsystematic information of the world to get intelligibly knowledge. With this kind of knowledge, artificial intelligence research is willing to develop systems, that are able to act and react properly to the world; And some day, are able to act intelligent, perhaps.

Hemispheric Distribution

After having dealt with how knowledge is stored in the brain we now turn to the question of whether the brain is specialized and if it is which functions can be located where. These questions can be subsumed under the topic “hemispheric specialisation” or “lateralization of processing” which looks at the differences in processing between the two hemispheres of the human brain. Differences between the hemispheres can be traced back to as long as 3.5 million years ago. Evidence for this are fossils of australopithecines (which is an extinct ancestor of homo sapiens). Because differences have been present for so long and survived the selective pressure they must be useful in some way for our cognitive processes.

Differences in Anatomy and Chemistry

Although at first glance the two hemispheres look identically they differ in fact quite a lot in various ways.

Concerning the anatomy, some areas are larger and the tissue contains more dendritic spines in one hemisphere than in the other. An example of this is what used to be called “Broca’s area” in the left hemisphere. This area which is –among other things- important for speech production shows greater branching in the left hemisphere than in the respective right hemisphere area. Because of the left hemisphere’s importance for language, with which we will deal later, one can conclude that anatomical differences have consequences for lateralization in function.

Neurochemistry is another domain the hemispheres differ in: The left hemisphere is dominated by the neurotransmitter dopamine, whereas the right hemisphere shows higher concentrations of norepinephrine. Theories suggest that modules specialized on cognitive processes are distributed over the brain according to the neurotransmitter needed. Thus, a cognitive function relying on dopamine would be located in the left hemisphere.

Historic Approaches

Hemispheric specialisation has been of interest since the days of Paul Broca and Karl Wernicke, who discovered the importance of the left hemisphere for speech in the 1860s. Broca examined a number of patients who could not produce speech but whose understanding of language was not severed, whereas Wernicke examined patients who suffered the opposite symptoms (i.e. who could produce speech but did not understand anything). Both Broca and Wernicke found that their patients’ brains had damage to distinct areas of the left hemisphere.

Because in these days language was seen as the cognitive process superior to all other processes, the left hemisphere was believed to be superior to the right which was expressed in the “cerebral dominance theory” developed by J.H. Jackson. The right hemisphere was seen as a “spare tire […] having few functions of its own” (Banich, S.94). This view was not challenged until the 1930s. In this decade and the following, research dramatically changed this picture. Of special importance for showing the role of the right hemisphere was Sperry, who conducted several experiments in 1974 for which he won the Nobel Prize in Medicine and Physiology in 1981.

Experiments with Split-Brain-Patients

Sperry’s experiments were held with people who suffered a condition called “split brain syndrom” because they underwent a commissurotomy. In a commissurotomy the corpus callosum which is the major cortical connection between the two hemispheres is cut so that communication between the hemispheres becomes severed in these patients. Before dealing with the role of the corpus callosum, we will talk about Sperry’s pioneering experiments with which he wanted to find out whether the left hemisphere really played such an important role in speech processing as was suggested by Broca and Wernicke.

Sperry used different experimental designs in his studies, but the basic assumption behind all experiments of this type was that perceptual information received at one side of the body is processed in the contralateral hemisphere of the brain.

In one of the experiments the subjects had to recognize objects by touching it with merely one hand, while being blindfolded. He then asked the patients to name the object they felt and found that people could not name it when touching it with the left hand (which is linked to the right hemisphere). The question that arose was whether this inability was due to a possible function of the right hemisphere as “spare tire” or due to something else. Sperry now changed the design of his experiment so that patients now had to show that they recognized the objects by using it the right way. For example, if they recognized a pencil they would use it to write. With this changed design, no difference in performance between both hands were found.

Another experiment conducted by Sperry involved chimeric pictures. A chimeric picture is a picture that is made up of two things that are each cut in half and then put together as one.

Experiments with Patients with other Brain-Lesions

Other experiments that were conducted with the aim to find out about hemispheric specialisation was done with individuals who were about to receive surgery in which parts of one of their
hemispheres was going to be removed due to epileptic seizures. Before the surgery was begun, it was important to find out which hemisphere was responsible for speech in this individual. This was done with the Wada-technique. Here, barbiturate was injected into one of the arteries supplying the brain with blood. Shortly after the injection, the contralateral side of the body is paralysed. If the person was now still able to speak, the doped hemisphere of the brain is not responsible for speech production in this individual. With the results of this technique it could be estimated that 95% of all adult righthanders use their left hemisphere for speech.


Research with people who suffer brain lesions or even a commissurotomy has some major draw backs: The reason why they had to undergo such surgery is usually epileptic seizures. Because of this, it is possible that their brains are not typical or have received damage to other areas during the surgery. Also, these studies have been performed with very limited numbers of subjects, so the statistical reliability might not be high.

Experiments with Neurologically Intact Individuals

In addition to experiments with brain-severed patients, studies with neurologically intact individuals have been conducted to measure perceptual asymmetries. These are usually performed with one of three methods: Namely the “divided visual field technique”, “dichaptic presentation” and “dichotic presentation”. Each of them again has as basic assumption the fact that perceptual information received at one side of the body is processed in the contralateral hemisphere.

The divided visual field technique is based on the fact that the visual field can be divided into the right (RVF) and left visual field (LVF). Each visual field is processed independently from

the other in the contralateral hemisphere. The divided visual fieldHighly simplified picture of the visual technique includes two different experimental designs: The pathway.

experimenter can present one picture in just one of the visual fields and then let the subject respond to this stimulus. The other possibility involves showing two different pictures in each visual field. A problem that can occur using the visual field technique is that the stimulus must be presented for less than 200 ms because this is how long the eyes can look at one point without shifting of the visual field.

In the dichaptic presentation technique the subject is presented two objects at the same time in each hand. (c.f. Sperry’s experiments)

The dichotic presentation technique enables researchers to study the processing of auditory information. Here, different information is presented simultaneously to each ear. Experiments with these techniques found that a sensory stimulus is processed 20 to 100 ms faster when it is initially directed to the specialized hemisphere for that task and the response is 10% more accurate.

Explanations for this include three hypotheses, namely the direct access theory, the callosal relay model and the activating-orienting model. The direct access theory assumes that information is processed in that hemisphere to which it is initially directed. This may result in less accurate responses,
if the initial hemisphere is the unspecialised hemisphere. The Callosal relay model states that information if initially directed to the wrong hemisphere is transferred to the specialized hemisphere over the corpus callosum. This transfer is time-consuming and is the reason for loss of information during transfer. The activating-orienting model assumes that a given input activates the specialized hemisphere. This activation then places additional attention on the contralateral side of the activated hemisphere, “making perceptual information on that side even more salient”. (Banich)


All experiments had some basic findings in common: The left hemisphere is superior at verbal tasks such as the processing of speech, speech production and recognition of letters whereas the right hemisphere excels at non-verbal tasks such as face recognition or tasks that involve spatial skills such as line orientation, or distinguishing different pitches of sound. This is evidence against the cerebral dominance theory which appointed the right hemisphere to be a spare tire! In fact both hemispheres are distinct and outclass at different tasks, and neither one can be omitted without this having high impact on cognitive performance.

Although the hemispheres are so distinct and are experts at their assigned functions, they also have limited abilities in performing the tasks for which the other hemisphere is specialized.

Do the Hemispheres Differ in What or How They Process?

There are two sets of approaches to the whole question of hemispheric specialisation. One set of theories goes about the topic by asking the question “What tasks is each hemisphere specialized for?”. Theories that belong to this set, assign the different levels of ability to process sensory information to the different levels of abilities for higher cognitive skills. One theory that belongs to this set is the “spatial frequency hypothesis”. This hypothesis states that the left hemisphere is important for fine detail analysis and high spatial frequency in visual images whereas the right hemisphere is important for low spatial frequency. We have

pursued this approach above. Experiment on local and global processing with patients with

left- or right-hemisphere damage

The other approach does not focus on what type of information is processed by each hemisphere but rather on how each hemisphere processes information. This set of theories assumes that the left hemisphere processes information in an analytic, detail- and function-focused way and that it places more importance on temporal relations between information, whereas the right hemisphere is believed to go about the processing of information in a holistic way, focusing on spatial relations and on appearance rather than on function.

The picture above shows an exemplary response to different target stimuli in an experiment on global and local processing with patients who suffer right- or left-hemisphere damage. Patients with damage to the right hemisphere often suffer a lack of attention to the global form, but recognize details with no problem. For patients with left-hemisphere-damage this is true the other way around. This experiment supports the assumption that the hemispheres differ in the way they process information.

Communication Between the Hemispheres via the Corpus Callosum

After we have looked at the different functions of each hemisphere and how researchers went about finding this out, we will now look at the role of the corpus callosum. With its 250 million nerve fibres the corpus callosum is like an Autobahn for neural data connecting the two hemispheres. There are in fact smaller connections between the hemispheres but these are little paths in comparison to the corpus callosum. All detailed higher order information must pass through the corpus callosum when being transferred from one hemisphere to the other. The transfer time which can be measured with ERP takes between 5 to 20 ms.

So why is this transfer needed at all if the hemispheres are so distinct concerning functioning, anatomy, chemistry and the transfer results in degrading of quality of information and takes time? The reason is that the hemispheres, although so different, do interact. This interaction has important advantages because as studies by Banich and Belger have shown it may “enhance the overall processing capacity under high demand conditions” (Banich). (Under low demand conditions the transfer does not make as much sense because the cost of transferring the information to the other hemisphere are higher than the advantages of parallel processing.)

The two hemispheres can interact over the corpus callosum in different ways. This is measured by first computing performance of each hemisphere individually and then measuring the overall performance of the whole brain.

In some tasks one hemisphere may dominate the other in the overall performance, so the overall performance is as good or bad as the performance of one of the single hemispheres. What’s surprising is that the dominating hemisphere may very well be the one that is less specialized, so here is another example of a situation where parallel processing is less effective than processing in just one half of the brain.

Another way of how the hemispheres interact is that overall processing is an average of performance of the two individual hemispheres.

The third, most surprising way the hemispheres can interact is that when performing a task together the hemispheres behave totally different than when performing the same task individually. This can be compared to social behavior of people: Individuals behave different in groups than they would when being by themselves.

Individual Factors may Influence Lateralization

After having looked at hemispheric specialization from a general point of view, we now want to focus on differences between individuals concerning hemispheric specialization. Aspects that may have an impact on lateralization might be age, gender or handed-ness.


Let’s first look at whether the age of an individual decides in how far each hemisphere is used at specific tasks. Researchers have suggested that lateralization develops with age until puberty. Thus infants should not have functionally-lateralized brains. Here are four pieces of evidence that speak against this hypothesis:

Infants already show the same brain anatomy as adults. This means the brain of a new born is already lateralized. Following the hypothesis that anatomy is linked to function this means that lateralization is not developed at a later period in life.

Differences in perceptual asymmetries that means superior performance at processing verbal vs. non-verbal material in the different hemispheres cannot be observed in children aged 5 to 13, i.e. children aged 5 process the material the same way 13 year olds do.

Experiments with 1-week-old infants showed that they responded with increased interest to verbal material when this was presented to the right ear than when presented to the left ear and increased interest to non-verbal material when presented to the left ear. The infants’ interest was hereby measured by the frequency of soother sucking.

Although children who underwent hemispherectomy (the surgical removal of one hemisphere) do develop the cognitive skills of the missing hemisphere (in contrast to adults or adolescents who cannot compensate for missing brain parts), they do not develop these skills to the same extent as a child with hemispherectomy of the other hemisphere. For example: A child whose right hemisphere has been removed will develop spatial skills but not to the extent that a child whose left hemisphere has been removed, and thus still possesses the right hemisphere.


Another factor that might influence brain lateralization is handedness. There is statistical evidence that left-handers have a different brain organization than right-handers. 10% of the population is left-handed. Whereas 95% of the right-handed people process verbal material in a superior manner in the left-hemisphere, there is no such a high figure for verbal superiority of one hemisphere in left-handers: 70% of the left-handers process verbal material in the left-hemisphere, 15% process verbal material in the right hemisphere (so the functions of the hemispheres are simply switched around), and the remaining 15% are not lateralized, meaning that they process language in both hemispheres. Thus as a group, left-handers seem to be less lateralized. However a single left-handed-individual can be just as lateralized as the average right-hander.


Gender is another aspect that is believed to have impact on the hemispheric specialization. In animal studies, it was found that hormones create brain differences between the genders that are related to reproductional functions. In humans it is hard to determine to which extent it is really hormones that cause differences and to which extent it is culture and schooling that are responsible.

One brain area for which a difference between the genders was observed is the corpus callosum.

Although one study found that the c.c. is larger in women than in men these results could not be replicated. Instead it was found that the posterior part of the c.c. is more bulbous in women than in men. This might however be related to the fact that the average woman has a smaller brain than the average man and thus the bulbousness of the posterior section of the c.c. might be related to brain size and not to gender.

In experiments that measure performance in various tasks between the genders the cultural aspect is of great importance because men and women might use different problem solving strategies due to schooling.


Although the two hemispheres look like each other’s mirror images at first glance, this impression is misleading. Looking closer, the hemispheres not only differ in their conformation and chemistry, but most importantly in their function. Although both hemispheres can perform all basic cognitive tasks, there exists a specialization for specific cognitive demands. In most people, the left hemisphere is an expert at verbal tasks, whereas the right hemisphere has superior abilities in non-verbal tasks. Despite the functional distinctness the hemispheres communicate with each other via the corpus callosum.

This fact has been utilized by Sperry’s experiments with split-brain-patients. These are outstanding among other experiments measuring perceptual asymmetries because they were the first experiments to refute the hemispheric dominance theory and received recognition through the Nobel Prize for Medicine and Physiology.

Individual factors such as age, gender or handed-ness have no or very little impact on hemispheric functioning.


Editors: Robert A. Wilson and Frank C. Keil.(Eds.) (online version july 2006). The MIT Encyclopedia of the Cognitive Sciences (MITECS), Bradford Books

Knowledge Representation

Goldstein, E. Bruce.(2005). Cognitive Psychology - Connecting, Mind Research, and Everyday Experience. Thomson, Wadsworth. Ch 8 Knowledge, 265-308.

Sowa, John F.(2000). Knowledge Representation - Logical, Philosophical, and Computational Foundations. Brooks/Cole.

Slides concerning Knowledge from: , Knowledge: Propositions and images. Knowledge: Concepts and categories.

Hemispheric Specialisation/Distribution

Banich, Marie T.(1997).Neuropsycology - The Neural Bases of Mental Function. Hougthon Mifflin Company. Ch 3 Hemispheric Specialisation, 90-123.

Hutsler, J. J., Gillespie, M. E., and Gazzaniga (2002). The evolution of hemispheric specialization. In Bizzi, E., Caliassano, P. and Volterra V. (Eds.) Frontiers of Life, Volume III: The Intelligent Systems Academic Press: New York.

Birbaumer, Schmidt(1996). Biologische Psychologie. Springer Verlag Berlin-Heidelberg. 3.Auflage. Ch 24 Plastizität, Lernen, Gedächtnis. Ch 27 Kognitive Prozesse (Denken).

Kandel, Eric R.; Schwartz, James H.; Jessel, Thomas M.(2000). Principles of Neural Science. Mc Graw Hill. edition. Part IX, Ch 62 Learning and Memory.

Ivanov, Vjaceslav V.(1983). Gerade und Ungerade - Die Assymmetrie des Gehirns und der Zeichensysteme. S.Hirzel Verlag Stuttgart.

David W.Green ; et al.(1996). Cognitive Science - An Introduction. Blackwell Publishers Ltd. Ch 10 Learning and Memory(David Shanks).


Knowledge Representation

From Stanford Encyclopedia of Philosophy: knowledge analysis, knowledge by acquaintance and knowledge by description

Lecture on Knowledge and Reasoning, University of Erlangen Germany

Links to Knowledge-Base and Ontology Projects Worldwide

Links on Ontologies and Related Subjects

Knowledge Representation: Logical, Philosophical, and Computational Foundations, by Sowa, John F.

Hemispheric Specialisation

Evolution of Hemispheric Specialisation, by Hutsler, Gillespie, Gazzaniga Cerebral specialisation and interhemispheric communication, by Gazzaniga,in Oxford Journals live version • discussion • edit lesson • comment • report an error


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