FOUNDATIONS OF COGNITIVE SCIENCE - of Kai-Uwe Carstensen

Foundations of Cognitive Science Carstensen 01.03.1999 

FOUNDATIONS OF COGNITIVE SCIENCE 

Kai-Uwe Carstensen 

Kai.Carstensen@CogSci.Uni-Osnabrueck.de 

University of Osnabrück Winter semester 1998/99 

Information about the lecture can be found at: 

http://www.cogsci.uni-osnabrueck.de/lectures/foundations/Foundationsscript.zip 

THE IMPORTANCE OF COGNITIVE SCIENCE 

This lecture introduces Cognitive Science, a new (inter-)discipline whose centerpiece is the investigation of 

cognition. Cognitive Science brings together evidence from various disciplines about how our mind and 

brain work, integrating the knowledge gained from different approaches to cognition. Cognitive Science 

views the mind as an information processing system. The results of empirical observation, theoretical 

analysis and computational modelling of mental activity are supposed to have a tremendous impact on future 

information technology: 

"I believe that the discovery by cognitive science and artificial intelligence of the technical challenges 

overcome by our mundane mental activity is one of the great revelations of science, an awakening of the 

imagination comparable to learning that the universe is made up of billions of galaxies or that a drop of pond 

water teems with microscopic life" 

(Steven Pinker, How the mind works, p. 4). 

MOTIVATION 

You may ask why a new discipline is needed for the study of cognition and why establishing this discipline 

should be regarded as a good idea. Here are a few reasons: 

Eternal questions are still unanswered 

Some questions about our mind have been asked for thousands of years but still haven´t been given satisfying 

answers: How do we think, understand, speak, learn …; what is the relation of body and mind; what is 

consciousness? These questions have been investigated in different, loosely related disciplines, but theoretical 

progress has been hindered by reformulations and reinterpretations of questions and (partial) answers. The 

new joint effort made in Cognitive Science can be expected to give answers to them by simply bringing 

together evidence from different viewpoints. 

Clear old problems recognized 

Perhaps the most famous theoretical problem is the "Frame problem" (McCarthy/Hayes 1969) well known 

in the field of Artificial Intelligence: The problem of specifying what exactly belongs to a certain piece of 

knowledge or, e.g., what is relevant for planning and performing a certain action. This is most clearly 

exemplified in Dennett´s story of the little robot. 

Once upon a time there was a robot, named R1 by its creators. Its only task was to fend for 

intself. One day its designers arranged for it to learn that its spare battery, its precious enerfy 

supply, was locked in a room with a time bomb set to go off soon. R1 located the room, and the 

key to the door, and formulated a plan to rescue its battery. There was a wagon in the room, and 

the battery was on the wagon, and R1 hypothesized that a certain action which it called 

PULLOUT(WAGON,ROOM) would result in the battery being removed from the room. 

Straighaway it acted, and did succeed in getting the battery out of the room before the bomb went 

off. Unfortunately, however, the bomb was also on the wagon. R1 knew that the bomb was on the 

wagon in the room, but didn't realize that pulling the wagon would bring the bomb out along with 

the battery. Poor R1 had missed that obvious implication of its planned act. 

Back to the drawing board. "The solution is obvious," said the designers. "Our next robot must be 

made to recognize not just the intended implications of its acts, but also the implications about 

their side-effects, by deducing these implications from the descriptions it uses in formulating its 

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plans." They called their next model the robot-deducer R1D1. They placed R1D1 in much the 

same predicament the R1 had succombed to, and as it too hit upon the idea of 

PULLOUT(WAGON,ROOM), it bafan, as designed to consider the implications of such a course 

of action. It had just finished deducing that pulling the wagon out of the room would not change 

the color of the room's walls, and was embarking on a proof of the further implication that pulling 

the wagon out would cause its wheels to turn more revolutions than there were wheels on the 

wagon - when the bomb went off. 

Back to the drawing board. "We must teach it the difference between relevant implications and 

irrelevant implications," said the designers. "And teach it to ignore the irrelevant ones." So they 

developed a method of tagging implications as either relevant or irrelevant to the project at hand, 

and installed the method in their net model, the robot-relevant-deducer, R2D1. When they 

subjected R2D1 to the test that had so unequivocally selected its predecessors for extinction, they 

were surprised to find it sitting, Hamlet-like, outside the room containing the bomb, the native 

hue of its resolution sicklied o'er with the pale case of thought, as Shakespeare has aptly put it. 

"DO something!" its creators yelled. 

"I am," it replied. "I'm busily ignoring some thousands of implications I have determined to be 

irrelevant. Just as soon as I find an irrelevant implication, I put it on the list of those I must 

ignore, and..." the bomb went off. 

from: Dennett, D., Cognitive Wheels: The Frame Problem in AI. In Minds, Machines, and 

Evolution. C. Hookway, ed. Pp. 128-151. Cambridge University Press, 1984. 

The robot´s failure can be blamed to two problematic aspects: First, the assumption that all problems can/have 

to be solved by conscious, rational thinking, that is, by the serial, step-by-step application of rules to arrive at 

a solution [an extreme version of this is also known as Ryle´s regress: the fallacious assumption that every 

conscious mental act needs deliberate planning (as well as its parts, its parts´ parts …)]. Second, seriality 

itself: although for small domains it is still a good idea to serially check the possibilities of what should/could 

be done in a certain situation (just look at the success of current chess computers), such serial thinking leads 

to a bottleneck in performance and is, in general, grossly inadequate both for biological (massive parallelity in 

the brain) and practical reasons. 

New insights gained 

The pop-out phenomenon and parallel computation: Why is it sometimes difficult to identify certain elements 

in our visual field (e.g., the "T" in a.) while sometimes certain elements can not be missed (they "pop up" in 

front of our "mental eye" as in b.)? We now know that there is a stage of parallel processing of different 

aspects of the visual field that must be distinguished from a stage of serial processing. An important aspect of 

our visual cognition is therefore a clever division of labour between "dumb" parallel processing (highlighting 

"interesting" locations in the visual field) and attentive, serial processing of such locations (identifying what is 

there, a kind of "looking at" these locations). [Mind the metaphors used here simply for abbreviation and 

illustration: Of course there is not a little person in our head (a homunculus) looking at our visual field (this is 

just what Ryle criticised)! In a sense, Cognitive Science is about replacing such inadequate metaphors by 

mechanisms.] 

Do you see the outstanding objects in both pictures? In the first one, it is not so easy to see the "T", because 

there are many vertical and horizontal lines in the display, making their combination less salient. 

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New technologies invented 

For a long time, the investigation of how the mind works has been in the hands of philosophers and 

psychologists. Now we do not only use computers for modelling the mind´s operations (or for building 

artificially intelligent systems), but with new neuroimaging techniques and devices (e.g., PET scans), we are 

even beginning to be able to "see the mind at work". 

Innovative methods found 

Artificial Neural Networks and Connectionism: In the past years, new methods of computing have been 

developped which are more or less inspired by the information processing in our brain. Some old and difficult 

problems (like face or voice recognition) can be handled much better with these methods. 

SYLLABUS 

· What is Cognitive Science? Basics. History. 

· The architecture of mind/cognition: CRUM (computational-representational understanding of the 

mind) 

· Non-classical approaches to cognition 

· The brain and its structure 

· The relation of mind and brain 

· Language 

· Artificial intelligence 

· Philosophy of mind 

· Visual cognition/ Imagery 

· Attention 

· Consciousness 

„COGNITION“ 

Derived from lat. cognoscere, gr. gignoskein (perceive, (get to) know) 

Introduced in modern psychology in demarcation to behaviorism 

· cognitive psychology (as a new branch of psychology established in the sixties) 

Cognition comprises, e.g., the functions of perception, imagination, planning, problem solving, 

remembering, recognizing, learning, language generation and understanding 

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WHAT IS THE AIM OF COGNITIVE SCIENCE? 

-> "reverse engineering" (How did nature do it?) 

rather than or before "engineering" 

Basic research questions (from Eckhardt 1995, What is Cognitive Science ?): 

• For the normal, typical adult, what is the capacity to ...? 

• In virtue of what does a normal, typical adult have the capacity to ...? 

• How does a normal, typical adult typically ...? 

• How does the capacity to … of the normal, typical adult interact with the rest of his or her cognitive 

capacities? 

CONTROVERSIAL AIMS 

· Does Cognitive Science include the study of the cognition (or intelligence) of man-made-computers? 

· Does Cognitive Science include the study of the cognition (or intelligence) of non-human animals? 

· Does Cognitive Science include the study of human mental phenomena other than cognition (such as 

emotions)? 

In my opinion, of course! 

THE RESEARCH FRAMEWORK OF COGNITIVE SCIENCE 

· The domain of Cognitive Science consists of the (human) cognitive capacities, which have a number of 

properties, among them 

· Intentionality (aboutness) 

Ð Productivity (i.e., to be used in novel ways) 

· The capacities make up a system according to which answers to the basic questions can be found 

· The (human,) cognitive mind/brain is a computational and representational device; hence, cognitive 

capacities consist of a system of computational and representational capacities 

REVISED RESEARCH QUESTIONS 

• For the normal, typical adult, what precisely is the information-processing function that underlies the 

capacity to ...? 

• When a normal, typical adult has the capacity to …, in virtue of what computational and 

representational resources is he or she able to compute the function ... ? 

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• When a normal, typical adult typically exercises his or her capacity to …, how is the function 

computed? 

• How does the information-processing function to … of the normal, typical adult interact with the rest 

of his or her information-processing functions? 

ASPECTS OF THE INVOLVED DISCIPLINES 

Philosophy: 

· Strong influences on theories of mind, language, and knowledge 

· Syllogisms (Aristotle) 

· „thinking as mental calculation“-view (Hobbes, Leibniz) 

· Mind-body-problem (Descartes) 

· Ideas, categories (Plato, Kant, Wittgenstein) 

· Logic (Frege, Carnap) 

Neuroscience: 

Ð Lashley (ablation technique, lesions) 

Ð Hebb (connectivity of cell assemblies) 

Ð McCulloch (+ Pitts) (logical abstractions of biological neurons) 

Ð Hubel/Wiesel (single-cell recordings) 

Psychology: 

Ð Information processing replaces behaviourism as the dominant paradigm (-> “cognitive" psych., -> 

George Miller) 

Ð Thinking as symbol manipulation, computer simulation as a method for the development of theories 

of the functioning of the mind 

Linguistics: 

· Generative grammar replaces structural linguistics (Noam Chomsky). (+ psycho-, computational-L.) 

Computer Science/Artificial Intelligence: 

· Symbolic computation replaces mere numerical calculation as main focus of the discipline (symbolic 

programming language LISP, ->McCarthy) 

· Programming in Logic (PROLOG) 

· Parallel Distributed Processing (PDP, ->Rumelhart/McClelland) 

(Cognitive) Anthropology: 

· Social/cultural variation of cognitive functions 

Mathematics (as a background discipline): 

· Establishment of basic formal concepts and formalisms 

· Mathematical logic (Frege, Russel/Whitehead) 

· Theory of computation (Turing, Church) 

· Formal semantics of natural language (Montague) 

THE RISE OF COGNITIVE SCIENCE 

• Hixon-Symposium (1948, California Institute of Technology) („Wie steuert das Nervensystem 

Verhalten?“): First counter-reaction against behaviorism (-> Skinner) 

participants: Lashley (Psych.), von Neumann (Math.), McCulloch (Neurophys.) 

• Symposium on Information Theory (1956) 

participants : George Miller (Psych.), Allan Newell/Herbert Simon (Inf.), Noam Chomsky (Ling.) 

• Meeting at the Dartmouth College (1956) 

participants : McCarthy, Minsky, Newell, Simon 

• Chomsky: review of Skinners „Verbal Behaviour“ (1959) 

• Establishment of the Center for Cognitive Studies in Harvard (Miller, Jerome Bruner) 

• Initiative of the Sloan Foundation (since 1975) 

• Establishment of the journal „Cognitive science“ (1977) 

• Establishment of the Cognitive Science Society (1979) 

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• Establishment of the journal „Kognitionswissenschaft“ (1990) 

• Establishment of the „Gesellschaft für Kognitionswissenschaft“ (GK) (1994) 

THE CLASSICAL VIEW OF COGNITIVE SCIENCE I 

„What has brought the field into existence is a common research objective: to discover the representational 

and computational capacities of the mind and their structural and functional representation in the brain“ 

(from a report of the Sloan foundation 1978) 

• Cognition is computation, the Turing-machine is the adequate most general construct for the 

description of computability 

• Cognitive processing is information processing 

• Information processing presupposes internal states, according to which outputs are computed with 

respect to given inputs 

• The basis of cognitive performance is the ability of cognitive systems to represent aspects of the 

environment relevant for action 

TURING MACHINE 

THE CLASSICAL VIEW OF COGNITIVE SCIENCE II 

• Aspects of representation can be formally (i.e., mathematic-logically) described and processing 

aspects can be modelled by general or specific inference mechanisms 

• Cognition is described at several levels 

- knowledge level, symbol level (representation), implementational level (physical, biological 

realization) 

- computational level, algorithmic level, implementational level (point of view: top-down) 

• Cognitive systems are composed of various components (modules) which constitute an information 

processing system (IPS) 

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Computational level: What information-processing problem is the system solving? 

Algorithmic level: What method is the system using to solve that informationprocessing 

problem? 

Implementational level: What physical properties are used to implement the (functional) 

method that the system uses to solve this information-processing 

problem? 

THE CLASSICAL VIEW OF COGNITIVE SCIENCE III 

• With the relations of their components, IPSs show a certain architecture, by which the behavior of the 

system is determined 

• IPSs are symbol processing systems which are somehow physically implemented 

(physical symbol systems hypothesis, Newell&Simon) 

"A physical symbol system consists of a set of entities, called symbols, which are physical 

patterns that can occur as components of another type of entity called an expression (or symbol 

structure). Thus, a symbol structure is composed of a number of instances (or tokens) of symbols 

related in some physical way (such as one token being next to another). At any instant of time 

the system will contain a collection of these symbol structures. Besides these structures, the 

system also contains a collection of processes that operate on expressions to produce other 

expressions: processes of creation, modification, reproduction and destruction. 

The Physical Symbol System Hypothesis. 

A physical symbol system has the necessary and sufficient means for general intelligent action. 

By necessary we mean that any system that exhibits intelligence will prove upon analysis to be a 

physical symbol system. By sufficient we mean that any physical symbol system of sufficient 

size can be organized further to exhibit general intelligence. By general intelligent action we 

wish to indicate the same scope of intelligence as we see in human action. " 

[from: Allen Newell and Herbert Simon, "Computer Science as Empirical Inquiry: Symbols and 

Search," Communications of the ACM, March 1976, pp 113-126.] 

• Cognition/mind can be described functionally (-> Functionalism), i.e. independently of its material 

realization 

Structure and behavior of IPSs can be reasonably put into analogy with the structure and behavior of 

existing computers (Von-Neumann-computer) 

Computer-Metaphor 

clear distinction of machine and program 

WHEN IS A MACHINE INTELLIGENT? TURING´S IDEA 

Turing believed that by the end of the century, machines would be able to converse and think to 

the point where no one would bother debating the issue anymore. The only problem was trying 

to figure out how we could tell if a machine was intelligent. 

After all, mankind has tried to define intelligence for ages and had made little progress except to 

decide that whatever it is, we've got it. 

Turing came up with a elegant solution. He constructed the simple proposition that if human 

beings are intelligent, and if a machine can imitate a human, then the machine, too, would have 

to be considered intelligent. 

The test may seem stupendously simplistic, but given the abysmally circular discussions about 

the nature of consciousness, meaning and thought, Turing's idea was at least a solid point of 

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reference that researchers could hold onto and discuss without getting lost in debates over how 

many bytes could dance on the head of pin. 

In Turing's proposal, a human interrogator sits in a room opposite a teletype or computer 

terminal. Hidden from the interrogator is a computer and another human being. The interrogator 

interviews both and tries to determine which is human and which is a computer. If the computer 

can fool the interrogator, it is deemed intelligent. 

Turing called this the "imitation game," although it is now universally known as the Turing Test. 

Given the simplicity of the Turing Test, it is surprising that for decades no one ever tried to 

actually conduct a Turing Test. Turing himself saw it as more a theoretical proposition to discuss 

the nature of machine intelligence. Over the years, perhaps researchers thought it obvious that no 

modern machine could yet pass the test. 

taken from: 

http://www-rci.rutgers.edu/~cfs/472_html/Intro/NYT_Intro/History/MachineIntelligence1.html 

see also 

http://www.cs.bilkent.edu.tr/~psaygin/ttest.html (great page on Turing test) 

VON-NEUMANN MACHINE 

IN SHORT: CRUM (COMPUTATIONAL-REPRESENTATIONAL 

UNDERSTANDING OF THE MIND) -> THAGARD 

· Thinking can best be understood in terms of 

· representational structures in the mind and 

· procedures operating on those structures 

· Analogy: Program Mind 

· data structures + algorithms = running programs 

· Mental representations + computational procedures = thinking 

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PROBLEMS OF THE CLASSICAL VIEW 

• The notion „representation“ is controversial 

• Symbol grounding is not ensured („symbol grounding problem“, -> Stevan Harnad) 

• The relation of mind and brain remains unclear 

• Inadequacy of the computer metaphor (massive parallel processing in the brain vs. sequential 

processing in the von Neumann-computer) 

• Top-down-conception 

• Missing content addressability 

There is a meanwhile classical while at the same time highly controversial critique of the functionalist 

(CRUM) view of cognition by the philosopher John R. Searle. 

Searle considers the following thought-experiment. Suppose that a person were given a set of 

purely formal rules for manipulating Chinese symbols. The person does not speak or understand 

written Chinese, and so he does not know what the symbols mean, though he can distinguish 

them by their differing shapes. The rules do not tell him what the symbols mean: they simply 

state that if a symbol of a certain shape comes into the room, then he should write down a 

symbol with a certain other shape on a piece of paper. The rules also state which groups of 

symbols can accompany one another, and in which order. The person sits in a room, and 

someone hands in a set of Chinese symbols. The person applies the rules, writes down a different 

set of Chinese symbols as specified by the rules on a sheet of paper, and hands the result to a 

person waiting outside the room. Unknown to the person in the room, the rules that he applies 

result in a grammatically correct conversation in Chinese. For example, if someone hands in a set 

of Chinese symbols that mean, "How do you feel today?" the symbols he writes down (as 

specified by the rules) mean, "Fine, thank you." In sum, the rules are a complete set of 

instructions that might be implemented on a computer designed to engage in grammatically 

correct conversations in Chinese. The person in the room, however, does not know this. He does 

not understand Chinese. 

taken from: http://oit.iusb.edu/~lzynda/cogsci_lecture16.html 

[see also http://www.cas.ilstu.edu/PT/chinroom.htm] 

Thus, according to Searle, although artificial systems may (seem to) be intelligent (that is, show intelligent 

behaviour), they differ fundamentally from us as their intelligence is not based on embodied cognition [there 

are many, who do not subscribe to this view, however]. 

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ALTERNATIVES/ ALTERNATIVE VIEWS 

• The architecture of the brain is relevant (neuroimaging, lesions) 

• connectionism/neuronal nets, ->subsymbolic representation and processing 

• ecological and social context are considered relevant (situated action) 

• „Natural Computation“ (i.e., „brain-like“ computation), Dana Ballard 

-> Important elements/concepts: 

• Minimal description length (programs as compact code) 

• Learning (developmental, behavioral, evolutionary) 

• Specialized architectures 

Minimum Description Length. The only answers that are practical to compute are those that 

retreat from the best answer in some sense. Answers can be just good, approximately correct, or 

correct to a certain probability. A universal metric for all these approximations is the minimum 

description length principle, which measures the cost of encoding regularity in data. 

Learning. Biological systems can amortize the cost of algorithms over their lifetime by learning 

from examples. Such learning can be seen as the ``on-line'' detection of regularity. 

Specialized Architectures. The massively parallel organization of the brain's neurons can 

compensate dramatically for their millisecond speeds. Particularly if the input is bounded at 

some fixed size, as it is with a retina or cochlea, then it can be very cost effective to design 

special-purpose architectures. In addition, to manage complexity the brain has evolved many 

hierachical structures. 

taken from: http://www.cs.rochester.edu/users/faculty/dana/comp.html 

GENERAL DISPUTES 

Ð Nativism vs. Empirism 

Ð Functionalism vs. eliminative Materialism 

Ð Associationism vs. Representationalism 

Ð Localizability of cognitive functions vs. distributedness of their representation 

Ð (Descartes, Gall, Broca/Wernicke, Hubel/Wiesel) 

Ð Modularity vs. Non-modularity of cognitive (sub)systems 

Ð Competence (ability to do sth.) vs. performance (behavior itself) 

Ð Weak psychological AI vs. strong psychological AI 

Strong AI aims at producing thinking machines, and assumes that implementing computer 

programs can be sufficient for producing a thinking thing. 

Weak AI aims at (a) producing machines that can perform complex tasks normally performed by 

intelligent beings, and/or (b) studying human cognitive processes by simulating them on a 

computer. 

Ð „GOFAI“ (Good Old Fashioned AI) vs. „New AI“ 

COGNITIVE SCIENCE AS AN “INTER”-DISCIPLINE 

Disciplinary structure at universities: Necessary, but not sufficient 

-> Cognitive Science as an interdisciplinary venture 

Problem of pure interdisciplinary proceeding: 

"Dabei hat sich jedoch gezeigt, daß die Schwierigkeiten interdisziplinärer Zusam-menarbeit im 

kognitionswissenschaftlichen Bereich viel größer sind als angenommen. Eine fruchtbare 

Zusammenarbeit kommt in vielen Fällen erst nach Jahren zustande. Das größte Hindernis bei der 

gemeinsamen Arbeit sind Statusprobleme der beteiligten Wissenschaften, gefolgt von der 

weitgehenden Unkenntnis des Problembewußtseins, der Begriffssysteme, des Wissensstandes und des 

methodisch-praktischen Vorgehens in den jeweils anderen Disziplinen" (Roth 1996:10). 

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Gardner: 

this is a „weak“ version of Cognitive Science "[das] kaum das Etikett einer bedeutenden neuen 

Wissenschaft [verdient]" (Gardner 1992:407) 

IS COGNITIVE SCIENCE A DISCIPLINE (IN A „STRONG“ SENSE)? 

If so, what is its content, what are its independent contributions? 

"Ich vertrete eine völlig andere, bislang noch umstrittene Meinung. Aus meiner [...] Sicht sind die 

wirklich wichtigen Grenzlinien in der Kognitionswissenschaft nicht die gleichen wie die der 

traditionellen Disziplinen, sondern vielmehr die zwischen speziellen kognitiven Inhalten. Darum 

sollten Wissenschaftler nach dem Kognitionsbereich definiert werden, der im Mittelpunkt ihrer Arbeit 

steht [...]" 

(Gardner 1992:407) 

Properties of the discipline : 

Ð Variety of methods (empirical, analytic, constructive) 

Ð accumulative as regards content 

Ð Information processing als paradigm 

Ð topic centered + multi-leveled 

TO WHAT EXTENT DOES COGNITIVE SCIENCE OFFER A GENERAL 

EDUCATION, TO WHAT EXTENT A JOB-RELATED ONE? 

-> both 

• generality of education achieved through multidisciplinary methodical abilities 

• specialization through thematic/topical deepening 

• education is basis for new cognitive information technology 

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ASPECTS OF COGNITION (CLASSICAL VIEW) 

COGNITIVE ARCHITECTURE 

¥ Basic assumption: IPSs are not (completely) homogeneously structured 

¥ IPSs consist of a set of interacting subsystems, which are functionally defined 

¥ Kinds of subsystems and their relations determine the „cognitive architecture“ of an IPS 

¥ Further assumption: cognitive architectures of normal, typical adults are roughly the same 

GLOBAL VIEW ON THE COGNITIVE ARCHITECTURE 

MEMORY 

¥ Long-term memory 

Ð declarative memory (-> Tulving) 

¥ semantic m. (concepts, “What is X”) 

¥ episodic (autobiographic) m. (events, “When did X happen”) 

Ð procedural memory (“How to do X”) 

¥ Short-term memory 

Ð Working memory (-> Baddeley) 

¥ central executive 

¥ specific subsystems (Visuo-spatial sketchpad, phonological loop) 

Ð Working memory (alternative view) 

¥ Set of active representations 

¥ Further distinction: explicit vs. implicit memory 

MEMORY REPRESENTATION 

¥ Memory 

Ð stores aspects of the perceived world 

Ð contains knowledge representation structures 

¥ For what? 

Ð Storing information makes it possible to learn, to recognize, categorize, plan, and reason 

Ð Storing information makes it possible to „represent the world“ 

¥ How much is stored? 

Ð Long term memory: everything? (problem of forgetting) 

Ð Short term memory: little (7±2 “chunks”, Miller) 

¥ How long? -> unlimited? 

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REPRESENTATION 

¥ Representation = 

(Symbolic data)structures + access processes 

¥ Different kinds (formats) of representation 

Ð Logic 

Ð Semantic networks, concept hierarchies 

Ð Schemata 

Ð Rules 

Ð mental images/models 

¥ Processes operate on representations 

Ð mental functions are realized 

Ð behavior is produced 

THEREFORE (REPRESENTATIONAL STANCE) 

Why do people have a particular kind of intelligent behavior? 

Explanatory pattern: 

¥ People have mental representations 

¥ People have algorithmic processes that operate on those representations 

¥ The processes, applied to representations, produce the behavior 

LOGIC 

¥ Motivation: 

Ð Many ideas about representation and computation come from the logic tradition 

Ð Languages of logic are suitable as universal knowledge representation languages (?) 

Ð Logic is a suitable/ the single suitable means for the formal representation of deduction (?) 

Ð Logical deduction corresponds (somehow) to natural reasoning (??) 

VARIOUS LOGICS 

¥ Propositional logic (Aussagenlogik) 

Ð Elements: Propositions p, q... 

Ð Connectors & (and), v (or), -> (if-then) 

Ð Negation (~p) 

¥ The relation of language, logic, and deduction: 

Ð “If it rains (p), then it is wet (q)”: p -> q 

Ð Patterns of inference/inference rules: 

¥ Modus ponens (MP) (If p -> q and p, then deduce q) 

¥ Modus tollens(MT) (If p -> q and ~q, then deduce ~p) 

Ð Thus: 

¥ “It rains” allows to infer “It is wet” (via MP) 

¥ “It is not wet” allows to infer “It does not rain” (via MT) 

¥ First order predicate logic (FOPL) 

Ð (Prädikatenlogik erster Stufe, PL/1) 

Ð allows statemenst about (relations holding between) objects 

Ð Quantification: 

¥ “All men are mortal”: "x man(x) -> mortal(x) 

¥ “There exists a mortal man”: $x man(x) & mortal(x) 

¥ Further logics (extensions of FOPL) for individual aspects of cognition/knowledge, z.B. 

Ð Modal logics (“it is necessary/possible that”) 

Ð Deontic logics (“may/must”) 

Ð Default-logics (“typically”) /Non-monotonic logics 

Ð Spatial, temporal, sortal … logics 

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Ð Many-valued logics, Fuzzy-Logic 

THEOREM PROVING: TYPES OF INFERENCE 

¥ Deduction (application of MP) 

Ð Is a monotonic procedure 

Ð Does not correspond to „learning“ 

¥ Induction (inferring a universal statement from single observations) 

Ð Could be expected to correspond to „learning“ 

Ð However: Induction is not correct, is “impossible” 

¥ Abduction (from p->q and q: infer p) 

Ð “It is wet” ---> “It has rained” 

Ð yields explanations 

Ð is defeasible 

COGNITIVE PLAUSIBILITY 

3 positions: 

Ð Formal logic is an important part of human reasoning (“mental logic”, Rips) 

Ð Formal logic is only distantly related to human reasoning, but that does not matter 

Ð Formal logic is only distantly related to human reasoning, so Cognitive Science should pursue other 

approaches (“mental models”, Johnson-Laird) 

THEREFORE (LOGICAL STANCE) 

Why do people make the inferences they do? 

Explanation: 

¥ People have representations similar to sentences in logic 

¥ People have deductive and inductive procedures that operate on those sentences 

¥ The deductive and inductive procedures, applied to the sentences, produce the inferences 

WASON’S SELECTION TASK 

¥ given: cards with letters on the one side and numbers on the other 

A B 4 7 

¥ Rule: If there is an A on the one side of a card, then there is a 4 on the other side 

¥ Question to subjects: Which card(s) must be turned over in order to verify the rule? 

¥ Result contradicts the assumption of a mental theorem prover 

RULES (PRODUCTIONS) 

¥ IF CONDITION THEN ACTION 

¥ ACTIONs are implication/deductions (A) or actions (B) 

¥ Logic Theorist (Newell, Shaw, Simon, 1958) 

Ð Modelling of human theorem proving in logic 

¥ GPS (General Problem solver, Newell/Simon 1972) 

Ð Generalization to human thinking and reasoning 

¥ ACT (“adaptive character of thought”, J. Anderson, 1983, 1993) 

¥ SOAR (“State, Operator, Apply and Result”, Newell,Laird, Rosenbloom 1993) 

DIFFERENCE TO LOGIC 

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¥ Simpler structure (condition-action) 

¥ Less strict: “p(x) -> q(x)” not necessarily interpreted as universal statement 

¥ Properties of the architecture 

Ð Working memory 

Ð Rule memory (procedural memory) 

Ð Control mechanism 

¥ Processing properties 

Ð E.g. “chunking” of rules 

PROBLEM SOLVING 

¥ Problem solving as search 

Ð Initial state 

Ð Goal state 

Ð Search space 

¥ Constraining the search space 

Ð by heuristic rules 

Ð by the constrained working memory 

¥ Different kinds of rule processing 

Ð forward 

Ð backward 

Ð bidirectional 

EXAMPLE: TIC-TAC-TOE 

¥ WIN: IF there is a row, colum, or diagonal with two of my pieces and a blank space THEN play the blank space to 

win 

¥ BLOCK: IF there is a row, colum, or diagonal with two of my opponent´s pieces and a blank space, THEN play the 

blank space to block the opponent 

¥ PLAY CENTER: IF the center is blank, THEN play the center 

¥ PLAY EMPTY CORNER: IF there is an empty corner, THEN move to an empty corner 

THEREFORE (RULE STANCE) 


Explanation: 

¥ People have mental rules 

¥ People have procedures for using these rules to search a space of possible solutions, and procedures for 

generating new rules 

¥ Procedures for using and forming rules produce the behavior 

CONCEPTS 

¥ What is X? 

[ 15 ]


Ð Plato: Knowledge about X is innate (Nativism) 

Ð Locke/Hume: Knowledge about X is gained through experience (Empirism) 

Ð Kant: both is correct 

¥ Concepts are used for categorization and inference 

Ð Input I is INSTANCE OF concept C 

Ð If I is INSTANCE OF C then it has properties P1, P2, … 

The purpose of categorisation is to avoid the consequences of miscategorisation. Categorisation 

means responding differentially to certain KINDs (or classes, or categories) of input. The 

purpose of the data-reduction is to get out of the […] problem of unique instances with which 

you can do nothing. To reduce is to select what is invariant in the inputs and will reliably allow 

them to be categorised correctly, and to ignore the rest. 

This is not just "processing economy": It is part to what it means to learn and to generalise. 

Stevan Harnad in an email 

( http://cogsci.soton.ac.uk/~harnad/Hypermail/Foundations.Cognition/0061.html ) 

[Just imagine that situation in which you have correctly categorized that animate thing with four legs coming 

running towards you as a bulldog. You should therefore be able to deduce the ‘may bite me‘ property and to 

draw the inference that it would be a good idea to run away or to hide. Luckily, there´s more to thinking and 

acting than only conscious deliberation…] 

¥ Approaches in AI /psychology 

Ð Networks of concepts (Semantic Nets (Quillian 1968)) 

➥ spreading activation Aktivationsausbreitung 

Ð Schemata (Frames (Minsky (1974), scripts (Schank/Abelson 1977)) 

SEMANTIC NETS 

¥ Relations between concepts 

¥ In general: links (of a certain type) between nodes (of a certain type) 

¥ Used for the representation of linguistic (semantic) knowledge 

¥ E.g., “Peter gives Mary a book” will be mapped on 

Sentences like the following can be ruled out as semantically ill-formed: 

*”The tower gives an ice to the idea” 

FRAMES 

Representation of (non-linguistic) knowledge (¹ semantic nets) 

Starting point: 

Emphasis on the structure of knowledge (as against unstructured sets of logical propositions) 

-> Schemata 

Frames as data structures for knowledge „chunks“ 

Ð stereotypical situations (“What happens on a birthday party?") 

Ð prototypical objects (representatives of a category, cp. “How does a typical chair look like?") 

Idea: Frames serve to 

Ð categorize sensoric input (by “Matching”) 

[ 16 ]


Ð memorize and organize new information relative to old information 

Ð infer further information 

FRAMES II 

¥ Nets of nodes and links (similar to semantic nets) 

¥ Object centered, independent data structure 

¥ Hierarchy of frames: Inheritance 

Ð Taxonomies (kind hierarchies, IS-A-hierarchies, ontologies), 

Ð Partonomies (part hierarchies) 

FRAMES III 

¥ Entries in a frame ("Slots") may have certain values ("Fillers") 

also: pointers to sub-frames 

¥ Restrictions on fillers 

Ð Value is of a certain type 

Ð complex complex conditions on relations between fillers 

Ð “attached procedures” for computing filler values 

¥ If-needed 

¥ If-added 

¥ "Default"-entries as fillers 

¥ typical values to be assumed in the lack of better evidence 

¥ may be overwritten 

EXAMPLE: A COURSE FRAME 

Course 

A_kind_of: process (systematic series of actions) 

Instructor: _ 

Room: _ 

Meeting_time: _ 

Requirements: exams, essays etc. 

Instances: Foundations_of_Cognitive_Science, Mathematics_I ... 

ASPECTS OF CONCEPT STRUCTURES 

Eleanor Rosch´s findings regarding structuring of concept knowledge: 

¥ Vertical dimension (inheritance) 

Ð Different levels of representation 

¥ Superordinate Level (furniture, tree) 

¥ Basic Level (chair, table, birch tree, oak tree) 

¥ Subordinate Level (stool, silver birch) 

Ð Basic Level is a distinguished level 

[The distinguished basic level may vary according to context and/or expertise; it is therefore sometimes called 

"entry" level.] 

¥ Horizontal dimension 

Ð Members of a category are not equal 

Ð There are no clear (necessary and sufficient) criteria for membership in a category 

Ð Prototypicality 

The traditional recipe of the Classic Theory is as follows: take a category and a set of defining features. The 

ingredients are: 

· categories are arbitrary; 

Ð categories have defining or critical attributes; 

Ð the intension (or set of attributes) determines the extension of a category (which items are members). 

[ 17 ]


The idea is that categories are held together by defining features, which are together necessary and jointly 

sufficient, and that allinstances of a category possess the requisite defining features. Someone who is an 

unmarried adult male belongs to the category *bachelor* having the defining properties *unmarried*, *adult*, 

and *male* (Some people may wonder why these properties have asterisks surrounding them, i.e. why they 

are treated as categories. Well, properties can themselves be categories, e.g. we can include the Dutch soccer 

team in the category of #orangeness#. Nothing prevents properties from being categories. 

This way of conceptualising categorization has its problems. The deathblow for this theory was given by the 

research on color terms. Before this research it was assumed that categorization and naming of colors were 

arbitrary; the lines between colors were arbitrary, drawn as seen fit by a culture. Individuals of a culture 

would simply mirror these boundaries in their own classificatory and mnemonic behavior. However, Berlin 

and Kay [69] showed with a series of anthropological experiments that every culture shares the same focal 

areas for colors, irrespective of whether or not they have names for them. That is, they all agree about what is 

a "good blue" or a "poor yellow", even if a culture lacks labels for them. Moreover, this universalist view on 

basic color categorization has been supported by developmental [Bornstein, Kessen, & Weiskopf 76] and 

animal studies [Sandell, Gross, & Bornstein 79]. Berlin and Kay also found a presence of fixed order in the 

construction of color lexicons. If a culture has only two color terms, terms will code for black and white. If a 

third term is added, it will be red; fourth and fifth will be yellow and green; blue and brown will be the next 

pair; and purple, pink, orange, and gray will be the last four names. These findings seem to be a consequence 

of the structure of the nervous system of the primate [ibid.; Gardner 87]. Rosch [73] continued on these 

experiments and found that while the naming practices turned out to be incidental, the categorization of colors 

seemed to reflect the organization of the nervous system, not the structure of particular lexicon. She found this 

in other domains as well. 

Rosch discovered more properties when she investigated categorization in other domains, namely, categories 

seemed to be organized in a taxonomy. Categorization is performed at the basic level (table, dog, NBAplayer). 

Above the level of basic objects is the layer of superordinate objects, being generalizations of the 

basic ones like furniture, animal, basketball-player. Below the basic layer is the subordinate layer containing 

the specializations like diner-table, Mastiff, Shawn Kemp. The possible reasons that we and especially 

children categorize at the basic level is that members of basic level categories tend to look alike, that the basic 

categories have many describable features (cows give milk, say "moooh", and have yellow labels in their ears 

(in the Netherlands)), that our physical interactions with members of such a category are the same (we use 

different chairs the same way), and that it allows easy communication in general situations (compare "A man 

was bitten by a dog" with "John was bitten by Fifi" and "A person was bitten by an animal" (Wolters, 

personal communication)). […] 

Other findings questioned the claim that categories have defining features. There are few categories, from 

natural ones like birds to artificial ones like tables, that seem to obey the classical rule of finite lists of critical 

features [see also Gardner 85]. Even when categories do have definitions, rules seem less important than 

prototypes (see below). In the case of the bachelor: Henry is unmarried, adult, and male. Then he is a bachelor 

according to the classicists. If we know that he lives together with his girlfriend, then he is certainly not a 

bachelor to us, but he still fits the necessary and sufficient conditions to be a bachelor. There are many other 

exemplars fitting the conditions: a homo-sexual, a monkey. Because these examples are not similar to the 

prototypical bachelor, they do not seem to belong in the category, even though technically they do [Barsalou 

92]. 

Wittgenstein [53] has given another and probably the most persuasive argument against Classical Theory. He 

showed that the concept *game* does not have unambiguous defining properties. In case of doubt try to 

characterize poker, tic-tac-toe, Russian roulette, chess, and trivial pursuit with defining features shared by all 

these instances. The concept *game* is kept together by a set of relationships and similarities. The principle is 

that each instance shares some subset of properties an instance of a category can have. Some, all, or none of 

the properties of this subset may be shared by another instance. This is called a family resemblance 

[Wittgenstein 53]. 

taken from: 

SCRIPTS 

http://www.soton.ac.uk/~coglab/coglab/Thesis/chptr3.html 

¥ Representation of situation specific knowledge (Schank, Abelson (1977): Scipts, Plans, Goals, and 

[ 18 ]


Understanding) 

¥ Example: What happens in a restaurant? 

Ð Entry conditions? 

Ð Entering etc. 

Ð Order meal 

Ð Eating 

Ð Paying 

Ð Exiting 

Ð Post conditions (results)? 

THEREFORE (CONCEPT STANCE) 


Explanation: 

¥ People have a set of concepts, organized via slots that establish kind and part hierarchies and other 

associations. 

¥ People have a set of procedures for concept application, including spreading activation, matching, and 

inheritance. 

[ 19 ]


¥ The procedures applied to the concepts produce the behavior. 

REPRESENTATION MEMORY 

¥ Necessary distinction: 

Ð Information stored in long term memory 

Ð Representation instantiated in working memory 

¥ Which information is filed in long term memory? 

Ð (only) Definitions (for concepts)? 

Ð (only) Abstractions/Schemata/Prototypes? 

Ð (only) Instances/Exemplars? 

¥ Propositional elements as basic cognitive building blocks ? 

¥ Pylyshyn [propositions are units of information] 

¥ “Language of thought” (Fodor) [propositions are units of representations] 

ANALOGIES AND CASES 

-> “Case based reasoning” 

Analogical Reasoning: 

¥ Starting point: Problem to be solved (target) 

¥ Remembering a similar problem (source) for which a solution is known 

¥ Structural comparison of source and target (putting their relevant components in correspondence with 

each other) 

¥ Adaptation of the source problem to produce a solution to the target problem 

¥ -> The tumor problem and the fortress story 

THEREFORE (ANALOGY STANCE) 


Explanation: 

¥ People have verbal and visual representations of situations that can be used as cases or analogs 

¥ People have processes of retrieval, mapping, and adaptation that operate on those analogs 

¥ The analogical processes, applied to the representations of analogs, produce the behavior 

MENTAL IMAGES 

For answering questions like the following, we obviously do not deduce an answer from a set of premises in a 

logic-like reasoning scheme, but we build a mental picture of the scene ("pictorial representation") in which 

the answer can be "read off": 

How many windows are there on the front of your house or apartment building? 

¥ Spatial aspects of pictorial representations and temporal aspects of their processing correspond to the 

represented aspects in the world (mental rotation, distance estimation) 

¥ Visual perception and mental imagery involve (in part) the same brain structures 

THEREFORE (IMAGERY STANCE) 


Explanation: 

¥ People have visual images of situations 

¥ People have processes such as scanning and rotation that operate on those images 

¥ The processes for constructing and manipulating images produce the intelligent behavior 

[ 20 ]


ASPECTS OF COGNITION (NON-CLASSICAL VIEW) 

CONNECTIONISM 

¥ Is also based on nets (networks) of nodes and links 

Ð Nodes: Cells, neurons, elements, units 

¥ However: (at least) the links do not have any meaning 

¥ Differentiation of 

Ð localistic networks (nodes still have a clear meaning) 

Ð distributed networks (even nodes have no meaning) 

¥ Related terms: 

Ð parallel distributed processing 

Ð artificial neural networks 

Ð subsymbolic paradigm 

¥ Historical starting point: Networks of McCulloch-Pitts cells 

THE ARCHITECTURE OF CONNECTIONIST NETWORKS 

ELEMENTS OF NEUR(ON)AL NETWORKS 

¥ Cells with 

Ð State of activation (a i(t)) 

Ð Activation function (f act) 

Given the threshold of activation q j 

¥ a j(t+1) = f act( a j(t), net j(t), q j ) 

Ð Output function f out 

¥ o j = f out(a j) 

¥ Network of arcs (directed graph) 

Ð with weights w ij between the cells i and j 

¥ Propagation function 

Ð net j (t) = S o i (t) w ij 

¥ Learning rule 

[ 21 ]


AN EXAMPLE: XOR-NETWORK 

TOPOLOGY (ARCHITECTURE) OF A NETWORK 

¥ Is crucial for what can be represented 

¥ In the second place: for what can be learned 

¥ Networks without hidden units 

Ð cannot represent XOR 

¥ Perceptrons (Rosenblatt 1958) 

¥ Critique: Minsky/Papert 1969 

Ð Are only suitable for linearly separable sets of inputs (¹XOR) 

THEORETICALLY POSSIBLE KINDS OF LEARNING 

IN NEURAL NETWORKS 

¥ Development of new connections 

¥ Extinction of existing connections 

¥ Modification of strength w ij of links 

¥ Modification of the threshold of neurons 

¥ Modification of activation, propagation or output function 

¥ Development of new cells 

¥ Deletion of cells 

[ 22 ]


LEARNING RULES 

TYPES OF LEARNING 

¥ Supervised learning (Überwachtes Lernen) 

Ð Desired output (teaching input) is given 

¥ Reinforcement learning (Bestärkendes Lernen) 

Ð Feedback whether classification was corrrect 

¥ Unsupervised learning (Unüberwachtes Lernen) 

Ð Selforganisation 

Ð Kohonen-maps 

ATTRACTIVITY OF CONNECTIONISM: 

NEURONAL PLAUSIBILITY 

¥ Similarity to biologiccal neural networks 

Ð Cells as processing units 

Ð Parallelity of processing 

Ð Learning as change of weights (synaptic strengths) 

Ð relatively few serial steps of processing (~100/sec in the brain) 

¥ Distributed representation 

Ð in case of lesions: “graceful degradation” 

¥ But in addition: Abstraction from given biological realisation 

ATTRACTIVITY OF CONNECTIONISM: 

COGNITIVE PLAUSIBILITY 

¥ Approach for representing the Microstructure of cognition 

¥ General principles are offered for the solution of basic problems in theories of cognition, e.g. 

Ð Categorization (e.g., taste, color, smell) 

Ð Prototypicality effects, context phenomena 

Ð Face recognition 

[ 23 ]


Ð Content addressable memory 

¥ Defeating the “brittleness” of systems in AI 

EXAMPLE: NETTALK 

(SEJNOWSKI/ROSENBERG 1987) 

¥ System mapping written onto spoken language 

¥ Feedforward-net 

Ð input layer with 203 units 

¥ divided into 7 groups (seven text symbols are coded) 

¥ each with 29 units (letters + blank + punctuation) 

Ð hidden layer with 80 units 

Ð output layer with 26 units 

¥ each unit codes a phonetic feature, syllable or intonation boundary 

Ð levelwise connectivity (18320 connections) 

¥ Supervised learning (via backpropagation) 

PERFORMANCE OF NETTALK 

¥ Text with 1024 words 

¥ 50 passes (ca. 250000 training pairs) 

¥ After that: 

Ð 95% correctness for elements of the same text 

Ð 78% correctness for elements of a new 439-word-text 

¥ distributed representation and graceful degradation 

THEREFORE (CONNECTIONIST STANCE) 


Explanation: 

¥ People have representations that involve simple processing units linked to each other by excitatory and 

inhibitory connections. 

¥ People have processes that spread activation between the units via their connections, as well as processes 

for modifying the connections 

¥ Applying spreading activation and learning to the units produces the behavior 

ADVANTAGES OF PDP 

¥ Speed and power (the parallelity aspect) 

¥ Functional persistence/fault tolerance (graceful degradation in case of damage) 

¥ Content addressability 

¥ Recovering information of a whole given only partial information 

¥ Categorization: Classifying new things as instances of known types 

¥ Recognition: Classifying input as being a certain known object (despite noise) 

¥ The „pop-out“ of relevant information (as opposed to exhaustive search) 

DIFFERENCES OF CONNECTIONIST MODELS TO THE BIOLOGICAL 

REALITY 

¥ Much lower number of neurons (10 2 -10 4 vs. 10 11 ) 

¥ Much lower number of connections (~10 5 connections in a net vs. 10 3 -10 4 synapses of a neuron) 

¥ Only one parameter of synaptic coupling (“weight” vs. different neuro transmitters) 

¥ Amplitude modulation vs. frequency modulation 

¥ Violation of the locality principle for synapses 

[ 24 ]


¥ No modelling of the synaptic structure of dendrites 

¥ No exact modelling of the temporal processes in neural circuits 

¥ Only homogeneous networks have been theoretically investigated 

¥ No consideration of chemical influences on neighbouring neurons 

¥ Biologically implausible learning rules (e.g. supervised learning) 

[ 25 ]


ASPECTS OF (COGNITIVE) NEUROSCIENCE 

recommended reading: W.H. Calvin, G.A. Ojeman (1994), Conversations with Neil´s Brain. 

http://weber.u.washington.edu/~wcalvin/bk7/bk7.htm 

QUESTIONS 

“Cognition is what the brain does” 

(S. Pinker, How the mind works) 

Ð How is information processed in the brain? 

¥ Neurophysiology 

Ð What kind of different structures are there in the brain and how are they related? 

¥ Neuroanatomy 

Ð How/where are specific cognitive structures/processes realized in the brain? 

¥ Cognitive Neuroscience 

Ð In which way can behavior (or higher cognitive functions) be explained by reference to brain 

structures/processes? 

¥ Neuropsychology 

MOTIVATION: THE VIEW FROM OUTSIDE THE BRAIN 

(STORIES, PHENOMENA, AND DISEASES) 

· Phrenology (Gall, Spurzheim): The view that ~35 brain functions can be localized in specific brain 

regions and that there is a direct relation of the intensity of a function and anatomical properties of the 

corresponding region (such that personal characteristics of a person can be judged by outward appearance 

of the skull). 

· Hemispheric differences: 

· Language and Aphasia 

· Broca and Wernicke (regions): The observation that specific brain regions relevant for language are left-hemispheric 

· Words and their context: Why aphasics laugh at the president´s speech (Sacks): People whose left hemisphere is damaged (i.e., 

aphasics) may not understand what is said but may still be able to evaluate a person´s speech with respect to the overall visua l 

and/or acoustic properties; on the other hand, people whose right hemisphere is damaged may not understand jokes any more 

· What people recognize/say/do when their corpus callosum is (partially) damaged 

· Confabulation 

· The woman whose right hand had to prevent herself from strangling herself with the left hand (Ramachandran) 

· Damage to the right hemisphere may lead to 

· not attending to the left side (unilateral neglect) 

· not recognizing their own deficits (anosognosia) 

· Processing of global/local information: Global information is processed preferably in the right hemisphere 

· Delis/Bihrle: the observation that down syndrome (known for disproportionately impaired language ability relative to other 

cognitive functions) correlates with preserved global processing 

· Frontal damage: 

· The story of Phineas Gage ( http://www.toto.com/butler/fam_tree/p_gage.htm ) 

· Occipital damage: 

· Blindsight (reacting correct to visual stimuli despite cortical blindness due to corresponding damage) 

· Parietal damage: 

· Spatial Disorientation, 

· Attentional failures (neglect, simultanagnosia): not being able to attend to the left side or to more than one object 

· ideomotor apraxia (left parietal): not being able to imagine an action 

· Temporal damage: 

· Seeing but not recognizing: 

· Visual Agnosia (The man who mistook his wife for a hat, Sacks), 

· Seeing but not believing: Capgras (Ramachandran): visually recognized close relatives are thought to be impostors 

· Loss of declarative memory: 

· Korsakow syndrome 

· The patient H.M. 

· Degenerative diseases: 

· Huntington 

· Parkinson 

[ 26 ]


· Alzheimer 

· Thunderstorms in the brain: Epilepsy 

· Specific phenomena (Ramachandran): Processing with respect to specific cognitive functions may be quite 

localized 

· The woman who died laughing 

· The man who can´t subtract three from seventeen 

· „Mirror“ neurons (which fire if an ape either performs or sees a certain gesture/manual action) 

· Mind blindedness (Baron-Cohen) 

LEVELS OF DESCRIPTION 

BASIC NEUROANATOMY 

Nervous system is divided in 

¥ central and peripheral NS 

¥ Central NS (Brain and spinal cord) 

Ð Endhirn (Telencephalon) 

¥ Subpallium (Septum, Basal ganglia/Striatum) and Pallium (Cortex) 

Ð Zwischenhirn (Diencephalon) 

¥ Epithalamus, Thalamus, Hypothalamus 

+ pituitary gland (Hirnanhangdrüse) 

Ð Mittelhirn (Mesencephalon) 

¥ Mittelhirndach/Tectum (superior and inferior colliculi), Tegmentum 

Ð Hinterhirn (Metencephalon) 

¥ Cerebellum (Kleinhirn) 

Ð Nachhirn (Myelencephalon) 

Ð Spinal cord (Rückenmark) 

SOME FUNCTIONAL ASPECTS 

¥ Spinal cord 

Ð Control of simple reflexes 

¥ Medulla 

Ð Regulation of heartbeat and breathing 

¥ Cerebellum 

Ð Coordinaton of fine muscle movement, balance 

¥ Pons (Brücke) 

Ð Relaying of information between cerebral cortex and cerebellum 

¥ Reticular formation(Formatio reticularis) 

Ð involved in the control of arousal and in the sleeping and waking cycles 

¥ Locus coeruleus: Destruction leads to coma 

¥ Raphe nuclei: Destruction leads to insomnia 

[ 27 ]


SOME FUNCTIONAL ASPECTS II 

¥ Hypothalamus 

Ð Important for the autonomic nervous system and the endocrine system 

Ð Besides projecting to other structures, influences activity of other neurons via neuromodulatory processes 

(hormone secretion) 

¥ Thalamus 

Ð Relay station between sensory areas and primary cortical sensory receiving areas 

Ð Connections from and to basal ganglia, cerebellum, cortex, and medial temporal lobe 

Ð Important substructure: pulvinar (-> attentional processing) 

¥ Basal ganglia( Caudate nucleus, Putamen, Globus pallidus ) 

Ð Planning of movements, control of action 

THE LIMBIC SYSTEM 

¥ Hippocampus 

¥ Amygdala (Mandelkern) 

¥ Cingulate gyrus 

¥ Septum 

¥ Mammillary bodies 

¥ Nucleus anterior thalami 

Important for emotional expression, valuation of events and (declarative) memory 

LOCATING STRUCTURES IN THE BRAIN: SPATIAL NOTIONS 

Orientations 

Ð Rostral / anterior = towards the nose or front end 

Ð Caudal /posterior = towards the tail in animals or towards the feet in humans 

Ð Dorsal = the back side 

Ð Ventral = the belly side 

Ð Lateral = toward the outside and away from the midline 

Ð Medial = toward the midline and away from the periphery 

Views 

Ð Sagittal view (from the side) 

Ð Transverse view (from above/below along the rostral/caudal dimension) 

Ð Horizontal (from above/below with respect to the ground, when the subject is standing) 

Ð Coronal view (from front/back with respect to the forebrain) 

IMPORTANT SPATIAL DIVISIONS 

Brain: Two halves, connected by the Corpus Callosum 

¥ Division of the cerebral cortex (Großhirnrinde ): 

Ð lateral Pallium (Paleocortex) 

Ð medial Pallium (Archicortex) 

Ð dorsal Pallium (Neocortex or Isocortex) 

¥ Division of the Isocortex: 

Ð Frontal lobe (Stirnlappen) 

Ð Parietal lobe (Scheitellappen) 

Ð Temporal lobe (Schläfenlappen) 

Ð Occipital lobe (Hinterhauptslappen) 

¥ Cytoarchitectonic divisions of the brain: 50 Brodman areas (Hirnrindenfelder) 

[ 28 ]


ROUGH FUNCTIONAL DIVISIONS OF ISOCORTEX 

¥ Frontal lobe: Motor areas (planning and execution of movements) 

Ð prefrontal cortex: processing of higher cognitive functions 

¥ Parietal lobe: Somatosensory areas 

Ð attentional and spatial processing 

¥ Temporal lobe: 

Ð Auditory processing, object recognition 

¥ Occipital lobe: Visual processing 

Ð Ventral pathway to the temporal lobe („what“, object processing) 

Ð Dorsal pathway to the parietal lobe („where“, spatial processing) 

¥ Association cortex 

ISOCORTEX 

¥ Thickness: 

Ð 2-5mm (Æ 3mm)(grey matter) 

¥ Surface area: 

Ð 2200-2400 cm 2 

¥ Highly folded surface 

Ð Gyri (crowns of the folded tissue) 

Ð Sulci (infoldings) 

¥ Consists of six layers 

CELLS OF THE NERVOUS SYSTEM 

¥ Glia cells 

¥ Neurons 

Ð Consist of 

¥ Dendritic tree (postsynaptic), in part with spines (Dornfortsätzen ) 

¥ Soma (cell body ) 

¥ Axon (presynaptic) 

Ð Up to 1m long 

Ð Contacts to up to 10000 other neurons via synapses (ø1000) 

Ð Transmission of action potentials (spikes) with ~100 m /sec 

Ð Different types 

Ð Different arrangement 

¥ in layers/lamina (e.g. in the cortex) 

Ð in mini columns (consisting of ca. 100 Neuronen), orthogonal to cortex surface, and macro columns (consisting of ca. 

300 mini columns) 

¥ in groups (-> nuclei) 

SYNAPSES 

¥ electrical synapses 

Ð electrical activity is transmitted directly (cells being very close to one another) 

Ð via plasma bridges (gap junctions) 

¥ chemical synapses 

Ð Presynapse, Postsynapse, synaptic cleft 

Ð transmission by transmitters 

¥ excitatory: 

Ð Acetylcholin, Noradrenalin, Serotonin, Dopamin, Glutamat 

¥ inhibitory: 

Ð Gamma-Aminobuttersäure (GABA), Glycin 

[ 29 ]


EXPERIMENTAL TECHNIQUES FOR THE INVESTIGATION OF NEURAL 

STRUCTURES 

¥ Lesion studies 

Ð Destruction of selected cells and observation of degeneration in other areas as a result of the lesion 

¥ Labeling procedures 

Ð Observation of the transportation of labeled chemicals, i.e., 

Ð of anterograde transportation (by autoradiographic tracing) 

Ð of retrograde transportation (from synaptic terminals back to the cell body) 

¥ Single cell recording 

Ð Observation whether or when a cell fires (by recording the electrical activity with microelectrodes) 

RESULTS OF MICROINVESTIGATIONS 

¥ Discovery of functional pathways 

Ð Transport of specific information through different brain areas 

¥ Discovery of columnar organization 

Ð Vertical organisation of cells in the cortex 

Ð Microcolums: Local processing of, e.g., orientation-specific or eye-specific information in the visual 

system 

¥ Discovery of topographic maps 

Ð Areas dedicated to the processing of specific information 

FUNCTIONAL NEUROIMAGING (FUNKTIONELLE BILDGEBENDE VERFAHREN): 

METABOLIC TECHNIQUES 

Emission Tomography 

Measuring brain activity by assessing regional cerebral blood flow (rCBF) 

¥ Single Photon Emission Tomography (SPECT) 

Ð Injection of HMPAO or inhaling air-xenon mixture 

¥ Positron Emission Tomography (PET) 

Ð Injection of labeled substances (e.g. water) 

¥ Functional Magnetic Resonance Imaging (fMRI) 

Ð Imaging of changes in the level of blood oxygenation 

FUNCTIONAL NEUROIMAGING: 

ELECTROPHYSIOLOGICAL TECHNIQUES 

¥ Electroencephalogram (EEG) 

Ð Detection of different rhythms of electrical activity (alpha, beta, theta etc.) 

¥ Event-Related Potentials (ERPs, Ereignis-korrelierte Potentiale) 

Ð Consist of a series of positive and negative changes from a baseline 

Ð Properties of the ERP (given the performance of a cognitive event) are analyzed 

¥ Probe-Evoked Potentials 

Ð How does performing a (cognitive) task influence a probe-dependent potential? 

¥ Magnetoencephalogram (MEG) 

Ð Detection of magnetic fields induced by active neurons , by a superconducting quantum interference device (SQUID) 

Ð Evoked fields (EF) or magnetic event-related fields (MEF) 

[ 30 ]


BRAINPART FUNCTIONS 

¥ Hippocampus: Anchoring of new information in declarative memory 

¥ Amygdala: emotional tagging of events (somatic markers) 

¥ Occipital cortex: primary visual system 

¥ Temporal cortex: object recognition 

¥ Parietal cortex: spatial orientation, visuo-spatial attention 

¥ Prefrontal cortex: 

Ð voluntary planning and acting (~ lateral) 

Ð personality / social behavior (~ ventromedial) 

MALFUNCTIONS OF THE BRAIN 

¥ Agnosia: not being able to recognize 

Ð objects/forms, colors, faces (prosopagnosia) 

Ð the own deficits (anosognosia) 

Ð more than one object at a time (simultanagnosia ) 

¥ Apraxia: not being able to act 

Ð Ideomotor apraxia: no voluntary movements (left parietal damage) 

¥ Anomia: not being able to name things 

¥ Alexia: not being able to read 

¥ Amusia: not being able to recognize melodies 

¥ Aphasia: language-related dysfunctions 

Ð Broca- (motoric) 

Ð Wernecke- (semantic) 

¥ Amnesia: not being able to memorize 

¥ Neglect: not being able to attend to 

EMOTIONS AND FEELINGS 

-> DAMASIO 1994, LEDOUX 1996 

¥ Emotions 

Ð Involve necessarily the amygdala and other parts of the limbic system 

Ð Can be induced by external stimuli or internal representations (e.g., fear vs. anxiety) 

Ð Correspond to activities in different emotional systems 

Ð Distinction of primary and secondary emotions 

¥ Primary e.: depend on limbic system circuitry (especially amygdala and anterior cingulate (e.g. stimulus-driven fear) 

¥ Secondary e.: based on systematic connections between categories of objects and situations, on the one hand, and primary 

emotions, on the other hand (involves also prefrontal and somatosensory cortices) 

¥ Emotional feelings 

Ð Correspond to being consciously aware of having an emotion 

¥ Somatic markers: 

Ð „gut feelings“ that result from emotion memory recall („how it feels“ if something happens) 

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VISUAL PERCEPTION AND ATTENTION 

VISION: THE PROBLEM 

¥ How do we attain constant visual experiences/knowledge about a structured three-dimensional world 

consisting of objects, given that 

Ð the input is solely the intensity of incoming light (luminance) in a certain range of wavelengths, projected onto a 

discontinuous two-dimensional surface (the retinae of both eyes) 

Ð the perceiving subject can be in motion 

Ð the perceived world may change continuously (e.g., moving objects) 

Ð a large number of goals relevant for behaviour must be fulfilled simultaneously 

Ð the ressources (memory, processing time) are limited 

ROOTS OF RESEARCH IN VISUAL PERCEPTION 

Helmholtz: 

depth clues 

Gestalt psychology (Wertheimer, Köhler, Koffka): 

Gestalt principles 

Gibson: 

ecological theory of vision, „direct perception“, extraction of invariants 

Cybernetics: 

Coupling of perception and action (control) 

Neisser: 

Preattentive/perceptual vs. attentive/cognitive 

Hubel/Wiesel: 

„detectors“, hypercolumns 

Marr: 

Computational vision 

Aloimonos: 

“Active vision“ 

NEUROPHYSIOLOGICAL ASPECTS: 

FROM THE RETINA TO THE VISUAL CORTEX 

¥ Photoreceptors, two types, distributed differently on the retina: 

Ð Cones (Zäpfchen) 

¥ Predominate in and around fovea 

¥ Require more intensive light 

¥ Essential for color vision 

¥ Three types of cones („blue“ (short wavelengths), „green“ (middle), „red“ (long)) with overlapping areas of response 

distribution 

Ð Rods (Stäbchen) 

¥ Percentage of rods increases towards periphery of retina 

¥ Sensitive to low levels of stimulation 

¥ Slow regeneration of photopigments 

¥ Interneurons 

Ð Receive activations from receptors, excitatory or inhibitory influence on retinal ganglion cells 


FROM THE RETINA TO THE VISUAL CORTEX II 

¥ Retinal ganglion cells with concentric receptive fields 

Ð [receptive field: area on the retina from which a cell receives activation] 

Ð Two types of cells 

¥ Parvocellular („color“ sensitive, high spatial resolution, less contrast sensitive, slow transmission of activation) [P system] 

¥ Magnocellular (large receptive fields) [M system] 

[ 32 ]


Ð Antagonistic behavior 

¥ On-center/off-surround-cells (react to bright spots in the center) 

¥ Off-center/on-surround-cells (react to dark spots) 

¥ Red-green and yellow-blue color antagonism 

Ð Project via the optic nerve 

¥ To the lateral geniculate nucleus (LGN) [~90% of the fibers] 

¥ To the superior colliculus 


FROM THE RETINA TO THE VISUAL CORTEX III 

¥ The nasal branch of each eyes´optic nerve projects to the contralateral brain half, crossing at the optic 

chiasm 

Ð Each half of the visual field is projected to the contralateral hemisphere! 

¥ Seitlicher Kniehöcker (corpus geniculatum laterale, LGN) 

Ð Six layers which alternately receive input from different eyes so that 

Ð corresponding receptive fields of both eyes are adjacent in LGN [here comes together what belongs together] 

Ð Fixed order of projection: 

¥ Ipsilateral retina -> layers 2, 3, 5 

¥ Contralateral retina -> layers 1, 4, 6 

¥ Layers 1 and 2 contain the magnocellular neurons 

Ð All layers project to layer 4 [of six cortical layers] of region V1 in primary visual cortex 


FROM THE RETINA TO THE VISUAL CORTEX IV 

¥ Streifencortex (striate cortex, V1, area 17) 

Ð Blobs: regions of high activityin layer 2 and 3 of V1 

¥ P cells project to blobs 

¥ M cells project to complementary interblob regions 

Ð Hypercolumnar organisation: 

¥ Alternating adjacent left eye/right eye information in layer 4 [ocular dominance colums] 

¥ Within them, smaller colums of cells react to stimuli with a certain orientation (steps of 15°) [orientation colums] 

FURTHER ASPECTS OF VISUAL PERCEPTION 

¥ Main functions of other visual areas 

Ð V2: binokular reaction, V3: Form and depth, but not color 

Ð V4: Color perception, V5: Motion perception, V6: Form perception 

¥ Types of cells: 

¥ Simple cells: mostly reaction to edges (position specific, in LGN) 

¥ Complex cells: react to contours of edges (not position specific) 

¥ Hypercomplex cells: react to corners and angles 

¥ Object recognition is assumed to derive from collective activation in different areas („feature maps“) 

Ð Ensemble hypothesis vs. „grandmother cell hypothesis“ 

¥ (the view that there are even more complex, „gnostic“ cells reacting to objects of specific types) 

ATTENTION 

"Everyone knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of 

what seems several simultaneously possible objects or trains of thought". 

William James 

But: 

"In reviewing the literature on attention we were struck by several observations. One was a widespread reluctance 

to define attention. Another was the ease with which competing theories can accommodate the same empirical 

phenomena. A third observation was the consistent appeal to some intelligent force or agent in explanations of 

attentional phenomena. [...] As a consequence , the more we read, the more bewildered we became" 

(Johnston/Dark 1986:43) 

[ 33 ]


3 WAYS TO VIEW „ATTENTION“ 

¥ As a process 

Ð managing the (limited) ressources needed for cognitive processes (-> control) 

Ð regulating the attentiveness to stimuli (-> alertness) 

Ð selecting relevant information (-> filter) 

¥ from different modalities (visuell vs. auditiv) 

¥ from different spatial locations 

¥ from different features (e.g., color vs. form) 

METAPHORS OF SELECTIVE ATTENTION 

¥ Attention as a 

Ð Spotlight (highlighting regions) 

Ð Zoom lens (focused vs. distributed attention) 

Ð Gradient (no sharp boundaries of the attended region) 

Ð Glue (attention is necessary for conjoining features) 

FEATURE INTEGRATION THEORY (TREISMAN) 

NETWORKS OF ATTENTION 

¥ Visuo-spatial orientating 

Ð DISENGAGE-mechanism (parietal Cortex) 

Ð MOVE-mechanism (Superior Colliculus) 

Ð ENGAGE (Pulvinar (Thalamus)) 

¥ Executive Network 

(anterior cyngulate gyrus) 

¥ Vigilance network (right parietal and right frontal lobes) 

[ 34 ]


APPENDIX:DER SCHRIFTSTELLER UND DIE VIELEN PROGRAMMIERER 

EIN MODERNES MÄRCHEN (?) 

erzŠhlt von Kai-Uwe Carstensen-Grimm 

Irgendwann einmal, in nicht allzu ferner Zukunft oder Vergangenheit, gibt es einen ganz 

au§ergewšhnlichen Romanschriftsteller. Au§ergewšhnlich deshalb, weil er nicht nur Romane verfa§t, 

sondern sich auch Gedanken darŸber macht, wie dies geschieht. Kein Mittel lŠ§t er unversucht, um 

herauszufinden, durch welche Prinzipien sein Tun geleitet wird. Schlie§lich versucht er sogar, 

Computerprogramme zu entwickeln, in denen sich seine natŸrliche schriftstellerische Kompetenz 

widerspiegeln soll, sprich, die selbst Romane schreiben kšnnen. 

Allerdings Ð er bleibt nicht der einzige, der das versucht. FŸr einige scheint es, aus verschiedenen 

GrŸnden, ebenfalls erstrebenswert, ein Schriftstellerei-Programm zu besitzen. Eines Tages gibt es solch ein 

Programm. Es ist ein Ideales Programm zum Schreiben (IPS), das seinen FŠhigkeiten nahe, in mancher 

Hinsicht sogar gleich kommt. Und so macht sich auch unser Schriftsteller auf, um es sich anzuschauen. Kaum 

hat er jedoch einen Blick auf Dokumentation und Programmcode des IPS geworfen, macht sich in ihm eine 

gro§e EnttŠuschung breit: Keine Kommentarzeile in dem ganzen Programm handelt von Schriftstellerei; 

stattdessen findet er nur Verweise auf BŸcher Ÿber Statistik und Differentialgleichungen. Und wie er genauer 

nachfragt, so bringt ihn die Unkenntnis der Programmierer Ÿber die vielfŠltigen Aspekte der Schriftstellerei 

ebenso zum Schaudern wie seine allmorgendliche kalte Dusche. "Wozu der analytische Aufwand", sagen sie 

nur, "unser Programm lernt alles von selbst." 

Insbesondere darŸber Šrgert sich unser Schriftsteller aber. Schlie§lich ist er der Fachmann fŸr 

Schriftstellerei. Wo genau stehen denn dort so wesentliche Wissensbausteine wie da§ man erst eine Idee 

haben mu§, bevor man Ÿberhaupt zu schreiben anfŠngt? Er selbst hingegen hat schon Programme entworfen, 

in denen Regeln wie "Wenn Du eine Idee hast, dann verfolge sie und arbeite einen Plot aus" eingebaut 

gewesen sind. Gro§e und komplexe Programme, die von vielen als "wundersam" angesehen worden sind (ob 

ihrer seltsamen Symbole wie z.B. '"' und '$'). All diese Programme haben aber nicht so richtig funktioniert. 

Weil er alles ganz genau hat hinschreiben mŸssen, gab es immer irgendetwas, das er nicht berŸcksichtigt hat. 

Vor allem ist ihm bis zuletzt nicht klar gewesen, was eigentlich eine 'Idee' ist, die man verfolgen mu§, und 

was ihr in seinen wundersamen Programmen entsprechen sollte. 

"Idee", denkt er eines Abends, den Kopf in die Ÿber seinem Laptop (auf dem das IPS lŠuft) 

verschrŠnkten Arme gelegt, "Idee, Idee, Idee". Und schon schlŠft er, mŸde vom langen Suchen und enttŠuscht 

Ÿber das, was er gefunden hat, ein. Die Gedanken verschwimmen, und plštzlich erscheint ihm eine Fee. "Du 

hast einen Wunsch frei", denkt er, ohne die Wšrter zu hšren. SelbstverstŠndlich wŸnscht er sich nichts 

sehnlicher als zu wissen, wer denn nun recht hat mit seiner Herangehensweise an die Schriftstellerei, er oder 

die Programmierer (so eine Frage kann wirklich nur in einem MŠrchen beantwortet werden). 

[ 35 ]


Als er erwacht, flimmern die folgenden Zeilen Ÿber das LCD-Display seines Notebooks: 

"Du mu§t die Forschung anderer tolerieren" 

"Du mu§t die Forschung anderer respektieren" 

"Du mu§t bei der Bewertung Deiner oder anderer Forschung alle relevanten Aspekte in Betracht ziehen" 

"Du mu§t Latein lernen" 

"Nec vitae, nec universitatis, sed posteritatis scientiae pervestigamus" 

Erstaunt stellt er fest, da§ ihm všllig klar ist, was diese SŠtze bedeuten (und da§ sich aus ihnen die Antwort 

auf seine Frage ableiten lŠ§t). "Das wird nicht jedem so gehen", denkt er. 

"Ich war eindeutig intolerant, gegenŸber dem Erfolg eines nicht nur kŸnstlichen, sondern auch 

undurchsichtigen schriftstellernden Systems" (tatsŠchlich war es diese besondere Mischung, die in ihm ein 

urtŸmliches Unbehagen hervorgerufen hat). 

"Und ich habe die IPS-Schriftstellerei nicht als solche anerkannt und respektiert. Wie borniert", 

tadelt er sich selbst. Schlie§lich lŠuft das Programm doch. Und da§ es bei der KomplexitŠt des Gegenstands 

nicht trivial sein kann, ist fŸr ihn nun offensichtlich. Wo war also das Problem? Es war ihm tatsŠchlich 

entfallen. Ach ja, die Bewertung. 

Da§ jeder seinen eigenen Ansatz zu ungunsten anderer bevorzugt und hervorhebt, glaubt er schon 

immer gewu§t zu haben. Deshalb auch sein Disput mit den Programmierern. Er erinnert sich der kontroversen 

Diskussionen, der beiderseitigen Arroganz und gegenseitigen Ignoranz. Schmunzelnd denkt er daran zurŸck, 

da§ er in den letzten Jahren zuerst insgeheim und dann immer offener ausgelacht wurde, ebenso wie man ihm 

zuletzt mit einem hŠmischen Grinsen (was er zu Recht als "na, willst Du jetzt endlich in Rente gehen?" 

interpretierte) das IPS Ÿberreicht hat. "Da haben wir wohl alle etwas Ÿbersehen", fŠllt ihm angesichts der 

Tatsache ein, da§ es sicherlich nicht darum gegangen sein konnte, das erste oder beste Schriftsteller-System 

zu bauen. Dies wollte er nicht einmal den Programmierern unterstellen. Schlie§lich orientieren die sich schon 

lange an biologischen Vorbildern. Unser Schriftsteller kennt sich da zugegebenerma§en nicht aus; ihm fallen 

nur Schlagworte ein: neuronal, genetisch, Virus, KŠfer (die er allerdings nicht zuordnen kann). 

"Wie konnte ich Ÿbersehen, da§ bei den unterschiedlichen Perspektiven auf und Herangehensweisen 

an die Schriftstellerei gar keine Vergleichbarkeit gegeben, also auch keine einheitliche Bewertung mšglich 

ist? ", fragt er sich verwundert. "Hey, ich will beschreiben und sie wollen konstruieren; ich will explizieren, 

und sie wollen generieren; ich will Kontrolle, sie wollen Selbstorganisation. Kein Wunder, da§ kaum eine 

gemeinsame Basis vorhanden ist." Kein Wunder auch, so wird ihm bewu§t, da§ er so viele Schwierigkeiten 

hatte bei dem Versuch, ein System zu bauen. 

"Das mit dem Latein lernen ist wohl ein Scherz", denkt er dann, "aber da§ wir fŸr die Nachwelt 

forschen, hatte ich tatsŠchlich vergessen." Hierzu gehšrt aber unbedingt eine auf prŠziser Analyse basierende 

explizite Wissensvermittlung. Denn was wŠre wohl, wenn er seinen Studenten im Theorieteil seines Kurses 

'Creative Writing:Theory and Praxis' einige Romane mit den Worten 'Lest dies und ihr wi§t, was 

Schriftstellerei ist' Ÿbergeben oder mit Šhnlich suggestiven Worten das IPS starten wŸrde? 

So sitzt er da, unser Schriftsteller, den Kopf sinnierend auf die rechte Hand gestŸtzt, und denkt an all 

die, die schon immer behauptet haben, da§ man Programmiererei und Schriftstellerei zwar nicht miteinander 

vereinbaren, aber doch immerhin unter- oder nebeneinander akzeptieren kann. "Wie gut hŠtten wir 

zusammenarbeiten kšnnen", grŸbelt er, einen wichtigen Aspekt hinzudenkend, "jeder hŠtte vom anderen 

lernen, von den Erkenntnissen des anderen profitieren kšnnen." Schlie§lich ist er Ñbei aller AnerkennungÑ 

keineswegs uneingeschrŠnkt zufrieden mit dem IPS. Sofort kommen ihm daher einige bedeutungsschwere 

WortungetŸme wie 'Interprofessionelle Kollaboration' oder 'Synergistische Progression' in den Sinn. 

"Praktisch, unter solchen cover terms subsummierbar zu sein", so sein vorlŠufiges Fazit, "aber es 

wird schwierig sein, allen Beteiligten ihre Bedeutung so zu vermitteln, da§ der Sinn und Zweck des ganzen 

Ÿber kurz oder lang nicht doch wieder in Frage gestellt wird und es zu denselben HahnenkŠmpfen kommt wie 

gerade gehabt. Na, und vom ErklŠrungsbedarf der Unbeteiligten mal ganz zu schweigen". 

Ganz plštzlich, als hŠtten diese †berlegungen eine Sperre beseitigt, kommt ihm die Idee zu einem 

neuen Roman. Ohne lange zu Ÿberlegen beginnt er mit der Arbeit. Wenn er seine Ideen ausgearbeitet hat und 

der Roman geschrieben ist (wie lange hŠtte es wohl gedauert, die entsprechenden Parameterwerte des IPS zu 

setzen?), dann will er wieder daran gehen herauszufinden, was eigentlich eine 'Idee' ist. Dazu wird er 

vielleicht ein weiteres wundersames Programm schreiben (ohne da§ er den Anspruch hat, da§ es jemals 

perfekt funktionieren wird). Jetzt wei§ er schlie§lich, in welcher Hinsicht eine solche Vorgehensweise als 

[ 36 ]


lobenswert, und in welcher sie als lŠcherlich zu bewerten ist. Reden will er z.B., mit Leuten (Kollegen, 

Interessierten, Nachkommen), die an Schriftstellerei interessiert sind, und nicht an Programmiererei, so 

schwierig und wichtig die auch immer ist. Mit diesen Leuten wird er sich dann vor seinen Laptop setzen, das 

Icon des IPS anklicken (es zeigt eine Vielzahl kleiner schwarzer Kisten, die in einer Art Netz miteinander 

verbunden sind) und ihnen beschreiben und gleichzeitig erklŠren, was gerade passiert. Wenn sie Fragen zu 

dem Programm selbst haben (was unweigerlich passieren wird), wird er sie an die Programmierer verweisen. 

Mit einigen von diesen hat er nŠmlich mittlerweile ein ÔInterdisziplinŠres Institut fŸr SchriftstellereiÕ 

gegrŸndet. Und selbstverstŠndlich gibt es einen Studiengang ÔComputational Creative WritingÕ, in dem seine 

Studenten auch lernen, IPSe zu entwickeln. Klar, da§ sie damit eine optimale Ausbildung erhalten und da§ 

sich Ihnen dadurch ganz neue Berufsperspektiven eršffnen (auch wenn es immer wieder schwierig ist, diese 

neuen QualitŠten nach au§en hin angemessen darzustellen). 

Und wenn er schon gestorben ist, werden ihm die Leute noch dankbar dafŸr sein, da§ sie Konzepte 

wie 'Idee' und ÔPlotÕ nicht immer wieder neu entdecken mŸssen. 

[ 37 ]

FOUNDATIONS OF COGNITIVE SCIENCE - of Kai-Uwe Carstensen

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