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Kognitive Modellierung - Cognitive Modeling
Vorlesung SS 2013
Kognitive Modellierung Cognitive Modeling
Prof. Dr. Tanja Schultz
Dipl.-Inform. Felix Putze
Dipl.-Inform. Dominic Heger
Donnerstag, 18.4.2013
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Allgemeine Informationen: VL KogMod
Vorlesung im Bachelor/Master/Hauptdiplom
– Vorkenntnisse sind nicht erforderlich
Kognitive Modellierung - Cognitive Modeling
Prüfungsmöglichkeit:
– Ja, für Informatik Bachelor/Master/Diplom und Inwi Master
– Mündlich oder schriftlich (Ankündigung folgt bald)
Turnus:
– Jährlich im SS, 2 SWS = 3 LP
Termine:
– Do 15:45 – 17:15 (50.34 SR 236)
– Start 18.04.2013, Ende 19.07.2013
DozentInnen:
– Prof. Dr. Tanja Schultz
– Dipl.-Inform. Felix Putze
– Dipl.-Inform. Dominic Heger
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Kognitive Modellierung - Cognitive Modeling
Allgemeine Informationen: Vorlesung
Alle Vorlesungsunterlagen befinden sich unter
http://csl.anthropomatik.kit.edu > Lehre > SS2013 > KM
– Alle Folien als pdf
– Aktuelle Änderungen, Ankündigungen, Syllabus
– Gegebenenfalls zusätzliches Material (Papers)
Grundlagen für Prüfungen:
– Vorlesungsinhalt, Folien, ggf. zusätzliches Material
Wir sind erreichbar:
CSL, Laborgebäude Kinderklinik, Geb. 50.21, Adenauerring 4
– Tanja Schultz ([email protected])
– Felix Putze ([email protected]),
Dominic Heger ([email protected])
– Sekretariat: Helga Scherer ([email protected])
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Forschung: Human-Centered Technologies
Anwendungsfeld Mensch-Maschine Interaktion
Herasusforerderungen und Aufgagen:
Produktivität und Usability
Kognitive Modellierung - Cognitive Modeling
Anwendungsfeld Mensch-Mensch Kommunikation
Herausforderung und Aufgaben:
Sprachenvielfalt, kulturelle Barrieren
Aufwand und Kosten
Kommunikation des Menschen mit seiner Umwelt
im weitesten Sinn:
Sprache, Bewegung, Biosignale
Technologien und Methoden:
Erkennen, Verstehen, Identifizieren
Statistische Modellierung, Klassifikation, ...
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Kognitive Modellierung - Cognitive Modeling
Lehre am CSL
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Multilinguale Mensch-Maschine Kommunikation (SS)
Biosignale und Benutzerschnittstellen (SS)
Kognitive Modellierung (SS)
Design und Evaluation Innovativer
Benutzerschnittstellen (WS)
• Praktikum: Biosignale – Aktivitätserkennung (SS)
• Praktikum: Biosignale – Emotion und Kognition (WS)
• Praktikum: Multilingual Speech Processing (WS)
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Kognitive Modellierung - Cognitive Modeling
What is this lecture about?
Design a robot that
is able to learn
complex tasks from
a human teacher
Automatically
optimize a GUI
by reducing its
cognitive
complexity
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Make computers
understand their
users‘ emotions
and how they
influence behavior
Motivation
Kognitive Modellierung - Cognitive Modeling
• Motivation of this course on “Cognitive Modeling”:
• Find answers to questions like:
• How do humans process information / plan / learn?
• Can computers or robots think / learn?
• Can we design better man-machine interfaces if we know about these
things?
 Example: If we know how human memory works and how it is
limited, can we derive how and how much information we can
present on the screen?
• These questions are typically asked by scientists in the area of
cognition and human behavior
• Goals of this course:
 Brief overview of cognitive science and methods
 Overview of cognitive and behavioral models
 Applications of cognitive and behavioral models
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Kognitive Modellierung - Cognitive Modeling
Outlook: Lecture Overview
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Cognitive Architectures
Memory Modeling
Multitasking
Attention and Visual Perception
Game Theory and Reinforcement Learning
Affective Models
Human and Machine Learning
Human Behavior Modeling and Decision Making
Empirical Cognitive Models
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Kognitive Modellierung - Cognitive Modeling
What is Cognition?
• Cognition
• (Latin: cognoscere, "to know”, "to conceptualize" , "to recognize")
• … is the scientific term for "the process of thought" (wikipedia)
• Usage of the term varies with disciplines:
• in psychology and cognitive science it refers to an information
processing view of an individual's psychological functions
• Cognition refers to a faculty for the processing of information,
applying knowledge, and changing preferences.
• Cognition can be natural or artificial, conscious or unconscious
• Cognition is not just a process in the head, but also an interaction
with the outside world. ([Taatgen et al., 2006])
• Cognition can be defined as "the act or process of knowing in the
broadest sense; specifically, an intellectual process by which
knowledge is gained from perception or ideas" (Webster's
Dictionary)
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Kognitive Modellierung - Cognitive Modeling
Cognitive Science
• Strube (2002): Focus of Cognitive Science (CS) is the study of
cognitive systems, their relevant structures, processes, and
resulting performance
• Cognitive Science is a truly young interdisciplinary science
which immerged around 1975 from the following disciplines:
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Psychology,
Philosophy,
Anthropology,
Neuroscience,
Computer science,
Linguistics
Computer
Science
Cognitive
Science
NeuroScience
Hexagon KW, nach Johannes Haack, Uni Potsdam
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Modeling
Kognitive Modellierung - Cognitive Modeling
• A model is a formal representation of a natural or artificial
physical object, concept, process, …
• Preserves main characteristics
• Abstraction and compression
• Allows to deduce information about the modeled entity
• Example in computer science:
• Entity: Street layout with intersections, routes, distances
• Model: Graph with nodes, edges, edge weights
• For a valid model, the following diagram commutates:
Model
Relation on model
Real entity
Related entity
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Cognitive Modeling
Kognitive Modellierung - Cognitive Modeling
• Cognitive Modeling is one aspect of Cognitive Science
• A cognitive model is a formal and computable description of a
mental process
• Deterministic mathematical functions
• Probabilistic models
• Algorithmic descriptions
• A cognitive model is typically…
• … designed according to psychological theories of the mind
• … adjusted to empirical data (e.g. parameter tuning)
• In line with the cognitive paradigm, the model
• … does not only describe behavior
• … but also its derivation from cognitive processes
• A cognitive model allows the prediction of cognitive processes
and human behavior in unseen situations
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Behaviorism
• Behaviorism was the dominant psychological
approach in the first half of the last century
Kognitive Modellierung - Cognitive Modeling
• Thorndike (1898), Pavlov (1905), Skinner (1956)
• Behaviorism:
• Goal: to predict and control behavior
• Rigid application of scientific method to psychology  controlled
laboratory experiments, dealing only with objective and
measurable parameters
• Consequence: Observation of only visible, measurable behavior
• Explain behavior by observing from the outside as trained
responses to perceived stimuli
 Example: Learn to execute actions which lead to rewards
• No consideration of internal or mental processes at all
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Conditioning
Kognitive Modellierung - Cognitive Modeling
• Conditioning is a classic explanation approach in Behaviorism
• Behavior = Learned pairs of stimulus  response
• Unconditioned Stimulus (US)
• Stimulus causing response (UR) without
previous learning
• Conditioned Stimulus (CS)
• Previously neutral stimulus (NS) that
causes response (CR) because of
multiple occurrences in combination
with US
• Example:
• Before training: Bell -> no specific reaction;
Food (US) -> salivation (UR)
• Training: Food (US) + bell (NS) -> salivation (UR)
• Result: Bell (CS) -> salivation (CR)
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Some Problems of Behaviorism
Kognitive Modellierung - Cognitive Modeling
• Around the 1950s, several experiments raised doubt in
explanations of Behaviorism
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Tolman (1948): Spatial Navigation
Chomsky (1959): Language Acquisition
Bandura’s Bobo-dolls experiment (1965)
…
• Phenomena were observed which could not be explained by
conditioning or other approaches of Behaviorism
• New explanations were developed which postulate a model of
certain cognitive processes
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Kognitive Modellierung - Cognitive Modeling
Example 1: Tolman’s Rat Maze (1948)
• Classical conditioning with rats in a maze:
• A food box contains a reward
• Untrained rats learn that a certain
behavior (taking directions)
• Learning by trial-and-error
• What happens, if no reward
is provided initially?
• What happens, if the maze
is modified?
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Example 1: Tolman’s Rat Maze (1948)
• Tolman’s modification to the maze task:
Kognitive Modellierung - Cognitive Modeling
• First days, they navigated it without reward
• Later, they received a reward (food) when they reached the exit
• Tolman observed that rats quickly improved in performance
(i.e. time to find exit) if reward was provided
• Not easily explained using classic behaviorism: Without
reward/stimulus, no learning should take place!
• Also, rats quickly adapted to modifications (e.g. new
obstacles) of the maze without repeated try-and-error
• Tolman’s explanation: The rats built “cognitive maps” of the
maze even when there was no reward
• Using this map, they can quickly locate the reward when placed or
alternate routes when old ones were blocked
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Example 2: Language Acquisition (1959)
Kognitive Modellierung - Cognitive Modeling
• Skinner: Verbal Behavior (1957): Behavioristic explanation of
language acquisition
• “Men act upon the world, and change it, and are changed in turn by
the consequences of their action.”
• Speech = Non-physical Behavior
• Reaction of other people reinforces
• Chomsky: Language acquisition cannot be explained this way
• Behavioral approach has no account for innate language skills
(indicated by common language structures all over the world; infants
can already recognize language structure)
• Proposes innate “universal grammar” (remember formal grammars!)
• Explains generation of new, i.e. unheard, combinations of words
• Chomsky claims LA is a process of hypothesis generation and testing
• Chomsky’s position is also disputed and seems outdated 
controversy lasts until today
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Example 3: Bandura’s Bobo-dolls (1965)
Kognitive Modellierung - Cognitive Modeling
• Groups of children were shown three different versions of a film
• All showed an adult abusing a human-like doll (punching,
kicking, insulting, …). There were three different endings:
• The adult is rewarded with candy
• A second person criticizes the actor and physically punishes him
• The behavior is not commented
• Later, the children are put in the movie setting themselves
• Children from the first and third group reproduced the aggressive
behavior towards the doll
• Children from the second group were much less aggressive
• When offered a reward however, also the children from the
second group reproduced the violent behavior
• Bandura’s interpretation: Difference between acquisition and
execution of behavior
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Kognitive Modellierung - Cognitive Modeling
Change of Paradigm
• Change of Paradigm: Cognitive Revolution („Kognitive Wende“)
• Started around 1950-1960
• 1956: Symposium on Information Theory (MIT) with participants like
Allen Newell, Herbert Simon (CMU), Marvin Minsky, Noam Chomsky
• Other players who were fundamental in changing the paradigm
• Norbert Wiener
 Kybernetik
• Alan Turing
 Turing Test
• George Kelly
 The Psychology of Personal Constructs
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Kognitive Modellierung - Cognitive Modeling
Main Ideas of the Cognitive Revolution
• Move away from the static and passive stimulus-response
paradigm of behaviorism
• To accurately predict how humans behave, it is necessary to
understand internal (potentially non-observable) processes
• Introduction of formal and mathematical models to describe
those internal processes
• Implementing those models in computer programs to predict
or replicate cognitive processes
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Behaviorism vs. Cognitive Science
Kognitive Modellierung - Cognitive Modeling
• Cognitive revolution actually is no revolution in a scientific sense
(acc. to Thomas Kuhn, The Structure of Scientific Revolutions):
• Behaviorism was not falsified (in the sense of Popper’s scientific method)
• Behaviorism did not “drown in a sea of anomalies”
• Instead, modern behaviorism co-exists with cognitive science
• Behavioristic experiments often yield new theories of cognition
• Behavioristic experiments are an important evaluation tool for cognitive
theories
• Modern behaviorism is still actively researched
• Border between cognitivism and behaviorism is often fuzzy
• Modern theories often combine cognitive and behavioristic ideas
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Contributing Factors
Kognitive Modellierung - Cognitive Modeling
• What developments made the cognitive revolution possible?
• Computers (1941: Zuse Z3, 1948: ENIAC, 1948: Mark I):
• Programmable computers allow to execute arbitrary code
• Processing of arbitrary information input is possible
• Computers solve problems which are considered as “intellectual
milestones”, e.g.
 outperform chess champion Kasparow, Deep Blue 1996,
 decipher cryptographic codes,
 win the game Jeopardy, IBM/CMU Watson 2011, ….
• Neuroscience/Neuroimaging (1940s: EEG, 1970s: MRI)
• Study the structure of the brain: Which regions are activated for
which tasks?
• Study the time course of activation: How long does a certain part of
processing last?
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Kognitive Modellierung - Cognitive Modeling
Human as Information Processing System
• Fundamental presumption:
• Mind as a computational model
• Differentiation between hardware (brain) and software (mind)
• Cognition is the sum of information processing and structures
of an intelligent system and independent from physical
substrate
• Cognition as general term for mental processes
• Human being is an information processing system
 With receptors and effectors
 With memory
 With mental representations (~ “data structures”)
 With behavior (~ “algorithms”)
 With a set of (parallel) information processing elements
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Kognitive Modellierung - Cognitive Modeling
Typical Information Processing Model
Sensor Input
Information Processing
Memory
Action
Generation
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Symbol-based Models: Rules
• Information is represented by semantically meaningful symbols
Kognitive Modellierung - Cognitive Modeling
• Words, numbers, objects, …
• Symbol form structures, e.g. a sentence formed by words
• Symbols and structures of symbols can be manipulated by rules
• A rule is of the form IF … THEN …
• IF part (precondition) matches the existing symbol structures
• THEN part says how the symbol structures are manipulated
• Rules can be used for planning (analogous to planning in AI)
• Example: Difference-Reduction  Apply rules which reduce the
difference between the current state and the goal state
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Other Forms of Symbol-Based Models
• Formal logic
Kognitive Modellierung - Cognitive Modeling
• Represent information as formulas (e.g. in first-order logic)
• Reasoning = conduct inference on set of formulas
• Concepts/Schemas
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Sets of similar features (e.g. “dog”  animal, four legs, barks, …)
Approximately match concepts with observation of world
Allows stereotypical (i.e. efficient) reasoning
Influence behavior and future information processing
• Analogies
• Representations of concrete situations
• Observation of the world is matched with similar situations
• Analogical process  Apply stored behavior to current situation
• Images
• Visual images of situations
• Perform operations like zooming, rotation, scanning, …
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Kognitive Modellierung - Cognitive Modeling
Connectionist Models
Why do people show intelligent behavior?
• Processing units = simple nodes of the neural network
• Nodes can be active or inactive
• Nodes are connected with each other by
• Excitatory connections
• Inhibitory connections
• Humans have methods to distribute activation across the units
• Humans have methods to modify the connections of these units
• Behavior = apply activations and learning (based on the units,
connections)  not explicitly encoded, but within the network topology
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Church-Turing Hypothesis
Kognitive Modellierung - Cognitive Modeling
• How close can computer models come to human cognition?
• Alan Turing was one of the first to reason about the
fundamental abilities of computers
• What functions can data processing machines compute?
(1937: On computable numbers, with an application to the
"Entscheidungsproblem")
• Can data processing machines mimic or actually have cognitive
abilities? (1950: Computing machinery and intelligence)
• All known sufficiently complex formal models of computation
are equivalent:
• Turing Machines, λ calculus, Recursive functions, WHILE programs, …
• (Strong) Church-Turing Hypothesis: Every effectively calculable
function can be calculated by a Turing Machine
• Does this also include human cognition?
•  Matter of fiercely fought discussions
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Turing Test
Kognitive Modellierung - Cognitive Modeling
• How can we determine whether we have a machine which
displays intelligent behavior?
• Make use of experts on intelligence  human judges:
• Have judge interact with a machine and a human via terminal
• Judge may ask any question and see the written answers
• A machine passes the test if the judge cannot distinguish it from the
human
• An entity which passes the test is considered intelligent
• Tests only for intelligent behavior, not for intelligent, human-like
thinking processes
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Criticism and Reality of Turing Test
Kognitive Modellierung - Cognitive Modeling
• Chinese Room Thought-Experiment (Searle, 1980):
• Room contains a person (who does not speak Chinese) with a rule book
• The book contains rules how to manipulate Chinese letters to transform
a Chinese question into the correct Chinese answer
• Person receives questions in Chinese as part of the Turing Test, applies
the rules from the book and returns the result
• Would you say that the person understands Chinese if it passes the test?
• ELIZA (Weizenbaum, 1966):
• Therapeutic conversation program that processed natural language
• Used shallow references to phrases of the user to do conversation:
 “I am feeling depressed.”  “Why are you feeling depressed?”
 “I have problems with my mother”  “Tell me about your family”
• Many people perceived ELIZA as empathic or even believed to interact
with a real human, even if aware of its main design principle
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Conclusion on Turing Test and AI
• There are many definitions of (artificial) intelligence and
arguments for and against each of them
Kognitive Modellierung - Cognitive Modeling
• Example: strong AI vs. weak AI
• Bottom line: Arguing about intelligence and artificial
intelligence is a philosophical mine field
• Our goal is not to synthesize human intelligence
• We want to improve human-machine interaction by a pragmatic
understanding of human cognition and its limitations
• We want human cognition as inspiration but not as blueprint for
artificial “cognition”
• Still, be aware of your vocabulary to talk precisely about
cognition and its simulation
• Example: If you say a robot “acts”, this implies it performs a conscious,
motivated behavior (which may be more than you intended)
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Criticism of Computer Analogy
• Intelligence is not independent of physical substrate
Kognitive Modellierung - Cognitive Modeling
• i.e. no separation between “hardware” and “software”
• Moravec's paradox: For robotics, the hard problems are easy and
the easy problems are hard
• Easy (for humans): Recognizing a face, lifting a pen, walking across a room
• Hard (for humans): Solving differential equations, predicting the stock market
• Moravec’s explanation: easy skills are unconscious and old, i.e. shaped and
optimized by million years of evolution; hard skills are conscious and new
• Embodiment: Cognitive abilities are shaped by the human body
• Cannot understand cognition without studying the body/physiology
• Also implies that we need to study the physiology of the brain to
understand cognitive processes  learn about neural structure!
•  Implications for design of robots and intelligent systems
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Kognitive Modellierung - Cognitive Modeling
The Brain and Modeling of Cognitive Processes
• All higher cognitive processes take place in the brain
• Cognitive processes are a result of low-level electrical
and chemical activity in the brain
• The brain consists of highly interconnected cells
• ~86 billion[1] neurons, with up to 10k synaptic connections each
• Computational Neuroscience
• Discipline of modeling certain aspects of brain function
 Single neurons, sensory perception, cognition, learning, etc.
• Models can be implemented in a computer
• Typical modeling aspects
 Brain localization, pathways, timings, etc.
• We will get to know some computational
neuroscience models throughout the lecture
[1] S. Herculano-Houzel (2009): The human brain in numbers: a linearly scaled-up primate brain, Frontiers in Human Neuroscience
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Neuroscientific Insights on Cognition
Kognitive Modellierung - Cognitive Modeling
• Some primary sensory and motor functions
are localizable at distinct centers of the brain
• Examples:
• Sensorimotor cortex
(-> Homunculus)
• Visual processing pathways
in occipital lobe
(more information in lecture on
perception and attention)
• Complex cognitive processes usually involve
activity in many different locations of the brain
• Most processes in the brain are still not completely
understood
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Measuring Brain Activity
Kognitive Modellierung - Cognitive Modeling
• To develop and verify models we need to measure brain activity
• Measurement of brain activity can be…
• … direct activity: electrical activity in active areas
 has very low voltage (hard to measure)
• … correlates of activity: higher oxygen concentration in active areas
 effects are more coarse (e.g. have lower temporal resolution)
• … invasive: directly accessing parts of the brain, requiring neurosurgery 
for medical applications, basic research with animals
• … non-invasive: refraining from direct access to the brain
 less detailed information, for studies with healthy subjects or for system
users
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A Few non-invasive Measurement Techniques
• EEG
Kognitive Modellierung - Cognitive Modeling
• Electrical Brain Waves
• Measured using
electrodes
• fMRI
• Strong magnetic field to
measure changes in spin
of oxygenated hemoglobin
• BOLD effect
• fNIRS
• Different absorption
rates of Near Infrared light
for oxygenated and
deoxygenated
hemoglobin
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Comparison of Techniques
Noninvasive Techniques
Kognitive Modellierung - Cognitive Modeling
EEG
MEG
fNIRS
fMRI
SPECT
PET
Invasiveness
Medium
(contact, gel)
Small
(no contact)
Small (contact,
but no gel)
Medium
(no contact,
noisy,
claustrophobic)
High (Injection
into blood)
High
(Injection into
blood)
Spatial
Resolution
Some
centimeters,
summation
Few
millimeters,
summation
~1-2cm
Voxel of about
3mm
Voxel of 1015mm
Voxel of 2-5mm
Temporal
Resolution
Milliseconds
Milliseconds
Some seconds
2-5 seconds
1 second
1 second
Physiological
Parameters
Electrical
neural activity
Electrical
neural activity
Hemaglobin
concentration
Hemaglobin
concentration
Blood flow
Blood flow
Ressources
Few Space
Few energy
Low costs
Lot of space
Lot of energy
High costs
Few Space
Few Energy
Reasonable
Costs
Lot of space
Lot of energy
High costs
Lot of space
High costs
Lot of space
High costs
Mobility /
Wearability
Yes
No
Yes
No
No
No
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What is possible using Brain Imaging?
Kognitive Modellierung - Cognitive Modeling
• Reconstruct movies from visual cortex brain activity
(University of California, Berkeley; Nishimoto et al., 2011)
• Record fMRI data of subject watching several movie trailers (for hours)
• Build regression models between movie and measured brain activity
• Predict brain activity from ~5000h of unseen YouTube videos
• Averaging of the 100 clips whose predicted activity is most similar to the
observed brain activity -> reconstruction
Original
Reconstructed
 Nice example, but in general it is extremely difficult to derive direct
information on behavior, thinking, and consciousness!
 We are still at the beginning and need new models how the brain works
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Human Brain Project
Kognitive Modellierung - Cognitive Modeling
• The Human Brain Project
• European Union FET Flagship Programme,
€ 100 Mio/year in 2013-2023
• Project Partners: ~100 European research
institutions, lead EPFL
• Vision: Model and simulate the brain using supercomputing technology
 Future Neuroscience:
 Consolidate enormous amount of research data/knowledge in
computational neuroscience into one model
 Future Computing :
 Energy efficient, adaptive, robust, … computing
 Create Artificial Intelligence models that integrate knowledge from
brain research
 Future Medicine:
 Understand brain diseases
 Simulate effects of drugs
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Human Brain Project
Kognitive Modellierung - Cognitive Modeling
• Some of the criticism on Human Brain Project
• Similar projects have only achieved so much more simple results
– it’s a lot of money for unclear outcomes
• Reductionist approach and building such a detailed model (cell level)
might not be the best approach to answer all questions
• HBP will may have a huge influence on brain research,
but unified models in research might limit its diversity
Images from Human Connectome Project
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Literature
Kognitive Modellierung - Cognitive Modeling
• No single standard textbook on cognitive modeling
• Check out „Cognitive Modeling Greatest Hits“
• http://cseweb.ucsd.edu/users/gary/CogSciLiterature.html
• List of groundbreaking modeling papers from the 1960ies to 2012
• Cover many of the aspects discussed in this lecture
• Any introductory text book on cognitive psychology, e.g.
• Eysenck & Keane: Cognitive Psychology – A student’s handbook (5th
edition), Psychology Press, 2005
• Betsch, Funke, Plessner: Denken – Urteilen, Entscheiden,
Problemlösen, Springer, 2011
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