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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 1/42 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 2/42 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]) 3/42 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, ... 4/42 Kognitive Modellierung - Cognitive Modeling Lehre am CSL • • • • 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) 5/42 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 6/42 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 7/42 Kognitive Modellierung - Cognitive Modeling Outlook: Lecture Overview • • • • • • • • • 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 8/42 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) 9/42 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: • • • • • • Psychology, Philosophy, Anthropology, Neuroscience, Computer science, Linguistics Computer Science Cognitive Science NeuroScience Hexagon KW, nach Johannes Haack, Uni Potsdam 10/42 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 11/42 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 12/42 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 13/42 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) 14/42 Some Problems of Behaviorism Kognitive Modellierung - Cognitive Modeling • Around the 1950s, several experiments raised doubt in explanations of Behaviorism • • • • 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 15/42 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? 16/42 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 17/42 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 18/42 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 19/42 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 20/42 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 21/42 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 22/42 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? 23/42 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 24/42 Kognitive Modellierung - Cognitive Modeling Typical Information Processing Model Sensor Input Information Processing Memory Action Generation 25/42 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 26/42 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 • • • • 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, … 27/42 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 28/42 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 29/42 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 30/42 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 31/42 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) 32/42 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 33/42 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 34/42 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 35/42 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 36/42 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 37/42 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 38/42 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 39/42 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 40/42 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 41/42 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 42/42