1. What is Cognitive Science? … is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology (Stanford Encyclopedia of Philosophy) http://plato.stanford.edu/entries/cognitive-science/

2. Most cognitive scientists are cognitive psychologists or computer scientists… (from: Schunn et al. 2005)

3. Cognitive Science Computer Science/ ArtificialIntelligence Neuroscience Philosophy Experimental Cognitive Psychology Linguistics Understanding Computation To understand limits of theories To understand structure of language To understand how the brain works For human data in various tasks We will focus mostly on insights from Cognitive Psychology Interdisciplinary study of intelligent behavior

4. Areas of Study Cognition is about internal processes that are often unobservable, e.g.: Perception, Attention, Memory, Visual Imagery, Language, Concept Learning, Reasoning Need converging evidence from different perspectives to really understand cognitive processes ?

5. Information Processing Information processing models resemble processing in computers – made cognitive psychology popular Information made available by the environment is processed by a series of processing systems Processing systems transform or alter the information in various systematic ways The major goal of research is to specify these processes and structures

6. Types of Processing Bottom-up processing Top-down processing Parallel processing Cascade processing

7. An early version of the information-processing approach  purely bottom up or stimulus-driven

8. A Demonstration of Top-Down Processing

9. Top-down processing Later stages of processing affect earlier stages  can explain effects of Knowledge, memory, expectations and context

10. (Kleffner & Ramachandran, ’92) Why do we seem to have a fairly robust interpretation of which shapes are concave and convex when the perceptual information is perfectly ambiguous? -> perception affected by knowledge Top-down processing: perception affected by knowledge of world

11. Top down processing: perception affected by memory First time, sine wave speech sounds incomprehensible (to most) After hearing the natural utterance, perception of sine- wave speech seems to be quite different "The steady drip is worse than a drenching rain." (for more info: http://www.haskins.yale.edu/haskins/MISC/SWS/SWS.html) http://psiexp.ss.uci.edu/research/teachingP140C/demos/sinewavespeech.aif http://psiexp.ss.uci.edu/research/teachingP140C/demos/naturalutterance.aif

12. Sound Induced Illusory Flashes Example of parallel and interactive processing: perception of visual event affected by perception of auditory events http://www.cns.atr.jp/~kmtn/soundInducedIllusor yFlash2/http://www.cns.atr.jp/~kmtn/soundInducedIllusor yFlash2/

13. McGurk Effect Perception of auditory event affected by visual processing Harry McGurk and John MacDonald in "Hearing lips and seeing voices", Nature 264, 746-748 (1976). AVI: http://psiexp.ss.uci.edu/research/teachingP140C/demos/McGurk_large.avihttp://psiexp.ss.uci.edu/research/teachingP140C/demos/McGurk_large.avi MOV: http://psiexp.ss.uci.edu/research/teachingP140C/demos/McGurk_large.movhttp://psiexp.ss.uci.edu/research/teachingP140C/demos/McGurk_large.mov

14. McGurk Effect Demonstrates parallel & interactive processing: speech perception is based on multiple sources of information, e.g. lip movements, auditory information. McGurk effect in video: –lip movements = “ga” –speech sound = “ba” –speech perception = “da” (for 98% of adults) Brain makes reasonable assumption that both sources are informative and “fuses” the information.

15. Four Main Approaches Experimental cognitive psychology Cognitive neuropsychology Computational cognitive science Cognitive neuroscience

16. COMPUTATIONAL COGNITIVE SCIENCE

17. Computer Models Computational modeling –Programming computers to model or mimic some aspects of human cognitive functioning. Modeling natural intelligence. Artificial intelligence –Constructing computer systems that produce intelligent outcomes  Simulations of behavior

18. Why do we need computational models? Provides precision need to specify complex theories. Makes vague verbal terms specific Provides explanations Can lead to predictions –just as meteorologists use computer models to predict tomorrow’s weather, the goal of modeling human behavior is to predict performance in novel settings

19. Production Systems

20. Connectionist Networks Also known as: –PDP: parallel distributed processing approach –Artificial Neural Networks Alternative to traditional information processing models Connectionist models are networks of simple processors that operate simultaneously Some biological plausibility

21.  idealized neurons (units) Output Processor Inputs Abstract, simplified description of a neuron

22. Connectionist Networks Inspired by real neurons and brain organization but are highly idealized Can spontaneously generalize beyond information explicitly given to network Retrieve information even when network is damaged (graceful degradation) Networks can be taught: learning is possible by changing weighted connections between nodes

23. Diagram showing how the inputs from a number of units are combined to determine the overall input to unit- i. Unit-i has a threshold of 1; so if its net input exceeds 1 then it will respond with  1, but if the net input is less than 1 then it will respond with –1

24. Different ways to represent information with connectionist networks: localist representation concept 1 concept 2 concept 3 Each unit represents just one item  “grandmother” cells 100000 000100 010000 Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 (activations of units; 0=off 1=on) Unit 6

25. Coarse Coding/ Distributed Representations concept 1 concept 2 concept 3 111000 101101 010101 (activations of units; 0=off 1=on) Each unit is involved in the representation of multiple items Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6

26. Advantage of Distributed Representations Efficiency –Solve the combinatorial explosion problem: With n binary units, 2 n different representations possible. (e.g.) How many English words from a combination of 26 alphabet letters? Damage resistance –Even if some units do not work, information is still preserved – because information is distributed across a network, performance degrades gradually as function of damage –(aka: robustness, fault-tolerance, graceful degradation)

27. Suppose we lost unit 5 concept 1 concept 2 concept 3 111000 101101 010101 (activations of units; 0=off 1=on) Can the three concepts still be discriminated? Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6

28. Multi-layered Connectionist Networks Activation flows from a layer of input units through a set of hidden units to output units Weights determine how input patterns are mapped to output patterns Network can learn to associate output patterns with input patterns by adjusting weights Hidden units tend to develop internal representations of the input-output associations Backpropagation is a common weight-adjustment algorithm hidden units input units output units

29. Example: NETtalk 7 groups of 29 input units 26 output units 80 hidden units _a_cat_ 7 letters of text input (after Hinton, 1989) target letter teacher /k/ target output Connectionist network learns to pronounce English words: i.e., learns spelling to sound relationships. Listen to this audio demo.Listen to this audio demo.

30. Other demos of neural networks Hopfield network http://www.cbu.edu/~pong/ai/hopfield/hopfieldapplet.html Backpropagation algorithm and competitive learning: http://www.psychology.mcmaster.ca/4i03/demos/demos.html Competitive learning: http://www.neuroinformatik.ruhr-uni- bochum.de/ini/VDM/research/gsn/DemoGNG/GNG.html Various networks: http://diwww.epfl.ch/mantra/tutorial/english/ Optical character recognition: http://sund.de/netze/applets/BPN/bpn2/ochre.html Brain-wave simulator http://www.itee.uq.edu.au/%7Ecogs2010/cmc/home.html

31. Limitations of Modeling Approach Computational models are rarely used to make new predictions Connectionist models do not resemble the human brain Numerous models can generally be found to “explain” the same set of findings Computational models often fail to capture the scope of cognitive phenomena Computational cognitive science may fail to deliver a general unified theory of cognition