1. Cognitive Science Definition: “the scientific study either of mind or of intelligence”  Essential Questions  What is intelligence?  How is it possible to model it computationally?  Takes ideas from  Psychology  Philosophy  Linguistics  Neuroscience  Artificial Intelligence / Computer Science  Maybe also minor contributions from:  Anthropology, Sociology, Emotion studies, Animal Cognition, Evolution

2. Origins of Cognitive Science  Psychology of the early 20 th century was dominated by “behaviourism”  Everything should be treated as a behaviour  “…purely objective experimental branch of natural science.” - John B. Watson  Goal: prediction and control of behaviour  “Introspection forms no essential part of its methods ” - John B. Watson  Should not have to describe things in terms of “hypothetical” internals  Such as the “mind”  “Consciousness” not an appropriate question for scientific inquiry  This changed around 1950s  Partly as a result of investigations in Artificial Intelligence, partly changing trends  People started talking about  Theories of mind  Internal representations  Computational procedures  Term “Cognitive Science” born in 1973  Came out of AI - Christopher Longuet-Higgins comment on “Lighthill report”

3. Cognitive Science – Information Processing  Cognitive Science views the mind as an information processing system  This is also called the computational view  From this perspective: a human mind’s activity consists of  Receive information  Store information  Retrieve information  Transmit information  Transform information  Example: a musician improvising  Listen to many tunes  Remember them  Find similarities  Come up with rules that say what sounds good together  Use those rules in real-time while playing

4. Understanding Information Processing Systems 1.We attribute non-behavioural properties to the system  We say that it has a purpose, goals or desires  We say that it has internal beliefs and knowledge and competence  We attribute meaning to its external behaviour and internal information  We treat other humans like this all the time, call it folk psychology 2.Representation: information in the system can represent real things  For example: symbols could represent objects and relationships  This would allow a clear separation of what and how oAlternatively: it could be a messy representation owhat and how tangled together 3.It has procedures for processing information  We call these procedures algorithms in computer speak  Describes how it does what it does  A clear set of steps that need to be followed  Like the recipe for making a cake  Like the instructions for long multiplication

5. Three Levels in Information Processing Systems (Marr’s three levels) What How Representation ties together Physical Implementation Procedure/Algorithm – clear set of instructions (how to process the input  output) What information is coming in? What information is outputted? What is the relationship? (also explains why it’s important) Must be physically carried out –Man with paper and pen –Mechanical computer –Modern PC –Human brain (neurons)

6. Three Levels in Information Processing Systems What How Representation ties together Physical Implementation Procedure/Algorithm – clear set of instructions What information is coming in? What information is outputted? What is the relationship? Interesting: Unlike other sciences we can study top two levels independently from the physical level Must be physically carried out –Man with paper and pen –Mechanical computer –Modern PC –Human brain (neurons) Caveat: This is a particular philosophical position, called “Functionalism”. Some philosophers do not accept it. Functionalism: mental states (beliefs, desires, being in pain, etc.) are constituted solely by their functional role; i.e. their causal relations to other mental states, sensory inputs, and behavioural outputs. Consequence: a mind can be implemented in lots of different physical hardware, so long as it performs the right functions.

7. Three Levels in Information Processing Systems What How Representation ties together Physical Implementation Procedure/Algorithm – clear set of instructions What information is coming in? What information is outputted? What is the relationship? Interesting: Unlike other sciences we can study top two levels independently from the physical level Must be physically carried out –Man with paper and pen –Mechanical computer –Modern PC –Human brain (neurons) Caveat: This is a particular philosophical position, called “Functionalism”. Some philosophers do not accept it. Functionalism: mental states (beliefs, desires, being in pain, etc.) are constituted solely by their functional role; i.e. their causal relations to other mental states, sensory inputs, and behavioural outputs. Consequence: a mind can be implemented in lots of different physical hardware, so long as it performs the right functions. What’s special about a mind then? We know it can do things a computer can’t do… A Functionalist claims that the special thing about the mind is the special information processing tasks, representations and algorithms it uses One could implement the same functions in a computer – don’t need organic neurons

8. Important to Study All Three Levels What How Physical Implementation Procedure/Algorithm – clear set of instructions What information is coming in? What information is outputted? What is the relationship? Could have elegant mathematical theory which no algorithm can implement Must be physically carried out –Man with paper and pen –Mechanical computer –Modern PC –Human brain (neurons)

9. Important to Study All Three Levels What How Physical Implementation Procedure/Algorithm – clear set of instructions What information is coming in? What information is outputted? What is the relationship? But without top level… Lose sight of what your information processing is trying to achieve Must be physically carried out –Man with paper and pen –Mechanical computer –Modern PC –Human brain (neurons)

10. Important to Study All Three Levels What How Physical Implementation Procedure/Algorithm – clear set of instructions What information is coming in? What information is outputted? What is the relationship? Could have a nice algorithm, but might take too much physical hardware to be practical Must be physically carried out –Man with paper and pen –Mechanical computer –Modern PC –Human brain (neurons)

11. Important to Study All Three Levels What How Physical Implementation Procedure/Algorithm – clear set of instructions What information is coming in? What information is outputted? What is the relationship? Focussing on the physical interactions here gives you no idea of what their purpose is Must be physically carried out –Man with paper and pen –Mechanical computer –Modern PC –Human brain (neurons)

12. Important to Study All Three Levels What How Physical Implementation Procedure/Algorithm – clear set of instructions What information is coming in? What information is outputted? What is the relationship? Insights from studying the brain could give clues about the algorithms and representations which are (or are not) being used Must be physically carried out –Man with paper and pen –Mechanical computer –Modern PC –Human brain (neurons)

13. Another Perspective on Cognitive Science  Studying different information processing tasks at different levels VisionLanguageMemoryProblem Solving Learning What (Info Proc Task) How (Algorithm) Physical Implementation

14. AI and Cognitive Science  Two way interaction between AI and Cognitive Science  AI informs Cognitive Science  Common to implement a cognitive theory in a computer  Run the program and see the ramifications of the theory  (Scientific hypothesis testing)  Running it may be necessary because theory is complicated  Also, existing AI theories may shed light on the way humans do it  Cognitive Science informs AI  Seeking inspiration to solve an AI problem  Study the way humans do it  Copy in computer  …or at least constrain the possible options under consideration

15. Herbert Simon “AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, then you're really doing cognitive science; you're using AI to understand the human mind.”

16. Applications of Cognitive Science  Education and Learning  From Cognitive Psychology:  Diagnose and treat children’s reading difficulties  Stroke Therapy  From Linguistics:  Understanding of speech impairments when stroke in left hemisphere of brain  … better therapy  Legal process  From Cognitive Psychology:  Understanding of reliability of memory  Question reliability of legal witnesses  Computing Technology  From AI:  You know loads of examples by now

17. Cognitive Science – Different Methods  Psychology  Controlled laboratory experiments  Detailed observations of behaviour  Philosophy  Thought experiments  Investigate consequences, and coherence of theories  Linguistics  Test speakers’ intuitions about “grammatical” sentences  Analyse children’s acquisition and errors  Neuroscience  Study active brain regions when doing something  Study neurons  Artificial Intelligence / Computer Science  Write programs, see where they succeed and fail

18. Cognitive Psychology  What are the mental processes in between stimulus and response? Categorisation Attention Memory Knowledge representation Numerical cognition Thinking Learning Language Sight Hearing Taste Smell Touch Balance Heat/cold … Voice Limbs Fingers Head … sensory input Motor output Sensory systems Central systems Motor systems (rough model - Boundaries are not clear in reality)

19. Cognitive Science – Different Methods Focus on central unit…  Thinking  Draw conclusions from facts, solve problems, plan actions…  In many diverse domains  Attention  Helps us focus on some task  Has limited capacity  Memory (includes Knowledge Representation)  Seems to be huge  Seems to be no limit on how well it retrieves relevant information  Learning  Acquire new knowledge and sensorimotor skills  How does this central unit work?

20. Physical Symbol System Hypothesis “A physical symbol system has the necessary and sufficient means of general intelligent action.” therefore… human thinking = symbol manipulation Newell & Simon, 1963.

21. Physical Symbol System Hypothesis “A physical symbol system has the necessary and sufficient means of general intelligent action.” Their symbols are taken to mean high level symbols  Directly correspond to objects in the world,  such as “monkey” and “table”. …but the weights and connections in a neural network could also be represented as symbols  Use this to make a “scruffy” representation of “monkey”  but that’s not considered to be what they meant

22. Physical Symbol System Hypothesis “A physical symbol system has the necessary and sufficient means of general intelligent action.”  Most AI people nowadays would not accept the idea of high level symbols being sufficient  Seems to work well for  playing chess, problem solving (if problem well defined)  but doesn’t work so well for some “easy” problems  Vision, moving around in the world  But most AI people would accept the computational theory of mind (i.e. Functionalism)

23. Universal Computing Machine  Turing machine:  Actions:  Head can move left and right over the tape  Can read and write symbols on the tape –Can overwrite symbols on tape  Machine has an internal state  Takes Action depending on state  Turing’s thesis: “If an algorithm exists then there is an equivalent Turing Machine”  Turing machine is the simplest possible description of a computer that can do anything  All modern computers can be simulated by a Turing machine  Only real difference: Turing machine has infinite tape, real computers have finite memory

24. Universal Computing Machine  How many symbols and states do you need?  Interesting…  If you make some really fancy machine…  Loads of states  Loads of possible symbols  Multiple tapes  Multiple stacks for storing things  Many heads working in parallel  You end up with something equivalent to the Turing machine StatesSymbols 242 103 74 55 25 46 3 218

25. Universal Computing Machine  The Turing machine has a set of rules  These determine how it acts  Can make a Universal Turing machine  Encode the rules you want it to use on the tape  The first thing it does is to read the rules  Then follow them…  Could also reprogram its rules as it goes along  Important ability for learning  Behaviour must change given experience

26. Universal Computing Machine  We said  “If an algorithm exists then there is an equivalent Turing Machine”  i.e. a (different) Turing machine is available to do any job we want to do  Now we can say  “If an algorithm exists then it can be simulated on a Universal Turing Machine”  i.e. all we need is a single Universal Turing Machine  This can do anything  This is the idea behind modern computers  Program instructions stored in memory just like any other data  Download a program off the web, and start running it  You don’t need a different computer for different jobs  One computer can do everything  Games, spreadsheet, database, music, movies, photo editor, word processor…

27. Is the Brain a Universal Computing Machine?  Warren McCullogh and Walter Pitts showed  Small collections of neurons can act as “logic gates” (building blocks of computers)  Brain could be viewed as a computing device, just like Turing machine  i.e. a brain can do what a computer can do  Other direction is a stronger claim  Can a computer do what a brain can do?  Can’t be proved  But universality of Turing machine suggests… maybe