Computational Cognitive Modelling

1. Computational Cognitive Modelling COGS 511-Lecture 1 General Introduction COGS 511 - Bilge Say

2. Related Readings From Course Pack • Cooper, R. Chapter 1: Modelling Cognition • McClelland (2009). The Place of Modeling in Cognitive Science. • References (extra and optional; given for a complete reference list – not in the course pack) • Carpenter and Just, Computational Modeling of High-Level Cognition versus Hypothesis Testing in Sternberg (ed), The Nature of Cognition, 1999. • Fernandez, J. Explanation by Computer Simulation in Cognitive Science, Minds and Machines, 13: 269-284, 2003. • Steedman, Chap. 5, of Scarborough and Sternberg (eds). • Morgan, M.S., & Morrison, M. (1999). Models as mediators (Ed). Cambridge: Cambridge University Press. COGS 511 - Bilge Say

3. Models • A representation of something that may be used in place of the real thing, abstracting away unimportant features but retaining the essential. (Cooper). • A good model is complete (does not abstract out important properties) and faithful (does not introduce features that are not in the original) with respect to its specific purpose. Helpful for understanding a complex system – cognition for the case of cognitive science. • Computational cognitive modelling is the development of computer models of cognitive processes and the use of such models to simulate and predict human behaviour. COGS 511 - Bilge Say

4. Models in Philosophy of Science • The task of Philosophy of science is: • Generate reflections on the theoretical and methodological issues in scientific practice. • Models function in a variety of different ways within sciences. • Analog models: Molecules – Billiard-balls, • Mechanical model: DNA molecule - Metal-made helix model • Scale models: Models in architecture, model airplanes, etc. • Treated in relation with theory and phenomena. COGS 511 - Bilge Say

5. Models in Philosophy of Science (cont.) • Semantic View • Models are abstract idealized systems which characterizehow the phenomena would have behaved if the idealized conditions were met (Suppe, 1989). • Thus, a theory characterizes the model which represents (certain aspects of) phenomena. COGS 511 - Bilge Say

6. Models in Philosophy of Science (cont.) • Morrison and Morgan (1999) • Models are evaluated in response to four questions: • How are models constructed? • What do they represent? • What role do they have/how do they function in scientific practices? • How do we learn from models? COGS 511 - Bilge Say

7. Models in Philosophy of Science (cont.) • Their general account based on case studies in physics, chemistry and economy proposes that: • Models are autonomous agents, i.e. they are only partially dependent on theories and phenomena • Models serve as instruments for investigation in science. COGS 511 - Bilge Say

8. Models in Philosophy of Science (cont.) • How are models constructed? • Not derived entirely from theory or phenomena • Involve both, and also additional “outside” elements (modeling decisions). COGS 511 - Bilge Say

9. Models in Philosophy of Science (cont.) • What do they represent? • Some aspect of the phenomena or some aspect of theories COGS 511 - Bilge Say

10. Models in Philosophy of Science (cont.) • What role do they have/how do they function in scientific practices? • Function as tools or instruments. COGS 511 - Bilge Say

11. Models in Philosophy of Science (cont.) • How do we learn from models? • Not by looking at a model, but by building and manipulating it. COGS 511 - Bilge Say

12. Program: data structures + algorithms= running programs Representation: Implied by the architecture, mathematical definition of the problem, design specification of the task, the software paradigm used Algorithms: Simplicity, efficiency and complexity trade-offs. Mind = mental representation + computational procedures = cognition Representation: Cognitive Architecture or the ontology of human mental process is not given. Hope: algorithms and representations posited will clarify the architecture, too. Algorithms: Performance on realistic data, simplicity in terms of plausibility Computer Science vs Cognitive Science COGS 511 - Bilge Say

13. The study and automation of intelligent behaviour (Luger & Stubblefield) Success: Commercial/Performance – as described by proposals such as Turing test (?) or in a limited domain aI: the study of human intelligence with computer as a tool (Yeap, 97) vs Ai: the study of machine intelligence as artificial intelligence Theoretical, experimental or applied (Rumelhart) Failures (?): Frame problem, syntax vs semantics/intentionality The study of cognition, mental activity involving acquisition, storage transformation and use of knowledge; study of mental processes such as memory, language, thought, perception, consciousness .... Success: “Competence” -explanatory power of a cognitive theory: pyschological and neurological plausibility, computational and representational power, practical applicability to education, design etc. Artificial Intelligence vs Cognitive Science COGS 511 - Bilge Say

14. An Example from Chess • Human experts use relatively shallow searches, averaging only three or four moves deep; perceptual patterns and their recognition play an important part in guiding the search. • Chess programs rely on extensive search and optimization of search techniques. Deep Blue evaluated 200 million moves per second in 1997. COGS 511 - Bilge Say

15. Computational Models in Cognitive Science • A computer program which implements a theory of some aspect of cognition (Green) • Representations and processes of some cognitive theory made precise by analogy with data structures and algorithms (Thagard) • Do computational models have to subscribe to strong AI view (aim: building machines that duplicate minds) to be useful as research tools in cognitive science? • Not necessarily! • Weak AI: Can machines be made to act as if they were intelligent? COGS 511 - Bilge Say

16. Some Philosophical Background • Functionalism: Most general features of cognition must be independent of neurology- the physical system – and the embodiment of mind. Mental states are abstract functions that get us from a given input to a given output. • Cognitivism: All there is to cognition is in mental states and thought. • Computational Theory of Mind ~Computational Representational Understanding of Mind: Human cognition can be best understood in terms of representational structures in the mind and computational procedures that operate on them. COGS 511 - Bilge Say

17. Computational Theory of Mind • Thought processes are computations on representations. • The mind can be realized/implemented outside of the brain eg. in a digital computer. • Is the mind a digital computer? • Church-Turing Thesis: The Universal Turing Machine can perform any calculation that can be described by an effective procedure. COGS 511 - Bilge Say

18. Misconceptions of Church-Turing Thesis • It doesn’t say that given a standard computer, you can compute any rule-governed input-output function.It doesn’t rule out machines (or brains) that compute non-Turing computable functions. Thus, it does not entail that brains can be simulated by a Universal Turing Machine. COGS 511 - Bilge Say

19. Questions • Can a certain approach contradict with Computational Theory of Mind (mental representation + computational processes = cognition) and still involve computational modelling ? (Yes –see dynamical approaches) • Do you have to ascribe to a functionalist view (mental states are abstract functions – can be described independent of brain states) to do computational modelling ? (No – see computational neuroscience) COGS 511 - Bilge Say

20. The Status of a Computational Model • “It is not the computer program that is the theory, at best they inspire the construction of a theory.” (Scheutz) • “Simulation is not a reasonable goal for cognitive science.” (Fodor) • “AI is to psychology as Disneyland is to physics.” (Green) • “Artificial Intelligence is to cognitive science as mathematics is to physics.” (Rumelhart) COGS 511 - Bilge Say

21. Marr’s Levels of Analysis • Computational: What information processing is being solved, and why? • Algorithmic: Representation and Programming. How is the problem being solved? • Implementational: What physical properties are required to build such a system? Hardware (e.g. brainstates) COGS 511 - Bilge Say

22. Subject Model Computational = Computational One-to-many One-to-many Algorithmic = Algorithmic Architectural = Architectural One-to-many One-to-many Implementational = Implementational (Dawson, 98)

23. Method Theory Computational  Behavioural Experiments One-to-many Algorithmic  Computer Simulations Architectural  One-to-many Cognitive and Computational Neuroscience Implementational  Adapted from (Brent, 96) COGS 511 - Bilge Say

24. The Function of Computational Models Computational Cognitive Model Simulates Generates Implements Behaviour Cognitive Process Describes Explains Theory Cooper (2002) – Ch.1 COGS 511 - Bilge Say

25. Causal Explanation: The system uses a program in order to compute a certain input-output mapping. Explaining how you cooked a tasty dish Do you have enough justification for that? Functional Analysis The system executes a program which amounts to computing a certain mapping. Explaining how an car manufacturing assembly line works Multiple realizability? Explanation by Computer Simulation (Fernandez, 2003) COGS 511 - Bilge Say

26. Advantages of Computational Modelling • Clarify, formally and unambiguously specify a certain cognitive theory • Create experimental participants that are durable, flexible etc. – in silico • Allow detailed evaluation and exploration of cognitive theories by means of raising new hypotheses • Enable interaction between studies in different disciplines • Not THE method, but a complementary method COGS 511 - Bilge Say

27. Strategies • Develop a model of some task or behaviour in order to learn more about it: “a fishing trip” • Implement a pre-existing, verbally specified highly complex theory to see if its theoretical assumptions are sufficient/necessary to account for the target behaviour. • Generate predictions/hypotheses to be then tested by behavioural experiments. Platform: Cognitive models of individual processes vs “unified” approach – cognitive architectures COGS 511 - Bilge Say

28. Evaluation of Models • Behavioural Outcome Modelling: Roughly showing similar behaviours as human beings • Qualitative Modelling: Same qualitative behaviours that characterize human behaviour, e.g. similar improvement, deteoriation • Quantitative Modelling: Similar quantitative behaviour as exhibited by humans, indicated by quantitative performance measures • A combination of the above (Sun, 98) COGS 511 - Bilge Say

29. Practical Problems with Cognitive Modelling • Goodness-of-fit problems • Individual Differences • Incidental Details Problems- scalability and sensitivity analysis needed • Problematic Predictive Power • Statistical interpretation varies as compared to hypothesis-testing statistics usage in psychology • Theory-model amalgamation • Complexity and understandability trade-offs • Isolated modelling – not enough interaction with different levels of theorizing and methods. COGS 511 - Bilge Say

30. Method Theory Computational  Behavioural Experiments One-to-many Algorithmic  Computer Simulations Architectural  One-to-many Cognitive and Computational Neuroscience Implementational  Adapted from (Brent, 96) COGS 511 - Bilge Say

31. Paradigms in Computational Modelling • Symbolic systems – best for accounting for rationality, systematicity etc. of symbol systems? • Connectionism – biologically plausible ? • Dynamicisim – best for exploring embodied, situated, temporal cognition? • Hybrid approaches • Similar Division in AI: GOFAI – Good, Old Fashioned AI vs NFAI – New Fangled AI COGS 511 - Bilge Say

32. Achievements for Cognitive Modelling • Shaping theories for various cognitive domains: language and skill acquisition, individual differences in working memory, cognitive lesioning simulations and neuropsychology. • Applied areas: Human-computer interaction, intelligent-tutoring systems COGS 511 - Bilge Say

33. Future for Cognitive Modelling • Integration of Computational Neuroscience and more abstract forms of cognitive modelling – e.g. Blue Brain project • More interaction between Artificial Intelligence and Cognitive Modelling – esp in Cognitive Architectures • More emphasis in hybrid models – symbolic, dynamic, connectionist, bayesian etc. COGS 511 - Bilge Say

34. Lecture 2 • Unified Theories of Cognition • Cognitive Architectures • Sample Architectures vs Frameworks • Reading: Langley, Laird and Rogers (2009) Cognitive Architectures • Start Readings for the project and think about your project groups. • Check Forum for online activity. COGS 511 - Bilge Say