From Autism to Expertise: Connecting Neural to Cognitive Understanding of Learni ng - PowerPoint PPT Presentation

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From Autism to Expertise: Connecting Neural to Cognitive Understanding of Learni ng

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  1. From Autism to Expertise: Connecting Neural to Cognitive Understanding of Learning Terry Bossomaier CRiCS (Centre for Research in Complex Systems) MIke Harré, Allan Snyder

  2. Overview • One of three talks: • This morning: expertise and cognition • This afternoon: complex systems • Wednesday: serious games • This talk: • Concepts in the brain • Patterns and expertise • Cognitive transitions

  3. Grand Challenges To build human-friendly artificial creative thinking systems which scale to arbitrary size To understand how social and organisational systems foster, or frustate, human creativity and how organisations can become themselves adaptive and creative.

  4. Evolution and Learning • Adaptability properties of entities (agents) in complex systems • Evolutionary forces and strategies • Phase transitions in systems and populations • Complexity of agent intelligence and relationship to evolutionary dynamics

  5. Tipping Points • Phase transitions and catastrophes • Second order transitions • very long correlation lengths • critical slowing down • increased variance • Mutual information peak • almost universal indicator

  6. Rain Man • Film starring Dustin Hoffman and Tom Cruise • Features high functioning autistic (DH) • Exhibits striking savant abilities • counting, subitisation • Problems with human relationships • theory of mind often a problem in autism

  7. Reorganisation

  8. Manacled by Mindsets • Concepts block access to detail (LATL) • Release savant skills with TMS, tDCS • Centre of the Mind (Allan Snyder) • Numerosity (inspired by Rain Man) • Change blindness • Proof reading • Absolute pitch… • Building a better brain?

  9. The quick brown fox jumps over the the lazy dog

  10. Patterns and the Brain • Autistic savants • exceptional detailed pattern memory • eidetic imagery • numerical (casino) skills • Expertise • 10,000 hours, 50K – 200K chunks • human expertise dominated by pattern memory • subtle. Not eidetic.

  11. Most difficult known game for computers Interesting problems in local-global order Huge search space – intractable Human expertise different to computer Strong use of pattern memory (we think) Marvin Minsky conundrum People get better the more they know, machines get slower. Go

  12. Avalanche Joseki

  13. Capture

  14. Liberties

  15. A Single Eye

  16. Suicide

  17. Capture

  18. Two Eyes

  19. Avalanche Joseki

  20. Studying Go Patterns • Use Go knowledge to select key patterns • Joseki and fuseki • Study variations from expertise • Two levels (amateur and professional) • Up to 9 dan levels in each • 9 Dan Professional, effectively grand master • Find probability distributions on moves

  21. Move Distributions • Ten moves found to be enough • 9 Dan tend to be a bit less diverse in move options • Middle ranks in between beginner and 9 Dan

  22. Local Global Order • Comparing sparse positions in game with all • Early positions involve global judgement • The divergence measure between each player rank and 9 Dan Professional shows no change until 1 Dan Amateur • Implies very little global understanding before several years of serious tournament play

  23. Transition to Expertise • Measuring the divergence between ranks shows a peak around 1 Dan Professional • Since performance is increasing uniformly without any sharp changes, it implies this is a reorganisation of knowledge rather than the learning of new techniques or strategies • See M. Harre ́, T. Bossomaier, C. Ranqing, and A.W. Snyder. The development of human expertise in a complex environment. Minds and Machines, 21:449–464, 2011.

  24. Game Tree Analysis • 8,500 starting corner positions • About 2,000 games • Compute game trees 6 pli deep • Compute entropies on • Ordered sequences of plays • Unordered (static positions) • Compute Mutual Information • Real indicator of phase transitions

  25. The Phase Transition • The game tree analysis shows a peak in Mutual Information at 1Dan Professional. • This is a strong indicator of a second order phase transition. • See M. Harre ́, T. Bossomaier, A. Gillett, and A.W. Snyder. The aggregate complexity of decisions in the game of Go. European Physical Journal B, 80:555–563, 2011.

  26. Perceptual Templates • To further understand the phase transition, a large number of game positions and moves were used to compute a Kohonen Self-Organising Map. • The maps were thresholded to create a set of several thousand perceptual templates • The amateur and professional templates are substantially different • See M. Harre ́, T.R.J. Bossomaier, and A.W. Snyder. The perceptual cues that reshape expert reasoning. Nature Scientific Reports, 2(502), 2012.

  27. Creativity • Many forms • replacement (eg Dali Lobster phone) • random acts (Dadaism) • bottom up (Jackson Pollack) • Deep creativity changes the foundations • Bach (equal temperament), Einstein (relativity) • Strong parallel between expertise and deep creativity

  28. Idea put forward by Grandin, Fitzgerald, Baron-Cohen and others, that great thinkers and creative minds of the past may have been autistic/Asbergers …It seems that for success in science or art, a dash of autism is essential Asberger (cited by Baron-Cohen) The Autistic Genius

  29. Asberger Geniuses? • Science: Einstein (Nobel Prize) • Poets: Yeats (Nobel Prize) • Philosophy: Wittgenstein • Computation: Wiener • Politics: Keith Joseph (Cabinet minister) From Michael Fitzgerald:Autism and Creativity

  30. The Paradox of Poets How can an autistic without a theory of mind be a poet? But poets work with sound and rhythm. Words strain, Crack and sometimes break, under the burden, Under the tension, slip, slide, perish, Decay with imprecision, will not stay in place, Will not stay still. -– T.S. Elliot

  31. Fundamental changes in the way we think may arise from low level play Tinkering with the building blocks Complexity theory emphasizes unpredictable emergent phenomena big system outcomes from changes at low level Complexity and Mindquakes

  32. Tinkering with the Foundations • Music: • Bach (equal temperament) • Wagner (chromaticism) • Schonberg (12 tone serialism) • Physics: • Einstein (speed of light) • Planck (quantisation) • Art: Breton, Dali (surrealism)

  33. Support for human creativity Simulating upwards from low level changes Searching for counter examples Computer creativity Building modular hierarchies with interchangeablility Teaching software agents to play Music synthesis for computer games Scenario modelling for security etc. Computers and Creativity

  34. Games with (more) ToM • Go involves very little Theory of Mind (ToM) • Bridge, Poker require judgements about players • A lot of online work in Poker (gambing driven) • Video games (MMOGs?) • Real life • Transitions in medicine • Financial trading

  35. Acknowledgements Michael Harréwas funded by an Australian Research Council Discovery Grant, DP0881829, to Snyder, Bossomaier and Harvey

  36. Envoi • Expertise goes through tipping point in Go • a general characteristic • applicable to ToM tasks too? • The savant brain has advantages • can we get the best of both worlds? • Next generation AI?