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Prof. Terry Bossomaier "From Autism to Expertise: Connecting Neural to Cognitive Understanding of Learning"
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Prof. Terry Bossomaier "From Autism to Expertise: Connecting Neural to Cognitive Understanding of Learning"


Published on …

9:30 – 10:30
Meng Wah Complex T4

Keynote 1:
From Autism to Expertise: Connecting Neural to Cognitive Understanding of Learning
By Prof. Terry Bossomaier, Charles Sturt University, Australia
Chair: Liaquat Hossain

Keynote 1:
From Autism to Expertise: Connecting Neural to Cognitive Understanding of Learning

In work stretching back a decade, evidence has been growing that not only is human knowledge hierarchical, but that dynamic inhibition blocks access to lower level detail, leaving only the global picture. Brain injury, pathology (such as strokes), conditions such as autism, and, now experimental procedures such as trans-cranial magnetic stimulation, allow access to this lower level detail. Sometimes access to this detail can enhance creativity or reveal better solutions to difficult problems.

Human expertise relies heavily on elaborate concept structures, but in some cases can lead to people being blinded by their expertise, in, for example, the Einstellung Effect. Much of our understanding of expertise has come from games such as Chess, but this talk will focus on Go, an equally difficult game for people, but a much more difficult game for computers. By analyzing large number of games online, some surprising characteristics of human cognition have emerged. The appreciation of global structures seems to occur at quite an advanced level, and that distinct transitions appear in the acquisition of expertise. Although the existence of such transitions has been conjectured, obtaining quantitative data of the kind we present has been made possible only through the availability of large numbers of decisions online. The talk will conclude with the implications for other games and the possible education opportunities.

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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. Go • 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.
  • 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. The Autistic Genius 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)
  • 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. 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 The Paradox of Poets How can an autistic without a theory of mind be a poet? But poets work with sound and rhythm.
  • 31. Complexity and Mindquakes • 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
  • 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. Computers and Creativity • 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.
  • 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 Harré was 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?