http://sol.edu.hku.hk/summerfest/#bossomaier-keynote-1-abstract 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.

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?