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Cognitive Science Computational modelling

Cognitive Science Computational modelling. Week 3 Linear separability Configuration files Reconstructing Cohen’s model of autism. Objectives of this workshop. To gain more familiarity with Tlearn To learn how to set up a network in Tlearn

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Cognitive Science Computational modelling

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  1. Cognitive ScienceComputational modelling Week 3 Linear separability Configuration files Reconstructing Cohen’s model of autism

  2. Objectives of this workshop • To gain more familiarity with Tlearn • To learn how to set up a network in Tlearn • To train and evaluate a backprop network learning "Exclusive OR" • To appreciate the difficulty of analysing network performance • To train and evaluate a backprop network model of "autism"

  3. "Exclusive OR“ & hidden units "John is a Tory or John is a Marxist" Either Tory, or Marxist, but not both. Compositional

  4. 1 0 1 0 Linear separability • XOR truth table as a graph • 2 dimensions (one for each input) • Plot the corresponding target output Tory 1 0 0 1 Marxist

  5. Exercise Draw the corresponding graph for ‘and’ e.g. Sue likes Radiohead and chocolate cake Is ‘and’ linearly separable?

  6. Number of inputs: 2 i1, i2 Number of hidden: ? two? #1, #2 Number of outputs: 1 #3 • xor-1501.wts contains the weights saved after 1501 learning trials with the set of training patterns For exercise follow from p117, Chapter 5

  7. Cohen's model of learning in autism • Too many and too few neurons and/or connections - Some things hard to learn - Poor generalisation • Model looks at effect of irrelevant inputs extra hidden units

  8. Happy face • mouth up -1 … 1 (+ve = smile) • eyebrows 0 … -1 (-ve = smile*) *roughly See Figure 11.3, but note that the vertical axis has the wrong values

  9. Reconstructing Cohen • Re-create input patterns • Re-create the target for each input pattern • Put those patterns into .data and .teach files • Create configuration file

  10. Input patterns 5 input values in each pattern 1st : mouth 2nd : eyebrow 3rd, 4th, 5th : mimic task-irrelevant features of the situation

  11. Values for ‘xtra’ inputs Random numbers should be noise easy way to do it is using SPSS … then “Save as…” comma delimitted

  12. Overview • Create training pattern inputs with 5 input values, n = 16 - and corresponding targets in a .teach file • Create 8 more in a separate .data file [why?] nb no .teach file needed for these • Create configuration file • Train; every so many trials, test both the training set & the configuration set

  13. Overview ctd Do it all again, with just one irrelevant xtra input Hint: you only need to make small changes to some of the files you already have

  14. Overview concluded Evaluate the results • Quantitatively error as learning progresses, on training set error as learning progresses, generalisation compare results for 1 irrelevant v 3 irrelevant • Qualitatively Mapping parameters onto theory eg number of inputs; what does it stand for from the theory Mapping to cognitive performance Mapping to biology

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