1 / 14

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

dmcclintock
Download Presentation

Cognitive Science Computational modelling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related