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Cognitive Modelling Assignment

Cognitive Modelling Assignment. Create Model. Examining the data for single classification. Step 1: Examine the training data and establish the patterns within. I took notes on two patterns:

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Cognitive Modelling Assignment

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  1. Cognitive Modelling Assignment Create Model

  2. Examining the data for single classification • Step 1: Examine the training data and establish the patterns within. • I took notes on two patterns: 1.How often a symptom occurred in one disease as a percentage of how often that symptom occurred overall. 2.What proportion one symptom was of all the examples of one disease.

  3. Patterns of Dimensions • A clear pattern emerged throughout, for each of the 3 disease categories it could be said that one dimension was more reliable than the others. • However, I noticed that this differed for each disease – unlike in the exemplar model

  4. How the question was asked • The importance of how the question was asked: Had it been presented differently it would have favoured the method applied by the exemplar model

  5. Part 1 of my model • I built this theory into my model by applying a “reliability parameter” βi. • Each dimension has a different reliability parameter β1β2β3 • These reliability parameters vary from disease to disease- the model has to be applied independently for each category

  6. Reliability parameters • Disease A: D1 (.9) most reliable to the point of outweighing D2(.1) or D3(.5) (especially if D1=A) • Disease B: D3(.7) seems most reliable, followed by D2(.5), then D1(.3) • Disease C: Again D1 (.9) most reliable, followed closely by D3(.8), D2 inconsistent (.3) • (These were worked out from combining the two patterns mentioned earlier)

  7. Part 2 of my model • The second part of my model was to define a membership score for each symptom for each disease. • To do this I took how often the symptom occurred in the disease as a proportion of how often it occurred overall and I then subtracted how often it occurred elsewhere • M= S-S’

  8. M=S-S’ • M=S-S’, S’=1-S • WHY?

  9. M=S-S’ • This formula provides a negative value if a symptoms membership in a different disease outweighs its membership in the tested disease • If it has even distribution between two then a value of zero is given • IF it is most common in this disease then it has a positive value

  10. Model • The model is created by combining part 1 and part two in a product. • The reliability parameter for a dimension is multiplied by the membership score for the given symptom • The products are then summed to give the overall similarity score for the test item

  11. Model for single classification

  12. Example: Model Applied to Disease A

  13. βiMi

  14. Similarity Scores

  15. Rescaled Data/People Scores

  16. Correlation to data

  17. Disease A

  18. Disease B

  19. Disease C

  20. Conjunction Classification • I took an integrative approach to my conjunctive classification. • The reason for this is that I don’t think people would combine their answers from earlier questions to answer a new question. • Instead I think people would recall the examples they had seen and combine these in some way

  21. Conjunction Classification • As such, I combined the data at the symptom membership score stage (i.e. When determining M) • I believe people would use A&B given as a sample of how to create conjunctions. • These examples include prominent members of both categories and I feel this would be a rule to go by.

  22. Max • One way I thought about incorporating the “most prominent members of both categories” idea into my model way by using the MAX of the two M values, however this results in obvious problems. (High values of A will be high values of AB and AC ignoring the other disease)

  23. Reliability parameter When choosing my reliability parameters for the conjunction categories I chose the max of the two included categories as I feel that this would be prioritised in the conjunction also

  24. Conjunction model • After trying Max I looked at the other functions and “SUM” and “ NORMALISED SUM” made the most intuitive sense as I think people would probably say “it could be in A or B then it might be in A&B” (The SUM models were rescaled from -6to6)

  25. β=MAX, M=SUM

  26. CORRELATION

  27. A&B

  28. A&C

  29. B&C

  30. Varying the Model • Using the Sum model didn’t always provide high values for the most prominent characteristics. As such, I retested the model inputting a value of 1 where any symptom had a value of 1 for either category

  31. Varied Model

  32. Correlations

  33. A&B

  34. B&C

  35. A&C

  36. NORMALISED SUM • Finally, I decided that using Normalised sum might be a better measure of the parameters I want to include. As such I applied the model with NORMALISED SUM and MAX reliability parameter • This data had to be rescaled from -9to9 to -10to10

  37. Β=MAX, M=NSUM

  38. CORRELATION

  39. A&B

  40. A&C

  41. B&C

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