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Cognitive Modelling Project

Cognitive Modelling Project. Aoife Kilduff 06355251. Classification Task. Classification involves generalizing from particular learned instances to novel situations This task: generalize information from training items (symptoms and diseases) to test items. How do people do this?

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Cognitive Modelling Project

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  1. Cognitive Modelling Project AoifeKilduff 06355251

  2. Classification Task • Classification involves generalizing from particular learned instances to novel situations • This task: generalize information from training items (symptoms and diseases) to test items. • How do people do this? • Prototype theory

  3. Prototype Theory • Prototype theory (Rosch, 1978) claims that some categories are seen as more central than others • The Prototype is the most central member of a category (summary representation of members) • When people classify objects they match them against this ‘prototype’

  4. My model: Single Classification • Prototype for dimension X=list of all possible values on that dimension • Each value is given a weight to determine its importance in the category. • Weight=No. of times symptom X occurs in Dim1Category Y No. of times all symptoms occur in Dim1Category Y • Weight = Proportion of times Symptom X Occurs in Dim1Category Y

  5. Determining Weights • Weight for Symptom C in Dim 1= 5/6 • Weight for Symptom C in Dim 2 = 0/6 • Weight for Symptom C in Dim 3 = 4/6

  6. Determining Weights • Computed this for every symptom, in every dimension, in every category. • Entered the data into a table like the one below

  7. Classifying Test Items • Add weights of items attributes in each category’s prototype • Dim1 more important so double this value. • Likelihood of Test Item ABY belonging to • Category A: • 2(4/6)+(1/6)+(2/6)=1.833 • Category B: • 2(1/6)+(3/6)+0/6=0.833 • Category C: • 2(0/6)+(0/6)+(1/6)=0.1667

  8. Results • Computed number of SD’s from the mean for both actual and predicted scores • Correlation of 0.916 between them (Very Strong) • Series 1=Actual Scores • Series 2=Predicted Scores

  9. Results Category A Category C Category B

  10. Results • Model was a good fit for good fit for participants average classification scores for items as members of Category A (r=0.932) • Model was also a good fit for category B (r=0.947) • Model was a good fit for Category C (r=0.934)

  11. My Model: Conjunctive Classification • Positive and Negative evidence involved. • Positive Evidence for Category A&B-Negative Evidence for Category A&B (Evidence for Category C) • Built on single classification model • Weight=(Proportion of times symptom X occurs in Dim1Category A + Proportion of times symptom X occurs in Dim1Category B)-(Proportion of times symptom X occurs in Dim1Category C)

  12. Determining Weights • Weight for Symptom A Dim 1 Category A&B: (4/6+1/6)-(0/6) = 0.833 • Weight for Symptom X Dim 1 Category A&B: (1/6+2/6)-(1/6) = 0.333 • Weight for Symptom Y Dim1 Category A&B: (1/6+1/6)-(0/6) = 0.333

  13. Determining Weights • Again, computed this for every symptom, in every dimension, in every conjunction category. • Entered the data into a table like the one below

  14. Classifying Test Items • Add weights of items attributes in each category’s prototype • Dim1 more important so double this value. • Also double values for Conjunction A&B because occurred in training-more likely • Likelihood of Test Item ABY belonging to • Category A&B: • 2[((4/6+1/6)-0/6)*2+((1/6+3/6)-0/6)+((2/6+0/6)-1/6)]=5 • Category B&C • ((1/6+0/6)-4/6)*2+((1/6+0/6)-1/6)+((0/6+1/6)-2/6)=-2.33 • Category A&C • ((4/6+0/6)-1/6)*2+((1/6+0/6)-3/6)+(2/6+1/6)-0/6)=2.33

  15. Results • Computed number of SD’s from the mean for both actual and predicted scores • Correlation of 0.581 between them (moderate) • Series 1: Predicted Scores • Series 2: Actual Scores

  16. Results Category A&C Category A&B Category B&C

  17. Assessment • Model was a good fit for participants average classification scores for items as members of Category A&B (r=0.957) • Maybe because A&B occurred in the training set • People use this rule when classifying item in A&B because they have seen training items in this category before • Model was not a good fit for Category B&C (r=-0.10) • Model was A moderate fit for Category A&C (r=0.683) • Have never seen items in these conjunction categories before so don’t use a rule.

  18. Assessment • Although the model does not fit some of the data well it does make some good predictions • Does not predict that an items classification score in a conjunction is always going to be between that item’s classification score in the constituents of that conjunction. • E.g. Model predicts test item 3 has a higher score in A&C than in A or C • However it does also make some inaccurate predictions • E.g. Test item 5 has a lower score in A&B than in A or B and the model does not predict this

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