Two Categories of Responders  Type 1 - Combinations of A and B treated as a fourth category (strategy evident in complete rejection of proposed categories A and C, and B and C)  Type 2 -Treat the combination of A and B as an example of how to combine categories My model will focus on the more numerous type 2 responders  Type 1 - Combinations of A and B treated as a fourth category (strategy evident in complete rejection of proposed categories A and C, and B and C)  Type 2 -Treat the combination of A and B as an example of how to combine categories My model will focus on the more numerous type 2 responders

Learning the Training Items  Learn the most reliable category feature first  Then to the next most reliable, and so.  Ability and motivation determine whether they weight all features  If the less reliable features are not weighted according to their reliability as a category indicator, they are attributed a low nominal value.  Learn the most reliable category feature first  Then to the next most reliable, and so.  Ability and motivation determine whether they weight all features  If the less reliable features are not weighted according to their reliability as a category indicator, they are attributed a low nominal value.

Attributing Weights to Features  A feature is weighted based on how representative it is of the category for that dimension, e.g. For Dim 1 Cat A, A = 3/6  Also based on how frequently it occurs within that category dimension, e.g. A = 3/4  These values are averaged, e.g. A = 3/6 + 3/4 = (To give a value between 0 and 1 for each symptom)  A feature is weighted based on how representative it is of the category for that dimension, e.g. For Dim 1 Cat A, A = 3/6  Also based on how frequently it occurs within that category dimension, e.g. A = 3/4  These values are averaged, e.g. A = 3/6 + 3/4 = (To give a value between 0 and 1 for each symptom) AXCcategory A AYY AAX YAY ZBBcategory B XBB

Negative Values  Negative values for symptoms that do not occur within a category are attributed based on how unrepresentative of the category they are  The value is determined by the symptoms proportional occurrence outside of the category  E.g. For category B symptom A in dim 1,  - 3/4 =  Negative values for symptoms that do not occur within a category are attributed based on how unrepresentative of the category they are  The value is determined by the symptoms proportional occurrence outside of the category  E.g. For category B symptom A in dim 1,  - 3/4 = AXCcategory A AYY AAX YAY ZBBcategory B XBB

Number Array for Positive Membership

Evaluating the Training Items  Based on the symptom values we can calculate how well these values categorize the training items  We can later use the averages and standard deviations of these values to help determine the membership scores for test items  Based on the symptom values we can calculate how well these values categorize the training items  We can later use the averages and standard deviations of these values to help determine the membership scores for test items

Cat ACat BCat C Mean for A items Mean for B items Mean for C items

Test Items for Categories A, B and C  Test items are put through the positive and negative arrays for each category and the values summed  I want them positive, so add 1  This value is then divided by the training item mean of that category to see how high it is relative to the training items e.g. test item 2 in Cat A = -0.75, plus 1 = 0.25, 0.25/1.62 = 0.15 From this value i minus the higher of the corresponding Cat values, (Cat C) =  Test items are put through the positive and negative arrays for each category and the values summed  I want them positive, so add 1  This value is then divided by the training item mean of that category to see how high it is relative to the training items e.g. test item 2 in Cat A = -0.75, plus 1 = 0.25, 0.25/1.62 = 0.15 From this value i minus the higher of the corresponding Cat values, (Cat C) = -1.25

Model vs Participants on individual Categories

Combining Categories  The test item is computed for each Category separately.  The strongest values from each are combined  The stronger single category total is subtracted from this to isolate the benefit of the combination.  This result is added to the strongest combination from both categories (already obtained)  The result is divided by the stronger of the two alternative 2 category combinations  Finally, minus 1 if a negative value was used in the combination  The test item is computed for each Category separately.  The strongest values from each are combined  The stronger single category total is subtracted from this to isolate the benefit of the combination.  This result is added to the strongest combination from both categories (already obtained)  The result is divided by the stronger of the two alternative 2 category combinations  Finally, minus 1 if a negative value was used in the combination

Model vs Participants on combined Categories