Description Usage Arguments Details Value Functions
Effects of Testing on un-encoded features and thresholds
1 2 3 4 5  | 
... | 
 Arugments passed to approriate functions based on type-checking. See details.  | 
mem | 
 Matrix of memory strengths.  | 
nFeatures | 
 Number of features the memory for each item has  | 
thresh | 
 Matrix of memory thresholds. Threshold is the number of active features needed to support pattern completetion  | 
acc | 
 Logical matrix of performance for previous test  | 
LR | 
 Learning Rate. Gives the probability of learning a new features through practice  | 
TR | 
 Threshold Reduction rate. Describes the probability of removing the need for a remembering a particular feature following successfull recall  | 
FR | 
 Forgetting Rate. Gives the probability of forgetting a feature  | 
thresh | 
 Matrix of memory thresholds. Threshold is the number of active features needed to support pattern completetion. Must hold integer values.  | 
acc | 
 Logical matrix of performance for previous test  | 
TR | 
 Threshold Reduction rate. Describes the probability of removing the need for a remembering a particular feature following successfull recall  | 
test is a conveince function, and will attempt to type-check
the codemem matrix to call the appropriate function (test_beta for doubles,
test_binomial for integers).
test_beta and test_binomial can (and should) be called directly though,
especially when fitting models and computational time matters. See Functions section
for more details.
List of two matrices. mem is a matrix of memory strengths following succcesful recall. thresh is a matrix of feature thresholds following succesful recall.
test_beta: test_beta is designed to be used with a matrix of
continous feature and threhsold values (represented as doubles) drawn from
a beta distribution
test_binomial: test_binomial is designed to be used with a matrix of
discrete feature and threshold values (represented as integers) drawn from
a binomial distribution
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