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last modified: December 17th, 2009;
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Research overview

My current research wants to explore the link that must exist between the learning curve seen in learning situations with the RT distribution that are seen at every single session of learning.

1. Skill acquisition and task learning
   1. The law of practice and curve analyses
      1. implementing predictions on RT
      2. general constraints to obtain a power-curve
      3. possible class of neural networks that have a power learning curve
   2. Familiarity and automaticity
      1. dividing the effect of automaticity and of familiarity into:
      2. structural component task learning
      3. specific task information learning
   3. RT distributions
   4. The role of categories in learning automatic task
   5. The role of similarity in learning automatic task
2. Visual search:
   1. Predicting slopes processing using feature based model
   2. Predicting intercepts processing using perceptual learningv
   3. Untangle the role of structural task learning from stimulus-specific learning:
      1. within-trial learning
      2. between-trial learning
3. Statistical methods:
   1. Distribution analysis (Weibull distribution, Lognormal distribution)
      1. Chi-square test of goodness of fit
      2. Log-likelihood fitting method
      3. Likelihood ratio test (LRT) used to compare two models
   2. Learning curve analysis (Power curve, Exponential curve)
      1. Chi-square test of goodness of fit, and RMSD index of fit
      2. Constraining SD curve to have the same rate than the RT curve
4. Paradigm
   1. visual and memory search task
   2. category learning task