Probabilistic association learning refers to a gradual feedback-based learning of probabilistically related cue outcome associations, without the necessity of conscious appreciation of the rules or strategies (Knowlton et al. 1994).
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The model studied here is remarkably robust across various modifications, including changes in how the landscape varies (gradual shift vs. feedback) and in the distribution of mutation sizes (uniform vs. normal).
The housekeeping performance improved in all units, and was maintained over a 3 4-year period in two out of four units after gradual termination of the feedback.
For example, Howison et al. (2011) demonstrated that young learners can use gesture to improve their understanding of proportionality, but this occurs through a gradual process that requires direct feedback on the quality of certain gestures for representing a specific ratio.
The other 15 subjects participated in experiment 2 and were trained in the gradual condition without the online cursor feedback.
The next question then is: Is the additional computation involved in the learning of the rotation in the gradual condition if the online visual feedback is not available?
