Why is training better when following an easy-to-difficult schedule? As suggested in the answer to this question, experimental results show that training is most effective when it follows an easy-to-difficult schedule. 

What theories and specifically computational models account for that?

In the simple computational models commonly used to demonstrate learning classifications for example, there is no such effect, as far as I know:


Binary Perceptron - Typical examples (closest to the cluster means, or far apart along the optimal separating vector) are the most informative, leading to a prediction that easy trials would result in faster learning. 


Some learning algorithms only update the network when an error is made, leading to a prediction that more difficult trials would result in faster learning. 

Support Vector Machinces (SVM) - only the few most difficult examples affect the final result, again leading to a prediction that more difficult trials would result in faster learning. 
Naive Bayes classifier - Typical examples help estimating the parameters with less errors, leading to a prediction that easy trials would result in faster learning.
SVM training is typically done in a batch processing, and thus the order of data presentation doesn't matter. You should consider online learning algorithms, for example, the perceptron learning rule. These algorithms are in general stochastic gradient descent optimization procedures, and easy examples early on with larger learning step would be much more efficient (faster convergence towards the correct answer) than learning with difficult examples first.