  Inspired by the importance of both communication and feedback on errors in
human learning, our main goal was to implement a similar mechanism in
supervised learning of artificial neural networks. The starting point in our
study was the observation that words should accompany the input vectors
included in the training set, thus extending the ANN input space. This had as
consequence the necessity to take into consideration a modified sigmoid
activation function for neurons in the first hidden layer (in agreement with a
specific MLP apartment structure), and also a modified version of the
Backpropagation algorithm, which allows using of unspecified (null) desired
output components. Following the belief that basic concepts should be tested on
simple examples, the previous mentioned mechanism was applied on both the XOR
problem and a didactic color case study. In this context, we noticed the
interesting fact that the ANN was capable to categorize all desired input
vectors in the absence of their corresponding words, even though the training
set included only word accompanied inputs, in both positive and negative
examples. Further analysis along applying this approach to more complex
scenarios is currently in progress, as we consider the proposed language-driven
algorithm might contribute to a better understanding of learning in humans,
opening as well the possibility to create a specific category of artificial
neural networks, with abstraction capabilities.
