ALCOVE model of category learning in PyTorch

Kruschke, J. K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99(1), 22.

You can run the main script as follows:

python alcove.py

This script trains ALCOVE on the 6 category learning problems of Shephard, Hovland, and Jenkins (1961) and plots their learning curves.

The main parameters are as follows:

• number of passes through exemplars (num_epochs)
• option to train ALCOVE or a multi-layer Perceptron
• use abstract binary data or real image data
• use hinge loss or log-likelihood loss
• learning rates for association weights and attention weights

There are a few modifications to Kruschke's original ALCOVE:

The original ALCOVE is modified as follows:

• With just two classes, we use only a single sigmoid output unit, instead of using a multi-class softmax output.
• Kruschke's original loss function has been replaced with either a maximum likelihood objective ("ll") or a hinge loss, which is a variant of the humble teacher used by Kruschke ("hinge")