Assessing similarity of emergent representations based on unsupervised learning
J. Raitio, Ricardo Vigário, Jaakko Särelä, Timo Honkela · 2005
According to a connectionist view, mental states consist of the activations of neural units in a connectionist network. We consider the similarity of representations that emerge in unsupervised, self-organization process of neural lattices when exposed to color spectrum stimuli. Self-organizing maps (SOM) are trained with color spectrum input, using various vectorial encodings for representation of the input. Further, the SOM is used for heteroassociative mapping to associate color spectrum with color names. Recall of association between the spectra and colors is assessed. It shows that the SOM learns representations for both stimuli and color names, and is able to associate them successfully. The resulting organization is compared through correlation of the activation patterns of the neural maps when responding to color spectrum stimuli. Experiments show that the emerged representations for stimuli are similar with respect to the partitioning-of-activation-space measure almost independently of the encoding used for input representation. This adds a new example in favour of the usability of the state space semantics.