Figure 5.

Shows simulation of a one-dimensional DNF model of the color memory game (top portion) and shape memory game (bottom portion). The top of panel A shows the metric organization of the color values used in the color memory game. This is the input to a one-dimensional working memory field for color (WM_{C_1D}), shown just below. WM_{C_1D} is coupled to a Hebbian layer for colors (HL_{C_1D}). Panel A also shows the model early in learning, after exposure to just a couple of colors in the color memory game. At this time, the model has acquired a couple weak memories in HL_{C_1D}. Panel C shows the model a bit later in learning. Now, the model has acquired more memories in HL_{C_1D}, some of which are robust. It is also maintaining multiple items in WM_{C_1D}, which helps the model acquire even more robust memories in HL_{C_1D}. By the end of learning, late in the color memory game, the model has acquired robust memories in HL_{C_1D} for all the colors in the color memory game.

The top of panel D shows the metric organization of the shape values used in the shape memory game. These are noticeably closer than those used in the color memory game, even though the distribution is the same. These values were input into a one-dimensional working memory field for shape (WM_{S_1D}). Panel D also shows the model early in learning, after exposure to just a couple of shapes in the shape memory game. The model has a couple of weak memories in the associated Hebbian layer, HL_{S_1D}. Panel E shows the model a bit later in learning. The model has acquired memories for several shapes in HL_{S_1D}. Notice that WM_{S_1D} is only maintaining one item, as compared to two in WM_{C_1D} at the same time in learning in the color memory game (see B). This is because the closeness of the shape values creates interference in WM_{S_1D}, effectively limiting it to remembering only one item at a time. This, in turn, hinders the ability of the model to form robust memories in HL_{S_1D} during the course of the shape memory game (F).