The Matrix Model

1. Items - Items can be any any sort of stimuli including words, pictures, melodies etc. For the most part, however, the experiments to which the model has been applied use words. Each item is modelled as a vector of feature weights. Feature weights are used to specify the degree to which certain features form part of an item.

   Item vectors are distinguished by subscripts (e.g. a_i). A distractor vector is indicated by a d.

2. Contexts - To distinguish between episodic and non-episodic tasks the Matrix Model assumes the episode or context in which items are studied is also represented by a vector of feature weights. In episodic tasks this context vector must be reinstated so that it may be used as a cue to the memory system. The context vector is represented by an x.

3. Associations - While individual items and contexts are represented as single vectors (a, b, x), associations between items and contexts are represented by matrices derived from the matrix product of these vectors. The resulting matrix product represents the association (or binding) between either items, or between items and context. The memory of the matrix Model is formed by adding these associations together.