One factor that has been empirically demonstrated to have a dramatic effect on human object recognition performance that has been relatively overlooked by artificial systems is the role of spatial context. However, there are many conditions under which both assumptions fail so that the objects cannot be uniquely identified in isolation, but in which spatial context can be used as a cue for disambiguation. Firstly, the architecture is hybrid in nature, combining the representational power of the symbolic approach with the flexibility of connectionism. This is important as neither approach in isolation is well suited to the task, with connectionist systems having difficulties in representing the complex spatial relations that are inherent to the problem and with symbolic systems being too brittle to handle noisy real-world data. Secondly, Copycat is specifically a model of analogy-making with mechanisms for equating percepts at high levels of abstraction.
Activation can spread between related concepts allowing for contextual influences on perception.
Plans for the extension of the model to incorporate spatial context will also be detailed.