Contextual conditioning and reinstatement of extinguished instrumental responding

In three experiments we studied the relationship between contextual conditioning and the reinstatement of extinguished lever pressing that occurs when noncontingent food is introduced following extinction. In all three experiments the non-contingent food was presented off-baseline (with the response levers not present). On subsequent tests, with the response levers present, animals that had been exposed to food showed more reinstatement of lever pressing than control animals. This finding rules out alternative mechanisms for the reinstated responding that rely on the interaction of non-contingent food and responding, such as superstitious reinforcement or the discriminative after-effects of food. In addition, in each experiment we demonstrated that manipulations known to affect contextual conditioning (signalling the food in Experiment 1, context extinction in Experiment 2, and switching contexts in Experiment 3) reduced the reinstatement. These results are consistent with the claim that contextual conditioning is important in controlling instrumental conditioning and closely parallel findings concerning the reinstatement of Pavlovian responsing following extinction.     

Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks

A novel modular connectionist architecture is presented in which the networks composing the architecture compete to learn the training patterns. An outcome of the competition is that different networks learn different training patterns and, thus, learn to compute different functions. The architecture performs task decomposition in the sense that it learns to partition a task into two or more functionally independent tasks and allocates distinct networks to learn each task. In addition, the architecture tends to allocate to each task the network whose topology is most appropriate to that task. The architecture's performance on “what” and “where” vision tasks is presented and compared with the performance of two multilayer networks. Finally, it is noted that function decomposition is an underconstrained problem, and, thus, different modular architectures may decompose a function in different ways. A desirable decomposition can be achieved if the architecture is suitably restricted in the types of functions that it can compute. Finding appropriate restrictions is possible through the application of domain knowledge. A strength of the modular architecture is that its structure is well suited for incorporating domain knowledge.     

Comparative oughts and comparative evils

My thanks to David Banach for commenting on an earlier draft of this essay.