The purpose of this paper is to extend an earlier theory of pleasure associated with harmonic sequences to melodic sequences. The theory stated that the sequences that will be pleasurable will be the ones that allow a coherent transition from one mental state to the next. This can be measured in a connectionist model by noting the strength of the activation boost in a competitive layer categorizing the elements of the sequence. It is suggested that a similar mechanism will work for melodic sequences if two conditions are met. First, the melodic sequences must be represented such that two sequences judged to be similar by the ear have an overlapping distributed representation. Next, a mechanism must be posited to separate melodic sequences into significant groups. The results of a network accomplishing these tasks are presented.

Previous efforts to integrate Explanation-Based Learning (EBL) and Similarity-Based Learning (SBL) have treated these two methods as distinct interactive processes. In contrast, the synthesis presented here views these techniques as emergent properties of a local associative learning rule operating within a neural network architecture. This architecture consists of an input layer, a layer buffering this input, but subject to descending influence from higher order units in the network, one or more hidden units encoding the previous knowledge of the network, and an output decision layer. S B L is accomplished in the normal manner by training the network with positive and negative examples. A single positive example only is required for E B L . Irrelevant feauires in the input are eliminated by the lack of top-down confirmation, and/or by descending inhibition. Associative learning then causes the strengthening of connections between relevant input features and activated hidden units, and the formation of "bypass" connections. O n future presentations of the same (or a similar) example, the network will then reach a decision more quickly, emulating the chunking of knowledge that takes place in symbolic E B L systems. Unlike tiiese programs, this integrated system can learn in the presence of an incomplete knowledge domain. A simulation program, ILn, provides partial verification of these claims.

The mere exposure effect, in which subjects prefer items they have previously been exposed to over unexposed items, is explained as the effect of competitive learning in a connectionist network. This type of unsupervised learning will cause the network to respond more strongly to patterns on which it has been trained. If it is assumed that positive affect is proportional to total activation, then the mere exposure effect is a direct consequence of this process. The addition of a habituation rule, with a dishabituating recovery element, can also explain factors which reduce or enhance the effect. These include the effect of exposure count, display presentation sequence, the complexity of the patterns, the effect of a delay after presentation, and finally, the effects of varying exposure duration. In the case of this last factor, in addition to showing that very short exposure durations can enhance the effect, the model reveals why it may be possible to respond positively to a stimulus that one cannot recall perceiving.