Raffaele Calabretta

ABSTRACT "Why can’t I decide to be happy?" This is the question that encapsulates the meaning behind Gabriele’s story, the main character of the novel Il film delle emozioni (The Movie of Emotions; Calabretta 2007a, in Italian). Gabriele is a victim of his negative emotions, and is completely in the power of his self-blame and self-devaluative thinking, which he learns to change only at the end of the novel, thanks to creativity and to the artistic expression of his own traumatic experiences (see Sparrow and Wegner 2006). The novel tells of Gabriele’s several attempts at writing, first, the script of an autobiographical "educational" movie on emotion regulation; and then at transforming it into a novel. In reality, it reveals itself as a book popularizing the most recent scientific knowledge on psychology of emotions; as a dramatization of a scientist’s life—of his interior, interpersonal, and social conflicts (as well as political ones); and as a dramatization of the creative process of writing. As we will see in this article, The Movie of Emotions is a very particular novel that has much to do with creativity viewed in three different aspects—creativity in the novel’s structure, creativity in the process of the novel’s creation, and proposed techniques to increase creativity.

The article examines the question of how learning multiple tasks interacts with neural architectures and the flow of information through those architectures. It approaches the question by using the idealization of an artificial neural network where it is possible to ask more precise questions about the effects of modular versus nonmodular architectures as well as the effects of sequential versus simultaneous learning of tasks. A prior work has demonstrated a clear advantage of modular architectures when the two tasks must be learned at the same time from the start, but this advantage may disappear when one task is first learned to a criterion before the second task is undertaken. Indeed, in some cases of sequential learning, nonmodular networks achieve success levels comparable to those of modular networks. In particular, if a nonmodular network is to learn two tasks of different difficulty and the more difficult task is presented first and learned to a criterion, then the network will learn the second, easier one without permanent degradation of the first one. In contrast, if the easier task is learned first, a nonmodular task may perform significantly less well than a modular one. It seems that the reason for this difference has to do with the fact that the sequential presentation of the more difficult task first minimizes interference between the two tasks. More broadly, the studies summarized in this article seem to imply that no single learning architecture is optimal for all situations