In search of an appropriate abstraction level for motif annotations

In: Proceedings of the 2012 Workshop on Computational Models of Narrative, (pp. 22-28).

The structure and evolution of story networks

With this study, we advance the understanding about the processes through which stories are retold. A collection of story retellings can be considered as a network of stories, in which links between stories represent pre-textual (or ancestral) relationships. This study provides a mechanistic understanding of the structure and evolution of such story networks: we construct a story network for a large diachronic collection of Dutch literary retellings of Red Riding Hood, and compare this network to one derived from a corpus of paper chain letters. In the analysis, we first provide empirical evidence that the formation of these story networks is subject to age-dependent selection processes with a strong lopsidedness towards shorter time-spans between stories and their pre-texts (i.e. ‘young’ story versions are preferred in producing new versions). Subsequently, we systematically compare these findings with and among predictions of various formal models of network growth to determine more precisely which kinds of attractiveness are also at play or might even be preferred as explicatory models. By carefully studying the structure and evolution of the two story networks, then, we show that existing stories are differentially preferred to function as a new version's pre-text given three types of attractiveness: (i) frequency-based and (ii) model-based attractiveness which (iii) decays in time

Competing goals attenuate avoidance behavior in the context of pain

Current fear-avoidance models consider pain-related fear as a crucial factor in the development of chronic pain. However, pain-related fear often occurs in a context of multiple, competing goals. This study investigated whether pain-related fear and avoidance behavior are attenuated when individuals are faced with a pain avoidance goal and another valued but competing goal, operationalized as obtaining a monetary reward. Fifty-five healthy participants moved a joystick toward different targets. In the experimental condition, a movement to one target (conditioned stimulus [CS+]) was followed by a painful unconditioned stimulus (pain-US) and a rewarding unconditioned stimulus (reward-US) on 50% of the trials, whereas the other movement (nonreinforced conditioned stimulus [CS)) movement was not. In the control condition, the CS+ movement was followed by the pain-US only. Results showed that pain-related fear was elevated in response to the CS+ compared to the CS movement, but that it was not influenced by the reward-US. Interestingly, participants initiated a CS+ movement slower than a CS movement in the control condition but not in the experimental condition. Also, in choice trials, participants performed the CS+ movement more frequently in the experimental than in the control condition. These results suggest that the presence of a valued competing goal can attenuate avoidance behavior. Perspective: The current study provides experimental evidence that both pain and competing goals impact on behavioral decision making and avoidance behavior. These results provide experimental support for treatments of chronic pain that include an individual's pursuit of valuable daily life goals, rather than limiting focus to pain reduction only. (C) 2014 by the American Pain Societ

Classifying evolutionary forces in language change using neural networks

A fundamental problem in research into language and cultural change is the difficulty of distinguishing processes of stochastic drift (also known as neutral evolution) from processes that are subject to selection pressures. In this article, we describe a new technique based on deep neural networks, in which we reformulate the detection of evolutionary forces in cultural change as a binary classification task. Using residual networks for time series trained on artificially generated samples of cultural change, we demonstrate that this technique is able to efficiently, accurately and consistently learn which aspects of the time series are distinctive for drift and selection, respectively. We compare the model with a recently proposed statistical test, the Frequency Increment Test, and show that the neural time series classification system provides a possible solution to some of the key problems associated with this test.Language Use in Past and Presen