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Previous research suggests that individuals abused as children are more likely to engage in risky sexual behavior during adulthood. The present study examined early maladaptive schemas as mediators of the child abuse-risky sexual behavior relationship among 653 college women. Self-report surveys assessed three forms of child abuse: Sexual, physical, and emotional, and assessed early maladaptive schemas within two domains: Disconnection/rejection and Other-Directedness. Disconnection/rejection schemas fully mediated the relation between child emotional abuse and number of sexual partners and partially mediated the relationship for sexual and physical abuse. However, when frequency of specific risky sexual acts (e.g., sex without contraception) was examined in the previous six months, only abandonment was a partial mediator. Implications for intervention and future research are discussed.

This work demonstrates an interface, Creative Help, that assists people with creative writing by automatically suggesting new sentences in a story. Authors can freely edit the generated suggestions, and the application tracks their modifications. We make use of a Recurrent Neural Network language model to generate suggestions in a simple probabilistic way. Motivated by the theorized role of unpredictability in creativity, we vary the degree of randomness in the probability distribution used to generate the sentences, and find that authors' interactions with the suggestions are influenced by this randomness.

The Story Cloze Test consists of choosing a sentence that best completes a story given two choices. In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct. The classifier is trained to distinguish correct story endings given in the training data from incorrect ones that we artificially generate. Our experiments evaluate different methods for generating these negative examples, as well as different embedding-based representations of the stories. Our best result obtains 67.2% accuracy on the test set, outperforming the existing top baseline of 58.5%.

We examine an emerging NLP application that supports creative writing by automatically suggesting continuing sentences in a story. The application tracks users' modifications to generated sentences, which can be used to quantify their "helpfulness" in advancing the story. We explore the task of predicting helpfulness based on automatically detected linguistic features of the suggestions. We illustrate this analysis on a set of user interactions with the application using an initial selection of features relevant to story generation.

Abstract. Seventy years ago, psychologists Fritz Heider and MarianneSimmel described an influential study of the perception of intention, where a simple movie of animated geometric shapes evoked in their subjects rich narrative interpretations involving their psychology and social relationships. In this paper, we describe the Heider-Simmel Interactive Theater, a web application that allows authors to create their own movies in the style of Heider and Simmel's original film, and associate with them a textual description of their narrative intentions. We describe an evaluation of our authoring tool in a classroom of 10th grade students, and an analysis of the movies and textual narratives that they created. Our results provide strong evidence that the authors of these films, as well as Heider and Simmel by extension, intended to convey narratives that are rich with social, cognitive, and emotional concerns.

People naturally anthropomorphize the movement of nonliving objects, as social psychologists Fritz Heider and Marianne Simmel demonstrated in their influential 1944 research study. When they asked participants to narrate an animated film of two triangles and a circle moving in and around a box, participants described the shapes' movement in terms of human actions. Using a framework for authoring and annotating animations in the style of Heider and Simmel, we established new crowdsourced datasets where the motion trajectories of animated shapes are labeled according to the actions they depict. We applied two machine learning approaches, a spatial-temporal bag-of-words model and a recurrent neural network, to the task of automatically recognizing actions in these datasets. Our best results outperformed a majority baseline and showed similarity to human performance, which encourages further use of these datasets for modeling perception from motion trajectories. Future progress on simulating human-like motion perception will require models that integrate motion information with top-down contextual knowledge.

This thesis explores the use of a recurrent neural network model for a novel story generation task. In this task, the model analyzes an ongoing story and generates a sentence that continues the story.