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Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading.Relying on over 44 billion classifications, our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network.These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages. NEURO-SEMANTIC REPRESENTATION OF STORIES 3 Decoding the Neural Representation of Story Meanings across LanguagesOne of the defining characteristics of human language is its capacity for semantic extensibility. Drawing from a common lexicon of morphemes and words, humans generate and comprehend sophisticated, higher-level utterances that transcend the sum of their individual units. This is perhaps best exemplified in stories, in which sequences of events invite inferences about the intentions and motivations of characters, about cause and effect, and about theme and message. The kind of meaning that emerges over time as one listens to a story is not easily captured by analysis at the word level alone.Further, a necessary condition for generating higher-level semantic constructs is that speakers of the same language infer similar meanings from expressions of both lower and higher level semantic units. For example, it can be assumed that when speakers of the same language listen to stories, the perceived meanings of these stories have much in NEURO-SEMANTIC REPRESENTATION OF STORIES 4In this work, our aim is to move beyond word-level semantics to investigate neuro-semantic representations at the story-level across three different languages.Specifically, we set out to determine if there are systematic patterns in the neuro-semantic representations of stories beyond those corresponding to word-level stimuli. Our aim is motivated by the long-standing understanding that discourse representations are different from the sum of all of their lexical or clausal parts. Most psycholinguistic models of discourse processing are concerned with the con...

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Abstract.Interactive storytelling is an interesting cross-disciplinary area that has importance in research as well as entertainment. In this paper we explore a new area of interactive storytelling that blurs the line between traditional interactive fiction and collaborative writing. We present a system where the user and computer take turns in writing sentences of a fictional narrative. Sentences contributed by the computer are selected from a collection of millions of stories extracted from Internet weblogs. By leveraging the large amounts of personal narrative content available on the web, we show that even with a simple approach our system can produce compelling stories with our users.

Narratives are an important component of culture and play a central role in transmitting social values. Little is known, however, about how the brain of a listener/reader processes narratives. A receiver's response to narration is influenced by the narrator's framing and appeal to values. Narratives that appeal to "protected values," including core personal, national, or religious values, may be particularly effective at influencing receivers. Protected values resist compromise and are tied with identity, affective value, moral decision-making, and other aspects of social cognition. Here, we investigated the neural mechanisms underlying reactions to protected values in narratives. During fMRI scanning, we presented 78 American, Chinese, and Iranian participants with real-life stories distilled from a corpus of over 20 million weblogs. Reading these stories engaged the posterior medial, medial prefrontal, and temporo-parietal cortices. When participants believed that the protagonist was appealing to a protected value, signal in these regions was increased compared with when no protected value was perceived, possibly reflecting the intensive and iterative search required to process this material. The effect strength also varied across groups, potentially reflecting cultural differences in the degree of concern for protected values.

Commonsense psychology refers to the implicit theories that we all use to make sense of people's behavior in terms of their beliefs, goals, plans, and emotions. These are also the theories we employ when we anthropomorphize complex machines and computers as if they had humanlike mental lives. In order to successfully cooperate and communicate with people, these theories will need to be represented explicitly in future artificial intelligence systems. This book provides a large-scale logical formalization of commonsense psychology in support of humanlike artificial intelligence. It uses formal logic to encode the deep lexical semantics of the full breadth of psychological words and phrases, providing fourteen hundred axioms of first-order logic organized into twenty-nine commonsense psychology theories and sixteen background theories. This in-depth exploration of human commonsense reasoning for artificial intelligence researchers, linguists, and cognitive and social psychologists will serve as a foundation for the development of humanlike artificial intelligence.

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.