Living Machines: A study of atypical animacy

This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use

Inducing Stereotypical Character Roles from Plot Structure

If we are to understand stories, we must understand characters: characters are central to every narrative and drive the action forward. Critically, many stories (especially cultural ones) employ stereotypical character roles in their stories for different purposes, including efficient communication among bundles of default characteristics and associations, ease understanding of those characters\u27 role in the overall narrative, and many more. These roles include ideas such as hero, villain, or victim, as well as culturally-specific roles such as, for example, the donor (in Russian tales) or the trickster (in Native American tales). My thesis aims to learn these roles automatically, inducing them from data using a clustering technique. The first step of learning character roles, however, is to identify which coreference chains correspond to characters, which are defined by narratologists as animate entities that drive the plot forward. The first part of my work has focused on this character identification problem, specifically focusing on the problem of animacy detection. Prior work treated animacy as a word-level property, and researchers developed statistical models to classify words as either animate or inanimate. I claimed this approach to the problem is ill-posed and presented a new hybrid approach for classifying the animacy of coreference chains that achieved state-of-the-art performance. The next step of my work is to develop approaches first to identify the characters and then a new unsupervised clustering approach to learn stereotypical roles. My character identification system consists of two stages: first, I detect animate chains from the coreference chains using my existing animacy detector; second, I apply a supervised machine learning model that identifies which of those chains qualify as characters. I proposed a narratologically grounded definition of character and built a supervised machine learning model with a small set of features that achieved state-of-the-art performance. In the last step, I successfully implemented a clustering approach with plot and thematic information to cluster the archetypes. This work resulted in a completely new approach to understanding the structure of stories, greatly advancing the state-of-the-art of story understanding

The nature of the animacy organization in human ventral temporal cortex

The principles underlying the animacy organization of the ventral temporal cortex (VTC) remain hotly debated, with recent evidence pointing to an animacy continuum rather than a dichotomy. What drives this continuum? According to the visual categorization hypothesis, the continuum reflects the degree to which animals contain animal-diagnostic features. By contrast, the agency hypothesis posits that the continuum reflects the degree to which animals are perceived as (social) agents. Here, we tested both hypotheses with a stimulus set in which visual categorizability and agency were dissociated based on representations in convolutional neural networks and behavioral experiments. Using fMRI, we found that visual categorizability and agency explained independent components of the animacy continuum in VTC. Modeled together, they fully explained the animacy continuum. Finally, clusters explained by visual categorizability were localized posterior to clusters explained by agency. These results show that multiple organizing principles, including agency, underlie the animacy continuum in VTC.Comment: 16 pages, 5 figures, code+data at - https://doi.org/10.17605/OSF.IO/VXWG9 Update - added supplementary results and edited abstrac

An introduction to time-resolved decoding analysis for M/EEG

The human brain is constantly processing and integrating information in order to make decisions and interact with the world, for tasks from recognizing a familiar face to playing a game of tennis. These complex cognitive processes require communication between large populations of neurons. The non-invasive neuroimaging methods of electroencephalography (EEG) and magnetoencephalography (MEG) provide population measures of neural activity with millisecond precision that allow us to study the temporal dynamics of cognitive processes. However, multi-sensor M/EEG data is inherently high dimensional, making it difficult to parse important signal from noise. Multivariate pattern analysis (MVPA) or "decoding" methods offer vast potential for understanding high-dimensional M/EEG neural data. MVPA can be used to distinguish between different conditions and map the time courses of various neural processes, from basic sensory processing to high-level cognitive processes. In this chapter, we discuss the practical aspects of performing decoding analyses on M/EEG data as well as the limitations of the method, and then we discuss some applications for understanding representational dynamics in the human brain

A motion system for social and animated robots

This paper presents an innovative motion system that is used to control the motions and animations of a social robot. The social robot Probo is used to study Human-Robot Interactions (HRI), with a special focus on Robot Assisted Therapy (RAT). When used for therapy it is important that a social robot is able to create an "illusion of life" so as to become a believable character that can communicate with humans. The design of the motion system in this paper is based on insights from the animation industry. It combines operator-controlled animations with low-level autonomous reactions such as attention and emotional state. The motion system has a Combination Engine, which combines motion commands that are triggered by a human operator with motions that originate from different units of the cognitive control architecture of the robot. This results in an interactive robot that seems alive and has a certain degree of "likeability". The Godspeed Questionnaire Series is used to evaluate the animacy and likeability of the robot in China, Romania and Belgium

Impaired identification of impoverished animate but not inanimate objects in adults with high-functioning autism spectrum disorder

The ability to identify animate and inanimate objects from impoverished images was investigated in adults with high-functioning autism spectrum disorder (HFA) and in matched typically developed (TD) adults, using a newly developed task. Consecutive frames were presented containing Gabor elements that slightly changed orientation from one frame to the next. For a subset of elements, the changes were such that these elements gradually formed the outline of an object. Elements enclosed within the object's outline gradually adopted one and the same orientation, outside elements adopted random orientations. The subjective experience was that of an object appearing out of a fog. The HFA group required significantly more frames to identify the impoverished objects than the TD group. Crucially, this difference depended on the nature of the objects: the HFA group required significantly more frames to identify animate objects, but with respect to the identification of inanimate objects the groups did not differ. The groups also did not differ with respect to the number and type of incorrect guesses they made. The results suggest a specific impairment in individuals with HFA in identifying animate objects. A number of possible explanations are discussed

Encapsulated social perception of emotional expressions

In this paper I argue that the detection of emotional expressions is, in its early stages, informationally encapsulated. I clarify and defend such a view via the appeal to data from social perception on the visual processing of faces, bodies, facial and bodily expressions. Encapsulated social perception might exist alongside processes that are cognitively penetrated, and that have to do with recognition and categorization, and play a central evolutionary function in preparing early and rapid responses to the emotional stimuli

Seeing Seeing

I argue that we can visually perceive others as seeing agents. I start by characterizing perceptual processes as those that are causally controlled by proximal stimuli. I then distinguish between various forms of visual perspective-taking, before presenting evidence that most of them come in perceptual varieties. In doing so, I clarify and defend the view that some forms of visual perspective-taking are “automatic”—a view that has been marshalled in support of dual-process accounts of mindreading