Understanding the Use of the Term "Weaponized Autism" in An Alt-Right Social Media Platform.

BACKGROUND: The term "weaponized autism" is frequently used on extremist platforms. To better understand this, we conducted a discourse analysis of posts on Gab, an alt-right social media platform. METHODS: We analyzed 711 posts spanning 2018-2019 and filtered for variations on the term "weaponized autism". RESULTS: This term is used mainly by non-autistic Gab users. It refers to exploitation of perceived talents and vulnerabilities of "Weaponized autists", described as all-powerful masters-of-technology who are devoid of social skills. CONCLUSIONS: The term "weaponized autism" is simultaneously glorified and derogatory. For some autistic people, the partial acceptance offered within this community may be preferable to lack of acceptance offered in society, which speaks to improving societal acceptance as a prevention effort.

The Emergence of Gender Associations in Child Language Development.

Gender associations have been a long-standing research topic in psychological and social sciences. Although it is known that children learn aspects of gender associations at a young age, it is not well understood how they might emerge through the course of development. We investigate whether gender associations, such as the association of dresses with women and bulldozers with men, are reflected in the linguistic communication of young children from ages 1-5. Drawing on recent methods from machine learning, we use word embeddings derived from large text corpora including news articles and web pages as a proxy for gender associations in society, and we compare those with the gender associations of words uttered by caretakers and children in children's linguistic environment. We quantify gender associations in childhood language through gender probability, which measures the extent to which word usage frequencies in speech to and by girls and boys are gender-skewed. By analyzing 4,875 natural conversations between children and their caretakers in North America, we find that frequency patterns in word usage of both caretakers and children correlate strongly with the gender associations captured in word embeddings through the course of development. We discover that these correlations diminish from the 1970s to the 1990s. Our work suggests that early linguistic communication and social changes may jointly contribute to the formation of gender associations in childhood.

Beyond the Benchmarks: Toward Human-Like Lexical Representations.

To process language in a way that is compatible with human expectations in a communicative interaction, we need computational representations of lexical properties that form the basis of human knowledge of words. In this article, we concentrate on word-level semantics. We discuss key concepts and issues that underlie the scientific understanding of the human lexicon: its richly structured semantic representations, their ready and continual adaptability, and their grounding in crosslinguistically valid conceptualization. We assess the state of the art in natural language processing (NLP) in achieving these identified properties, and suggest ways in which the language sciences can inspire new approaches to their computational instantiation.

Probing Lexical Ambiguity: Word Vectors Encode Number and Relatedness of Senses.

Lexical ambiguity-the phenomenon of a single word having multiple, distinguishable senses-is pervasive in language. Both the degree of ambiguity of a word (roughly, its number of senses) and the relatedness of those senses have been found to have widespread effects on language acquisition and processing. Recently, distributional approaches to semantics, in which a word's meaning is determined by its contexts, have led to successful research quantifying the degree of ambiguity, but these measures have not distinguished between the ambiguity of words with multiple related senses versus multiple unrelated meanings. In this work, we present the first assessment of whether distributional meaning representations can capture the ambiguity structure of a word, including both the number and relatedness of senses. On a very large sample of English words, we find that some, but not all, distributional semantic representations that we test exhibit detectable differences between sets of monosemes (unambiguous words; N = 964), polysemes (with multiple related senses; N = 4,096), and homonyms (with multiple unrelated senses; N = 355). Our findings begin to answer open questions from earlier work regarding whether distributional semantic representations of words, which successfully capture various semantic relationships, also reflect fine-grained aspects of meaning structure that influence human behavior. Our findings emphasize the importance of measuring whether proposed lexical representations capture such distinctions: In addition to standard benchmarks that test the similarity structure of distributional semantic models, we need to also consider whether they have cognitively plausible ambiguity structure.

A comparison of homonym meaning frequency estimates derived from movie and television subtitles, free association, and explicit ratings.

Most words are ambiguous, with interpretation dependent on context. Advancing theories of ambiguity resolution is important for any general theory of language processing, and for resolving inconsistencies in observed ambiguity effects across experimental tasks. Focusing on homonyms (words such as bank with unrelated meanings EDGE OF A RIVER vs. FINANCIAL INSTITUTION), the present work advances theories and methods for estimating the relative frequency of their meanings, a factor that shapes observed ambiguity effects. We develop a new method for estimating meaning frequency based on the meaning of a homonym evoked in lines of movie and television subtitles according to human raters. We also replicate and extend a measure of meaning frequency derived from the classification of free associates. We evaluate the internal consistency of these measures, compare them to published estimates based on explicit ratings of each meaning's frequency, and compare each set of norms in predicting performance in lexical and semantic decision mega-studies. All measures have high internal consistency and show agreement, but each is also associated with unique variance, which may be explained by integrating cognitive theories of memory with the demands of different experimental methodologies. To derive frequency estimates, we collected manual classifications of 533 homonyms over 50,000 lines of subtitles, and of 357 homonyms across over 5000 homonym-associate pairs. This database-publicly available at: www.blairarmstrong.net/homonymnorms/ -constitutes a novel resource for computational cognitive modeling and computational linguistics, and we offer suggestions around good practices for its use in training and testing models on labeled data.

More Than the Eye Can See: A Computational Model of Color Term Acquisition and Color Discrimination.

We explore the following two cognitive questions regarding crosslinguistic variation in lexical semantic systems: Why are some linguistic categories-that is, the associations between a term and a portion of the semantic space-harder to learn than others? How does learning a language-specific set of lexical categories affect processing in that semantic domain? Using a computational word-learner, and the domain of color as a testbed, we investigate these questions by modeling both child acquisition of color terms and adult behavior on a non-verbal color discrimination task. A further goal is to test an approach to lexical semantic representation based on the principle that the more languages label any two situations with the same word, the more conceptually similar those two situations are. We compare such a crosslinguistically based semantic space to one based on perceptual similarity. Our computational model suggests a mechanistic explanation for the interplay between term frequency and the semantic closeness of learned categories in developmental error patterns for color terms. Our model also indicates how linguistic relativity effects could arise from an acquisition mechanism that yields language-specific topologies for the same semantic domain. Moreover, we find that the crosslinguistically inspired semantic space supports these results at least as well as-and in some aspects better than-the purely perceptual one, thus confirming our approach as a practical and principled method for lexical semantic representation in cognitive modeling.

Modeling Reference Production as the Probabilistic Combination of Multiple Perspectives.

While speakers have been shown to adapt to the knowledge state of their addressee in choosing referring expressions, they often also show some egocentric tendencies. The current paper aims to provide an explanation for this "mixed" behavior by presenting a model that derives such patterns from the probabilistic combination of both the speaker's and the addressee's perspectives. To test our model, we conducted a language production experiment, in which participants had to refer to objects in a context that also included a visually misleading object (e.g., a crayon shaped like a Lego brick) whose function was either known to both partners, or known just to the speaker but not the addressee. Modeling results indicate that the experimental findings cannot be explained by assuming that speakers tailor a referring expression solely to their own perspective or to the perspective of their addressee. Instead, accounting for the behavioral pattern requires an approach where both perspectives influence the choice of referring expressions. Nevertheless, in our situation, speakers consider their partner's perspective more than their own.

Perspective-taking behavior as the probabilistic weighing of multiple domains.

Our starting point is the apparently-contradictory results in the psycholinguistic literature regarding whether, when interpreting a definite referring expressions, listeners process relative to the common ground from the earliest moments of processing. We propose that referring expressions are not interpreted relative solely to the common ground or solely to one's Private (or egocentric) knowledge, but rather reflect the simultaneous integration of the two perspectives. We implement this proposal in a Bayesian model of reference resolution, focusing on the model's predictions for two prior studies: Keysar, Barr, Balin, and Brauner (2000) and Heller, Grodner and Tanenhaus (2008). We test the model's predictions in a visual-world eye-tracking experiment, demonstrating that the original results cannot simply be attributed to different perspective-taking strategies, and showing how they can arise from the same perspective-taking behavior.

A probabilistic computational model of cross-situational word learning.

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.

Using language to learn structured appearance models for image annotation.

Given an unstructured collection of captioned images of cluttered scenes featuring a variety of objects, our goal is to simultaneously learn the names and appearances of the objects. Only a small fraction of local features within any given image are associated with a particular caption word, and captions may contain irrelevant words not associated with any image object. We propose a novel algorithm that uses the repetition of feature neighborhoods across training images and a measure of correspondence with caption words to learn meaningful feature configurations (representing named objects). We also introduce a graph-based appearance model that captures some of the structure of an object by encoding the spatial relationships among the local visual features. In an iterative procedure, we use language (the words) to drive a perceptual grouping process that assembles an appearance model for a named object. Results of applying our method to three data sets in a variety of conditions demonstrate that, from complex, cluttered, real-world scenes with noisy captions, we can learn both the names and appearances of objects, resulting in a set of models invariant to translation, scale, orientation, occlusion, and minor changes in viewpoint or articulation. These named models, in turn, are used to automatically annotate new, uncaptioned images, thereby facilitating keyword-based image retrieval.

A computational model of early argument structure acquisition.

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions-associations between syntactic forms and properties of meaning that form the basis for a number of linguistic and psycholinguistic theories of language. This article presents a computational model for the representation, acquisition, and use of verbs and constructions. The Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behavior of the model over the timecourse of acquisition mimics, in relevant aspects, the stages of learning exhibited by children. Therefore, this proposal sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions.

Phase 1 study of weekly polyethylene glycol-camptothecin in patients with advanced solid tumors and lymphomas.

PURPOSE: To determine the maximal tolerated dose and dose-limiting toxicities (DLT) of pegamotecan (polyethylene glycol-camptothecin) in patients with advanced malignancies when administered in cycles of once weekly for 3 of 4 weeks. EXPERIMENTAL DESIGN: Eligible patients had advanced solid tumors that failed to respond to standard therapy or for which no standard therapy was available, including also the following criteria: measurable disease, Eastern Cooperative Oncology Group performance status of < or =2, and acceptable organ function. Pegamotecan was administered as a 60-minute infusion, with successive patient cohorts receiving escalating doses from 800 to 4,300 mg/m(2). The primary end point was to determine the maximal tolerated dose. Other end points were toxicity, pharmacokinetics, pharmacodynamics, and efficacy. Pharmacokinetic analysis measured free camptothecin. Pharmacodynamic analysis correlated drug effects with pegamotecan dose and pharmacokinetic variables. RESULTS: Twenty-seven patients were enrolled. The maximal tolerated dose was 3,240 mg/m(2). Grade 4 neutropenia, the DLT, was noted in two of four patients treated at 4,300 mg/m(2). Other grade 3 and 4 toxicities were anemia, thrombocytopenia, fatigue, prolonged partial thromboplastin time, hemorrhagic cystitis, dysuria, and urinary frequency. Pharmacokinetic analysis showed the apparent terminal elimination half-life to be 46 +/- 12.8 hours. Pharmacodynamic analysis showed that hematuria occurred in 8 of 15 patients with an area under the curve extrapolated to infinity (AUC(0-infinity)) > 20 ng h/mL and 0 of 10 patients with an AUC(0-infinity) < or = 20 ng h/mL. Unconfirmed partial responses were observed in two patients, one with metastatic small bowel adenocarcinoma and the other with metastatic esophageal cancer. CONCLUSIONS: The maximal tolerated dose of pegamotecan when administered weekly for 3 of 4 weeks is 3,240 mg/m(2). The DLT was neutropenia. Among nonhematologic toxicities, the incidence of gastrointestinal toxicity was low, but genitourinary toxicity seems to occur in the same effective dose range as noted with native camptothecin in earlier trials (27-43 mg/m(2)). The observed antitumor activity suggests that pegamotecan has single-agent activity and merits further investigation in phase 2 studies.