281 research outputs found

    Coreferential Interpretations of Reflexives in Picture Noun Phrases: An Experimental Approach

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    In English, reflexives (himself) and pronouns (him) have a nearly complementary distribution. That is, in the same structural position, a reflexive and a pronoun will have complementary referential domains: (1) Keni saw himselfi/*j

    Pseudoscientific health beliefs and the perceived frequency of causal relationships

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    Beliefs about cause and effect, including health beliefs, are thought to be related to the frequency of the target outcome (e.g., health recovery) occurring when the putative cause is present and when it is absent (treatment administered vs. no treatment); this is known as contingency learning. However, it is unclear whether unvalidated health beliefs, where there is no evidence of cause– effect contingency, are also influenced by the subjective perception of a meaningful contingency between events. In a survey, respondents were asked to judge a range of health beliefs and estimate the probability of the target outcome occurring with and without the putative cause present. Over-all, we found evidence that causal beliefs are related to perceived cause–effect contingency. Interestingly, beliefs that were not predicted by perceived contingency were meaningfully related to scores on the paranormal belief scale. These findings suggest heterogeneity in pseudoscientific health beliefs and the need to tailor intervention strategies according to underlying causes

    Towards a Robuster Interpretive Parsing

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    The input data to grammar learning algorithms often consist of overt forms that do not contain full structural descriptions. This lack of information may contribute to the failure of learning. Past work on Optimality Theory introduced Robust Interpretive Parsing (RIP) as a partial solution to this problem. We generalize RIP and suggest replacing the winner candidate with a weighted mean violation of the potential winner candidates. A Boltzmann distribution is introduced on the winner set, and the distribution’s parameter TT is gradually decreased. Finally, we show that GRIP, the Generalized Robust Interpretive Parsing Algorithm significantly improves the learning success rate in a model with standard constraints for metrical stress assignment

    Speech Facilitates the Categorization of Motions in 9-Month-Old Infants

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    Two experiments were used to investigate the influence of both native and non-native speech on the categorization of a set of an object’s motions by 9-month-olds. In Experiment 1, infants were habituated to a set of three object motions and tested with familiar and novel motions. Results of Experiment 1 show that infants were more likely to categorize the motion stimuli if they listened to either the native or non-native speech during the categorization process than if they listened to music or heard nothing at all. Results of Experiment 2 show that discrimination of the motions was not impaired by the presence of the labeling phrases. These results are consistent with a number of findings that report a unique influence of labels on categorization of static objects in infancy and extend those findings to categorization of motions

    What is typical about the typicality effect in category-based induction?

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    ITI-007 demonstrates brain occupancy at serotonin 5-HT2A and dopamine D2 receptors and serotonin transporters using positron emission tomography in healthy volunteers

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    © 2015 Springer-Verlag Berlin Heidelberg.Rationale: Central modulation of serotonin and dopamine underlies efficacy for a variety of psychiatric therapeutics. ITI-007 is an investigational new drug in development for treatment of schizophrenia, mood disorders, and other neuropsychiatric disorders. Objectives: The purpose of this study was to determine brain occupancy of ITI-007 at serotonin 5-HT2A receptors, dopamine D2 receptors, and serotonin transporters using positron emission tomography (PET) in 16 healthy volunteers. Methods: Carbon-11-MDL100907, carbon-11-raclopride, and carbon-11-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile) (carbon-11-DASB) were used as the radiotracers for imaging 5-HT2A receptors, D2 receptors, and serotonin transporters, respectively. Brain regions of interest were outlined using magnetic resonance tomography (MRT) with cerebellum as the reference region. Binding potentials were estimated by fitting a simplified reference tissue model to the measured tissue-time activity curves. Target occupancy was expressed as percent change in the binding potentials before and after ITI-007 administration. Results: Oral ITI-007 (10-40 mg) was safe and well tolerated. ITI-007 rapidly entered the brain with long-lasting and dose-related occupancy. ITI-007 (10 mg) demonstrated high occupancy (>80 %) of cortical 5-HT2A receptors and low occupancy of striatal D2 receptors (~12 %). D2 receptor occupancy increased with dose and significantly correlated with plasma concentrations (r 2∈=∈0.68, p∈=∈0.002). ITI-007 (40 mg) resulted in peak occupancy up to 39 % of striatal D2 receptors and 33 % of striatal serotonin transporters. Conclusions: The results provide evidence for a central mechanism of action via dopaminergic and serotonergic pathways for ITI-007 in living human brain and valuable information to aid dose selection for future clinical trials

    Modeling human performance in statistical word segmentation

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    The ability to discover groupings in continuous stimuli on the basis of distributional information is present across species and across perceptual modalities. We investigate the nature of the computations underlying this ability using statistical word segmentation experiments in which we vary the length of sentences, the amount of exposure, and the number of words in the languages being learned. Although the results are intuitive from the perspective of a language learner (longer sentences, less training, and a larger language all make learning more difficult), standard computational proposals fail to capture several of these results. We describe how probabilistic models of segmentation can be modified to take into account some notion of memory or resource limitations in order to provide a closer match to human performance.National Science Foundation (U.S.) (Grant BCS-0631518

    Learning and Long-Term Retention of Large-Scale Artificial Languages

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    Recovering discrete words from continuous speech is one of the first challenges facing language learners. Infants and adults can make use of the statistical structure of utterances to learn the forms of words from unsegmented input, suggesting that this ability may be useful for bootstrapping language-specific cues to segmentation. It is unknown, however, whether performance shown in small-scale laboratory demonstrations of “statistical learning” can scale up to allow learning of the lexicons of natural languages, which are orders of magnitude larger. Artificial language experiments with adults can be used to test whether the mechanisms of statistical learning are in principle scalable to larger lexicons. We report data from a large-scale learning experiment that demonstrates that adults can learn words from unsegmented input in much larger languages than previously documented and that they retain the words they learn for years. These results suggest that statistical word segmentation could be scalable to the challenges of lexical acquisition in natural language learning.National Science Foundation (U.S.) (NSF DDRIG #0746251
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