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Singular quantified terms
(2016)
In this paper, I discuss the behavior of singular partitives, focusing on Hebrew. I show that group noun-headed singular quantified terms behave essentially different from other singular quantified terms. Specifically, the domain of quantification in the former is a discrete set (the members of the group), while in the latter the domain of quantification is a set of mass entities. I propose a preliminary analysis of singular quantified terms in Hebrew, respecting the properties peculiar to this language as well as the observations about group vs. non-group singular quantified terms. This analysis is based on a novel class of quantifiers I name ’Measure Quantifiers’, which instantiate relations between algebraic sums. Using shifts between algebraic sums, we can represent the different readings of singular and plural individual or group terms.
We study biased Maker-Breaker positional games between two players, one of whom is playing randomly against an opponent with an optimal strategy. In this paper we consider the scenario when Maker plays randomly and Breaker is "clever", and determine the sharp threshold bias of classical graph games, such as connectivity, Hamiltonicity, and minimum degree-k. We treat the other case, that is when Breaker plays randomly, in a separate paper. The traditional, deterministic version of these games, with two optimal players playing, are known to obey the so-called probabilistic intuition. That is, the threshold bias of these games is asymptotically equal to the threshold bias of their random counterpart, where players just take edges uniformly at random. We find, that despite this remarkably precise agreement of the results of the deterministic and the random games, playing randomly against an optimal opponent is not a good idea: the threshold bias tilts significantly more towards the random player. An important qualitative aspect of the probabilistic intuition carries through nevertheless: the bottleneck for Maker to occupy a connected graph is still the ability to avoid isolated vertices in her graph. (C) 2016Wiley Periodicals, Inc.
It has been proposed that in online sentence comprehension the dependency between a reflexive pronoun such as himself/herself and its antecedent is resolved using exclusively syntactic constraints. Under this strictly syntactic search account, Principle A of the binding theory—which requires that the antecedent c-command the reflexive within the same clause that the reflexive occurs in—constrains the parser's search for an antecedent. The parser thus ignores candidate antecedents that might match agreement features of the reflexive (e.g., gender) but are ineligible as potential antecedents because they are in structurally illicit positions. An alternative possibility accords no special status to structural constraints: in addition to using Principle A, the parser also uses non-structural cues such as gender to access the antecedent. According to cue-based retrieval theories of memory (e.g., Lewis and Vasishth, 2005), the use of non-structural cues should result in increased retrieval times and occasional errors when candidates partially match the cues, even if the candidates are in structurally illicit positions. In this paper, we first show how the retrieval processes that underlie the reflexive binding are naturally realized in the Lewis and Vasishth (2005) model. We present the predictions of the model under the assumption that both structural and non-structural cues are used during retrieval, and provide a critical analysis of previous empirical studies that failed to find evidence for the use of non-structural cues, suggesting that these failures may be Type II errors. We use this analysis and the results of further modeling to motivate a new empirical design that we use in an eye tracking study. The results of this study confirm the key predictions of the model concerning the use of non-structural cues, and are inconsistent with the strictly syntactic search account. These results present a challenge for theories advocating the infallibility of the human parser in the case of reflexive resolution, and provide support for the inclusion of agreement features such as gender in the set of retrieval cues.
Referential Choice
(2016)
We report a study of referential choice in discourse production, understood as the choice between various types of referential devices, such as pronouns and full noun phrases. Our goal is to predict referential choice, and to explore to what extent such prediction is possible. Our approach to referential choice includes a cognitively informed theoretical component, corpus analysis, machine learning methods and experimentation with human participants. Machine learning algorithms make use of 25 factors, including referent’s properties (such as animacy and protagonism), the distance between a referential expression and its antecedent, the antecedent’s syntactic role, and so on. Having found the predictions of our algorithm to coincide with the original almost 90% of the time, we hypothesized that fully accurate prediction is not possible because, in many situations, more than one referential option is available. This hypothesis was supported by an experimental study, in which participants answered questions about either the original text in the corpus, or about a text modified in accordance with the algorithm’s prediction. Proportions of correct answers to these questions, as well as participants’ rating of the questions’ difficulty, suggested that divergences between the algorithm’s prediction and the original referential device in the corpus occur overwhelmingly in situations where the referential choice is not categorical.
Referential Choice
(2016)
We report a study of referential choice in discourse production, understood as the choice between various types of referential devices, such as pronouns and full noun phrases. Our goal is to predict referential choice, and to explore to what extent such prediction is possible. Our approach to referential choice includes a cognitively informed theoretical component, corpus analysis, machine learning methods and experimentation with human participants. Machine learning algorithms make use of 25 factors, including referent’s properties (such as animacy and protagonism), the distance between a referential expression and its antecedent, the antecedent’s syntactic role, and so on. Having found the predictions of our algorithm to coincide with the original almost 90% of the time, we hypothesized that fully accurate prediction is not possible because, in many situations, more than one referential option is available. This hypothesis was supported by an experimental study, in which participants answered questions about either the original text in the corpus, or about a text modified in accordance with the algorithm’s prediction. Proportions of correct answers to these questions, as well as participants’ rating of the questions’ difficulty, suggested that divergences between the algorithm’s prediction and the original referential device in the corpus occur overwhelmingly in situations where the referential choice is not categorical.
TripleA is a workshop series founded by linguists from the University of Tübingen and the University of Potsdam. Its aim is to provide a forum for semanticists doing fieldwork on understudied languages, and its focus is on languages from Africa, Asia, Australia and Oceania. The second TripleA workshop was held at the University of Potsdam, June 3-5, 2015.
Predicate focus
(2016)
Aphasia, the language disorder following brain damage, is frequently accompanied by deficits of working memory (WM) and executive functions (EFs). Recent studies suggest that WM, together with certain EFs, can play a role in sentence comprehension in individuals with aphasia (IWA), and that WM can be enhanced with intensive practice. Our aim was to investigate whether a combined WM and EF training improves the understanding of spoken sentences in IWA. We used a pre-post-test case control design. Three individuals with chronic aphasia practised an adaptive training task (a modified n-back task) three to four times a week for a month. Their performance was assessed before and after the training on outcome measures related to WM and spoken sentence comprehension. One participant showed significant improvement on the training task, another showed a tendency for improvement, and both of them improved significantly in spoken sentence comprehension. The third participant did not improve on the training task, however, she showed improvement on one measure of spoken sentence comprehension. Compared to controls, two individuals improved at least in one condition of the WM outcome measures. Thus, our results suggest that a combined WM and EF training can be beneficial for IWA.