Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: When Suboptimal Behavior is Optimal and Why: Modeling the Acquisition of Noun Classes in Tsez Annie Gagliardi (acg39@umd.edu) Naomi H. Feldman (nhf@umd.edu) Jeffrey Lidz (jlidz@umd.edu) Department of Linguistics, 1401 Marie Mount Hall, University of Maryland, College Park, MD 20742 USA Abstract behavior suggests that there is more to language acquisition than a simple mapping of external statistical information to an internal representation of this distribution. In particular it suggests that properties of the learner shape the statistical information in the input into the subset of information that is used to guide inferences in language acquisition: the intake.  use a Bayesian model of noun classification to probe what underlies the difference in the measureable input and the intake that children use to acquire noun classes. As a general framework,  assume that optimal performance in an experimental task involves the following four components: (1) Accumulation of knowledge of the statistical distribution of features relating to some phenomenon (2) Observation of features in a novel experimental item (3) Knowledge of which features are relevant for the statistical computation (4) Bayesian computation to determine how to generalize the phenomenon in question to the novel instance (1) depends on the learner’s ability to observe and encode a statistical distribution of features pertaining to some phenomenon. (2) is similar to (1), but refers to encoding these features given a situation where the learner will be performing a computation to classify or otherwise deal with a novel instance. (3) requires the learner to know which features are relevant for a computation and is by no means trivial, as not every feature related to every phenomenon is relevant to the associated computation. (4) is an assumption that  are making about the kind of computations that learners use distributional information for. While step (4) is often assumed to be the culprit when learners show suboptimal performance in experimental tasks, in principle steps (1) through (3) can also contribute to suboptimal performance. Our case study on Tsez noun classification examines how each of these pieces could result in a reshaping of the statistical information in the input.  begin with an outline of the distributional information that characterizes Tsez noun classes.  then compare children’s use of this information in classification with that of a naive Bayesian classifier. Finally,  explore three models that introduce uncertainty in levels (1)-(3) from above, in an effort to determine what underlies the difference beten children’s performance and predictions made by the Bayesian model. Children acquiring languages with noun classes (grammatical gender) have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier.  propose three models that introduce uncertainty into the optimal Bayesian classifier and find that all three provide ways to account for the difference beten children’s behavior and the optimal classifier. These results suggest that children may be classifying optimally with respect to a distribution that doesn’t match the surface distribution of these statistical features. Keywords: language acquisition; noun classes; Bayesian classification; statistical learning. Learners are surrounded by statistical information. Considerable evidence suggests that they can make use of statistics to learn about their environment. For example, when acquiring artificial languages, children track distributional cues that allow them to discover phonetic categories (Maye, rker & Gerken, 2002), word boundaries (Saffran, Newport & Aslin, 1996), grammatical categories (Mintz, 2003; Reeder, Newport & Aslin 2009, 2010), grammatical dependencies (Gomez & Maye, 2005; Saffran, 2001) and phrase structure (Takahashi, 2009). This leads to a commonly held belief in the language acquisition literature that children are perfect statistical learners (e.g. Elman, Bates, Johnson Karmiloff-Smith, Parisi & Plunkett The hypothesis that children are perfect statistical learners predicts that when tested on their ability to generalize aspects of their native language in an experimental setting, children’s linguistic knowledge should always reflect the distribution of statistical information in the input. Hover, this is not always the case. Work by Hudson-Kam and Newport (2009), for example, suggests that children are not perfectly veridical learners, in that they sometimes override statistical patterns in the service of amplifying some other facet of the language they are acquiring. As this work has largely focused on artificial language learning, here  examine another type of non-veridical statistical learning involving the acquisition of noun class (grammatical gender) in a natural language, Tsez.  present evidence showing that children exhibit behavior that is inconsistent with the statistical information available in the input when assigning novel nouns to noun classes. This inconsistent
['Annie Gagliardi', 'Naomi H. Feldman', 'Jeffrey Lidz']
I am writing a research paper on a idea When Suboptimal Behavior is Optimal and Why: Modeling the Acquisition of Noun Classes in Tsez
[{"from": "human", "value": "I am writing a research paper on a idea When Suboptimal Behavior is Optimal and Why: Modeling the Acquisition of Noun Classes in Tsez"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: When Suboptimal Behavior is Optimal and Why: Modeling the Acquisition of Noun Classes in Tsez Annie Gagliardi (acg39@umd.edu) Naomi H. Feldman (nhf@umd.edu) Jeffrey Lidz (jlidz@umd.edu) Department of Linguistics, 1401 Marie Mount Hall, University of Maryland, College Park, MD 20742 USA Abstract behavior suggests that there is more to language acquisition than a simple mapping of external statistical information to an internal representation of this distribution. In particular it suggests that properties of the learner shape the statistical information in the input into the subset of information that is used to guide inferences in language acquisition: the intake.  use a Bayesian model of noun classification to probe what underlies the difference in the measureable input and the intake that children use to acquire noun classes. As a general framework,  assume that optimal performance in an experimental task involves the following four components: (1) Accumulation of knowledge of the statistical distribution of features relating to some phenomenon (2) Observation of features in a novel experimental item (3) Knowledge of which features are relevant for the statistical computation (4) Bayesian computation to determine how to generalize the phenomenon in question to the novel instance (1) depends on the learner\u2019s ability to observe and encode a statistical distribution of features pertaining to some phenomenon. (2) is similar to (1), but refers to encoding these features given a situation where the learner will be performing a computation to classify or otherwise deal with a novel instance. (3) requires the learner to know which features are relevant for a computation and is by no means trivial, as not every feature related to every phenomenon is relevant to the associated computation. (4) is an assumption that  are making about the kind of computations that learners use distributional information for. While step (4) is often assumed to be the culprit when learners show suboptimal performance in experimental tasks, in principle steps (1) through (3) can also contribute to suboptimal performance. Our case study on Tsez noun classification examines how each of these pieces could result in a reshaping of the statistical information in the input.  begin with an outline of the distributional information that characterizes Tsez noun classes.  then compare children\u2019s use of this information in classification with that of a naive Bayesian classifier. Finally,  explore three models that introduce uncertainty in levels (1)-(3) from above, in an effort to determine what underlies the difference beten children\u2019s performance and predictions made by the Bayesian model. Children acquiring languages with noun classes (grammatical gender) have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier.  propose three models that introduce uncertainty into the optimal Bayesian classifier and find that all three provide ways to account for the difference beten children\u2019s behavior and the optimal classifier. These results suggest that children may be classifying optimally with respect to a distribution that doesn\u2019t match the surface distribution of these statistical features. Keywords: language acquisition; noun classes; Bayesian classification; statistical learning. Learners are surrounded by statistical information. Considerable evidence suggests that they can make use of statistics to learn about their environment. For example, when acquiring artificial languages, children track distributional cues that allow them to discover phonetic categories (Maye, rker & Gerken, 2002), word boundaries (Saffran, Newport & Aslin, 1996), grammatical categories (Mintz, 2003; Reeder, Newport & Aslin 2009, 2010), grammatical dependencies (Gomez & Maye, 2005; Saffran, 2001) and phrase structure (Takahashi, 2009). This leads to a commonly held belief in the language acquisition literature that children are perfect statistical learners (e.g. Elman, Bates, Johnson Karmiloff-Smith, Parisi & Plunkett The hypothesis that children are perfect statistical learners predicts that when tested on their ability to generalize aspects of their native language in an experimental setting, children\u2019s linguistic knowledge should always reflect the distribution of statistical information in the input. Hover, this is not always the case. Work by Hudson-Kam and Newport (2009), for example, suggests that children are not perfectly veridical learners, in that they sometimes override statistical patterns in the service of amplifying some other facet of the language they are acquiring. As this work has largely focused on artificial language learning, here  examine another type of non-veridical statistical learning involving the acquisition of noun class (grammatical gender) in a natural language, Tsez.  present evidence showing that children exhibit behavior that is inconsistent with the statistical information available in the input when assigning novel nouns to noun classes. This inconsistent"}]