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Alfonso Caramazza - One of the best experts on this subject based on the ideXlab platform.
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the noun verb dissociation in language production varieties of causes
Cognitive Neuropsychology, 2004Co-Authors: Marcella Laiacona, Alfonso CaramazzaAbstract:Abstract We report the performance of two patients who presented with complementary deficits in naming nouns relative to verbs: EA performed far worse with nouns than verbs, while MR performed worse with verbs than nouns. The two patients' Grammatical Category-specific deficits could not easily be explained in terms of damage to specific types of semantic knowledge prototypically associated with nouns (visual properties) and verbs (action features). One of the two patients, MR, also presented with a selective deficit in processing verbal as opposed to nominal morphology, in line with her impairment in naming verbs. The other patient, EA, showed no impairment in producing nominal and regular verbal morphology. The contrasting patterns of Grammatical Category-specific deficits in naming and morphological processing, along with other recently reported patterns, are interpreted as providing support for the claim that semantic and Grammatical properties independently contribute to the organisation of lexical p...
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The representation of Grammatical categories in the brain.
Trends in cognitive sciences, 2003Co-Authors: Kevin A. Shapiro, Alfonso CaramazzaAbstract:Language relies on the rule-based combination of words with different Grammatical properties, such as nouns and verbs. Yet most research on the problem of word retrieval has focused on the production of concrete nouns, leaving open a crucial question: how is knowledge about different Grammatical categories represented in the brain, and what components of the language production system make use of it? Drawing on evidence from neuropsychology, electrophysiology and neuroimaging, we argue that information about a word's Grammatical Category might be represented independently of its meaning at the levels of word form and morphological computation.
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selective difficulties with spoken nouns and written verbs a single case study
Journal of Neurolinguistics, 2002Co-Authors: Brenda Rapp, Alfonso CaramazzaAbstract:Abstract We describe an individual who exhibits greater difficulties in speaking nouns than verbs and greater difficulties in writing verbs than nouns across a range of both single word and sentence production tasks. This double dissociation of Grammatical Category by modality within a single individual represents an important challenge to the claim that all apparent Category Grammatical deficits can be reduced to the effects of the various semantic variables. The modality-specific nature of the findings clearly support the representation of Grammatical Category distinctions at post-semantic levels of representations and processing and they raise a number of questions regarding the specific instantiation of these distinctions within current theoretical frameworks of language production.
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Representation of Grammatical categories of words in the brain
Journal of cognitive neuroscience, 1995Co-Authors: Argye E. Hillis, Alfonso CaramazzaAbstract:We report the performance of a patient who, as a consequence of left frontal and temporoparietal strokes, makes far more errors on nouns than on verbs in spoken output tasks, but makes far more errors on verbs than on nouns in written input tasks. This double dissociation within a single patient with respect to Grammatical Category provides evidence for the hypothesis that phonological and orthographic representations of nouns and verbs are processed by independent neural mechanisms. Furthermore, the opposite dissociation in the verbal output modality, an advantage for nouns over verbs in spoken tasks, by a different patient using the same stimuli has also been reported (Caramazza & Hillis, 1991). This double dissociation across patients on the same task indicates that results cannot be ascribed to "greater difficulty" with one type of stimulus, and provides further evidence for the view that Grammatical Category information is an important organizational principle of lexical knowledge in the brain.
Elissa L Newport - One of the best experts on this subject based on the ideXlab platform.
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exploring the role of representation in models of Grammatical Category acquisition
Cognitive Science, 2012Co-Authors: Ting Qian, Patricia A Reeder, Richard N Aslin, Joshua B Tenenbaum, Elissa L NewportAbstract:Exploring the Role of Representation in Models of Grammatical Category Acquisition Ting Qian (tqian@bcs.rochester.edu) 1 Patricia A. Reeder (preeder@bcs.rochester.edu) 1 Richard N. Aslin (aslin@cvs.rochester.edu) 1 Josh B. Tenenbaum (jbt@mit.edu) 2 Elissa L. Newport (newport@bcs.rochester.edu) 1 1 Department of Brain & Cognitive Sciences, University of Rochester of Brain & Cognitive Sciences, MIT 2 Department Abstract tween linguistic elements. Although studies have shown that human learners and computational models can successfully learn Grammatical categories when only these cues are avail- able, the question of representation still remains poorly un- derstood. How do learners represent the knowledge of pre- viously encountered linguistic items in order to generalize to novel ones? The aim of the present work is to ask what types of repre- sentations are used by human learners in an artificial grammar learning (AGL) task that includes many of the distributional properties of spoken language. We focus on how learners in- duce Grammatical categories and assign words to them. Our approach involves computational modeling, comparing the simulated learning outcome of three different models, each of which makes a different assumption about how learners rep- resent the learned grammar. We assess the models by com- paring the generalization patterns of each model and those of human learners. Our experimental data come from our pre- vious findings across 10 AGL experiments (Reeder et al., in review; Schuler et al., in prep). In the next section, we first provide a brief summary of these results. Importantly, the goal of our modeling work is not to mirror every detail of hu- man behavior in AGL experiments: to do so, one must con- sider psychological variables such as memory and attention, which are currently not included in our models. Instead, we are interested in exploring the representational assumptions that human learners have adopted in our experiments. One major aspect of successful language acquisition is the abil- ity to generalize from properties of experienced items to novel items. We present a computational study of artificial language learning, where the generalization patterns of three generative models are compared to those of human learners across 10 ex- periments. Results suggest that an explicit representation of word categories is the best model for capturing the generaliza- tion patterns of human learners across a wide range of learning environments. We discuss the representational assumptions implied by these models. Introduction Learning the grammar of a language consists of at least two important tasks. First, learners must discover the cues in the linguistic input that are useful for constructing the grammar of the language. Second, learners must represent their knowl- edge of the grammar in a form that makes it possible to assess the Grammaticality of future input. With an appropriate repre- sentation of the grammar, learners can generalize from prop- erties of the small set of experienced items to predicted prop- erties of novel items. This ability for generalization is crucial for language acquisition, as the input for learning is naturally limited. Such generalization should extend to only the novel items that are actually licensed by the language, no more (over-generalization) and no less (under-generalization). Previous research has offered several hypotheses regarding the cues that learners use and the representations of gram- mar they form. In the realm of syntactic Category acquisition, one hypothesis is that the categories (but not their contents) are innately specified prior to receiving any linguistic input, with the assignment of words to categories accomplished with minimal exposure (e.g. McNeill, 1966). On this view, both the cues and the representations are predefined and indepen- dent of linguistic input. A contrasting view states that gram- matical categories are learned, though different hypotheses appeal to the importance of different cues or cue combina- tions during the learning process (such as semantic cues, e.g., Bowerman, 1973). Within this class of non-nativist hypothe- ses, several studies have suggested that distributional cues may be sufficient for extracting the grammar of the input language (e.g., Braine, 1987; Maratsos & Chalkley, 1980; Mintz et al., 2002). Distributional cues are defined over pat- terns in the linguistic input, such as token frequencies, co- occurrence statistics, and latent structural dependencies be- Background on Behavioral Results The behavioral data come from a series of 10 experiments with adult participants in which we created an artificial gram- mar with the structure (Q)AXB(R). Each letter represents a Category of nonsense words. Q and R words served as op- tional categories that made sentences of the language vary in length from 3 to 5 words and made words of the language observe patterning in terms of relative order but not fixed po- sition. The sizes of the categories varied across experiments, leading to different numbers of possible sentences in the lan- guage. For ease of presentation, we will number the experi- ments. In Experiments 1-4 (Reeder et al., 2009), there were 108 possible sentences that could be created from this gram- mar; in Experiment 5 (Reeder et al., 2009), there were 576 possible sentences; in Experiments 6-10 (Reeder et al., 2010;
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CogSci - Exploring the Role of Representation in Models of Grammatical Category Acquisition
Cognitive Science, 2012Co-Authors: Ting Qian, Patricia A Reeder, Richard N Aslin, Joshua B Tenenbaum, Elissa L NewportAbstract:Exploring the Role of Representation in Models of Grammatical Category Acquisition Ting Qian (tqian@bcs.rochester.edu) 1 Patricia A. Reeder (preeder@bcs.rochester.edu) 1 Richard N. Aslin (aslin@cvs.rochester.edu) 1 Josh B. Tenenbaum (jbt@mit.edu) 2 Elissa L. Newport (newport@bcs.rochester.edu) 1 1 Department of Brain & Cognitive Sciences, University of Rochester of Brain & Cognitive Sciences, MIT 2 Department Abstract tween linguistic elements. Although studies have shown that human learners and computational models can successfully learn Grammatical categories when only these cues are avail- able, the question of representation still remains poorly un- derstood. How do learners represent the knowledge of pre- viously encountered linguistic items in order to generalize to novel ones? The aim of the present work is to ask what types of repre- sentations are used by human learners in an artificial grammar learning (AGL) task that includes many of the distributional properties of spoken language. We focus on how learners in- duce Grammatical categories and assign words to them. Our approach involves computational modeling, comparing the simulated learning outcome of three different models, each of which makes a different assumption about how learners rep- resent the learned grammar. We assess the models by com- paring the generalization patterns of each model and those of human learners. Our experimental data come from our pre- vious findings across 10 AGL experiments (Reeder et al., in review; Schuler et al., in prep). In the next section, we first provide a brief summary of these results. Importantly, the goal of our modeling work is not to mirror every detail of hu- man behavior in AGL experiments: to do so, one must con- sider psychological variables such as memory and attention, which are currently not included in our models. Instead, we are interested in exploring the representational assumptions that human learners have adopted in our experiments. One major aspect of successful language acquisition is the abil- ity to generalize from properties of experienced items to novel items. We present a computational study of artificial language learning, where the generalization patterns of three generative models are compared to those of human learners across 10 ex- periments. Results suggest that an explicit representation of word categories is the best model for capturing the generaliza- tion patterns of human learners across a wide range of learning environments. We discuss the representational assumptions implied by these models. Introduction Learning the grammar of a language consists of at least two important tasks. First, learners must discover the cues in the linguistic input that are useful for constructing the grammar of the language. Second, learners must represent their knowl- edge of the grammar in a form that makes it possible to assess the Grammaticality of future input. With an appropriate repre- sentation of the grammar, learners can generalize from prop- erties of the small set of experienced items to predicted prop- erties of novel items. This ability for generalization is crucial for language acquisition, as the input for learning is naturally limited. Such generalization should extend to only the novel items that are actually licensed by the language, no more (over-generalization) and no less (under-generalization). Previous research has offered several hypotheses regarding the cues that learners use and the representations of gram- mar they form. In the realm of syntactic Category acquisition, one hypothesis is that the categories (but not their contents) are innately specified prior to receiving any linguistic input, with the assignment of words to categories accomplished with minimal exposure (e.g. McNeill, 1966). On this view, both the cues and the representations are predefined and indepen- dent of linguistic input. A contrasting view states that gram- matical categories are learned, though different hypotheses appeal to the importance of different cues or cue combina- tions during the learning process (such as semantic cues, e.g., Bowerman, 1973). Within this class of non-nativist hypothe- ses, several studies have suggested that distributional cues may be sufficient for extracting the grammar of the input language (e.g., Braine, 1987; Maratsos & Chalkley, 1980; Mintz et al., 2002). Distributional cues are defined over pat- terns in the linguistic input, such as token frequencies, co- occurrence statistics, and latent structural dependencies be- Background on Behavioral Results The behavioral data come from a series of 10 experiments with adult participants in which we created an artificial gram- mar with the structure (Q)AXB(R). Each letter represents a Category of nonsense words. Q and R words served as op- tional categories that made sentences of the language vary in length from 3 to 5 words and made words of the language observe patterning in terms of relative order but not fixed po- sition. The sizes of the categories varied across experiments, leading to different numbers of possible sentences in the lan- guage. For ease of presentation, we will number the experi- ments. In Experiments 1-4 (Reeder et al., 2009), there were 108 possible sentences that could be created from this gram- mar; in Experiment 5 (Reeder et al., 2009), there were 576 possible sentences; in Experiments 6-10 (Reeder et al., 2010;
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The distributional structure of Grammatical categories in speech to young children
Cognitive Science, 2002Co-Authors: Toben H. Mintz, Elissa L Newport, Thomas G. BeverAbstract:We present a series of three analyses of young children’s linguistic input to determine the distributional information it could plausibly offer to the process of Grammatical Category learning. Each analysis was conducted on four separate corpora from the CHILDES database (MacWhinney, 2000) of speech directed to children under 2;5. We show that, in accord with other findings, a distributional analysis which categorizes words based on their co-occurrence patterns with surrounding words successfully categorizes the majority of nouns and verbs. In Analyses 2 and 3, we attempt to make our analyses more closely relevant to natural language acquisition by adopting more realistic assumptions about how young children represent their input. In Analysis 2, we limit the distributional context by imposing phrase structure boundaries, and find that categorization improves even beyond that obtained from less limited contexts. In Analysis 3, we reduce the representation of input elements which young children might not fully process and we find that categorization is not adversely affected: Although noun categorization is worse than in Analyses 1 and 2, it is still good; and verb categorization actually improves. Overall, successful categorization of nouns and verbs is maintained across all analyses. These results provide promising support for theories of Grammatical Category formation involving distributional analysis, as long as these analyses are combined with appropriate assumptions about the child learner’s computational biases and capabilities.
Michael Tomasello - One of the best experts on this subject based on the ideXlab platform.
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twenty five month old children do not have a Grammatical Category of verb
Cognitive Development, 1993Co-Authors: Raquel Olguin, Michael TomaselloAbstract:This study investigated experimentally the nature and development of children's early productivity with verb-argument structure and verb morphology. Twenty-two to 25-month-old boys and girls were, in the context of playing a game over a several week period, exposed to eight novel verbs modeled with experimentally controlled argument structures and verb inflections. The question was whether, when, and in what ways the children would become productive with these verbs in their spontaneous speech, going beyond the particular linguistic forms they had heard. In terms of verb-argument structure, the results showed that children most often followed the surface structure of the model, regardless of the argument they were trying to express. Thus, when children had heard an argument expressed for a verb, they almost always marked that argument correctly in their own utterances; when they had not heard an argument expressed for a particular verb, their correct marking dropped to chance levels. The children showed no signs of productive verb morphology, but they did use the newly learned verbs in some creative ways involving noun-like uses and the appending of locatives. Results are discussed in terms of Tomasello's (1992) Verb Island hypothesis.
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Twenty-Three-Month-Old Children Have a Grammatical Category of Noun.
Cognitive Development, 1993Co-Authors: Michael Tomasello, Raquel OlguinAbstract:This study investigated experimentally the nature and development of children's early productivity with nouns, both in verb-argument structure and with plural morphology. Eight 20- to 26-month-old boys and girls were, in the context of playing a game over a several week period, exposed to four novel nouns, modeled in experimentally controlled ways. The question was whether, when, and in what ways the children would become productive with these nouns in their spontaneous speech, going beyond the particular linguistic forms they had heard. In terms of verb-argument structure, 7 of the 8 children used their nouns in productive argument roles, that is, in semantic roles they had not heard them used in. Five of the 8 children used the plural morpheme productively with the novel nouns as well. Implications for theories of Grammatical Category formation are discussed.
Raquel Olguin - One of the best experts on this subject based on the ideXlab platform.
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twenty five month old children do not have a Grammatical Category of verb
Cognitive Development, 1993Co-Authors: Raquel Olguin, Michael TomaselloAbstract:This study investigated experimentally the nature and development of children's early productivity with verb-argument structure and verb morphology. Twenty-two to 25-month-old boys and girls were, in the context of playing a game over a several week period, exposed to eight novel verbs modeled with experimentally controlled argument structures and verb inflections. The question was whether, when, and in what ways the children would become productive with these verbs in their spontaneous speech, going beyond the particular linguistic forms they had heard. In terms of verb-argument structure, the results showed that children most often followed the surface structure of the model, regardless of the argument they were trying to express. Thus, when children had heard an argument expressed for a verb, they almost always marked that argument correctly in their own utterances; when they had not heard an argument expressed for a particular verb, their correct marking dropped to chance levels. The children showed no signs of productive verb morphology, but they did use the newly learned verbs in some creative ways involving noun-like uses and the appending of locatives. Results are discussed in terms of Tomasello's (1992) Verb Island hypothesis.
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Twenty-Three-Month-Old Children Have a Grammatical Category of Noun.
Cognitive Development, 1993Co-Authors: Michael Tomasello, Raquel OlguinAbstract:This study investigated experimentally the nature and development of children's early productivity with nouns, both in verb-argument structure and with plural morphology. Eight 20- to 26-month-old boys and girls were, in the context of playing a game over a several week period, exposed to four novel nouns, modeled in experimentally controlled ways. The question was whether, when, and in what ways the children would become productive with these nouns in their spontaneous speech, going beyond the particular linguistic forms they had heard. In terms of verb-argument structure, 7 of the 8 children used their nouns in productive argument roles, that is, in semantic roles they had not heard them used in. Five of the 8 children used the plural morpheme productively with the novel nouns as well. Implications for theories of Grammatical Category formation are discussed.
Michael H. Kelly - One of the best experts on this subject based on the ideXlab platform.
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Using sound to solve syntactic problems: the role of phonology in Grammatical Category assignments.
Psychological review, 1992Co-Authors: Michael H. KellyAbstract:One ubiquitous problem in language processing involves the assignment of words to the correct Grammatical Category, such as noun or verb. In general, semantic and syntactic cues have been cited as the principal information for Grammatical Category assignment, to the neglect of possible phonological cues. This neglect is unwarranted, and the following claims are made: (a) Numerous correlations between phonology and Grammatical class exist, (b) some of these correlations are large and can pervade the entire lexicon of a language and hence can involve thousands of words, (c) experiments have repeatedly found that adults and children have learned these correlations, and (d) explanations for how these correlations arose can be proposed and evaluated. Implications of these phenomena for language representation and processing are discussed. One of the many problems that must be solved in language acquisition, comprehension, and production is how to assign words to the appropriate Grammatical categories, such as noun and verb. In particular, during language acquisition children must learn the Grammatical categories of their language and learn which words fall into these different classes. Thus, German children must eventually learn that /ir/ is a pronoun (ihr), whereas English children must learn that the same sound is a noun (ear). During language comprehension, adults also must classify words into Grammatical classes quickly (i.e., in a fraction of a second) and accurately. Furthermore, the correct classification might not be immediately clear to the listener. For instance, The team bats. . . could correspond to an article-adjective-noun structure or an article-noun-verb structure, and the listener may have to wait for further information before making a classification decision. Finally, during language production, speakers must access words of the appropriate Grammatical class to formulate an acceptable utterance. General success at this task is illustrated by the fact that speech errors seem to be strongly constrained by Grammatical Category. For example, inadvertent word substitutions, such as saying "apartment" instead of "appointment," almost always preserve Grammatical class (Fay & Cutler, 1977; see also Garrett, 1982). Investigations of these problems have focused much attention on the sources of information available for making Grammatical Category assignments. In general, such investigations focus almost exclusively on semantic and syntactic information for Grammatical class. The semantic approach argues that major Grammatical classes are universally associated with certain
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Phonological information for Grammatical Category assignments
Journal of Memory and Language, 1991Co-Authors: Kimberly Wright Cassidy, Michael H. KellyAbstract:Abstract During language acquisition, children must learn how to classify words into the appropriate Grammatical Category, such as noun or verb. Adults must also assign words to Grammatical categories quickly and accurately. Most theories of this task focus on strategies that exploit semantic and/or syntactic correlates of Grammatical class. This paper examines a relatively neglected source of information for Grammatical Category: phonology. Study one demonstrates that English verbs contain fewer syllables than English nouns, a difference that appears strongly in both adult-adult language and parental speech to children. Studies three and four provide evidence that adults and children are sensitive to this difference. Study three reports that adults use pseudowords more often in sentences as verbs if their syllable number is small, whereas they use pseudowords as nouns more often if their syllable number is large. Study four reports that 4-year old children associate pseudowords with actions (the prototypical verb meaning) more often than objects (the prototypical noun meaning) if the pseudowords contain one rather than three syllables. The relevance of the noun-verb syllable difference for connectionist models of linguistic knowledge is discussed. In addition, possible causes of the syllable number difference between nouns and verbs are proposed and evaluated in study two.
