Eliana Colunga | University of Colorado, Boulder (original) (raw)

Papers by Eliana Colunga

Cognitive Science, 2011

In typical development, word learning goes from slow and laborious to fast and seemingly effortle... more In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds are so skilled at learning noun categories that they seem to intuit the whole range of things in the category from hearing a single instance named-they are biased learners. This is not the case for children below the 20th percentile on productive vocabulary (late talkers). This paper looks at the vocabulary composition of age-matched 18-30-month-old late-and early-talking children. The results of Experiment 1 show that late talkers' vocabularies are more variable than early talker's vocabularies. Crucially, Experiment 2 shows that neural networks trained on the vocabularies of individual late talkers learn qualitatively different biases than those trained on early talker vocabularies. These simulations make testable predictions for world learning biases of late-vs. early-talking children. The implications for diagnosis and intervention are discussed.

Proceedings of the Annual Meeting of the Cognitive Science Society, 2006

One account of word learning suggests that children learn that words are unique as labels among p... more One account of word learning suggests that children learn that words are unique as labels among perceptual signals because of the way they tend to systematically co-occur with categories of objects. Twenty-month-old's use of mutual exclusivity with different types of labels emanating from different sources was investigated in order to evaluate this account. Specifically, words and animal sounds were investigated. Results showed that children applied mutual exclusivity to both words and animal sounds produced by mouths, but not to words or animal sounds produced by noisemakers. This suggests the importance of including the regularities of social context and pragmatics to the associationist account of word learning.

Peer review is a core component of scientific practice. Although peer review ideally improves res... more Peer review is a core component of scientific practice. Although peer review ideally improves research and promotes rigor, it also has consequences for what types of research are published and cited, and who wants to (and is able to) advance in research-focused careers. Despite these consequences, few reviewers or editors receive training or oversight to ensure their feedback is helpful, professional, and culturally sensitive. Here, we critically examine the peer review system in psychology and neuroscience at multiple levels, from ideas to institutions, interactions, and individuals. We highlight initiatives that aim to change the normative negativity of peer review and provide authors with constructive, actionable feedback that is sensitive to diverse identities, methods, topics, and environments. We conclude with a call to action for how individuals, groups, and organizations can improve the culture of peer review. We provide examples of how changes in the peer review system can ...

Creativity and Cognition

Interactions between children and their caregivers represent an important factor of child develop... more Interactions between children and their caregivers represent an important factor of child development. Book sharing and other play interactions are common ways in which caregivers and their preschool-age children interact. Shared book reading has many benefits in early childhood, but some researchers have suggested that children may become passive in such interactions. Additionally, with caregivers having sole access to the information in the text, they may be less open to contributions the child puts forth if they conflict with the text. In contrast, a more symmetrical and cooperative activity, such as putting together a puzzle, may elicit more participation from the child and less categorical input from the caregiver. In a study with 59 2-and 3-year-olds and their caregivers engaging in one of these two activities, we find that interactions centered around the puzzle artifact are characterized by more meaningful participation on the part of the child and less definitive corrections on the part of the caregiver compared to book-based interactions. These findings suggest that alternatives to shared book reading with 2-and 3-year-olds may nudge children to express themselves more creatively when interacting with caregivers. Implications for the design of learning experiences for preschoolers are discussed. CCS CONCEPTS • Social and professional topics → Children; • Human-centered computing → Empirical studies in interaction design.

Findings of the Association for Computational Linguistics: ACL 2022

Automatic morphological processing can aid downstream natural language processing applications, e... more Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for endangered languages. Having long been multilingual, the field of computational morphology is increasingly moving towards approaches suitable for languages with minimal or no annotated resources. First, we survey recent developments in computational morphology with a focus on low-resource languages. Second, we argue that the field is ready to tackle the logical next challenge: understanding a language's morphology from raw text alone. We perform an empirical study on a truly unsupervised version of the paradigm completion task and show that, while existing state-of-the-art models bridged by two newly proposed models we devise perform reasonably, there is still much room for improvement. The stakes are high: solving this task will increase the language coverage of morphological resources by a number of magnitudes.

Marcus (2001) argues that only those connectionist models that incorporate (classical) rules can ... more Marcus (2001) argues that only those connectionist models that incorporate (classical) rules can account for the phenomenon of transfer of learning in infants. Seidenberg and Elman (1999) have tried to counter to Marcus by means of a simple recurrent network (SRN) trained on a categorization task. In this paper we show how a prediction-SRN, trained on a simple but structured pre-training set, can preserve its computational equivalency with respect to classical counterparts while eschewing the need to posit rule-governed underlying mechanisms; a criticism that has been raised against Seidenberg and Elman's categorization-based reply.

Comparing Apples to Fruit: Parent’s Comparisons of Labels are Related to First and Second Label L... more Comparing Apples to Fruit: Parent’s Comparisons of Labels are Related to First and Second Label Learning Chandra L. Brojde (chandrab@colorado.edu) Department of Psychology and Neuroscience, CB345 Boulder, CO 80309 USA Eliana Colunga (eliana.colunga@colorado.edu) Department of Psychology and Neuroscience, CB345 Boulder, CO 80309 USA Abstract Young children often find it difficult to learn two labels for a single object. However, there is a great deal of variability across studies in children’s bias to reject second labels. In this study, we investigated three possible factors affecting this variability including age, task, and parental input in a cross- sectional sample of children from 12- to 28-months-old. We show that children reject second labels differently depending on their age, task demands, and the amount and type of parental input. Importantly, there is also a correlation between the ways in which parent’s use second labels and children’s acceptance of first and second labe...

Infants’ Associations of Words and Sounds to Animals and Vehicles Eliana Colunga (ecolunga@cs.ind... more Infants’ Associations of Words and Sounds to Animals and Vehicles Eliana Colunga (ecolunga@cs.indiana.edu) Computer Science Department; Lindley Hall 215 Bloomington, IN 47405 USA Linda B. Smith (smith4@indiana.edu) Department of Psychology; 1101 East Tenth Street Bloomington, IN 47405 USA In a recent study, Woodward and Hoyne (1999) showed that 13-month-olds readily associate both words coming from the experimenter’s mouth and non-linguistic sounds coming from a hand-held noisemaker with object categories. In contrast, 20-month-olds associate words but not non- linguistic sounds with object categories. Woodward and Hoyne suggest that words become privileged as possible names; that the forms a name can take are open at the be- ginning and become more restricted with development. Are children learning what forms count as words? If so, just what defining features are they learning? In the research presented here, we attempt to answer these questions. In our account, words become privil...

Cognitive Science, 2013

Using Complex Network Analysis in the Cognitive Sciences Nicole M. Beckage (Nicole.Beckage@Colora... more Using Complex Network Analysis in the Cognitive Sciences Nicole M. Beckage (Nicole.Beckage@Colorado.edu) University of Colorado, Boulder Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA Michael S. Vitevitch (mvitevit@ku.edu) University of Kansas Department of Psychology, 1415 Jayhawk Blvd Lawrence, KS 66045 USA Alexander Mehler (mehler@em.uni-frankfurt.de) Goethe University, Frankfurt Department of Computer Science and Mathematics, Robert-Mayer Strase 10 Frankfurt am Main, 60325 Germany Eliana Colunga (Eliana.Colunga@Colorado.edu) University of Colorado, Boulder Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA the idea of small-world structure, which has been shown to allow for efficient processing and navigation of information. From there we introduce the idea that network statistics change with the size and density of a graph. That brings up concepts of randomization and statistical tests. While these will be handled initially as def...

One account of word learning suggests that children learn that words are unique as labels among p... more One account of word learning suggests that children learn that words are unique as labels among perceptual signals because of the way they tend to systematically co-occur with categories of objects. Twenty-month-old’s use of mutual exclusivity with different types of labels emanating from different sources was investigated in order to evaluate this account. Specifically, words and animal sounds were investigated. Results showed that children applied mutual exclusivity to both words and animal sounds produced by mouths, but not to words or animal sounds produced by noisemakers. This suggests the importance of including the regularities of social context and pragmatics to the associationist account of word learning.

Cognitive Science, 2013

Using the words toddlers know now to predict the words they will learn next Nicole M. Beckage (Ni... more Using the words toddlers know now to predict the words they will learn next Nicole M. Beckage (Nicole.Beckage@Colorado.edu) Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA Eliana Colunga (Eliana.Colunga@Colorado.edu) Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA is, we ask: can we use the words a child knows now to predict the words that a child will learn next? Abstract We set forth to show that lexical connectivity plays a role in understanding early word learning. By considering words that are learned in temporal proximity to one another to be related, we are able to better predict the words next learned by toddlers. We build conditional probability models based on data from the growing vocabularies of 77 toddlers, followed longitudinally for a year. This type of conditional probability model outperforms the current norms based on baseline probabilities of learning given age alone. This is a first step to capturing the interacti...

Cognitive Science, 2015

We examine the nature of phonological and semantic similarity in early language learning. We cons... more We examine the nature of phonological and semantic similarity in early language learning. We consider how the use of this information might change over the course of development. To this end, we represent the lexicon as either a phonological or semantic network and model the growth of this network. Constructing normative vocabularies from the Communicative Development Inventory norms, we utilize a preferential attachment growth algorithm. We predict and quantify the words which will be learned next, comparing the two network representations. We consider the effect of age, total vocabulary size and language ability as measured through CDI percentile. Our findings suggest that the semantic representation does not outperform the baseline bag-of-words model, whereas the phonological representation conditionally does. More generally, we show that the network representation influences the ability of a model to capture vocabulary growth. We further offer a method of analysis for testing re...

Complexity, 2019

Network models of language provide a systematic way of linking cognitive processes to the structu... more Network models of language provide a systematic way of linking cognitive processes to the structure and connectivity of language. Using network growth models to capture learning, we focus on the study of the emergence of complexity in early language learners. Specifically, we capture the emergent structure of young toddler’s vocabularies through network growth models assuming underlying knowledge representations of semantic and phonological networks. In construction and analyses of these network growth models, we explore whether phonological or semantic relationships between words play a larger role in predicting network growth as these young learners add new words to their lexicon. We also examine how the importance of these semantic and phonological representations changes during the course of development. We propose a novel and significant theoretical framework for network growth models of acquisition and test the ability of these models to predict what words a specific child is ...

IEEE Transactions on Cognitive and Developmental Systems, 2019

Can we predict the words a child is going to learn next given information about the words that a ... more Can we predict the words a child is going to learn next given information about the words that a child knows now? Do different representations of a child's vocabulary knowledge affect our ability to predict the acquisition of lexical items for individual children? Past research has often focused on population statistics of vocabulary growth rather than prediction of words an individual child is likely to learn next. We consider a neural network approach to predict vocabulary acquisition. Specifically, we investigate how best to represent the child's current vocabulary in order to accurately predict future learning. The models we consider are based on qualitatively different sources of information: descriptive information about the child, the specific words a child knows, and representations that aim to capture the child's aggregate lexical knowledge. Using longitudinal vocabulary data from children aged 15-36 months, we construct neural network models to predict which words are likely to be learned by a particular child in the coming month. Many models based on child-specific vocabulary information outperform models with child information only, suggesting that the words a child knows influence prediction of future language learning. These models provide an understanding of the role of current vocabulary knowledge on future lexical growth.

How does a child's vocabulary production change and expand over time? Past research has often foc... more How does a child's vocabulary production change and expand over time? Past research has often focused on characterizing population statistics of vocabulary growth. In this work, we develop models that attempt to predict when a specific word will be learned by a particular child. The models are based on two qualitatively different sources of information: a representation describing the child (age, sex, and quantifiers of vocabulary skill) and a representation describing the specific words a child knows. Using longitudinal data from children aged 15-36 months collected at the University of Colorado, we constructed logistic regression models to predict each month whether a word would be learned in the coming month. Models based on either the child representation or the word representation outperform a baseline model that utilizes population acquisition norms. Although the child-and word-representation models perform comparably, an ensemble that averages the predictions of the two separate models obtains significantly higher accuracy, indicating that the two sources of information are complementary. Through the exploration of such models, we gain an understanding of the factors that influence language learning, and this understanding should inform cognitive theories of development. On a practical level, these models support the development of interventions to boost language acquisition.

aaai.org

In this statement, we discuss two kinds of properties that a grounded model of the learning of wo... more In this statement, we discuss two kinds of properties that a grounded model of the learning of word mean-ing should have, those related to the way in which linguistic and non-linguistic processing should inter-act and those related to the representational demands placed on such a ...

Citeseer

One account of word learning suggests that children learn that words are unique as labels among p... more One account of word learning suggests that children learn that words are unique as labels among perceptual signals because of the way they tend to systematically co-occur with catego-ries of objects. Twenty-month-old's use of mutual exclusivity with different types of labels ...

Cognitive Science, 2011

In typical development, word learning goes from slow and laborious to fast and seemingly effortle... more In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds are so skilled at learning noun categories that they seem to intuit the whole range of things in the category from hearing a single instance named-they are biased learners. This is not the case for children below the 20th percentile on productive vocabulary (late talkers). This paper looks at the vocabulary composition of age-matched 18-30-month-old late-and early-talking children. The results of Experiment 1 show that late talkers' vocabularies are more variable than early talker's vocabularies. Crucially, Experiment 2 shows that neural networks trained on the vocabularies of individual late talkers learn qualitatively different biases than those trained on early talker vocabularies. These simulations make testable predictions for world learning biases of late-vs. early-talking children. The implications for diagnosis and intervention are discussed.

Proceedings of the Annual Meeting of the Cognitive Science Society, 2006

One account of word learning suggests that children learn that words are unique as labels among p... more One account of word learning suggests that children learn that words are unique as labels among perceptual signals because of the way they tend to systematically co-occur with categories of objects. Twenty-month-old's use of mutual exclusivity with different types of labels emanating from different sources was investigated in order to evaluate this account. Specifically, words and animal sounds were investigated. Results showed that children applied mutual exclusivity to both words and animal sounds produced by mouths, but not to words or animal sounds produced by noisemakers. This suggests the importance of including the regularities of social context and pragmatics to the associationist account of word learning.

Peer review is a core component of scientific practice. Although peer review ideally improves res... more Peer review is a core component of scientific practice. Although peer review ideally improves research and promotes rigor, it also has consequences for what types of research are published and cited, and who wants to (and is able to) advance in research-focused careers. Despite these consequences, few reviewers or editors receive training or oversight to ensure their feedback is helpful, professional, and culturally sensitive. Here, we critically examine the peer review system in psychology and neuroscience at multiple levels, from ideas to institutions, interactions, and individuals. We highlight initiatives that aim to change the normative negativity of peer review and provide authors with constructive, actionable feedback that is sensitive to diverse identities, methods, topics, and environments. We conclude with a call to action for how individuals, groups, and organizations can improve the culture of peer review. We provide examples of how changes in the peer review system can ...

Creativity and Cognition

Interactions between children and their caregivers represent an important factor of child develop... more Interactions between children and their caregivers represent an important factor of child development. Book sharing and other play interactions are common ways in which caregivers and their preschool-age children interact. Shared book reading has many benefits in early childhood, but some researchers have suggested that children may become passive in such interactions. Additionally, with caregivers having sole access to the information in the text, they may be less open to contributions the child puts forth if they conflict with the text. In contrast, a more symmetrical and cooperative activity, such as putting together a puzzle, may elicit more participation from the child and less categorical input from the caregiver. In a study with 59 2-and 3-year-olds and their caregivers engaging in one of these two activities, we find that interactions centered around the puzzle artifact are characterized by more meaningful participation on the part of the child and less definitive corrections on the part of the caregiver compared to book-based interactions. These findings suggest that alternatives to shared book reading with 2-and 3-year-olds may nudge children to express themselves more creatively when interacting with caregivers. Implications for the design of learning experiences for preschoolers are discussed. CCS CONCEPTS • Social and professional topics → Children; • Human-centered computing → Empirical studies in interaction design.

Findings of the Association for Computational Linguistics: ACL 2022

Automatic morphological processing can aid downstream natural language processing applications, e... more Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for endangered languages. Having long been multilingual, the field of computational morphology is increasingly moving towards approaches suitable for languages with minimal or no annotated resources. First, we survey recent developments in computational morphology with a focus on low-resource languages. Second, we argue that the field is ready to tackle the logical next challenge: understanding a language's morphology from raw text alone. We perform an empirical study on a truly unsupervised version of the paradigm completion task and show that, while existing state-of-the-art models bridged by two newly proposed models we devise perform reasonably, there is still much room for improvement. The stakes are high: solving this task will increase the language coverage of morphological resources by a number of magnitudes.

Marcus (2001) argues that only those connectionist models that incorporate (classical) rules can ... more Marcus (2001) argues that only those connectionist models that incorporate (classical) rules can account for the phenomenon of transfer of learning in infants. Seidenberg and Elman (1999) have tried to counter to Marcus by means of a simple recurrent network (SRN) trained on a categorization task. In this paper we show how a prediction-SRN, trained on a simple but structured pre-training set, can preserve its computational equivalency with respect to classical counterparts while eschewing the need to posit rule-governed underlying mechanisms; a criticism that has been raised against Seidenberg and Elman's categorization-based reply.

Comparing Apples to Fruit: Parent’s Comparisons of Labels are Related to First and Second Label L... more Comparing Apples to Fruit: Parent’s Comparisons of Labels are Related to First and Second Label Learning Chandra L. Brojde (chandrab@colorado.edu) Department of Psychology and Neuroscience, CB345 Boulder, CO 80309 USA Eliana Colunga (eliana.colunga@colorado.edu) Department of Psychology and Neuroscience, CB345 Boulder, CO 80309 USA Abstract Young children often find it difficult to learn two labels for a single object. However, there is a great deal of variability across studies in children’s bias to reject second labels. In this study, we investigated three possible factors affecting this variability including age, task, and parental input in a cross- sectional sample of children from 12- to 28-months-old. We show that children reject second labels differently depending on their age, task demands, and the amount and type of parental input. Importantly, there is also a correlation between the ways in which parent’s use second labels and children’s acceptance of first and second labe...

Infants’ Associations of Words and Sounds to Animals and Vehicles Eliana Colunga (ecolunga@cs.ind... more Infants’ Associations of Words and Sounds to Animals and Vehicles Eliana Colunga (ecolunga@cs.indiana.edu) Computer Science Department; Lindley Hall 215 Bloomington, IN 47405 USA Linda B. Smith (smith4@indiana.edu) Department of Psychology; 1101 East Tenth Street Bloomington, IN 47405 USA In a recent study, Woodward and Hoyne (1999) showed that 13-month-olds readily associate both words coming from the experimenter’s mouth and non-linguistic sounds coming from a hand-held noisemaker with object categories. In contrast, 20-month-olds associate words but not non- linguistic sounds with object categories. Woodward and Hoyne suggest that words become privileged as possible names; that the forms a name can take are open at the be- ginning and become more restricted with development. Are children learning what forms count as words? If so, just what defining features are they learning? In the research presented here, we attempt to answer these questions. In our account, words become privil...

Cognitive Science, 2013

Using Complex Network Analysis in the Cognitive Sciences Nicole M. Beckage (Nicole.Beckage@Colora... more Using Complex Network Analysis in the Cognitive Sciences Nicole M. Beckage (Nicole.Beckage@Colorado.edu) University of Colorado, Boulder Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA Michael S. Vitevitch (mvitevit@ku.edu) University of Kansas Department of Psychology, 1415 Jayhawk Blvd Lawrence, KS 66045 USA Alexander Mehler (mehler@em.uni-frankfurt.de) Goethe University, Frankfurt Department of Computer Science and Mathematics, Robert-Mayer Strase 10 Frankfurt am Main, 60325 Germany Eliana Colunga (Eliana.Colunga@Colorado.edu) University of Colorado, Boulder Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA the idea of small-world structure, which has been shown to allow for efficient processing and navigation of information. From there we introduce the idea that network statistics change with the size and density of a graph. That brings up concepts of randomization and statistical tests. While these will be handled initially as def...

One account of word learning suggests that children learn that words are unique as labels among p... more One account of word learning suggests that children learn that words are unique as labels among perceptual signals because of the way they tend to systematically co-occur with categories of objects. Twenty-month-old’s use of mutual exclusivity with different types of labels emanating from different sources was investigated in order to evaluate this account. Specifically, words and animal sounds were investigated. Results showed that children applied mutual exclusivity to both words and animal sounds produced by mouths, but not to words or animal sounds produced by noisemakers. This suggests the importance of including the regularities of social context and pragmatics to the associationist account of word learning.

Cognitive Science, 2013

Using the words toddlers know now to predict the words they will learn next Nicole M. Beckage (Ni... more Using the words toddlers know now to predict the words they will learn next Nicole M. Beckage (Nicole.Beckage@Colorado.edu) Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA Eliana Colunga (Eliana.Colunga@Colorado.edu) Department of Psychology and Neuroscience, 345 UCB Boulder, CO 80309 USA is, we ask: can we use the words a child knows now to predict the words that a child will learn next? Abstract We set forth to show that lexical connectivity plays a role in understanding early word learning. By considering words that are learned in temporal proximity to one another to be related, we are able to better predict the words next learned by toddlers. We build conditional probability models based on data from the growing vocabularies of 77 toddlers, followed longitudinally for a year. This type of conditional probability model outperforms the current norms based on baseline probabilities of learning given age alone. This is a first step to capturing the interacti...

Cognitive Science, 2015

We examine the nature of phonological and semantic similarity in early language learning. We cons... more We examine the nature of phonological and semantic similarity in early language learning. We consider how the use of this information might change over the course of development. To this end, we represent the lexicon as either a phonological or semantic network and model the growth of this network. Constructing normative vocabularies from the Communicative Development Inventory norms, we utilize a preferential attachment growth algorithm. We predict and quantify the words which will be learned next, comparing the two network representations. We consider the effect of age, total vocabulary size and language ability as measured through CDI percentile. Our findings suggest that the semantic representation does not outperform the baseline bag-of-words model, whereas the phonological representation conditionally does. More generally, we show that the network representation influences the ability of a model to capture vocabulary growth. We further offer a method of analysis for testing re...

Complexity, 2019

Network models of language provide a systematic way of linking cognitive processes to the structu... more Network models of language provide a systematic way of linking cognitive processes to the structure and connectivity of language. Using network growth models to capture learning, we focus on the study of the emergence of complexity in early language learners. Specifically, we capture the emergent structure of young toddler’s vocabularies through network growth models assuming underlying knowledge representations of semantic and phonological networks. In construction and analyses of these network growth models, we explore whether phonological or semantic relationships between words play a larger role in predicting network growth as these young learners add new words to their lexicon. We also examine how the importance of these semantic and phonological representations changes during the course of development. We propose a novel and significant theoretical framework for network growth models of acquisition and test the ability of these models to predict what words a specific child is ...

IEEE Transactions on Cognitive and Developmental Systems, 2019

Can we predict the words a child is going to learn next given information about the words that a ... more Can we predict the words a child is going to learn next given information about the words that a child knows now? Do different representations of a child's vocabulary knowledge affect our ability to predict the acquisition of lexical items for individual children? Past research has often focused on population statistics of vocabulary growth rather than prediction of words an individual child is likely to learn next. We consider a neural network approach to predict vocabulary acquisition. Specifically, we investigate how best to represent the child's current vocabulary in order to accurately predict future learning. The models we consider are based on qualitatively different sources of information: descriptive information about the child, the specific words a child knows, and representations that aim to capture the child's aggregate lexical knowledge. Using longitudinal vocabulary data from children aged 15-36 months, we construct neural network models to predict which words are likely to be learned by a particular child in the coming month. Many models based on child-specific vocabulary information outperform models with child information only, suggesting that the words a child knows influence prediction of future language learning. These models provide an understanding of the role of current vocabulary knowledge on future lexical growth.

How does a child's vocabulary production change and expand over time? Past research has often foc... more How does a child's vocabulary production change and expand over time? Past research has often focused on characterizing population statistics of vocabulary growth. In this work, we develop models that attempt to predict when a specific word will be learned by a particular child. The models are based on two qualitatively different sources of information: a representation describing the child (age, sex, and quantifiers of vocabulary skill) and a representation describing the specific words a child knows. Using longitudinal data from children aged 15-36 months collected at the University of Colorado, we constructed logistic regression models to predict each month whether a word would be learned in the coming month. Models based on either the child representation or the word representation outperform a baseline model that utilizes population acquisition norms. Although the child-and word-representation models perform comparably, an ensemble that averages the predictions of the two separate models obtains significantly higher accuracy, indicating that the two sources of information are complementary. Through the exploration of such models, we gain an understanding of the factors that influence language learning, and this understanding should inform cognitive theories of development. On a practical level, these models support the development of interventions to boost language acquisition.

aaai.org

In this statement, we discuss two kinds of properties that a grounded model of the learning of wo... more In this statement, we discuss two kinds of properties that a grounded model of the learning of word mean-ing should have, those related to the way in which linguistic and non-linguistic processing should inter-act and those related to the representational demands placed on such a ...

Citeseer

One account of word learning suggests that children learn that words are unique as labels among p... more One account of word learning suggests that children learn that words are unique as labels among perceptual signals because of the way they tend to systematically co-occur with catego-ries of objects. Twenty-month-old's use of mutual exclusivity with different types of labels ...