Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Goldilocks Forgetting in Cross-Situational Learning

Given that there is referential uncertainty (noise) when learning words, to what extent can forgetting filter some of that noise out, and be an aid to learning? Using a Cross Situational Learning model we find a U-shaped function of errors indicative of a "Goldilocks" zone of forgetting: an optimum store-loss ratio that is neither too aggressive nor too weak, but just the right amount to produce better learning outcomes. Forgetting acts as a high-pass filter that actively deletes (part of) the referential ambiguity noise, retains intended referents, and effectively amplifies the signal. The model achieves this performance without incorporating any specific cognitive biases of the type proposed in the constraints and principles account, and without any prescribed developmental changes in the underlying learning mechanism. Instead we interpret the model performance as more of a by-product of exposure to input, where the associative strengths in the lexicon grow as a function of linguistic experience in combination with memory limitations. The result adds a mechanistic explanation for the experimental evidence on spaced learning and, more generally, advocates integrating domain-general aspects of cognition, such as memory, into the language acquisition process

Computational and Robotic Models of Early Language Development: A Review

International audienceWe review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments

The role of feedback and instruction on the cross-situational learning of vocabulary and morphosyntax:Mixed effects models reveal local and global effects on acquisition

First language acquisition is implicit, in that explicit information about the language structure to be learned is not provided to children. Instead, they must acquire both vocabulary and grammar incrementally, by generalizing across multiple situations that eventually enable links between words in utterances and referents in the environment to be established. However, this raises a problem of how vocabulary can be acquired without first knowing the role of the word within the syntax of a sentence. It also raises practical issues about the extent to which different instructional conditions – about grammar in advance of learning or feedback about correct decisions during learning – might influence second language acquisition of implicitly experienced information about the language. In an artificial language learning study, we studied participants learning language from inductive exposure, but under different instructional conditions. Language learners were exposed to complex utterances and complex scenes and had to determine the meaning and the grammar of the language from these co-occurrences with environmental scenes. We found that learning was boosted by explicit feedback, but not by explicit instruction about the grammar of the language, compared to an implicit learning condition. However, the effect of feedback was not general across all aspects of the language. Feedback improved vocabulary, but did not affect syntax learning. We further investigated the local, contextual effects on learning, and found that previous knowledge of vocabulary within an utterance improved learning but that this was driven only by certain grammatical categories in the language. The results have implications for theories of second language learning informed by our understanding of first language acquisition as well as practical implications for learning instruction and optimal, contingent adjustment of learners’ environment during their learning

HETEROGENEOUS DATA AND PROBABILISTIC SYSTEM MODEL ANALYSES FOR ENHANCED SITUATIONAL AWARENESS AND RESILIENCE OF CRITICAL INFRASTRUCTURE SYSTEMS

The protection and resilience of critical infrastructure systems (CIS) are essential for public safety in daily operations and times of crisis and for community preparedness to hazard events. Increasing situational awareness and resilience of CIS includes both comprehensive monitoring of CIS and their surroundings, as well as evaluating CIS behaviors in changing conditions and with different system configurations. Two frameworks for increasing the monitoring capabilities of CIS are presented. The proposed frameworks are (1) a process for classifying social media big data for monitoring CIS and hazard events and (2) a framework for integrating heterogeneous data sources, including social media, using Bayesian inference to update prior probabilities of event occurrence. Applications of both frameworks are presented, including building and evaluating text-based machine learning classifiers for identifying CIS damages and integrating disparate data sources to estimate hazards and CIS damages. Probabilistic analyses of CIS vulnerabilities with varying system parameters and topologies are also presented. In a water network, the impact of varying parameters on component performance is evaluated. In multiple, small-size water networks, the impacts of system topology are assessed to identify characteristics of more resilient networks. This body of work contributes insights and methods for monitoring CIS and assessing their performance. Integrating heterogeneous data sources increases situational awareness of CIS, especially during or after failure events, and evaluating the sensitivity of CIS outcomes to changes in the network facilitates decisions for CIS investments and emergency response.Ph.D

A computational model of the cultural co-evolution of language and mindreading

Contains fulltext : 226366.pdf (publisher's version ) (Open Access)39 p

Intentional Training With Speech Production Supports Children’s Learning the Meanings of Foreign Words : A Comparison of Four Learning Tasks

To determine the best techniques to teach children foreign words, we compared the effectiveness of four different learning tasks on their foreign-word learning (i.e., learning word forms and word meanings). The tasks included incidental learning, intentional learning with production, intentional learning without production, and crosssituational statistical learning. We also analyzed whether children’s age and cognitive skills correlate with the learning of word forms and word meanings. Forty-four 5–8-yearold children participated in the study. The results reveal that the children were able to learn the correct word forms from all four tasks and no differences emerged between the effectiveness of the tasks on the learning of word-forms. The children also learned the word meanings with all four tasks, yet the intentional task with production was more effectivethantheincidentaltask. Thissuggeststhattheabilityofchildrentolearnforeign words benefited from them knowing that they were supposed to learn new words and producingthemaloudwhiletraining.Theageofthechildrencorrelatedwiththeirlearning results for word forms and meanings on the intentional task without production. The older children learned more effectively than the younger children in this task. Children’s phonological processing skills were correlated with learning the word meanings from the incidental task, suggesting that children with better phonological skills were able to benefit from incidental learning more than children with poorer phonological skills. Altogether, the results suggest that children’s foreign-language learning benefits from intentional training with speech production regardless of their age or cognitive skills.Peer reviewe

Research and Applications of the Processes of Performance Appraisal: A Bibliography of Recent Literature, 1981-1989

[Excerpt] There have been several recent reviews of different subtopics within the general performance appraisal literature. The reader of these reviews will find, however, that the accompanying citations may be of limited utility for one or more reasons. For example, the reference sections of these reviews are usually composed of citations which support a specific theory or practical approach to the evaluation of human performance. Consequently, the citation lists for these reviews are, as they must be, highly selective and do not include works that may have only a peripheral relationship to a given reviewer\u27s target concerns. Another problem is that the citations are out of date. That is, review articles frequently contain many citations that are fifteen or more years old. The generation of new studies and knowledge in this field occurs very rapidly. This creates a need for additional reference information solely devoted to identifying the wealth of new research, ideas, and writing that is changing the field