Online research in philosophy

We propose that Bloom's focus on cognitive factors involved in word learning still lacks a broader perspective. We emphasize the crucial relevance of working memory in learning elements of language. Specifically, we demonstrate through our data that in impaired populations knowledge of some linguistic elements can be dissociated according to the subcomponent of working memory (visual or verbal) involved in a task. Further, although Bloom's concentration on theory of mind as a precondition for word learning is certainly correct, theory of mind being a necessary condition does not make it a sufficient one. On the basis of our studies we point out the importance of a theory of mind related goal preference in acquiring spatial language. In general, we claim that more specific cognitive preferences and constraints should be outlined in detail for the preconditions of acquiring linguistic elements.

Abstract Based on recalling two characteristic features of Bayesian statistical inference in commutative probability theory, a stability property of the inference is pointed out, and it is argued that that stability of the Bayesian statistical inference is an essential property which must be preserved under generalization of Bayesian inference to the non?commutative case. Mathematical no?go theorems are recalled then which show that, in general, the stability can not be preserved in non?commutative context. Two possible interpretations of the impossibility of generalization of Bayesian statistical inference to the non?commutative case are offered, none of which seems to be completely satisfying.

Based on recalling two characteristic features of Bayesian statistical inference in commutative probability theory, a stability property of the inference is pointed out, and it is argued that that stability of the Bayesian statistical inference is an essential property which must be preserved under generalization of Bayesian inference to the non-commutative case. Mathematical no-go theorems are recalled then which show that, in general, the stability can not be preserved in non-commutative context. Two possible interpretations of the impossibility of generalization of Bayesian statistical inference to the non-commutative case are offered, none of which seems to be completely satisfying.

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.

Bloom provides a detailed account of children's word learning and comprehension. Yet, this book falls short of explaining the developmental process of word learning. The studies reviewed do not explain how infants begin to map words onto objects or the environment's facilitative role. Researchers must describe how several factors interact and explain the relative importance of each during the development of word learning.

At 14 months, children appear to struggle to apply their fairly well-developed speech perception abilities to learning similar sounding words (e.g., bih/dih; Stager & Werker, 1997). However, variability in nonphonetic aspects of the training stimuli seems to aid word learning at this age. Extant theories of early word learning cannot account for this benefit of variability. We offer a simple explanation for this range of effects based on associative learning. Simulations suggest that if infants encode both noncontrastive information (e.g., cues to speaker voice) and meaningful linguistic cues (e.g., place of articulation or voicing), then associative learning mechanisms predict these variability effects in early word learning. Crucially, this means that despite the importance of task variables in predicting performance, this body of work shows that phonological categories are still developing at this age, and that the structure of noninformative cues has critical influences on word learning abilities.

Statistical Learning Theory (e.g., Hastie et al., 2001; Vapnik, 1998, 2000, 2006) is the basic theory behind contemporary machine learning and data-mining. We suggest that the theory provides an excellent framework for philosophical thinking about inductive inference.

Normal children learn tens of thousands of words, and do so quickly and efficiently, often in highly impoverished environments. In How Children Learn the Meanings of Words, I argue that word learning is the product of certain cognitive and linguistic abilities that include the ability to acquire concepts, an appreciation of syntactic cues to meaning, and a rich understanding of the mental states of other people. These capacities are powerful, early emerging, and to some extent uniquely human, but they are not special to word learning. This proposal is an alternative to the view that word learning is the result of simple associative learning mechanisms, and it rejects as well the notion that children possess constraints, either innate or learned, that are specifically earmarked for word learning. This theory is extended to account for how children learn names for objects, substances, and abstract entities, pronouns and proper names, verbs, determiners, prepositions, and number words. Several related topics are also discussed, including naïve essentialism, children's understanding of representational art, the nature of numerical and spatial reasoning, and the role of words in the shaping of mental life. Key Words: cognitive development; concepts; meaning; semantics; social cognition; syntax; theory of mind; word learning.

Bloom provides a masterful synthesis of recent advances in word-learning, placing them within the framework of abiding theoretical issues. I will augment and challenge his approach by underscoring the significance of word extension for questions concerning (a) the origin and evolution of infants' expectations, and (b) domain-specificity in word-learning.

Infant and adult learners are able to identify word boundaries in fluent speech using statistical information. Similarly, learners are able to use statistical information to identify word–object associations. Successful language learning requires both feats. In this series of experiments, we presented adults and infants with audio–visual input from which it was possible to identify both word boundaries and word–object relations. Adult learners were able to identify both kinds of statistical relations from the same input. Moreover, their learning was actually facilitated by the presence of two simultaneously present relations. Eight-month-old infants, however, do not appear to benefit from the presence of regular relations between words and objects. Adults, like 8-month-olds, did not benefit from regular audio–visual correspondences when they were tested with tones, rather than linguistic input. These differences in learning outcomes across age and input suggest that both developmental and stimulus-based constraints affect statistical learning.