The effects of evolutionary distance on TWINSCAN, an algorithm for pair-wise comparative gene prediction.
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Biomedical subjects
M R Brent
Publications and source records attributed to M R Brent.
The role of exposure to isolated words in early vocabulary development.
Fluent speech contains no known acoustic analog of the blank spaces between printed words. Early research presumed that word learning is driven primarily by exposure to isolated words. In the last decade there has been a shift to the view that exposure to isolated words is unreliable and plays little if any role in early word learning. This study revisits the role of isolated words. The results show (a) that isolated words are a reliable feature of speech to infants, (b) that they include a variety of word types, many of which are repeated in close temporal proximity, (c) that a substantial fraction of the words infants produce are words that mothers speak in isolation, and (d) that the frequency with which a child hears a word in isolation predicts whether that word will be learned better than the child's total frequency of exposure to that word. Thus, exposure to isolated words may significantly facilitate vocabulary development at its earliest stages.
Adult↗
Integrating genomic homology into gene structure prediction.
TWINSCAN is a new gene-structure prediction system that directly extends the probability model of GENSCAN, allowing it to exploit homology between two related genomes. Separate probability models are used for conservation in exons, introns, splice sites, and UTRs, reflecting the differences among their patterns of evolutionary conservation. TWINSCAN is specifically designed for the analysis of high-throughput genomic sequences containing an unknown number of genes. In experiments on high-throughput mouse sequences, using homologous sequences from the human genome, TWINSCAN shows notable improvement over GENSCAN in exon sensitivity and specificity and dramatic improvement in exact gene sensitivity and specificity. This improvement can be attributed entirely to modeling the patterns of evolutionary conservation in genomic sequence.
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On the discovery of novel wordlike units from utterances: an artificial-language study with implications for native-language acquisition.
In 4 experiments, adults were familiarized with utterances from an artificial language. Short utterances occurred both in isolation and as part of a longer utterance, either at the edge or in the middle of the longer utterance. After familiarization, participants' recognition memory for fragments of the long utterance was tested. Recognition was greatest for the remainder of the longer utterance after extraction of the short utterance, but only when the short utterance was located at the edge of the long utterance. These results support the incremental distributional regularity optimization (INCDROP) model of speech segmentation and word discovery, which asserts that people segment utterances into familiar and new wordlike units in such a way as to minimize the burden of processing new units. INCDROP suggests that segmentation and word discovery during native-language acquisition may be driven by recognition of familiar units from the start, with no need for transient bootstrapping mechanisms.
Adult↗
Syntactic categorization in early language acquisition: formalizing the role of distributional analysis.
We propose an explicit, incremental strategy by which children could group words with similar syntactic privileges into discrete, unlabeled categories. This strategy, which can discover lexical ambiguity, is based in part on a generalization of the idea of sentential minimal pairs. As a result, it makes minimal assumptions about the availability of syntactic knowledge at the onset of categorization. Although the proposed strategy is distributional, it can make use of categorization cues from other domains, including semantics and phonology. Computer simulations show that this strategy is effective at categorizing words in both artificial-language samples and transcripts of naturally-occurring, child-directed speech. Further, the simulations show that the proposed strategy performs even better when supplied with semantic information about concrete nouns. Implications for theories of categorization are discussed.
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Distributional regularity and phonotactic constraints are useful for segmentation.
In order to acquire a lexicon, young children must segment speech into words, even though most words are unfamiliar to them. This is a non-trivial task because speech lacks any acoustic analog of the blank spaces between printed words. Two sources of information that might be useful for this task are distributional regularity and phonotactic constraints. Informally, distributional regularity refers to the intuition that sound sequences that occur frequently and in a variety of contexts are better candidates for the lexicon than those that occur rarely or in few contexts. We express that intuition formally by a class of functions called DR functions. We then put forth three hypotheses: First, that children segment using DR functions. Second, that they exploit phonotactic constraints on the possible pronunciations of words in their language. Specifically, they exploit both the requirement that every word must have a vowel and the constraints that languages impose on word-initial and word-final consonant clusters. Third, that children learn which word-boundary clusters are permitted in their language by assuming that all permissible word-boundary clusters will eventually occur at utterance boundaries. Using computational simulation, we investigate the effectiveness of these strategies for segmenting broad phonetic transcripts of child-directed English. The results show that DR functions and phonotactic constraints can be used to significantly improve segmentation. Further, the contributions of DR functions and phonotactic constraints are largely independent, so using both yields better segmentation than using either one alone. Finally, learning the permissible word-boundary clusters from utterance boundaries does not degrade segmentation performance.
Adult↗
Advances in the computational study of language acquisition.
This paper provides a tutorial introduction to computational studies of how children learn their native languages. Its aim is to make recent advances accessible to the broader research community, and to place them in the context of current theoretical issues. The first section locates computational studies and behavioral studies within a common theoretical framework. The next two sections review two papers that appear in this volume: one on learning the meanings of words and one or learning the sounds of words. The following section highlights an idea which emerges independently in these two papers and which I have dubbed autonomous bootstrapping. Classical bootstrapping hypotheses propose that children begin to get a toc-hold in a particular linguistic domain, such as syntax, by exploiting information from another domain, such as semantics. Autonomous bootstrapping complements the cross-domain acquisition strategies of classical bootstrapping with strategies that apply within a single domain. Autonomous bootstrapping strategies work by representing partial and/or uncertain linguistic knowledge and using it to analyze the input. The next two sections review two more more contributions to this special issue: one on learning word meanings via selectional preferences and one on algorithms for setting grammatical parameters. The final section suggests directions for future research.
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