The differential role of phonological and distributional cues in grammatical categorisation

Recognising the grammatical categories of words is a necessary skill for the acquisition of syntax and for on-line sentence processing. The syntactic and semantic context of the word contribute as cues for grammatical category assignment, but phonological cues, too, have been implicated as important sources of information. The value of phonological and distributional cues has not, with very few exceptions, been empirically assessed. This paper presents a series of analyses of phonological cues and distributional cues and their potential for distinguishing grammatical categories of words in corpus analyses. The corpus analyses indicated that phonological cues were more reliable for less frequent words, whereas distributional information was most valuable for high frequency words. We tested this prediction in an artificial language learning experiment, where the distributional and phonological cues of categories of nonsense words were varied. The results corroborated the corpus analyses. For high-frequency nonwords, distributional information was more useful, whereas for low-frequency words there was more reliance on phonological cues. The results indicate that phonological and distributional cues contribute differentially towards grammatical categorisation.     

Limited information estimation and testing of discretized multivariate normal structural models

Discretized multivariate normal structural models are often estimated using multistage estimation procedures. The asymptotic properties of parameter estimates, standard errors, and tests of structural restrictions on thresholds and polychoric correlations are well known. It was not clear how to assess the overall discrepancy between the contingency table and the model for these estimators. It is shown that the overall discrepancy can be decomposed into a distributional discrepancy and a structural discrepancy. A test of the overall model specification is proposed, as well as a test of the distributional specification (i.e., discretized multivariate normality). Also, the small sample performance of overall, distributional, and structural tests, as well as of parameter estimates and standard errors is investigated under conditions of correct model specification and also under mild structural and/or distributional misspecification. It is found that relatively small samples are needed for parameter estimates, standard errors, and structural tests. Larger samples are needed for the distributional and overall tests. Furthermore, parameter estimates, standard errors, and structural tests are surprisingly robust to distributional misspecification. This research was supported by the Department of Universities, Research and Information Society (DURSI) of the Catalan Government, and by grants BSO2000-0661 and BSO2003-08507 of the Spanish Ministry of Science and Technology.     

Analyzing distributional properties of interference effects across modalities: chances and challenges

In research investigating Stroop or Simon effects, data are typically analyzed at the level of mean response time (RT), with results showing faster responses for compatible than for incompatible trials. However, this analysis provides only limited information as it glosses over the shape of the RT distributions and how they may differ across tasks and experimental conditions. These limitations have encouraged the analysis of RT distributions using delta plots. In the present review, we aim to bring together research on distributional properties of auditory and visual interference effects. Extending previous reviews on distributional properties of the Simon effect, we additionally review studies reporting distributional analyses of Stroop effects. We show that distributional analyses of sequential effects (i.e., taking into account congruency of the previous trial) capture important similarities and differences of interference effects across tasks (Simon, Stroop) as well as across sensory modalities, despite some challenges associated to this approach.     

Infant-directed speech supports phonetic category learning in English and Japanese

Across the first year of life, infants show decreased sensitivity to phonetic differences not used in the native language [Werker, J. F., & Tees, R. C. (1984). Cross-language speech perception: evidence for perceptual reorganization during the first year of life. Infant Behaviour and Development, 7, 49-63]. In an artificial language learning manipulation, Maye, Werker, and Gerken [Maye, J., Werker, J. F., & Gerken, L. (2002). Infant sensitivity to distributional information can affect phonetic discrimination. Cognition, 82(3), B101-B111] found that infants change their speech sound categories as a function of the distributional properties of the input. For such a distributional learning mechanism to be functional, however, it is essential that the input speech contain distributional cues to support such perceptual learning. To test this, we recorded Japanese and English mothers teaching words to their infants. Acoustic analyses revealed language-specific differences in the distributions of the cues used by mothers (or cues present in the input) to distinguish the vowels. The robust availability of these cues in maternal speech adds support to the hypothesis that distributional learning is an important mechanism whereby infants establish native language phonetic categories.     

Neoliberalism versus distributional autonomy: the skipped step in rawls’s the law of peoples

Debates about global distributive justice focus on the gulf between the wealthy North and the impoverished South, rather than on issues arising between liberal democracies. A review of John Rawls’s approach to international justice discloses a step Rawls skipped in his extension of his original-position procedure. The skipped step is where a need for the distributional autonomy of sovereign liberal states reveals itself. Neoliberalism denies the possibility and the desirability of distributional autonomy. A complete Rawlsian account of global justice shows the necessity and possibility of a charter between liberal states, assuring each a proper minimum degree of distributional autonomy     

Interactions between statistical and semantic information in infant language development

Infants can use statistical regularities to form rudimentary word categories (e.g. noun, verb), and to learn the meanings common to words from those categories. Using an artificial language methodology, we probed the mechanisms by which two types of statistical cues (distributional and phonological regularities) affect word learning. Because linking distributional cues vs. phonological information to semantics make different computational demands on learners, we also tested whether their use is related to language proficiency. We found that 22-month-old infants with smaller vocabularies generalized using phonological cues; however, infants with larger vocabularies showed the opposite pattern of results, generalizing based on distributional cues. These findings suggest that both phonological and distributional cues marking word categories promote early word learning. Moreover, while correlations between these cues are important to forming word categories, we found infants' weighting of these cues in subsequent word-learning tasks changes over the course of early language development.     

The effects of distributional learning on rats' sensitivity to phonetic information

During the first year of life, infants show decreased sensitivity to phonetic differences not used in their native language and increased sensitivity to the differences that are used. It has been shown that this change in speech perception is a function of the distributional properties of the input. The present study explores whether the mechanism responsible for the developmental changes regarding the organization of phonetic categories is a general mechanism shared with other animals. The results demonstrate that the distributional exposure to a phonetic continuum affects the subsequent discrimination of these phonemes in rats, indicating that the ability to use distributional cues to change the phonetic category structure extends beyond humans.     

Distributional analyses in the picture–word interference paradigm: Exploring the semantic interference and the distractor frequency effects

The present study explores the distributional features of two important effects within the picture–word interference paradigm: the semantic interference and the distractor frequency effects. These two effects display different and specific distributional profiles. Semantic interference appears greatly reduced in faster response times, while it reaches its full magnitude only in slower responses. This can be interpreted as a sign of fluctuant attentional efficiency in resolving response conflict. In contrast, the distractor frequency effect is mediated mainly by a distributional shift, with low-frequency distractors uniformly shifting reaction time distribution towards a slower range of latencies. This finding fits with the idea that distractor frequency exerts its effect by modulating the point in time in which operations required to discard the distractor can start. Taken together, these results are congruent with current theoretical accounts of both the semantic interference and distractor frequency effects. Critically, distributional analyses highlight and further describe the different cognitive dynamics underlying these two effects, suggesting that this analytical tool is able to offer important insights about lexical access during speech production.     

Distributional analyses in auditory lexical decision: Neighborhood density and word-frequency effects

In the present article, the effects of phonological neighborhood density and word frequency in spoken word recognition were examined using distributional analyses of response latencies in auditory lexical decision. A density × frequency interaction was observed in mean latencies; frequency effects were larger for low-density words than for high-density words. Distributional analyses further revealed that for low-density words, frequency effects were reflected in both distributional shifting and skewing, whereas for high-density words, frequency effects were purely mediated by distributional skewing. The results suggest that word frequency plays a role in early auditory word recognition only when there is relatively little competition between similar-sounding words, and that frequency effects in high-density words reflect postlexical checking.     

The influence of emotion on lexical processing: Insights from RT distributional analysis

In two lexical decision experiments, the present study was designed to examine emotional valence effects on visual lexical decision (standard and go/no-go) performance, using traditional analyses of means and distributional analyses of response times. Consistent with an earlier study by Kousta, Vinson, and Vigliocco (Cognition 112:473–481, 2009), we found that emotional words (both negative and positive) were responded to faster than neutral words. Finer-grained distributional analyses further revealed that the facilitation afforded by valence was reflected by a combination of distributional shifting and an increase in the slow tail of the distribution. This suggests that emotional valence effects in lexical decision are unlikely to be entirely mediated by early, preconscious processes, which are associated with pure distributional shifting. Instead, our results suggest a dissociation between early preconscious processes and a later, more task-specific effect that is driven by feedback from semantically rich representations.     

The phonological-distributional coherence hypothesis: cross-linguistic evidence in language acquisition

Several phonological and prosodic properties of words have been shown to relate to differences between grammatical categories. Distributional information about grammatical categories is also a rich source in the child's language environment. In this paper we hypothesise that such cues operate in tandem for developing the child's knowledge about grammatical categories. We term this the Phonological-Distributional Coherence Hypothesis (PDCH). We tested the PDCH by analysing phonological and distributional information in distinguishing open from closed class words and nouns from verbs in four languages: English, Dutch, French, and Japanese. We found an interaction between phonological and distributional cues for all four languages indicating that when distributional cues were less reliable, phonological cues were stronger. This provides converging evidence that language is structured such that language learning benefits from the integration of information about category from contextual and sound-based sources, and that the child's language environment is less impoverished than we might suspect.     

Distributional learning has immediate and long-lasting effects

Evidence of distributional learning, a statistical learning mechanism centered on relative frequency of exposure to different tokens, has mainly come from short-term learning and therefore does not ostensibly address the development of important learning processes. The present longitudinal study examines both short- and long-term effects of distributional learning of phonetic categories on non-native sound discrimination over a 12-month period. Two groups of listeners were exposed to a two-minute distribution of auditory stimuli in which the most frequently presented tokens either approximated or exaggerated the natural production of the speech sounds, whereas a control group listened to a piece of classical music for the same length of time. Discrimination by listeners in the two distribution groups improved immediately after the short exposure, replicating previous results. Crucially, this improvement was maintained after six and 12 months, demonstrating that distributional learning has long-lasting effects.     

iMinerva: A Mathematical Model of Distributional Statistical Learning

Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non‐adjacent relations. On the surface, these different aspects share few commonalities. Despite this, we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain‐general characteristics of memory.     

公正的启发理论述评

组织公正感包括分配公正、程序公正、互动公正等重要的组成要素。尽管这些公正感的构成要素有不同的内涵和影响因素,然而在许多情况下,这些要素之间有较高的一致性。与过去注重这些要素的差异性相反,Lind看到了这些公正感要素之间的一致性,提出了试图整合这些一致性的公正理论：公正的启发理论。该文比较全面地介绍了公正的启发理论的含义,如何启发等核心概念,并对启发理论存在的问题进行了分析,指出了启发理论研究的方向。     

Spicy Adjectives and Nominal Donkeys: Capturing Semantic Deviance Using Compositionality in Distributional Spaces

Sophisticated senator and legislative onion. Whether or not you have ever heard of these things, we all have some intuition that one of them makes much less sense than the other. In this paper, we introduce a large dataset of human judgments about novel adjective‐noun phrases. We use these data to test an approach to semantic deviance based on phrase representations derived with compositional distributional semantic methods, that is, methods that derive word meanings from contextual information, and approximate phrase meanings by combining word meanings. We present several simple measures extracted from distributional representations of words and phrases, and we show that they have a significant impact on predicting the acceptability of novel adjective‐noun phrases even when a number of alternative measures classically employed in studies of compound processing and bigram plausibility are taken into account. Our results show that the extent to which an attributive adjective alters the distributional representation of the noun is the most significant factor in modeling the distinction between acceptable and deviant phrases. Our study extends current applications of compositional distributional semantic methods to linguistically and cognitively interesting problems, and it offers a new, quantitatively precise approach to the challenge of predicting when humans will find novel linguistic expressions acceptable and when they will not.     

Probing Lexical Ambiguity: Word Vectors Encode Number and Relatedness of Senses

Lexical ambiguity—the phenomenon of a single word having multiple, distinguishable senses—is pervasive in language. Both the degree of ambiguity of a word (roughly, its number of senses) and the relatedness of those senses have been found to have widespread effects on language acquisition and processing. Recently, distributional approaches to semantics, in which a word's meaning is determined by its contexts, have led to successful research quantifying the degree of ambiguity, but these measures have not distinguished between the ambiguity of words with multiple related senses versus multiple unrelated meanings. In this work, we present the first assessment of whether distributional meaning representations can capture the ambiguity structure of a word, including both the number and relatedness of senses. On a very large sample of English words, we find that some, but not all, distributional semantic representations that we test exhibit detectable differences between sets of monosemes (unambiguous words; N = 964), polysemes (with multiple related senses; N = 4,096), and homonyms (with multiple unrelated senses; N = 355). Our findings begin to answer open questions from earlier work regarding whether distributional semantic representations of words, which successfully capture various semantic relationships, also reflect fine-grained aspects of meaning structure that influence human behavior. Our findings emphasize the importance of measuring whether proposed lexical representations capture such distinctions: In addition to standard benchmarks that test the similarity structure of distributional semantic models, we need to also consider whether they have cognitively plausible ambiguity structure.     

The distributional structure of grammatical categories in speech to young children

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 showthat, in accord with other findings, a distributional analysis which categorizeswords based on their co‐occurrence patterns with surroundingwords 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 howyoung 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.     

What plausibly affects plausibility? Concept coherence and distributional word coherence as factors influencing plausibility judgments

Our goal was to investigate the basis of human plausibility judgements. Previous research had suggested that plausibility is affected by two factors: concept coherence (the inferences made between parts of a discourse) and word coherence (the distributional properties of the words used). In two experiments, participants were asked to rate the plausibility of sentence pairs describing events. In the first, we manipulated concept coherence by using different inference types to link the sentences in a pair (e.g., causal or temporal). In the second, we manipulated word coherence by using latent semantic analysis, so two sentence pairs describing the same event had different distributional properties. The results showed that inference type affects plausibility; sentence pairs linked by causal inferences were rated highest, followed by attributal, temporal, and unrelated inferences. The distributional manipulations had no reliable effect on plausibility ratings. We conclude that the processes involved in rating plausibility are based on evaluating concept coherence, not word coherence.     

Rapid acquisition of phonological alternations by infants

We explore whether infants can learn novel phonological alternations on the basis of distributional information. In Experiment 1, two groups of 12-month-old infants were familiarized with artificial languages whose distributional properties exhibited either stop or fricative voicing alternations. At test, infants in the two exposure groups had different preferences for novel sequences involving voiced and voiceless stops and fricatives, suggesting that each group had internalized a different familiarization alternation. In Experiment 2, 8.5-month-olds exhibited the same patterns of preference. In Experiments 3 and 4, we investigated whether infants' preferences were driven solely by preferences for sequences of high transitional probability. Although 8.5-month-olds in Experiment 3 were sensitive to the relative probabilities of sequences in the familiarization stimuli, only 12-month-olds in Experiment 4 showed evidence of having grouped alternating segments into a single functional category. Taken together, these results suggest a developmental trajectory for the acquisition of phonological alternations using distributional cues in the input.     

The phonological-distributional coherence hypothesis: Cross-linguistic evidence in language acquisition

Several phonological and prosodic properties of words have been shown to relate to differences between grammatical categories. Distributional information about grammatical categories is also a rich source in the child’s language environment. In this paper we hypothesise that such cues operate in tandem for developing the child’s knowledge about grammatical categories. We term this the Phonological-Distributional Coherence Hypothesis (PDCH). We tested the PDCH by analysing phonological and distributional information in distinguishing open from closed class words and nouns from verbs in four languages: English, Dutch, French, and Japanese. We found an interaction between phonological and distributional cues for all four languages indicating that when distributional cues were less reliable, phonological cues were stronger. This provides converging evidence that language is structured such that language learning benefits from the integration of information about category from contextual and sound-based sources, and that the child’s language environment is less impoverished than we might suspect.