A microscopic view of the yielding transition in concentrated emulsions

We use a custom shear cell coupled to an optical microscope to investigate at the particle level the yielding transition in concentrated emulsions subjected to an oscillatory shear deformation. By performing experiments lasting thousands of cycles on samples at several volume fractions and for a variety of applied strain amplitudes, we obtain a comprehensive, microscopic picture of the yielding transition. We find that irreversible particle motion sharply increases beyond a volume-fraction dependent critical strain, which is found to be in close agreement with the strain beyond which the stress-strain relation probed in rheology experiments significantly departs from linearity. The shear-induced dynamics are very heterogenous: quiescent particles coexist with two distinct populations of mobile and `supermobile' particles. Dynamic activity exhibits spatial and temporal correlations, with rearrangements events organized in bursts of motion affecting localized regions of the sample. Analogies with other sheared soft materials and with recent work on the transition to irreversibility in sheared complex fluids are briefly discussed.Comment: 11 pages, 10 figures. Submitted to Soft Matte

Modeling the optional infinite stage in MOSAIC: A generalization to Dutch

This paper presents a model of a stage in children’s language development known as the optional infinitive stage. The model was originally developed for English, where it was shown to provide a good account of several phenomena. The model, which uses a discrimination network, analyzes the distribution of words in the input, and derives word classes from them by linking words that are used in a similar context. While the earlier version of the model is sensitive only to characteristics of phrases that follow target words, the present version also takes preceding input into consideration. Also, the present version uses a probabilistic rather than a deterministic learning mechanism. Generalisation of the model to Dutch is considered a strong test of the model, since Dutch displays the optional infinitive phenomenon, while its syntax differs substantially from that of English. The model was presented with child-directed input from two Dutch mothers, and its output was compared to that of the respective children. Despite the fact that the model was developed for a different language, it captures the optional infinitive phenomenon in Dutch as it does in English, while showing sensitivity to Dutch syntax. These results suggest that a simple distributional analyzer can capture the regularities of different languages despite the apparent differences in their syntax

Meter based omission of function words in MOSAIC

MOSAIC (Model of Syntax Acquisition in Children) is augmented with a new mechanism that allows for the omission of unstressed function words based on the prosodic structure of the utterance in which they occur. The mechanism allows MOSAIC to omit elements from multiple locations in a target utterance, which it was previously unable to do. It is shown that, although the new mechanism results in Optional Infinitive errors when run on children’s input, it is insufficient to simulate the high rate OI errors in children’s speech unless combined with MOSAIC’s edge-first learning mechanism. It is also shown that the addition of the new mechanism does not adversely affect MOSAIC’s fit to the Optional Infinitive phenomenon. The mechanism does, however, make MOSAIC’s output more child-like, both in terms of the range of utterances it can simulate, and the level and type of determiner omission that the model displays

Simulating the temporal reference of Dutch and English Root Infinitives.

Hoekstra & Hyams (1998) claim that the overwhelming majority of Dutch children’s Root Infinitives (RIs) are used to refer to modal (not realised) events, whereas in English speaking children, the temporal reference of RIs is free. Hoekstra & Hyams attribute this difference to qualitative differences in how temporal reference is carried by the Dutch infinitive and the English bare form. Ingram & Thompson (1996) advocate an input-driven account of this difference and suggest that the modal reading of German (and Dutch) RIs is caused by the fact that infinitive forms are predominantly used in modal contexts. This paper investigates whether an input-driven account can explain the differential reading of RIs in Dutch and English. To this end, corpora of English and Dutch Child Directed Speech were fed through MOSAIC, a computational model that has already been used to simulate the basic Optional Infinitive phenomenon. Infinitive forms in the input were tagged for modal or non-modal reference based on the sentential context in which they appeared. The output of the model was compared to the results of corpus studies and recent experimental data which call into question the strict distinction between Dutch and English advocated by Hoekstra & Hyams

Simulating the Noun-Verb Asymmetry in the Productivity of Children’s Speech

Several authors propose that children may acquire syntactic categories on the basis of co-occurrence statistics of words in the input. This paper assesses the relative merits of two such accounts by assessing the type and amount of productive language that results from computing co-occurrence statistics over conjoint and independent preceding and following contexts. This is achieved through the implementation of these methods in MOSAIC, a computational model of syntax acquisition that produces utterances that can be directly compared to child speech, and has a developmental component (i.e. produces increasingly long utterances). It is shown that the computation of co-occurrence statistics over conjoint contexts or frames results in a pattern of productive speech that more closely resembles that displayed by language learning children. The simulation of the developmental patterning of children’s productive speech furthermore suggests two refinements to this basic mechanism: inclusion of utterance boundaries, and the weighting of frames for their lexical content

Subject omission in children's language; The case for performance limitations in learning.

Several theories have been put forward to explain the phenomenon that children who are learning to speak their native language tend to omit the subject of the sentence. According to the pro-drop hypothesis, children represent the wrong grammar. According to the performance limitations view, children represent the full grammar, but omit subjects due to performance limitations in production. This paper proposes a third explanation and presents a model which simulates the data relevant to subject omission. The model consists of a simple learning mechanism that carries out a distributional analysis of naturalistic input. It does not have any overt representation of grammatical categories, and its performance limitations reside mainly in its learning mechanism. The model clearly simulates the data at hand, without the need to assume large amounts of innate knowledge in the child, and can be considered more parsimonious on these grounds alone. Importantly, it employs a unified and objective measure of processing load, namely the length of the utterance, which interacts with frequency in the input. The standard performance limitations view assumes that processing load is dependent on a phrase’s syntactic role, but does not specify a unifying underlying principle

Unifying cross-linguistic and within-language patterns of finiteness marking in MOSAIC

MOSAIC, a model that has already simulated cross-linguistic differences in the occurrence of the Optional Infinitive phenomenon, is applied to the simulation of the pattern of finiteness marking within Dutch. This within-language pattern, which includes verb placement, low rates of Optional Infinitives in Wh-questions and the correlation between finiteness marking and subject provision, has been taken as evidence for the view that children have correctly set the clause structure and inflectional parameters for their language. MOSAIC, which employs no built-in linguistic knowledge, clearly simulates the pattern of results as a function of its utterance-final bias, the same mechanism that is responsible for its successful simulation of the crosslinguistic data. These results suggest that both the crosslinguistic and within–language pattern of finiteness marking can be understood in terms of the interaction between a simple resource-limited learning mechanism and the distributional statistics of the input to which it is exposed. Thus, these phenomena do not provide any evidence for abstract or innate knowledge on the part of the child

Modelling the Development of Children’s use of Optional Infinitives in Dutch and English using MOSAIC

In this study we use a computational model of language learning (MOSAIC) to investigate the extent to which the Optional Infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in two children learning Dutch and two children learning English as the average length of their utterances increases. These results suggest that it is possible to explain the key features of the OI phenomenon in both Dutch and English in terms of the interaction between an utterancefinal bias in learning and the distributional characteristics of child-directed speech in the two languages. They also show how computational modelling techniques can be used to investigate the extent to which cross-linguistic similarities in the developmental data can be explained in terms of common processing constraints as opposed to innate knowledge of Universal Grammar

Resolving ambiguities in the extraction of syntactic categories through chunking.

In recent years, several authors have investigated how co-occurrence statistics in natural language can act as a cue that children may use to extract syntactic categories for the language they are learning. While some authors have reported encouraging results, it is difficult to evaluate the quality of the syntactic categories derived. It is argued in this paper that traditional measures of accuracy are inherently flawed. A valid evaluation metric needs to consider the well-formedness of utterances generated through a production end. This paper attempts to evaluate the quality of the categories derived from cooccurrence statistics through the use of MOSAIC, a computational model of syntax acquisition that has already been used to simulate several phenomena in child language. It will be shown that derived syntactic categories which may appear to be of high quality will quickly give rise to errors which are not typical of child speech. A solution to this problem is suggested in the form of a chunking mechanism which serves to differentiate between alternative grammatical functions of identical word forms. Results are evaluated in terms of the error rates in utterances produced by the system as well as the quantitative fit to the phenomenon of subject omission

Modelling the development of Dutch Optional Infinitives in MOSAIC.

This paper describes a computational model which simulates the change in the use of optional infinitives that is evident in children learning Dutch as their first language. The model, developed within the framework of MOSAIC, takes naturalistic, child directed speech as its input, and analyses the distributional regularities present in the input. It slowly learns to generate longer utterances as it sees more input. We show that the developmental characteristics of Dutch children’s speech (with respect to optional infinitives) are a natural consequence of MOSAIC’s learning mechanisms and the gradual increase in the length of the utterances it produces. In contrast with Nativist approaches to syntax acquisition, the present model does not assume large amounts of innate knowledge in the child, and provides a quantitative process account of the development of optional infinitives