Modelling the acquisition of syntactic categories

This research represents an attempt to model the child’s acquisition of syntactic categories. A computational model, based on the EPAM theory of perception and learning, is developed. The basic assumptions are that (1) syntactic categories are actively constructed by the child using distributional learning abilities; and (2) cognitive constraints in learning rate and memory capacity limit these learning abilities. We present simulations of the syntax acquisition of a single subject, where the model learns to build up multi-word utterances by scanning a sample of the speech addressed to the subject by his mother

Inflexibility of experts – Reality or myth? Quantifying the Einstellung effect in chess masters

How does the knowledge of experts affect their behaviour in situations that require unusual methods of dealing? One possibility, loosely originating in research on creativity and skill acquisition, is that an increase in expertise can lead to inflexibility of thought due to automation of procedures. Yet another possibility, based on expertise research, is that experts’ knowledge leads to flexibility of thought. We tested these two possibilities in a series of experiments using the Einstellung (set) effect paradigm. Chess players tried to solve problems that had both a familiar but non-optimal solution and a better but less familiar one. The more familiar solution induced the Einstellung (set) effect even in experts, preventing them from finding the optimal solution. The presence of the non-optimal solution reduced experts' problem solving ability was reduced to about that of players three standard deviations lower in skill level by the presence of the non-optimal solution. Inflexibility of thought induced by prior knowledge (i.e., the blocking effect of the familiar solution) was shown by experts but the more expert they were, the less prone they were to the effect. Inflexibility of experts is both reality and myth. But the greater the level of expertise, the more of a myth it becomes

Why good thoughts block better ones: The mechanism of the pernicious Einstellung (set) effect.

The Einstellung (set) effect occurs when the first idea that comes to mind, triggered by familiar features of a problem, prevents a better solution being found. It has been shown to affect both people facing novel problems and experts within their field of expertise. We show that it works by influencing mechanisms that determine what information is attended to. Having found one solution, expert chess players reported that they were looking for a better one. But their eye movements showed that they continued to look at features of the problem related to the solution they had already thought of. The mechanism which allows the first schema activated by familiar aspects of a problem to control the subsequent direction of attention may contribute to a wide range of biases both in everyday and expert thought - from confirmation bias in hypothesis testing to the tendency of scientists to ignore results that do not fit their favoured theories

Specialization effect and its influence on memory and problem solving in expert chess players

Expert chess players, specialized in different openings, recalled positions and solved problems within and outside their area of specialization. While their general expertise was at a similar level players performed better with stimuli from their area of specialization. The effect of specialization on both recall and problem solving was strong enough to override general expertise – players remembering positions and solving problems from their area of specialization performed at around the level of players one standard deviation above them in general skill. Their problem solving strategy also changed depending on whether the problem was within their area of specialization or not. When it was, they searched more in depth and less in breadth; with problems outside their area of specialization, the reverse. The knowledge that comes from familiarity with a problem area is more important than general purpose strategies in determining how an expert will tackle it. These results demonstrate the link in experts between problem solving and memory of specific experiences and indicate that the search for context independent general purpose problem solving strategies to teach to future experts is unlikely to be successful

Stochastic methods for solving high-dimensional partial differential equations

We propose algorithms for solving high-dimensional Partial Differential Equations (PDEs) that combine a probabilistic interpretation of PDEs, through Feynman-Kac representation, with sparse interpolation. Monte-Carlo methods and time-integration schemes are used to estimate pointwise evaluations of the solution of a PDE. We use a sequential control variates algorithm, where control variates are constructed based on successive approximations of the solution of the PDE. Two different algorithms are proposed, combining in different ways the sequential control variates algorithm and adaptive sparse interpolation. Numerical examples will illustrate the behavior of these algorithms

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

Modelling children's negation errors using probabilistic learning in MOSAIC.

Cognitive models of language development have often been used to simulate the pattern of errors in children’s speech. One relatively infrequent error in English involves placing inflection to the right of a negative, rather than to the left. The pattern of negation errors in English is explained by Harris & Wexler (1996) in terms of very early knowledge of inflection on the part of the child. We present data from three children which demonstrates that although negation errors are rare, error types predicted not to occur by Harris & Wexler do occur, as well as error types that are predicted to occur. Data from MOSAIC, a model of language acquisition, is also presented. MOSAIC is able to simulate the pattern of negation errors in children’s speech. The phenomenon is modelled more accurately when a probabilistic learning algorithm is used

Modeling children’s case marking errors with MOSAIC

We present a computational model of early grammatical development which simulates case-marking errors in children’s early multi-word speech as a function of the interaction between a performance-limited distributional analyser and the statistical properties of the input. The model is presented with a corpus of maternal speech from which it constructs a network consisting of nodes which represent words or sequences of words present in the input. It is sensitive to the distributional properties of items occurring in the input and is able to create ‘generative’ links between words which occur frequently in similar contexts, building pseudo-categories. The only information received by the model is that present in the input corpus. After training, the model is able to produce child-like utterances, including case-marking errors, of which a proportion are rote-learned, but the majority are not present in the maternal corpus. The latter are generated by traversing the generative links formed between items in the network

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