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Elicited imitation is a widely used method for testing a child&#39;s knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing.

CogSci 2022

Modelling Children&#39;s Sentence Recall using an Encoder-Decoder Network

## Welcome to EMNLP 2024! 
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Elicited imitation is a widely used method for testing a child's knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing.

Modelling Children's Sentence Recall using an Encoder-Decoder Network

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Query-Based Memory (QBM) models are heavily used in machine learning, though their relevance to human cognition is unclear. In this paper, we explore QBM models through both formal exploration and a simulation study to address this question. We found that QBM models are theoretically motivated, as they approximate rational induction with neurally-plausible mechanisms. Additionally, a simple implementation of the model could readily reproduce four benchmark findings in infant statistical learning. These results provide an encouraging starting point for further research using these formal tools to understand cognition across development.

Query-Based Memory Approximates Rational Induction: Applications to Infant Statistical Learning

Zipf’s law asserts that words form a power law distribution: word frequency is inversely proportional to rank. Relatively recent cognitive and usage-based linguistics argue that speech differs structurally from writing. Except for a few older analyses performed on tiny corpora, studies of Zipf’s law prior to 2021 have been done on written corpora and use informal methods to determine Zipfianness. 
We argue that recent work indicating that transcribed speech forms a Zipfian distribution can be extended to the speech of traditionally developing children. Further, we show that the transcribed speech of children with a clinical diagnosis of autism spectrum disorder is non-Zipfian. These judgements are made using formal statistical techniques developed in Clauset (2009). They include the Kolmogorov-Smirnov statistic for goodness-of-fit and likelihood ratio to rule out other distributions.

A First Look at Zipf's Law and the Speech of Children with Autism Spectrum Disorder

Individuals with Williams Syndrome (WS) display an unusual cognitive profile with severe deficits in spatial skills along with fluent and arguably complex language. Our experiment focused on the comprehension of scalar expressions, such as `some', `two', and `or' as a window to study their semantic and pragmatic competence. We compared performance of individuals with WS (mean age = 16,4 (year, month), age range = 11,10-21,11) to children matched by Mental Age (MA, (mean age = 6,1, age range = 5,2-7,8) and typical adults. No differences between the WS and MA groups were found in their knowledge of truth conditions of scalar terms. We further tested whether participants accept the statements with scalar terms in contexts featuring their logical (semantic) readings. Individuals with WS accepted logical readings more often than children matched by MA, suggesting that individuals with WS have access to the abstract meaning of scalar expressions.

Understanding of Linguistic Scales in Speakers with Williams Syndrome

Semantic representations of words can vary from person to person and can undergo change over the lifespan. Although a number of studies have explored differences in affective variables (e.g. valence), there has not been any large-scale investigation of the way socio-semantic information is represented, nor whether there is variation (or stability) in these representations across a diverse participant sample. We report on a study where 2,700 Czech words were rated by 1,450 Czech speakers along the dimensions of gender (feminine/masculine), location (rural/urban), political alignment (conservative/liberal), valence (negative/positive) and age (old/young). The ratings were analysed based on gender and age demographics. Our results demonstrate that distinct categories of words, e.g. those associated with mental wellbeing (such as úzkost [anxiety]) are self-associated – young participants rate the words as more young, older participants as more old. Our results shed light on the variation that exists in socio-semantic representations within different populations.

Exploring age and gender differences in socio-semantic representations of words

Polarity-sensitivity is a typologically general linguistic phenomenon. We focus on negative polarity items (NPIs, e.g. English 'any') -- expressions that are licensed only in negative contexts. The relevant notion of 'negative context' could be defined lexically, syntactically or semantically. There is psycholinguistic evidence in favour of semantics as a driving factor for some NPIs in a couple of languages (Chemla, Homer, & Rothschild, 2011; Denić, Homer, Rothschild, & Chemla, 2021). Testing the scale of this analysis as a potential cross-linguistic universal experimentally is extremely hard. We turn to recent multilingual pre-trained language models -- multilingual BERT (Devlin, Chang, Lee, & Toutanova, 2018) and XLM-RoBERTa (Conneau et al., 2019) -- and evaluate the models' recognition of polarity-sensitivity and its cross-lingual generality. Further, using the artificial language learning paradigm, we look for the connection in neural language models between semantic profiles of expressions and their ability to license NPIs. We find evidence for such connection for negation but not for other items we study.

The driving forces of polarity-sensitivity: Experiments with multilingual pre-trained neural language models

This paper proposes new empirical classifiers for evaluative concepts, including thin concepts like 'good' or 'bad' and thick concepts such as 'honest' or 'disgusting', based on quantitative corpus linguistics. Prior work in experimental philosophy has shown that sentiment analysis can be used to track differences between concept classes. Building on this, Task 1 investigates whether the relationship between sentiment and evaluativeness is parabolic rather than linear. Task 2 extends this question to the differences between evaluative and non-evaluative concept classes. The results of both Tasks show that the linear and the parabolic logistic regression classifiers perform equally well. Interestingly, this study also finds that adjectives attributed to animate entities (e.g. "generous customer") generally have a higher probability to be evaluative concepts than those attributed to inanimate entities (e.g."dry soil").

Why are reckless socks not (more of) a thing? Towards an empirical classification of evaluative concepts.

Models of word meaning, like the Topics model (Griffiths et al., 2007) and word2vec (Mikolov et al., 2013), condense word-by-context co-occurrence statistics to induce representations that organize words along semantically relevant dimensions (e.g., synonymy, antonymy, hyponymy etc.). However, their reliance on latent representations leaves them vulnerable to interference and makes them slow learners. We show how it is possible to construct the meaning of words online during retrieval to avoid these limitations. 
We implement our spreading activation account of word meaning in an associative net, a one-layer highly recurrent network of associations, called a Dynamic-Eigen-Net, that we developed to address the limitations of earlier variants of associative nets when scaling up to deal with unstructured input domains such as natural language text. After fixing the corpus across models, we show that spreading activation using a Dynamic-Eigen-Net outperforms the Topics model and word2vec in several cases when predicting human free associations and word similarity ratings. We argue in favour of the Dynamic-Eigen-Net as a fast learner that is not subject to catastrophic interference, and present it as an example of delegating the induction of latent relationships to process assumptions instead of assumptions about representation.

Constructing Word Meaning without Latent Representations using Spreading Activation

Skilled readers are typically most accurate at identifying words when fixating them slightly left of the central character, the so-called optimal viewing position. There are two main explanations for this effect, which are not mutually exclusive. The first claims that the optimal viewing position lies left-of-center due to the particular constraints of the human perceptual system. The second explains the effect in terms of the beginnings of words generally being more informative about word identity. The complexities of natural languages make it difficult to tease apart the relative contribution of each explanation. We explore this issue through the lens of a Bayesian cognitive model and two experiments using artificial lexicons in which we can carefully control how information is distributed across wordforms. Our results replicate previous findings and further suggest that readers may use implicit knowledge about information distribution to minimize uncertainty when targeting words.

Minimizing Expected Uncertainty in Visual Word Recognition: Are Readers Sensitive to the Distribution of Information across Word Forms?

Is the visual word recognition system sensitive to compound (e.g., SNOWBALL) and pseudocompound (e.g., CARPET) constituents? To answer this question, we employed red-blue anaglyphs glasses to parse red-blue colored (pseudo)compounds at the fovea—by hypothesis split along the vertical meridian—and allowing for the independent processing of word segments in the visual word form area. Seventy-one participants performed a visual masked lexical decision task with 133 millisecond stimuli presentations. Compounds and pseudocompounds were either split at the (pseudo)morpheme boundary (legally-split) or within a (pseudo)morpheme (illegally-split). We found a statistically significant interaction effect between word type and legality. Planned comparisons revealed an advantage in accuracy for legally-split compounds and pseudocompounds over their respective illegal splits. Furthermore, legally-split compounds elicited more accurate responses than legally-split pseudocompounds. In line with previous findings supporting a purely morpho-orthographic prelexical process with faster presentation times, these findings suggest a temporally distinct mechanism during visual word recognition.

Parsing Compounds and Pseudocompounds at the Fovea

Learning to read is a critical skill; yet only a small portion of children in the United States are reading at or above grade level. Attention is one crucial process that affects the
acquisition of reading skills. The process involves selectively choosing task relevant information and requires monitoring competing demands. Many books for beginning readers include illustrations, but this design choice may require learners to split their attention between multiple sources of information. This study employed eye tracking to examine whether embedding text within illustrations in children’s e-books inadvertently induces attentional competition. The results showed that spatially separating illustrations from the text in beginning reader books reduces attentional competition and improves children’s reading comprehension. This study shows that changes to the design of books for beginning readers can help promote literacy development in children.

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Query-Based Memory Approximates Rational Induction: Applications to Infant Statistical Learning

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