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A challenge in learning phonological grammars is learning how phonological processes interact. It has been argued that some process interactions are easier to learn than others. One basis for this argument is asymmetries observed in experimental settings: artificial languages generated from certain process interactions are more likely to be successfully reproduced by participants than others. In this paper, we argue that asymmetries in production do not necessarily provide direct support that some phonological interactions are easier to learn. Rather, we show that these asymmetries can instead emerge due to differences in the number of consistent or nearly-consistent grammars each pattern has. We present a noisy channel model of morpho-phonological learning and apply it to a recent behavioral study examining the learnability of phonological process interactions. We find that, due to the relative difference in the number of grammars that can exactly match or nearly match the observed data, the model achieves the same qualitative results as those observed in experimental settings.

CogSci 2022

Assessing the learnability of process interactions using grammatical spaces

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

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Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)
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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Previous research suggests that a verb’s meaning is learned partly through the aggregated profile of syntactic frames associated with it. For example, “turn” occurs with transitive and intransitive frames in causative alternation (“He turned the car”/“The car turned”), indicating it is a causal verb. Some evidence demonstrates that young children combine multiple frames to map verbs to appropriate events. However, previous work always presented these frames together, in a single dialogue. What remains unknown is how verb learning occurs when the frames are separated, uttered in different referential contexts, as is likely in children’s everyday life. In a series of cross-situational word-learning experiments, we show that adult learners update their hypothesis about a novel verb's meaning when they encounter the verb again in a new frame, integrating their previous hypothesis about the verb’s meaning with the new frame. These results shed light on the cross-situational mechanisms of syntactic bootstrapping.

​​Learners Integrate Syntactic Frames and Semantic Hypotheses in Cross-situational Verb Learning

A central question for language development is whether very early telegraphic speech, ungrammatical but intelligible, reflects adult-like abstract linguistic competence, or instead is the result of statistical learning without the reliance on the abstract knowledge. In this paper, we develop a simulation paradigm to evaluate the predictive fit of different accounts of three-word utterances children (<20 months) produced. The simulation subjects are a) human adults with full linguistic competence, b) a statistical model that captures local statistical regularities of language input, and c) a deep neural model that learns and processes language input with global context-aware learning mechanisms. The statistical language model predicted child three-word utterances, both grammatical and ungrammatical ones, better than the neural models and even better (but not statistically) than human adults. The findings suggest simple local statistical regularities underlying child early telegraphic speech.

Key Open Door: Simulating Children’s Early Three-word Utterances Reveals Simple Statistical Regularities Underlying Telegraphic Speech

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

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.

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Learners Integrate Syntactic Frames and Semantic Hypotheses in Cross-situational Verb Learning