United States

To convey meaning, language relies on hierarchically organized, long-range relationships spanning words, phrases, sentences, and discourse. As the distances between elements in language sequences increase, the strength of the long range relationships between those elements decays following a power law. This power-law relationship has been attributed variously to long-range sequential organization present in language syntax, semantics, and discourse structure. However, non-linguistic behaviors in numerous phylogenetically distant species, ranging from humpback whale song to fruit fly motility, demonstrate similar long-range statistical dependencies. Therefore, we hypothesized that long-range statistical dependencies in speech may occur independently of linguistic structure. To test this hypothesis, we measured long-range dependencies in speech corpora from children (aged 6 months -- 12 years). We find that adult-like power-law statistical dependencies are present in human vocalizations prior to the production of complex linguistic structure. These linguistic structures cannot, therefore, be the sole cause of long-range statistical dependencies in language.

CogSci 2021

Long-range sequential dependencies precede complex syntactic production in language acquisition

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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.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
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**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


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.

poster

As time series analysis continues to capture the interest of cognitive and behavioral researchers, it is increasingly important to evaluate these methods and compare their respective insights. Here, we evaluate three popular analyses: vector autoregression, cross-correlation, and cross-recurrence quantification analysis. Using social cohesion data derived from Twitter and daily counts of real-world events during the Arab Spring, we present a case study using these methods and evaluate their benefits, limitations, and differences in results. We propose that researchers interested in time series analysis consider these differences and use multiple methods to assure reliability of their results.

Vector Autoregression, Cross-Correlation, and Cross-Recurrence Quantification Analysis: A Case Study in Social Cohesion and Collective Action

Previous studies have found that bilingual children receive different Give-Number task knower-levels across their languages as subset-knowers. However, recent work reveals that the reliability of Give-Number task is moderate for subset-knower levels. This raises the possibility that the differences found in knower-levels across bilinguals’ languages are explained by a lack of reliability of Give-Number. To address this possibility, we presented bilingual children with Give-Number and a subitizing task that they had to perform in both languages. If differences in knower-levels are due to true differences in children’s understanding of small numbers and not random noise, then differences in subitizing abilities should also be non-random. Data collection is still in progress, but preliminary planned analyses (N=13/64) revealed no differences in subitizing abilities across languages, based on knower-levels classification. We discuss implications of these findings on knowledge transfer across bilinguals’ languages.

Subitizing Abilities of Bilingual Subset-Knowers

Children typically do not spontaneously recognize accented productions of known words until approximately 19 months. In 15-month-olds, however, this ability is correlated with vocabulary size. Vocabulary size may support cross-accent accommodation by decreasing the likelihood that a variant production is considered to be an unknown word. We simulated a cross-accent word identification experiment, with word tokens generated from a two-dimensional Gaussian space, and accented productions simulated via linear transforms. Simulated participants were Bayesian classifiers with large or small vocabularies. Our large vocabulary group accurately classified more accented tokens and were less likely to classify an accented token as an unknown word. Thus, one way a growing vocabulary size may foster cross-accent accommodation is through increasing infants’ propensity to fit accented variants to known words, rather than treating them as unknown words.

Modeling a direct role of vocabulary size in driving cross-accent word identification

Speakers use different language to communicate with partners in different communities. But how do we learn and represent which conventions to use with which partners? In this paper, we argue that solving this challenging computational problem requires speakers to supplement their lexical representations with knowledge of social group structure. We formalize this idea by extending a recent hierarchical Bayesian model of convention formation with an intermediate layer explicitly representing the latent communities each partner belongs to, and derive predictions about how conventions formed within a group ought to extend to new in-group and out-group members. We then present evidence from two behavioral experiments testing these predictions using a minimal group paradigm. Taken together, our findings provide a first step toward a formal framework for understanding the interplay between language use and social group knowledge.

Respect the code: Speakers expect novel conventions to generalize within but not across social group boundaries

Human language is often assumed to make "infinite use of finite means" - that is, to generate an infinite number of possible utterances from a finite number of building blocks. From an acquisition perspective, this assumed property of language is interesting because learners must acquire their languages from a finite number of examples. To acquire an infinite language, learners must therefore generalize beyond the finite bounds of the linguistic data they have observed. In this work, we use an artificial language learning experiment to investigate whether people generalize in this way. We train participants on sequences from a simple grammar featuring center embedding, where the training sequences have at most two levels of embedding, and then evaluate whether participants accept sequences of a greater depth of embedding. We find that, when participants learn the pattern for sequences of the sizes they have observed, they also extrapolate it to sequences with a greater depth of embedding. These results support the hypothesis that the learning biases of humans favor languages with an infinite generative capacity.

Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar

Previous literature suggests that the language processor generates expectations about upcoming material. Several studies have found evidence for a prediction error cost in cases where the comprehender encountered not the predicted word but a plausible unexpected continuation instead. This cost is argued to be a result of an inhibitory process that suppresses activation of the originally predicted word. Others have found no such evidence for a prediction cost. In a probe recognition memory task, we find evidence for interference from an incorrectly predicted word, and in a self-paced reading study, we find evidence for facilitation when the originally predicted word is encountered later on. Taken together, our results provide evidence against a strong version of the suppression account, in which incorrectly predicted words are fully inhibited. Instead we argue in favor of a passive lingering activation account, in which activation for the disconfirmed prediction gradually decays over time.

Unexpected guests: When disconfirmed predictions linger

Infant vocabulary development is inevitably dependent on the speech they hear in their environment. This paper reports an investigation of the vocabulary statistics that predict a word’s age of acquisition, focusing on frequency and contextual diversity as derived from child-directed speech data along with associative norms generated by adults. Age of acquisition is operationalised using parental-report of infant word knowledge in a British English-speaking population. The work can be considered an extension of Hills, Maouene, Riordan, and Smith (2010) using a fully British English dataset. We found significant effects of both word frequency and word associations on age of acquisition. Interestingly, the strength of these predictors differed between word classes, with frequency being the strongest predictor for nouns and associations the strongest predictor for function words.

Exploring the variable effects of frequency and semantic diversity as predictors for a word's ease of acquisition in different word classes

The speed-accuracy tradeoff (SAT) method has produced several prominent findings in sentence processing. While a substantial number of SAT studies has yielded statistical null results regarding the degree to which certain factors influence the speed of sentence processing operations, the statistical power of the SAT paradigm is not known. As a result, it is not entirely clear how to interpret these findings. We addressed this problem by means of a simulation study in which we simulated SAT experiments for a range of known effect sizes in order to determine the statistical power in typical SAT experiments. We found that while SAT experiments appear to have quite satisfactory power to detect differences in asymptotic accuracy, that is not the case for speed-related parameters. We conclude that the failure to find an effect in speed-related parameters in SAT experiments may be less meaningful than previously thought.

Statistical Power in Response Signal Paradigm Experiments

Features of the physical world may be acquired from the statistical properties of language. Here we investigate how the Transformer Language Model T5 is able to gain knowledge of the visual world without being able to see or feel. In a series of four studies, we show that T5 possesses an implicit understanding of the relative sizes of animals, their weights, and their shapes, but not their colors, that aligns well with that of humans. As the size of the models was increased from 60M to 11B parameters, we found that the fit to human judgments improved dramatically, suggesting that the difference between humans and these learning systems might ultimately disappear as the parameter sizes grow even larger. The results imply that knowledge of the perceptual world—and much of semantic memory—might be acquired in dis-embodied learning systems using real-time inferential processes

What Transformers Might Know About the Physical World: T5 and the Origins of Knowledge

Bilinguals are reported to have language learning advantages. One possible pathway is a language-specific transfer effect, whereby sensitivity to structural regularities in known languages can be brought to novel languages that share features. To test for specific linguistic feature transfer, we designed a task for bilinguals with homogeneous language exposure (bilingual in same languages) and heterogeneous feature representation (differing proficiencies). As Hindi and Mandarin have retroflexion in phoneme contrasts, we conducted a pre-registered study with a statistical learning task of a Hindi dental-retroflex contrast on parallel English-Mandarin bilinguals with varied Mandarin proficiency. Unlike the pilot study (N = 15), the main study (N = 50) found no evidence for a learning effect, and language-experience did not explain learning variance. As these stimuli have shown learning effects in children, learning effects for this feature may be fragile in this adult population, and language-specific neural commitments may prevent learning of the contrast.

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Next from CogSci 2021

Vector Autoregression, Cross-Correlation, and Cross-Recurrence Quantification Analysis: A Case Study in Social Cohesion and Collective Action

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