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Does knowing certain words help children learn other words? We hypothesized that knowledge of more general (more superordinate) words at time1 would lead to faster vocabulary growth as measured through vocabulary checklists administered at later timepoints. We find that this is indeed the case. Children who have similar vocabularies at time1 , but differ in their productive knowledge of more general words such as “animal,” “picture,” and “get” go on to have different rates of word learning. Knowledge of more general words is associated with faster vocabulary growth, particularly of words semantically related to the superordinate terms they are reported to produce. This positive relationship between knowledge of more general words and word learning remains even when controlling for measures of verbal and nonverbal intelligence.

CogSci 2021

Superordinate Word Knowledge Predicts Longitudinal Vocabulary Growth

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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,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**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.

technical paper

Categorizing spatial relations is central to the development of visual understanding and spatial cognition, with roots in the first few months of life. Quinn (2003) reviews two findings in infant relation categorization: categorizing one object as above/below another precedes categorizing an object as between other objects, and categorizing relations over specific objects predates abstract relations over varying objects. We model these phenomena with deep neural networks, including contemporary architectures specialized for relational learning and vision models pretrained on baby headcam footage (Sullivan et al., 2020). Across two computational experiments, we can account for most of the developmental findings, suggesting these neural network models are useful for studying the computational mechanisms of infant categorization.

Examining Infant Relation Categorization Through Deep Neural Networks

A simple memory component is amended to local (“Pursuit”; Stevens,  Gleitman,  Trueswell, and Yang (2017)) and  global (e.g., Yu  and  Smith  (2007);  Fazly,  Alishahi,  and  Stevenson(2010)) models of cross-situational word learning. Only a finite (and small) number of words can be concurrently learned; successfully  learned  words  are  removed  from  the  memory buffer  and  stored  in  the  lexicon. The  memory  buffer  improves the empirical coverage for both local and global learn-ing models.  However, the complex task of homophone learn-ing (Yurovsky & Yu, 2008) proves a more decisive advantage for the local model (dubbed Memory Bound Pursuit;  MBP). Implications and limitations of these results are discussed.

Memory Constraints on Cross Situational Word Learning

Recent work (Xu et al., 2020) has suggested that numeral systems  in  different  languages  are  shaped  by  a  functional  need for efficient communication in an information-theoretic sense. Here we take a learning-theoretic approach and show how efficient communication emerges via reinforcement learning.  In our  framework,  two  artificial  agents  play  a  Lewis  signaling game  where  the  goal  is  to  convey  a  numeral  concept.   The agents  gradually  learn  to  communicate  using  reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al.(2015); Gibson et al. (2017).  They are also shown to be similar to human numeral systems of same type.  Our results thus provide a mechanistic explanation via reinforcement learning of the recent results in Xu et al. (2020) and can potentially be generalized to other semantic domains.

Learning Approximate and Exact Numeral Systems via Reinforcement Learning

Across the world's languages, children reliably learn nouns more easily than verbs. Attempts to understand the difficulty of verb learning have focused on determining whether the challenge stems from differences in the linguistic usage of nouns and verbs, or instead conceptual differences in the categories that they label. We introduce a novel metric to quantify the contributions of both sources of difficulty using unsupervised learning models trained on corpora of language and images. We find that there is less alignment between the linguistic usage of verbs and their categories than for nouns and their categories. However, this difference is driven almost entirely by differences in the structure of their visual categories: Relative to nouns, events described by the same verb are more variable and events described by two different verbs are more similar. We conclude that differences between noun and verb learning need not be due to fundamental differences in learning processes, but may instead be driven by the difficulty of one-shot generalization from verbs' visual categories.

A common framework for quantifying the learnability of nouns and verbs

People learn by both decomposing and combining concepts; most accounts of combination are either compositional or conjunctive. We augment the DORA model of representation learning to build new predicate representation by combining (or compressing) existing predicate representations (e.g., building a predicate a_b by combining predicates a and b). The resulting model learns structured relational representations from experience and then combines these relational concepts to form more complex, compressed concepts. We show that the resulting model provides an account of a category learning experiment in which categories are defined as novel combinations of relational concepts.

Compression: A Lossless Mechanism for Learning Complex Structured Relational Representations

Curiosity is a fundamental driver of human behavior, and yet because of its open-ended nature and the wide variety of behaviors it inspires in different contexts, it is remarkably difficult to study in a laboratory context. A promising approach to developing and testing theories of curiosity is to instantiate them in artificial agents that are able to act and explore in a simulated environment, and then compare the behavior of these agents to humans exploring the same stimuli. Here we propose a new experimental paradigm for examining children's -- and AI agents' -- curiosity about objects' physical interactions. We let them choose which object to drop another object onto in order to create the most interesting effect. We compared adults' (N=155) and children's choices (N=66; 3-7 year-olds) and found that both children and adults show a strong preference for choosing target objects that could potentially contain the dropped object. Adults alone also make choices consistent with achieving support relations. We contextualize our results using heuristic computational models based on 3D physical simulations of the same scenarios judged by participants.

Predicting children's and adults' preferences in physical interactions via physics simulation

Despite being designed for performance rather than cognitive plausibility, transformer language models have been found to be better at predicting metrics used to assess human language comprehension than language models with other architectures, such as recurrent neural networks. Based on how well they predict the N400, a neural signal associated with processing difficulty, we propose and provide evidence for one possible explanation—their predictions are affected by the preceding context in a way analogous to the effect of semantic facilitation in humans.

Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?

What do infants and young children tend to see in their everyday lives? Relatively little work has examined the categories and objects that tend to be in the infant view during everyday experience, despite the fact that this knowledge is central to theories of category learning. Here, we analyzed the prevalence of the categories (e.g., people, animals, food) in the infant view in a longitudinal dataset of egocentric infant visual experience. Overall, we found a surprising amount of consistency in the broad characteristics of children's visual environment across individuals and across developmental time, in contrast to prior work examining the changing nature of the social signals in the infant view. In addition, we analyzed the distribution and identity of the categories that children tended touch and interact with in this dataset, generalizing previous findings that these objects tended to be distributed in a Zipfian manner. Taken together, these findings take a first step towards characterizing infants' changing visual environment, and call for future work to examine the generalizability of these results and to link them to learning outcomes.

Characterizing the object categories two children see and interact with in a dense dataset of naturalistic visual experience

Infants learn the meaning of words from accumulated experiences of real-time interactions with their caregivers. To study the effects of visual sensory input on word learning, we recorded infant's view of the world using head-mounted eye trackers during free-flowing play with a caregiver. While playing, infants were exposed to novel label-object mappings and later learning outcomes for these items were tested after the play session. In this study we use a classification based approach to link properties of infants' visual scenes during naturalistic labeling moments to their word learning outcomes. We find that a model which integrates both highly informative and ambiguous sensory evidence is a better fit to infants' individual learning outcomes than models where either type of evidence is taken alone, and that raw labeling frequency is unable to account for the word learning differences we observe. Here we demonstrate how a computational model, using only raw pixels taken from the egocentric scene image, can derive insights on human language learning.

In-the-Moment Visual Information from the Infant's Egocentric View Determines the Success of Infant Word Learning: A Computational Study

Two very different kinds of views exist of how infants learn about physics: on the one hand, through domain-specific rules, on the other, through general purpose simulation.
We attempt to reconcile these two views through a model that uses simulation to bootstrap rule learning. This model makes a variety of predictions about rapid concept acquisition in young infants which are consistent with experiments performed by developmental cognitive scientists. Consistent with the developmental literature, our model learns physical rules from just a few examples, but only when those examples are consistent with general physical principles. A model without simulation shows no such biases. Our approach provides a general mechanism for explaining how simulation and rule learning might bootstrap off of each other throughout development, and opens up a number of new questions about how children learn physical representations.

Superordinate Word Knowledge Predicts Longitudinal Vocabulary Growth

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Examining Infant Relation Categorization Through Deep Neural Networks

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Superordinate Word Knowledge Predicts Longitudinal Vocabulary Growth

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Next from CogSci 2021

Examining Infant Relation Categorization Through Deep Neural Networks

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I Know You Know I’m Signaling: Novel gestures are designed to guide observers’ inferences about communicative goals

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