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Learning to count is an important example of the broader human capacity for systematic generalization, and the development of counting is often characterized by an inflection point when children rapidly acquire proficiency with the procedures that support this ability. We aimed to model this process by training a reinforcement learning agent to select N items from a binary vector when instructed (known as the give-$N$ task). We found that a memory-augmented modular network architecture based on the recently proposed Emergent Symbol Binding Network (ESBN) exhibited an inflection during learning that resembled human development. This model was also capable of systematic extrapolation outside the range of its training set - for example, trained only to select between 1 and 10 items, it could succeed at selecting 11 to 15 items as long as it could make use of an arbitrary count sequence of at least that length. The close parallels to child development and the capacity for extrapolation suggest that our model could shed light on the emergence of systematicity in humans.

CogSci 2021

Modelling the development of counting with memory-augmented neural networks

The AGI conference series is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.

Underline hosted the two main days of AGI 2020 on Wednesday June 24th and Thursday June 25th. Both days were **live-streamed** and you can read the full program bellow. All presentations are available for a **asynchronous viewing**. All presenters are grouped into panels. You can also hear guest talks by **Ben Goertzel** (SingularityNET), **Valery Tarasov** (Semenov Institute of Chemical Physics), **Tatiana Shavrina** (Sberbank) and **Joscha Bach** (MIT Media Lab). 

AGI-20

The AGI conference series is the only major conference series devoted wholly and specifically to the creation of AI systems possessing general intelligence at the human level and ultimately beyond. By gathering together active researchers in the field, for presentation of results and discussion of ideas, we accelerate our progress toward our common goal.

technical paper

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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.

In many team-based activities, members search for information to gain situational awareness and thereby structure their own behavior. The extent to which members are coupled and in control of their surrounding environment can be accessed via the fluctuations of their searching behaviors. To facilitate prospective control, assistive technologies such as a head-up display (HUD) can alleviate the demands of search and facilitate team performance. This study investigated how three-person teams divided their labor and structured their search behavior when playing a multiplayer search-and-retrieval task where first-person visibility and access to a HUD were manipulated. Results showed that access to task relevant information facilitated performance and division of labor, and increased prospective control of searching behaviors, as indexed by detrended fluctuation analysis (DFA). Over multiple sessions, teams learned to use the HUD to structure their behavior to achieve the task goal. Results indicate the potential in using DFA for monitoring prospective control in team contexts.

The Structure of Team Search Behaviors with Varying Access to Information

People perceive, think, and act in a multitude of different ways across cultures, and there is an extensive history of research documenting these differences. At the heart of much of this work is a contrast between Western and East Asian cultures that has inspired important efforts to document human psychology in populations outside of the WEIRD (Western, Educated, Industrial, Rich, Democratic) demographic, which is much overrepresented within psychological research (Henrich, Heine, & Norenzayan, 2010). Recent recommendations for measuring cultural distance (Muthukrishna et al., 2020) profile the US and China as focal points for cultural comparisons, but define cultural distance using explicit self-report measures. Here, we evaluate cross-cultural differences between the US and China using implicit and experimental measures. We attempt to reproduce and test extensions of prior work demonstrating cross-cultural differences in reasoning, vision, and social cognition with convenience snowball samples of university students. Few of these differences appeared in our sample.

Investigating cross-cultural differences in reasoning, vision, and social cognition through replication

To communicate successfully, we need to use words that our conversational partner understands. Adults maintain precise models of the words people are likely to know, using both prior experience with their conversational partner and general metalinguistic information. Do children also know what words others are likely to know? We asked children ages 4-8 (n = 62) to predict whether a very young child would know each of 15 familiar animal words. With minimal information, even children as young as 4 made reliable predictions about the target child's vocabulary knowledge. Children were more likely to judge that a younger child would know an early-acquired word (e.g., dog) than a late-acquired word (e.g., lobster), and this pattern became more robust over development. Thus, even preschool age children are adept at inferring other children's vocabulary knowledge, and they could leverage this information to communicate effectively.

Children know what words other children know

Norms are central to social life. They help people select actions that benefit the community and facilitate behavior prediction and coordination. However, little is known about the cognitive properties of norms. Here we focus on norm activation, context specificity, and how those properties differ for the two major types of norms: prescriptions and prohibitions. In two studies, participants are exposed to a variety of contexts by way of scene images and either (a) freely generate norms that apply to the context or (b) decide whether each of a series of candidate norms applies to a given context. Across both studies, people showed high levels of context specificity and fast norm activation, and these properties were substantially stronger for prescriptions than for prohibitions.

Cognitive Properties of Norm Representations

Collective problem solving is supposed to benefit from cognitive diversity (e.g., when a team consists of individuals with different learning strategies). However, recent evidence for this claim fails to rule out an alternative explanation: that the benefit is due to moderate non-conformity, not diversity. We extend a previous agent-based simulation to distinguish these hypotheses, and demonstrate that diverse learning strategies alone do not yield the expected benefit. We extend the model further, based on an idea from the philosophy of science: Group-level benefits in complex problem solving often entail individual-level failures. Accordingly, we parameterize tolerance for failure, and show that there is an interaction between tolerance for failure and diversity. When tolerance for failure is zero, heterogeneous and homogeneous groups perform equally; when non-zero, diverse groups can outperform heterogeneous groups. Our agent-based simulations help clarify when cognitive diversity benefits collective problem solving.

Tolerance to failure unleashes the benefits of cognitive diversity in collective problem solving

Inferences about other people's knowledge and beliefs are central to social interaction. In many situations, however, it's not possible to be sure what other people know because their behavior is consistent with a range of potential epistemic states. Nonetheless, this behavior can give us coarse intuitions about how much someone might know, even if we cannot pinpoint the exact nature of this knowledge. We present a computational model of this kind of broad epistemic-state inference, centered on the expectation that agents maximize epistemic utilities. We evaluate our model in a graded inference task where people had to infer how much an agent knew based on the actions they chose. Critically, the agent's behavior was always under-determined, but nonetheless contained information about how much knowledge they possessed. Our model captures nuanced patterns in participant judgments, revealing a quantitative capacity to infer amorphous knowledge from minimal behavioral evidence.

I can tell you know a lot, although I'm not sure what: Modeling broad epistemic inference from minimal action

Reasoning beyond available data is a ubiquitous feature of human
cognition. But while the availability of first-hand data typically diminishes
as the concepts we reason about become more complex, our ability to draw
inferences seems not to. We may offset the sparsity of direct evidence by
observing the statements of others, but such social meta-inference comes with
challenges of its own. The strength of socially-provided evidence depends on
multiple factors which themselves must be inferred, like the knowledge, social
goals, and independence of the people providing the data. Here, we present the
results of an experiment aimed at examining how people draw conclusions from
information provided by others in the context of social media posts. By
systematically varying the degree of consensus along with the number of people
and distinct arguments involved we are able to assess how much each factor
affects the conclusions reasoners draw. Across a range of topics we find that
while people are influenced by the number of people on each side of an
argument, the number of posts is the dominant factor driving belief
revision. In contrast to well established findings in simpler domains, we find
that people are largely insensitive to the diversity of the arguments made.

Social meta-inference and the evidentiary value of consensus

How do adults understand children's speech? Children's productions over the course of language development often bear little resemblance to typical adult pronunciations, yet caregivers nonetheless reliably recover meaning from them. Here, we employ a suite of Bayesian models of spoken word recognition to understand how adults overcome the noisiness of child language, showing that communicative success between children and adults relies heavily on adult inferential processes. By evaluating competing models on phonetically-annotated child language from the Providence corpus, we show that adults' recovered meanings are best predicted by prior expectations fitted specifically to the child language environment, rather than to typical adult-adult language. After quantifying the contribution of this "child-directed listening" over developmental time, we discuss the consequences for theories of language acquisition, as well as the implications for commonly-used methods for assessing children's linguistic proficiency.

Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication

Because words have multiple meanings, language users must often choose appropriate meanings according to the context of use. How this potential ambiguity affects first language learning, especially word learning, is unknown. Here, we present the first large-scale study of how children are exposed to, and themselves use, ambiguous words in their language learning environments. We tag 180,000 words in two longitudinal child language corpora with word senses from WordNet, focusing between 9 and 51 months and limiting to words from a popular parental vocabulary report. We then compare the diversity of sense usage in adult speech around children to that observed in a sample of adult language, as well as the diversity of sense usage in children's own productions. To accomplish this we use a Bayesian model-based estimate of sense entropy, a measure of diversity that takes into account uncertainty inherent in small sample sizes. This reveals that sense diversity in caregivers' speech to children is similar to that observed in a sample of adult-directed written material, and that children's use of nouns---but not verbs---is similarly diverse to that of adults. Finally, we show that sense entropy is a significant predictor of vocabulary development: children begin to produce words with a higher diversity of adult sense usage at later ages. We discuss the implications of our findings for theories of word learning.

Quantifying Lexical Ambiguity in Speech To and From English-Learning Children

Frequency of occurrence in the input is a main factor determining the ease of acquisition in first language learners. However, little is known about the factors relevant for the acquisition of low-frequency items. We examine the use of aspectual markers in a longitudinal corpus of Chintang (Sino-Tibetan, Nepal) children (ages 2;1-4;5). Only 7.7% of all Chintang verbs are overtly marked for aspect. Chintang has three aspect markers, one of which is substantially more frequent than the others. One of the low-frequency markers is positionally and prosodically more salient, appearing at the word-boundary. 
Using a Bayesian beta-binomial model, we assess the distribution and flexibility of use of aspectual markers in the input and children's production. Our analysis shows that the most frequent marker was acquired earliest, as predicted. For the low-frequency markers, position, segmentability and uniformity are better predictors of ease of acquisition.

Modelling the development of counting with memory-augmented neural networks

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