United States

Planning is hard. The use of subgoals can make planning more tractable, but selecting these subgoals is computationally costly. What algorithms might enable us to reap the benefits of planning using subgoals while minimizing the computational overhead of selecting them? We propose visual scoping, a strategy that interleaves planning and acting by alternately defining a spatial region as the next subgoal and selecting actions to achieve it. We evaluated our visual scoping algorithm on a variety of physical assembly problems against two baselines: planning all subgoals in advance and planning without subgoals. We found that visual scoping achieves comparable task performance to the subgoal planner while requiring only a fraction of the total computational cost.Together, these results contribute to our understanding of how humans might make efficient use of cognitive resources to solve complex planning problems.

CogSci 2021

Visual scoping operations for physical assembly

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers.<br><br><br>
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**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.

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.

Modelling the development of counting with memory-augmented neural networks

Prediction error is known to enhance priming effects for familiar syntactic structures; it also strengthens the formation of new declarative memories. Here, we investigate whether violating expectations may aid the acquisition of new abstract syntactic structures, too, by enhancing memory for individual instances which can then form the basis for abstraction. In a cross-situational artificial language learning paradigm, participants were exposed to novel syntactic structures in ways that either violated their expectations (Surprisal group) or that conformed to them (Control group). Results from a delayed post-test show that participants in the Surprisal group developed stronger representations of the structures’ form-meaning mappings and were better able to generalize them to new instances, relative to the Control group.

Expectation Violation Leads to Generalization: The Effect of Prediction Error on the Acquisition of New Syntactic Structures

Symmetry is ubiquitous in nature, in logic and mathematics, and in perception, language, and thought. Although humans are exquisitely sensitive to visual symmetry (e.g., of a butterfly), linguistic symmetry goes far beyond visuospatial properties: Many words refer to abstract, logically symmetrical concepts (e.g., equal, marry). This raises a question: Do representations of symmetry correspond across language and vision, and if so, how? To address this question, we used a cross-modal matching paradigm. On each trial, adult participants observed a visual stimulus (either symmetrical or non-symmetrical) and had to choose between a symmetrical and non-symmetrical English predicate unrelated to the stimulus (e.g., "negotiate" vs. "propose"). In a first study with visual events (symmetrical collision or asymmetrical launch), participants reliably chose the predicate matching the event's symmetry. A second study showed that this "matching" generalized to static objects, and was weakened when the stimuli's binary-relational nature was made less apparent (i.e., one object with a symmetrical contour, rather than two symmetrically configured objects). Taken together, our findings support the existence of an abstract relational concept of symmetry which humans access via both perceptual and linguistic means. More broadly, this work sheds light on the rich, structured nature of the language-cognition interface, and points towards a possible avenue for acquisition of word-to-world mappings for the seemingly inaccessible logical symmetry of linguistic terms.

Where Word and World Meet: Intuitive Correspondence Between Visual and Linguistic Symmetry

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

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.

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