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We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an important aspect of concept development in language acquisition (Carlson &amp; Pelletier, 1995; Gelman, 2009). We extend a computational framework designed to model grounded language acquisition by introducing the concept network. This new layer of abstraction enables the system to encode knowledge learned from generic statements and represent the associations between concepts learned by the system. Through three tasks that utilize the concept network, we demonstrate that our extensions to ADAM can acquire generic information and provide an example of how ADAM can be used to model language acquisition.

CogSci 2021

A Grounded Approach to Modeling Generic Knowledge 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.

We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an important aspect of concept development in language acquisition (Carlson & Pelletier, 1995; Gelman, 2009). We extend a computational framework designed to model grounded language acquisition by introducing the concept network. This new layer of abstraction enables the system to encode knowledge learned from generic statements and represent the associations between concepts learned by the system. Through three tasks that utilize the concept network, we demonstrate that our extensions to ADAM can acquire generic information and provide an example of how ADAM can be used to model language acquisition.

poster

Political ideology leads educated adults–especially the highly numerate–to selectively reason about numbers that support their beliefs (“motivated numeracy”). We investigated whether supports that help children’s quantitative reasoning (number-lines) might also help political partisans. To test this, we asked 429 adults to interpret fictional data, in table or number-line format, about the effect of gun control on crime or the effect of a skin cream on rashes. We found data presented in number-line formats yielded greater accuracy than table formats controlling for numeracy skills (χ2 (1) = 21.88, p < .001), regardless of whether the true interpretation of data affirms, neutral to, or disaffirms participants’ political outlooks. Solving table problems after number-line problems yielded greater accuracy compared to solving table problems first (χ2 (1) = 4.78, p < .005), suggesting number-line practice is educational. Our research has important implications for communicating policy data and improving objectivity.

Cognitive Supports for Objective Numeracy

We make frequent decisions about how to manage our health, yet do so with information that is highly complex or received piecemeal. Causal models can provide guidance about how components of a complex system interact, yet models that provide a complete causal story may be more complex than people can reason about. Prior work has provided mixed insights into our ability to make decisions with causal models, showing that people can use them in novel domains but that they may impede decisions in familiar ones. We examine how tailoring causal information to the question at hand may aid decision making, using simple diagrams with only the relevant causal paths (Experiment 1) or those paths highlighted within a complex causal model (Experiment 2). We find that diagrams tailored to a choice improve decision accuracy over complex diagrams or prior knowledge, providing new evidence for how causal models can aid decisions.

It's Complicated: Improving Decisions on Causally Complex Topics

Children can represent, compute, and manipulate numbers from very early in development. Additionally, beginning in infancy, children appear to have intuitions about probability, correctly anticipating the outcomes of simple sampling events. In two experiments, we examined 3- to 7-year-olds’ (N=196) ability to compare the number of items across sets in games of chance. In Experiment 1, children were asked to select between two games with different numbers of hiding locations to either find or hide a gold coin. Using a similar set up, in Experiment 2, they were asked to select the game that would make it easy or hard for another player to find the coin. Results from both experiments suggest that by around age 5, children can use numerical reasoning to compare odds: they were more likely to select the game with more cups when asked to help hide the gold coin than find it (Experiment 1) and when asked to make the game hard rather than easy (Experiment 2).

Can Children use Numerical Reasoning to Compare Odds in Games?

The development of mobility technologies has led to the concept of shared spaces. In the shared space, mobilities and pedestrians share a single common space. Compared to conventional separated spaces, cooperative behaviors are critical in shared spaces because all agents can move freely at their own speed and in their directions with few constraints. An experiment was conducted using indices for own cost, others' benefit, and own loss to reveal the nature of the cooperative behaviors associated with moving. We found that compared to when people are encouraged to behave without urgency, they frequently change their speed and direction so as not to interrupt others and reach their destination more quickly when people are required to behave cooperatively. Therefore, it was concluded that both others' benefit and one's own benefit are critical for cooperative behaviors when moving in shared spaces.

What is the Cooperative Behavior of Moving in Shared Spaces?

Social media users are generally exposed to information that is predominantly consistent with their attitudes and beliefs (i.e., filter bubbles), which can increase polarization and decrease understanding of complex and controversial topics. One potential approach to mitigating the negative consequences of filter bubbles is intentional exposure to information that is inconsistent with attitudes. However, it is unclear how exposure to attitude-inconsistent information in social media contexts influences memory for controversial information. To fill this gap, this study examines the effects of presenting participants (n = 96) with Twitter content on a controversial topic (i.e., labor unions) that was either pro-union, anti-union, or neutral. Participants then read a media article including both pro-union and anti-union information. Participants who saw Twitter content that was inconsistent with their prior attitudes regarding labor unions recalled less of the article content compared to those who saw Twitter content that was consistent with their prior attitudes. The findings suggest that Twitter users’ memory for information related to controversial topics may not benefit from exposure to messages outside their filter bubble.

Social Media Spillover: Attitude-Inconsistent Tweets Reduce Memory for Subsequent Information

Physical social contact, such as grooming in primates or touch in humans, is fundamental to create and maintain social bonds. The Brain Opioid Theory of Social Attachment postulates that µ-opioids play a central role in social connection. Accordingly, pharmacological studies in isolated animals indicate that µ-opioid agonists reduce, and µ-opioid antagonists increase distress responses and motivation for social contact. Despite the abundance of animal studies, human evidence is still lacking.
Here, we investigated the neurochemical basis of social motivation under stress in healthy human volunteers, following morphine (µ-opioid agonist) or placebo administration. By adopting a translational approach, real physical effort and facial hedonic reactions, together with self-reports of wanting and liking for social touch, were assessed.
Preliminary results revealed increased adverse response to stress following morphine administration. In line with animal models and previous evidence in humans, this enhanced stress response led to increased motivation to obtain social touch.

Effect of morphine administration on human social motivation during stress

Humans perceive the world through a rich, object-centric lens. We are able to infer 3D geometry and features of objects from sparse and noisy data. Gestalt rules describe how perceptual stimuli tend to be grouped based on properties like proximity, closure, and continuity. However, it remains an open question how these mechanisms are implemented algorithmically in the brain, and how (or why) they functionally support 3D object perception. Here, we describe a computational model which accounts for the Gestalt principle of Common Fate - grouping stimuli by shared motion statistics. We argue that this mechanism can be explained as bottom-up neural amortized inference in a top-down generative model for object-based scenes. Our generative model places a low-dimensional prior on the motion and shape of objects, while our inference network learns to group feature clusters using inverse renderings of noisily textured objects moving through time, effectively enabling 3D shape perception.

Explaining the Gestalt principle of common fate as amortized inference

To infer what others know, we must consider under what epistemic states their actions were both rational and probable. We test whether preschoolers can compare the probability of different actions (and outcomes) under different epistemic states—and use this to evaluate what others know. Specifically, four- to six-year-olds (n=90) were asked to help evaluate an agent’s knowledge state by asking the agent to complete either an “undiagnostic” task (where success was assured), or a “diagnostic task” (where the probability of random success was low). By age six, children understood that the “diagnostic” task would more likely reveal the agent’s knowledge state; four- and five-year-olds had no reliable preference, although children in all age groups understood that the “diagnostic” task was harder. These results suggest that, by the end of preschool, children understand how agents’ epistemic states and environment jointly determine success—considering whether agents’ actions imply knowledge, or just luck.

Children consider the probability of random success when evaluating knowledge

Listening to music activates representations of movement and social agents. Why? We ask whether high-level causal reasoning about how music was generated can lead people to link musical sounds with animate agents. To test this, we asked whether people (N=60) make flexible inferences about whether an agent caused musical sounds, integrating information from the sounds’ timing and from the visual context in which it was produced. Using a 2x2 within-subject design, we found evidence of causal reasoning: In a context where producing a musical sequence would require self-propelled movement, people inferred that an agent had been present causing the sounds. When the context provided an alternative possible explanation, this ‘explained away’ the agent, reducing the tendency to infer an agent was present for the same acoustic stimuli. People can use causal reasoning to infer whether an agent produced musical sounds, suggesting that high-level cognition can link music with social concepts.

From music to animacy: Causal reasoning links animate agents with musical sounds

Recently, there has been research on what factors influence a user’s password setting practices, which include various types of emotions such as anger, risk-taking tendencies, etc. However, research has shown that factors such as memorability and perceived memorability have a greater influence on password choice. Some recent research has shown a negative correlation between the perceived memorability and the perceived security of passwords, particularly passphrases (that are technically more secure). However, it is unclear whether this effect can be extended to groups with good experiences with digital spaces (IT professionals, entrepreneurs, etc.). Furthermore, it has not been determined whether random, uncommonly-worded, or complex structure passphrases would also maintain the correlation, as opposed to relatively less secure, common/simple passphrases. This study examines this problem using a diverse demographic and different categories of passphrases.

A Grounded Approach to Modeling Generic Knowledge Acquisition

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