United States

People watching is a ubiquitous component of human activities. An important aspect of such activities is the aesthetic experience that arises naturally from seeing how elegant people move their bodies in performing different actions. What makes some body movements look better than others? We examined how visual processing contributes to the aesthetics experience from seeing actions, using point-light “creatures” generated by spatially scrambling locations of a point-light walker’s joints. Observers rated how aesthetically pleasing and lifelike each creature looked in a video of the creature moving from left to right. They viewed four kinds of creatures: The joints’ trajectories were either from an upright walker (thus exhibiting gravitational acceleration) or an inverted walker (thus defying gravity) and were either congruent to the direction of global body displacements or incongruent (as in the moonwalk). Observers gave both higher aesthetic and animacy ratings for creatures with upright versus inverted trajectories, and congruent versus incongruent movements. Moreover, after regressing out the influence of animacy, the creatures that move in a natural causal manner (in accordance with gravity and their body displacements) were still preferred. The subtle differences between different kinds of creatures suggest a role of automatic perceptual mechanisms in these preferences. Thus, while our thinking minds may enjoy watching the magical moonwalk, our automatic minds, with a taste for causality, may curtail the impression of its visual beauty.

CogSci 2021

Aesthetic experience is influenced by causality in biological movements

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
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IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

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! 

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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.

There are many ways to go wrong when evaluating new information, e.g. by putting unwarranted trust in non-experts, or failing to scrutinize information about threats. We examined how effective people were at evaluating information about the COVID-19 pandemic. Early in the course of the pandemic, we recruited 1791 participants from six countries with varying levels of pandemic severity, and asked them to evaluate true and false pandemic-related statements (assertions and prescriptions) sampled from the media. We experimentally manipulated the source of each statement (a doctor, a political/religious leader, social media, etc.). Overall, people proved to be epistemically vigilant: they distinguished between true and false statements, especially prescriptions, and they trusted doctors more than other sources. These effects were moderated by feeling threatened by the pandemic, and by strong identification with some sources (political/religious leaders). These findings provide optimism in the fight against misinformation, while highlighting challenges posed by politics and ideology.

Evaluating Information and Misinformation during the COVID-19 Pandemic: Evidence for Epistemic Vigilance

Coordinated hunting is widely observed in animals, and sharing rewards is often considered a major incentive for this success. However, it is unclear what causal roles are played by this reward-sharing mechanism. In order to systematically examine the effects of sharing rewards in animal coordinated hunting, we conduct a suite of modeling experiments using a state-of-the-art multi-agent reinforcement learning algorithm. The models are trained and evaluated with a task that simulates real-world collective hunting. We manipulate four evolutionarily important variables: reward distribution, hunting party size, free-rider problems, and hunting difficulty. Our results indicate that individually rewarded predators outperform predators that share rewards, especially when the hunting is difficult, the group size is large, and the action cost is high. Moreover, predators with shared rewards suffer from the free-rider problem. We conclude that sharing reward is neither necessary nor sufficient for modeling animal coordinated hunting through reinforcement learning.

Sharing is not Needed: Modeling Animal Coordinated Hunting with Reinforcement Learning

When people are unsure of the intended meaning of a word, they often ask for clarification. One way of doing so-often assumed in models of communication-is to point at a potential target: "Do you mean [points at the rabbit]?'' However, what if the target is unavailable? Then the only recourse is language itself, which seems equivalent to pulling oneself up from a swamp by one's hair. 
We created two computational models of communication, one able to point and one not. The latter incorporates inference to resolve the meaning of non-pointing signals. Simulations show agents in both models reach perceived understanding equally quickly. While this means agents think they are successfully communicating, non-pointing agents understand each other only at chance level.
This shows that state-of-the-art computational explanations have difficulty explaining how people solve the puzzle of underdetermination, and that doing so will require a fundamental leap forward.

Computational challenges in explaining communication: How deep the rabbit hole goes

People think they ought to make some decisions on the basis of deliberative analysis, and others on the basis of intuitive, gut feelings. What accounts for this variation in people’s preferences for intuition versus deliberation? We propose that intuition might be prescribed for some decisions because people’s folk theory of decision-making accords a special role to authenticity, where authenticity is uniquely associated with intuitive choice. Two pre-registered experiments find evidence in favor of this claim. In Experiment 1 (N=631), we find that decisions made on the basis of intuition (vs. deliberation) are more likely to be judged authentic, especially in domains where authenticity is plausibly valued. In Experiment 2 (N=177), we find that people are more likely to prescribe intuition as a basis for choice when the value of authenticity is heightened experimentally. These effects hold beyond previously recognized influences, such as computational costs, presumed efficacy, objectivity, complexity, and expertise.

Deciding to be Authentic: Intuition is Favored Over Deliberation for Self-Reflective Decisions

Cultural attractor landscapes describe the time-evolution of cultural variants over transmission events. When variants sit at a local minimum of a stable attractor landscape, there will be no cumulative error over transmissions, laying the foundation for cumulative culture. But because cultural attractors are emergent products of dynamic populations of cognitive landscapes, which are in turn emergent products of individual experience within a culture, stable cultural attractor landscapes cannot be taken for granted. Yet, little is known about how cultural attractors form or stabilize. We present an agent-based model of cultural attractor dynamics, which adapts a cognitive model of unsupervised learning of phoneme categories in individual learners to a multi-agent, sociocultural setting wherein individual learners provide the training input to each other. We find that constraints at the level of cognition, development, and demographic structure determine the tendency for populations to self-organize into and dynamically stablilize a cultural attractor landscape.

The Emergence of Cultural Attractors: An Agent-Based Model of Collective Perceptual Alignment

We explore whether people’s judgments about the possibility of events are predicted by their knowledge of similar events. Participants read 80 events from a list including events that were ordinary, unusual, and impossible. Participants rated whether the events were possible or whether the events were similar to events they knew to have happened. The averaged ratings for each judgment were strongly correlated, and the correlation remained significant in an analysis limited to a subset of the events that were neither viewed as totally impossible, or as extremely similar to known events. These findings provide preliminary evidence that adults may judge whether events are possible by relying on a memory-based heuristic which aims to identify whether these events are similar to known events.

Possibility judgments may depend on assessments of similarity to known events

Do nonhuman animals (hereafter “animals”) possess empathy and if so to which degree? Can we develop a conceptual framework that allows us to characterize similarities and differences between implementations of empathy in humans and animals? We aim to answer these questions in two steps. First, we develop a new conceptual framework by distinguishing different levels of empathy starting with paradigmatic cases of human empathy developing in human ontogeny. Second, we describe in detail which of these levels of empathy can be found in other species based on animal studies. This approach allows a detailed characterization of the relation of empathy in humans and other animals.

A conceptual framework for empathy and its application to investigate nonhuman animals

Social interactions serve as the primary training ground for many of a child’s positive cognitive and developmental abilities. Parent responsiveness has been identified as one key mechanism through which children gain more mature skills, but the question of how children elicit responses from their parents remains to be fully investigated. In this study, we utilized head-mounted eye trackers to track moment-by-moment shifts in gaze, manual action, and speech during parent-child toy play. This allowed us to identify the moments preceding a parent response and the type and timing of the parent’s response relative to the child’s behaviors. We found that child events of attention and action – where they were both touching and looking at a toy – were more successful in eliciting parent responses overall and in eliciting multimodal parent responses than events of just child look or child touch. The parent’s latency to respond to their child differed by event type and duration, suggesting that child behaviors influence parent responses. Implications and future directions are discussed.

Multimodal Behaviors from Children Elicit Parent Responses in Real-Time Social Interaction

Preference reversals in risky choice -- where people favor low-risk prospects in binary choice but assign higher prices to high-risk prospects -- have led to models of response processes that differentiate pricing from choice. Theories of intertemporal choice do not distinguish between response processes, assuming instead that eliciting choices or prices will lead to the same inferences about people’s preferences for delayed outcomes. Here, we show that this assumption is incorrect. Participants in a price-choice experiment showed systematic preferences for smaller-sooner (SS) over larger-later (LL) options in binary choice, but reversed this apparent preference by pricing the exact same LL options higher than the SS options. This reversal in pricing results in less impulsive behavior, suggesting that pricing frames may reduce choice impulsivity. To explain these diverging price and choice findings in a common framework, we propose a variant of a pricing model from risky choice that accommodates these effects.

Preference reversals between intertemporal choice and pricing

Recency effects—giving exaggerated importance to recent outcomes—are a common aspect of decision tasks. In the current study, we explore two explanations of recency-based decision making, that it is (1) a deliberate strategy for adaptive decision making in real-world environments which tend to be dynamic and autocorrelated, and/or (2) a product of processing limitations of working memory. Supporting explanation 1, we found that participants strategically adjusted their recency levels across trials to achieve optimal levels in a range of tasks. Furthermore, they started with default recency values that had high aggregate performance across environments. However, only some correlations between recency values and WM scores were significant, providing no clear conclusion regarding explanation 2. Ultimately, we propose that recency involves a combination of the two—people can strategically change recency within the limits of WM capacities to adapt to external environments.

Aesthetic experience is influenced by causality in biological movements

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Evaluating Information and Misinformation during the COVID-19 Pandemic: Evidence for Epistemic Vigilance

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Aesthetic experience is influenced by causality in biological movements

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Evaluating Information and Misinformation during the COVID-19 Pandemic: Evidence for Epistemic Vigilance

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Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

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