United States

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.

CogSci 2021

Possibility judgments may depend on assessments of similarity to known events

**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.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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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.

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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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**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.

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

Selective attention involves attending to task-relevant information and inhibiting task-irrelevant information. While spatial priming is known to efficiently shape selective attention, the nature of language-mediated effects on selective attention is not well-understood, particularly in young toddlers. We com- pare the impact of language-mediated and spatially-mediated attention in an eye-tracking paradigm in which two objects are presented in one of four possible locations and one of the objects is highlighted. The impact of labelling on attention orienting during a prime phase was tested in a subsequent probe phase, where either the identity, location or both were manipulated, and compared to the impact of spatial priming. To elucidate the role of development on these effects, the study was conducted with 18- and 26-month-old toddlers. The results revealed that both language-mediated and spatially-mediated priming lead to attention orienting during the probe phase: at- tended information during the prime phase facilitates attention during the probe phase while ignored features are inhibited. However, in contrast to spatially-mediated attention during the prime phase, language-mediated attention can override these inhibitory effects. The impact of language on overcoming inhibitory effects is particularly noteworthy in the older age group.

Language- and spatially-mediated attention in toddlers

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

Discrete emotions are known to elicit changes in decision-making. Previous research has found that affect biases response times and the perception of evidence for choices, among other key factors of decision-making. However, little is known how affect influences the specific cognitive mechanisms that underlie decision-making. We investigated these mechanisms by fitting a hierarchical reinforcement-learning decision diffusion model to participant choice data. Following the collection of baseline decision-making data, participants took part in a writing exercise to generate neutral or discrete emotions. Following the writing exercise, participants made additional decisions. We found that exposure to discrete emotions modulates decision-making through several mechanisms including rates of learning and evidence accumulation, separation of decision thresholds, and sensitivity to noise. Furthermore, we found that exposure to each of the four discrete emotions modulated decision-making differently. These findings integrate learning and decision process models to expand on previous research and elucidate processes of affective decision-making.

Incidental discrete emotions influence processes of evidence accumulation in reinforcement-learning

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.

Possibility judgments may depend on assessments of similarity to known events

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

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Possibility judgments may depend on assessments of similarity to known events

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

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Towards evaluating the impact of swarm robotic control strategy on operators' cognitive load

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