United States

Species such as humans rely on their excellent visual abilities to perceive and navigate the world. Dogs have co-habited with humans for millennia, yet we know little about how they gather and use visual information to guide decision-making. Across five experiments, we presented pet dogs (N=49) with two foods of unequal value in a 2-alternative choice task, and measured whether dogs showed preferential gazing, and whether visual attention patterns were associated with item choice. Overall, dogs looked significantly longer at the preferred (high value) food over the low value alternative. There was also evidence of item-dependent predictive gaze—dogs looked proportionally longer at the item they subsequently chose. Surprisingly, dogs’ choice behavior was only slightly above chance, despite visual discrimination. These results suggest that dogs use visual information in the environment to inform their choice behavior, but that other factors may also contribute to their decision-making.

CogSci 2021

Domestic dogs’ gaze and behaviour in 2-alternative choice tasks

**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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### 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! 

**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.

This paper explores previous arguments that language 
evolved not from animal communication, as naturally 
assumed by most scholars, but instead out of animal 
cognition. It is proposed here that additional support for this 
argument comes from Cognitive Linguistics, an
interdisciplinary branch of linguistics. Cognitive Linguistics
mediates communication and cognition but, as with Cognitive 
Discourse Analysis, studies language use in terms of what it 
demonstrates about underlying cognitive processes and 
concepts. The paper demonstrates key examples of animal 
cognition’s links to language, with Cognitive Linguistics 
support, as well as the approach’s application to animal 
cognition in terms of domain-general symbolism, beyond 
verbal language. However, because communication remains a 
major function of language, the communicative aspect ought 
to be maintained in the explanation of language evolution.

Cognitive Linguistics Support for the Evolution of Language from Animal Cognition

Effective social co-ordination benefits from mentally representing a partner’s actions. Chimpanzees can successfully work together, but the cognitive mechanisms they employ to aid social co-ordination remain unclear. Studies of action planning show that, like humans, chimpanzees demonstrate the end-state-comfort effect; considering the end of an action sequence during motor-planning, e.g. using an initially awkward grasp when handling an overturned glass to facilitate how it will end up being held. Human research shows that we extend this to a partner; we pass objects in a way that facilitates the action to be performed with them. Here, we assessed the location in which chimpanzees passed a tool to an experimenter to investigate action accommodation. We manipulated experimenter hand location and their ease of access to locations and found some effect on passing behaviour indicating that, under certain conditions, chimpanzees consider a partner’s actions when planning their own actions in a co-ordination context.

Do chimpanzees represent the actions of a co-ordination partner?

As the mental health crisis deepens with the prolonged COVID-19 pandemic, there is an increasing need for understanding individuals’ emotional experiences. We have built a large-scale Korean text corpus with five self-labeled psychological ground-truths: empathy, loneliness, stress, personality, and emotions. We collected 19,025 documents of daily emotional experiences from 3,805 Korean residents from October to December 2020. We collected 42,128 sentences with different levels of theory-of-mind. Each sentence was annotated by trained psychology students and reviewed by experts. Participants varied in their ages from the early 20s to late 80s and had various social and economic statuses. The pandemic impacted the majority of daily lives, and participants often reported negative emotional experiences. We found the most frequent topics: responses to confirmed cases, health concerns of family members, anger towards people without masks, stress-relief strategies, change of the lifestyle, and preventive practices. We then trained the Word2Vec model to observe specific words that match each topic from the topic model. The current dataset will serve as benchmark data for large-scale and computational methods for identifying mental health levels based on text. This dataset is expected to be used and transformed in many creative ways to mitigate COVID-19-related mental health problems

Building a Psychological Ground Truth Dataset with Empathy and Theory-of-Mind During the COVID-19 Pandemic

It has long been debated whether aesthetics is something uniquely human. Our ongoing project aims to empirically address comparative aesthetics in a collaboration between psychologists and biologists. Defining aesthetic responses as approach behaviour associated with sensory pleasure and corresponding physiological states, we are exploring whether humans and non-human animals share similar mechanisms in their evaluation of sexually attractive stimuli. Specifically, we compare humans and ring doves (Streptopelia risoria), a species whose courtship behaviour and physiology have been extensively investigated in behavioural and neuroendocrinology research. Both species form pair bonds, communicate using visual and auditory signals, and can be tested in laboratory conditions with conspecifics or using video and auditory stimuli. Truly comparative work involves using as similar an experimental approach as possible for both human and animal participants. Here we report our comparative methods and experimental paradigms, and some recent results.

Comparative Aesthetics: A novel approach to investigate multimodal attractiveness in humans and animals

Reasoning about what other people know is an important cognitive ability, known as epistemic reasoning, which has fascinated psychologists, economists, and logicians. In this paper, we propose a computational model of humans’ epistemic reasoning, including higher-order epistemic reasoning—reasoning about what one person knows about another person’s knowledge—that we test in an experiment using a deductive card game called “Aces and Eights”. Our starting point is the model of perfect higher-order epistemic reasoners given by the framework of dynamic epistemic logic. We modify this idealized model with bounds on the level of feasible epistemic reasoning and stochastic update of a player’s space of possibilities in response to new information. These modifications are crucial for explaining the variation in human performance across different participants and different games in the experiment. Our results demonstrate how research on epistemic logic and cognitive models can inform each other.

Does Amy Know Ben Knows You Know Your Cards? A Computational Model of Higher-Order Epistemic Reasoning

We investigated how the language background (L1) of bilinguals influences the representation and use of the second language (L2) through computational models. With the essays part from The International Corpus Network of Asian Learners of English (ICNALE), we compared variables indicating syntactic complexity in their L2 production to predict L1. We then trained neural language models based on BERT to predict the L1 of these English learners. Results showed the systematic influence of L1 syntax properties on English learners' L2 production, which further confirmed integrations of syntactic knowledge across languages in bilingual speakers. Results also found neural models can learn to represent and detect such L1 impacts, while multilingually trained models have no advantage in doing so.

Language representations in L2 learners: Toward neural models

Dogs are not particularly known for complex physical cognitive abilities. However, a number of recent violation-of-expectation studies have challenged this view. In the current eye-tracking study, we further investigated dogs’ (N=15) reaction to physically implausible events, particularly in the context of support, occlusion, and launching events. In Experiment 1, the dogs watched a rolling ball moving over a gap in a surface either falling down or hovering over the gap. In Experiment 2, the dogs saw a ball rolling behind a narrow pole either disappearing behind it or re-appearing on the other side. In Experiment 3, the dogs observed launching events either with or without contact between the balls. The dogs’ pupil dilation response and looking times suggest that they form implicit expectations about occlusion and launching events but not about gravity-related events at least in the context of animated objects on a screen.

Expectancy violations about physical properties of animated objects in dogs

Multimodal signals consist of multiple components in multiple sensory channels and are common in animal courtship. Signal components can carry unique or redundant information about the courting animal. The response to such multimodal displays might additionally reveal multisensory integration such that the response to the whole display is not simply the sum of the responses to the individual parts. In this study, we used high-quality audiovisual recordings of courting male ring doves and measured female behavioural responses to video playback. 21 females were split into three conditions: one featured multimodal, audiovisual playback, while in the other two, either the visual or auditory courtship component was occluded using familiar stimuli (foliage; vacuum cleaner sound). We analysed female behaviours associated with sexual stimulation and compared frequency of behaviours across conditions and between playback and control intervals. Additionally, we measured blood levels of oestradiol before and after testing.

Using playback to investigate multimodal signalling of attractiveness in ring doves (Streptopelia risoria)

People ask questions that are far richer, more informative, and more creative than current AI systems. We propose a neuro-symbolic framework for modeling human question asking, which represents questions as formal programs and generates programs with an encoder-decoder based deep neural network. From extensive experiments using an information-search game, we show that our method can predict which questions humans are likely to ask in unconstrained settings. We also propose a novel grammar-based question generation framework trained with reinforcement learning, which is able to generate creative questions without supervised human data.

Modeling Question Asking Using Neural Program Generation

Empathy is a complex cognitive ability once thought to be unique to humans (Batson, 2003). However, studies suggest dogs can exhibit empathetic behaviors towards owners in distress (Sanford et al., 2018; Bourg et al., 2020). The current study examines the empathetic capacities of dogs presented with a trapped stranger crying or humming behind a see-through closed door. Opening behavior and physiological markers of stress including heart rate variability (HRV) and coded stress behaviors were measured. Unlike in past research, dogs did not open more or faster for the distressed stranger than the non-distressed stranger. This fits with findings on the importance of familiarity on empathetic responding (deWaal, 2008). Additionally, the HRV and owner-reported fear of dogs in the crying condition were lower for dogs that opened than those that did not, suggesting that like children, dogs must have low personal distress to show empathy (Eisenberg et al., 1996).

Domestic dogs’ gaze and behaviour in 2-alternative choice tasks

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Cognitive Linguistics Support for the Evolution of Language from Animal Cognition

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