United States

Predictive Processing (PP) has been suggested to account for early cognitive development (Köster et al., 2020). In this paper, we extend its application to early social coordination in parent-infant interactions. Interpersonal neural synchrony in parent-child interactions is hypothesized to be a function of the coupling of internal generative models for mutual prediction. By aligning these internal models and reducing prediction errors, social uncertainty is reduced, and interpersonal neural synchrony is enhanced. Support for this hypothesis is provided by assessing neural synchrony during mother-infant interaction.

CogSci 2021

Infants’ Social Communication from a Predictive Processing Perspective

**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

### 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.

Humans have a remarkable ability to go beyond the observable. From seeing the current state of our shared kitchen, we can infer what happened and who did it. Prior work has shown how the physical state of the world licenses inferences about the causal history of events, and the agents that participated in these events. Here, we investigate a previously unstudied source of evidence about what happened: social evaluations. In our experiment, we present situations in which a group failed to optimally coordinate their actions. Participants learn how much each agent was blamed for the outcome, and their task is to make inferences about the situation, the agents' actions, as well as the agents' capabilities. We develop a computational model that accurately captures participants' inferences. The model assumes that people blame others by considering what they should have done, and what causal role their action played. By inverting this generative model of blame, people can figure out what happened.

Who went fishing? Inferences from social evaluations

Music is highly complex and provides a rich variety of insights into the human mind, its mental structures, and processes. Experienced musicians are able to create complex structures in real time effortlessly, yet there is at present no successful model of full musical structure. The integration of different musical aspects such as melody, rhythm, voice leading, and form as well as the representation of long-term structure are particularly challenging. To open new possibilities for the study of higher-order structure in music and its perceptual correlates, cognitive music research would benefit from further mutual integration of theoretical, mathematical, computational, and psychological research, similar to advancements in linguistics. This symposium therefore focuses on the formal understanding and empirical investigation of music-theoretically motivated research questions in music cognition. It connects perspectives from music theory, behavioral research, corpus research, and computational modeling, and aims to initiate interdisciplinary discussions about the currently most challenging topics related to the cognition of higher-order structures in music.

Music Cognition: The Complexity of Musical Structure

Psychological similarity—the subjective distance between objects in the world or memory—is a highly influential concept in many areas of cognitive psychology, such as learning, memory, categorization, judgment, and preferential choice. The contributions within this symposium will evaluate the fundamental role that similarity plays in human judgment and decision making. We bring together experts from distinct subdisciplines of psychology, who examine the influence of similarity on categorization, consumer choice, risky choice, social norms, and in memory-based choices. Specifically, the contributions elaborate on three key questions repeatedly pursued within cognitive psychology: 1) how does similarity activate previous experiences and renders them available within a given choice context? 2) how does similarity interact with feature or knowledge abstraction processes? 3) how is similarity represented psychologically? To reach this goal, the contributions within this symposium focus on reinstating similarity-based processes within formal cognitive models and test their predictions experimentally.



Similarity-based Influences in Judgment and Decision Making

Over their first years of life, children learn not just the words of their native languages, but how to use them to communicate. Because manual annotation of communicative intent does not scale to large corpora, our understanding of communicative act development is limited to case studies of a few children at a few time points. We present an approach to automatic identification of communicative acts using a Hidden Topic Markov Model, applying it to the CHILDES database. We first describe qualitative changes in parent-child communication over development, and then use our method to demonstrate two large-scale features of communicative development: (1) children develop a parent-like repertoire of our model's communicative acts rapidly, their learning rate peaking around 14 months of age, and (2) this period of steep repertoire change coincides with the highest predictability between parents' acts and children's, suggesting that structured interactions play a role in learning to communicate.

Infants’ Social Communication from a Predictive Processing Perspective

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Who went fishing? Inferences from social evaluations

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Motivational Gestures in Robot-Assisted Language Learning: A Study of Cognitive Engagement using EEG Brain Activity

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