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Despite their apparent importance for the acquisition of full-fledged human intelligence, mechanisms of intrinsically motivated autonomous learning are poorly understood. How do humans identify useful sources of knowledge and decide which learning situations to approach in the absence of external rewards? While the recognition of this important problem has grown in psychological sciences over the recent years, an intriguing proposition for the possible mechanism comes from artificial intelligence, where efficient autonomous learning is achieved by programming agents to follow the heuristic of maximizing learning progress (LP) during exploration. In this study, we set out to examine the empirical evidence for this idea. Using computational modeling, we demonstrate that humans show signs of following LP while they freely explore and practice a set of multiple learning activities of varying difficulty, including an activity that is impossible to learn. Different approaches to operationalizing the notion of LP and their plausibility in light of empirical data are also discussed. We also show that models combining several types of intrinsic rewards fit better human exploration data than single component models considered so far in theoretical accounts.

CogSci 2021

Intrinsic Rewards in Human Curiosity-Driven Exploration: An Empirical Study

<center>Louis Stokes Regional Center of Excellence for the Study of STEM Interventions<center>

<CENTER>Translating Critical, Strengths-Based Conceptual Frameworks to STEM Programming Design & Dissemination<CENTER>


 **Sponsored in Partnership by:**
 National Science Foundation, Understanding Interventions, University of Michigan and University of Missouri
 
 
 		
	
Dear Colleagues,

Welcome to the inaugural Louis Stokes Regional Center of Excellence for the Study of STEM Interventions ‎‎(LSCRE ‎‎‎‎SOSI) Summer Institute. SOSI is an NSF funded initiative that reflects a collaboration of STEM and STEM Education Scholars ‎and Program Administrators from the University of Missouri-Columbia, Understanding ‎Interventions, Inc., and the University of Michigan.‎ We offer training and development for STEM program administrators, faculty, and other ‎stakeholders ‎in critical, strengths-based theories and evidence-based practices. Key to our work is providing the tools and avenues for our participants to inform their practice and contribute to the body of translational research that facilitate ‎underrepresented students’ success in STEM. Beyond the Summer Institutes, SOSI, through the Understanding Interventions Index, supports resources for access to the STEM research-to-practice translational literature, a database of journals that publish these works, relevant published measures, and the Understanding Interventions Journal as an outlet for dissemination. This work also extends to webinars and workshops held throughout the year as an integral part of the collaboration between SOSI and its partners. 

This 2021 Summer Institute is designed to address participant needs across a range of experience in our concurrent sessions. Concurrent Sessions 1 are tailored for those of us who have professional training outside the social and behavioral sciences but are looking to develop proficiency, scholarship, and utilize methodologies that test theories like Community Cultural Wealth (CCW), Phenomenological Variant of Ecological Systems Theory (PVEST) and Social Cognitive Career Theory (SCCT). Concurrent Sessions 2 are tailored to those with some experience in these areas and are looking to engage more deeply. Some sessions like those that address measures, implementation, and outcomes will be joint. For the current topic sessions, participants will not be limited a specific concurrent session but will have the flexibility to attend the session that they feel most appropriate to their level of experience. 

We look forward to an exciting and productive institute as we all leverage our varied experiences, perspectives, and ideas to this growing community of practice. We would also like to extend our deep gratitude to the National Science Foundation for its generous support of this work.


Best,

SOSI Leadership Team

SOSI Virtual Summer Institute 2021

poster

### 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,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

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.

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.

Infants’ Social Communication from a Predictive Processing Perspective

In order to communicate, humans flatten complex ideas and their attributes into a sequence of words. Humans can use this ability to express and understand complex hierarchical and relational concepts, such as kinship relations and logical deduction chains. We simulate communication of relational and hierarchical concepts using artificial agents. We propose a new set of graph communication games, which show that agents parametrized by graph neural networks develop a more compositional language compared to bag-of-words and sequence models. Graph-based agents are also more successful at systematic generalization to new combinations of familiar features. We release the implementation to probe research on emergent communication over complex data.

Structural Inductive Biases in Emergent Communication

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.

Intrinsic Rewards in Human Curiosity-Driven Exploration: An Empirical Study

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Infants’ Social Communication from a Predictive Processing Perspective

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