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Spacing is a highly effective encoding strategy that has been shown to benefit memory in a variety of domains. Recent work has emphasized the evaluation of spaced practice under conditions that more closely reflect daily life. This work found that spacing repetitions over days, weeks, or months is effective over retention intervals as long as one year. One aspect of spaced study that has received less attention, however, is the relationship between the number of acquisition sessions and final retention. That is, if a student preparing for an exam plans to allocate 10 hours to preparing for that exam, is there an optimal, or perhaps minimal, number of study sessions that they should engage in to best leverage the benefits of spacing? In the present experiment we had participants complete 16 practice tests of Japanese-English pairs (e.g., boushi – hat). These practice tests were either all completed in one session, or distributed across two, three, or four sessions. These sessions were spaced either 1, 7, 30, or 90 days apart. Participants completed four test trials following a retention interval of 90 days, 180 days, or some variable length. Our results suggest that the number of acquisition sessions monotonically enhanced first-trial test performance as well as relearning, though evidence for enhanced relearning between one and two sessions was ambiguous. Unexpectedly, these monotonic trends were stable across practice lags and retention intervals. These findings suggest that, in addition to the temporal lag between practice episodes, the number of sessions over which one elects to distribute those episodes also has ramifications for long-term retention, and that each additional session yields meaningful benefits.

CogSci 2021

Additional acquisition sessions monotonically benefit retention and relearning

**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)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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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,**
![](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.

Previous research suggests that humans use multimodal information during emotional communication. Only a few studies have investigated how emotional information is presented in non-verbal channels. We examined the role of hand gestures in communicating emotions. In a between-subject design, we analyzed the size, frequency, and type of gestures under encouraged and spontaneous gesturing conditions. Participants (N=36) were asked to describe narratives with emotional content. Preliminary analyses with 18 individuals show that, interestingly, gesture frequencies and gesture use with specific emotional phrases did not differ between groups. Also, there was not a significant difference between the two groups in the types of gestures they produced. However, the gestures in the encouraged condition were significantly bigger in size compared to gestures in the spontaneous condition. These diverse preliminary results suggest that people’s hand gestures during emotional communication should be investigated with all their dimensions instead of focusing on one aspect.

The Role of Hand Gestures in Emotion Communication

How do people ask questions to zero in on a correct answer? Although we can formally define an optimal query to maximize information gain, algorithms for finding this optimal guess may impose large resource costs in space (memory) and time (computation). 
To understand how people trade off the information gain and the computational difficulty of choosing the ideal query, we turned to a large dataset of 380,000 guesses made during a number-guessing game with Amazon Alexa. We analyzed whether the arithmetic difficulty of following the optimal strategy predicts how far a guess deviates from theoretically optimal query. We find that when memory load is higher, and when more arithmetic operations need to be performed, human guesses deviate more from the most informative query.
These results suggest human computational resource constraints limit how people seek out informative questions.

Cognitive cost and information gain trade off in a large-scale number guessing game

The COVID-19 pandemic and its profound global effects may be changing the way we think about illness. We surveyed 120 American adults to explore the effects of the pandemic on their mental models of illness. Participants read three vignettes: one relating to COVID-19, one to cancer, and another to the common cold, and were asked questions relating to the diagnosis, treatment, prevention, time-course, and transmission of each disease. Results showed that participants were more likely to correctly diagnose COVID-19 (93% accuracy) compared to a cold (60% accuracy) or cancer (51% accuracy). Of the 40 participants that incorrectly diagnosed the cold vignette, 7 misdiagnosed a cold as COVID-19. These and other preliminary findings suggest a distinct mental model for COVID-19 compared to other illnesses. The prevalence of COVID-19 in everyday discourse may lead to biased responding, similar to errors in medical diagnosis that result from physicians’ expertise (Hashem et al., 2003).

Mental Models of Illness in the COVID-19 Era

Across the lifespan, humans direct their learning towards information they are curious to know. However, it is unclear what elicits curiosity, and whether and how this changes across development. Is curiosity triggered by surprise and uncertainty, as prior research suggests, or by expected learning, which is often confounded with these features? In the present research, we use a Bayesian reinforcement learning model to quantify and disentangle surprise, uncertainty, and expected learning. We use the resulting model-estimated features to predict curiosity ratings from 5- to 9-year-olds and adults in an augmented multi-armed bandit task. Like adults’ curiosity, children’s curiosity was best predicted by expected learning. However, after accounting for expected learning, children (but not adults) were also more curious when uncertainty was higher and surprise lower. This research points to developmental changes in what elicits curiosity and calls for a reexamination of research that confounds these elicitors.

Developmental Change in What Elicits Curiosity

We investigate whether reasoners are sensitive to the underlying causal structure of an event when evaluating its likely causes. Participants read stories in which two unknown causes led to an outcome in either a converging or linear structure. They were then asked to select two of three possible causes to complete the story. Two candidates were semantically-related, direct causes of the outcome. The third was an unrelated, indirect cause of the outcome that was conditional on a directly-related event. Differences in abstract structure, and not association, guided people’s evaluations of the most likely causes (e.g., ‘breeze blowing’ was judged an unlikely direct cause of a noisy room compared to ‘alarm ringing’ or ‘door slamming’, but a likely indirect cause, conditional on the door slamming). Results demonstrate that people consider abstract information about the structure of an event when evaluating causes. Knowledge of causal structure may therefore guide hypothesis evaluation.

Knowing the Shape of the Solution: Causal Structure Constrains Evaluation of Possible Causes.

Elementary schools provide students with their first encounters with formal science, creating both foundations for students’ knowledge of science content, and impressions of what it means to learn science. Here, we examined adult, including K-12 teachers’, intuitions regarding different types of content relevant to elementary school science, namely, labels, function, and mechanism. We focused primarily on perceptions of mechanistic explanations—causal explanations that explain how something works. This focus stems from children’s curiosity and aptitude for mechanism. Across four studies we predicted, and found, that participants deprioritize mechanistic explanations relative to more superficial explanation types: labels and function. This intuition, which appears to be reflected in formal science curricula, misestimates children’s abilities and attitudes towards mechanistic information.

Adult Intuitions about Mechanistic Content in Elementary School Science Lessons

The recent pandemic has emphasized the need for developing more effective pedagogical practices in online education. While activities that require more deep connection of concepts can enhance overall learning versus mere rote rehearsal, are conceptual activities also viable options for inclusion in online coursework, given the asynchronous and flexible nature of online learning? To answer this question, students in 2 online courses either completed concept-mapping exercises, or control exercises (i.e., definitions of concepts). Results indicate that while initially concept-mapping is no better than memorization, as experience with mapping increases, a growth in learning emerges later in the course. Students also rate the educational utility and enjoyablity of conceptual activities similarly to control exercises, suggesting that students do not perceive these conceptual activities more negatively. As such, concept-mapping does have a utility for online learning, but there is a necessary a warm-up period before benefits are realized.

Concept mapping produces delayed benefits in online learning.

According to theories of event-predictive cognition, neural processing focuses on the next relevant interaction targets. Evidence for this notion comes from the anticipatory crossmodal congruency effect (aCCE), which implies that spatial body representations are mapped onto future goal locations in advance of a goal-directed action. Here we present a free energy based normative process model that accounts for the aCCE quantitatively by applying crossmodal mappings between vision and touch as well as active inference. A comparison with a diffusion model shows that our model accounts for the response time distributions and the aCCE with a sparser set of parameters. However, the temporal dynamics of the model require further fine tuning to account for all aspects of the aCCE. The model shows how the free energy framework can be used to account for behavioral data in general and how to implement theories of event-predictive cognition in a normative cognitive process model.

Modeling the Anticipatory Remapping of Spatial Body Representations: A Free Energy Approach

Core knowledge concepts such as object behavior principles provide a rich inventory of primitives for thinking and learning in the natural world. However, it remains unexplored how these concepts are reused for problem-solving and communication in highly abstract domains.

We analyze a large-scale natural language study drawing on the Abstraction and Reasoning Corpus (ARC), a set of highly abstract visual tasks where solvers construct outputs from input grids according to an inferred pattern. ARC explicitly incorporates core knowledge principles without any real-world objects. In the study, subjects solved and communicated the inferred patterns of ARC tasks via written explanations for other subjects attempting to solve tasks using only the explanations.

We examine how subjects solve, communicate, and interpret these explanations, and we show that subjects use fundamentally abstract core knowledge properties–object cohesion and contact causality–to reason about, understand, and communicate the inference tasks with language.

Core knowledge objects in reasoning and language use for highly abstract inductive tasks

Young infants reason about the goals, preferences, and actions of others. State of the art computational models, however, still fail in such reasoning. The Baby Intuitions Benchmark (BIB) was designed to test agency reasoning in AI using an infant behavioral paradigm. While BIB’s presentation of simple animations makes it particularly suitable for testing AI, such vignettes have yet to be validated with infants.

In this pilot, 11-month-old infants watched two sets of animations from BIB, one on agents’ consistent preferences and the other on agents’ efficient actions. Infants looked longer towards violations in agents’ behavior in both the preference (N = 24, β = 3.24 p = .040) and efficiency task (N = 24, β = 4.50 p = .016).

These preliminary results suggest that infants’ agency reasoning is abstract enough to be elicited by simple animations and validate BIB as a test of agency reasoning for humans and AIs.

Additional acquisition sessions monotonically benefit retention and relearning

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The Role of Hand Gestures in Emotion Communication

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