United States

We perceive and categorize the world constrained by the restrictions of our sensory and neural systems. Additionally, naming modulates how we categorize the things we see. Here we explore a third influence on visual categorization, we study how the perceived statistical regularities of shapes and colors modulate our experience in categorizing objects. We conducted our analyses in artificial systems. We used computer vision to process pictures of real objects, and artificial neural networks to categorize them. We found that the statistical regularities of different object sets produced either shape or color biases, depending on the nature of the set. Our statistical-based categorization approach presents complementary mechanisms of categorization biases, relevant for a more comprehensive understanding of the linguistic shape bias, the color bias of food, and it let us hypothesize why categorization may vary across populations.

CogSci 2021

The Statistical Properties of Color and Shape of Objects in Visual Categorization

**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,**
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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.

Many researchers have demonstrated the change of body representation, including the rubber hand illusion and pinocchio illusion. However, they focused on the change of a single body part. The human body has a symmetric structure; therefore, the modification of body representation can be facilitated by the corresponding movement of symmetric parts. In our four experiments, participants moved their hands in different manners with distorted vision and were measured whether their behavior changed throughout the task. The four tasks differed in whether participant's hands were moving simultaneously or separately and whether they moved their hands to the same point or a different point. A behavioral change occurred in all experiments. When the participants moved their hands to the same point simultaneously the greatest behavioral change was facilitated. Neither moving both hands simultaneously or moving to the same position facilitated the change.

Change of Body Representation in Symmetric Body Parts

How should we represent social categories? Essentialism, which posits an internal category essence, has negative consequences such as group-based generalization and intolerance of nonconformity. Structural representations, which consider categories as situated in a larger context, could be more constructive. When considering a neutral context, adults who learned a structural (food availability) cause for a novel group’s diet, versus a biological (allergies, digestion) or cultural (taboo) cause, generalized in a context-sensitive manner, considered nonconformity to be more possible and acceptable, and suggested intervening on the structural context rather than the group to change the property. When considering social stratification, adults who learned a structural (discrimination) cause for a novel group working a low-status job, versus a biological (physically well-suited) or cultural (traditions/values) cause, showed more context-sensitive generalization, considered the present disparity to be more unacceptable, and suggested more structural interventions. Structural representations may consequently be a more constructive alternative to essentialism.

Let’s talk structure: the positive consequences of structural representations

Performance on cognitive tasks is typically affected by many factors simultaneously. For example, performance on a retrieval practice task is determined not only by memory encoding and retrieval processes, but also by motor processes, executive functions, affective state, etc. Often, these contributing processes also vary over time. This makes it challenging to attribute performance changes to a single mechanism, especially in real-world settings.

Here we analyse performance data from multiple retrieval practice sessions completed by special forces trainees before and after a high-intensity speed march. Learning outcomes improved from session to session, which suggests increased memory function. However, a linear ballistic accumulator fitted to the data showed that changes in non-memory processes—a decrease in non-retrieval time in particular—sufficed to explain the observed improvements.

This work demonstrates that even with a simple task, assuming that performance changes are attributable to a single cognitive process can lead to erroneous conclusions.

Memory Performance in Special Forces: Speedier Responses Explain Improved Retrieval Performance after Physical Exertion

The category variability effect is referred to as that the middle item between two categories is more similar to the low-variability category but tends to be classified as the high-variability category, which challenges the exemplar model. We however hypothesized that this effect can result from the use of the single-category strategy in a binary categorization task, specifically when only the low-variability category is referenced for categorization. One experiment was conducted with a recognition task inserted in the categorization task to selectively deepen the processing for the exemplars of the high-variability category, low-variability category, or both categories. The results showed that the strongest category variability effect occurred when the low-variability category was emphasized in the recognition task. The exemplar model SD-GCM provided a good account for the category variability effect, with a large weight for the low-variability category and a small weight for the high-variability category, hence verifying our hypothesis.

Exemplar Account for Category Variability Effect: Single Category based Categorization

Spacing out information promotes retention more than massing information – a robust finding in psychological science. Research on the spacing effect has primarily manipulated aspects of the learning schedule (e.g., item repetitions, retention interval). Limited work has considered how familiarity with the to-be-learned information impacts the spacing effect. The current study addressed this gap by examining how word frequency/familiarity affects retention on a massed or spaced schedule. One-hundred adults were presented with 24 high familiarity (e.g., apple), low familiarity (e.g., vestige), or nonsense (e.g., blicket) words on a massed and spaced schedule. Retrieval was tested after a 5-minute delay, revealing a significant spacing effect regardless of word familiarity. Furthermore, overall performance was significantly greater for highly familiar items. These results suggest that spacing is effective regardless of general word familiarity. Future studies will assess how learners’ prior knowledge of test items – as measured by a vocabulary test – impacts the spacing effect.

Does the spacing effect depend on prior knowledge? Evaluating the role of word familiarity in learning from spaced vs. massed schedules

We trust ingroups more than outgroups (Balliet et al. 2014, Psychological Bulletin), but to correctly identify group members we need reliable markers of group membership (Cohen 2012, Current Anthropology). Artificial language experiments show that linguistic tags can serve as such group markers (Roberts 2013, Language and Linguistics Compass). We now tested whether sharing a language also promotes trust, in comparison to other physical and cultural ingroup markers. We created an online alien game in which participants assumed the identity of an alien and played a simultaneous trust game (Berg et al. 1995, Games and Economic Behavior) with two other aliens exhibiting the ingroup versus outgroup marker types (artificial languages, body parts, costumes). GLM analyses showed that participants significantly entrusted aliens sharing their own social marker with more money, independently of marker type. Thus, a considerable number of people trusted those who spoke or looked like themselves more.

Do I trust you more if you speak like me?

Understanding how people mentally represent expenditures is crucial to understanding how they manage their money. In this paper, we report three studies that investigate people’s representation of budgeting categories by asking people to categorize common expenditures of money (e.g., rent, dining out, etc.). We then examine the implications of these taxonomic representations of expenditures for how people selectively restrict their uses of money (e.g., when overspending on one item, for which other items would people choose to spend less). We found that there is consensus in people’s representations of expenditures, and that both the category membership and taxonomic distance between items predict how people restrict their spending.

Mental Representation of Budgeting Categories

COVID-19 and the 2021 U.S. Capitol attacks have highlighted the potential dangers of pseudoscientific and conspiratorial belief adoption. Approaches to combating misinformed beliefs have tried to "pre-bunk" or "inoculate" people against misinformation adoption and have yielded only modest results. These approaches presume that some citizens may be more gullible than others and thus susceptible to multiple misinformed beliefs. We provide evidence of an alternative account: it's simply too hard for all people to be accurate in all domains of belief, but most individuals are trying. We collected data on a constellation of human beliefs across domains from more than 1,700 people on Amazon Mechanical Turk, and find misinformed beliefs to be broadly, but thinly, spread among the population. Further, we do not find that individuals who adopt one misinformed belief are more likely to engage in pseudoscientific or conspiratorial thinking across the board, in opposition to "slippery slope” notions of misinformation adoption.

"Fringe" beliefs aren't fringe

Human Theory of Mind enables us to attribute mental states like beliefs and desires based on how other people act. However, in many social interactions (particularly ones that lack observable action), people also directly think about other people's thinking. Here we present a computational framework, Bayesian inverse reasoning, for thinking about other people's thoughts. Our framework formalizes inferences about thinking by inferring a generative model of reasoning decisions and computational processes, structured around a principle of rational mental effort -- the idea that people expect other agents to allocate thinking rationally. We show that this model quantitatively predicts human judgements in a task where participants must infer the mental causes behind an agent's pauses as they navigate and solve a maze. Our results contribute to our understanding of the richness of the human ability to think about other minds, and to even think about thinking itself.

hinking about thinking through inverse reasoning

People's visual experiences are easy to examine along natural language boundaries, e.g., by categories or attributes. However, it is more difficult to elicit detailed visuospatial information about what a person attends to, e.g., the specific shape of a tree. Paying attention to the shapes of things not only feeds into tasks like visual category learning, but also enables us to differentiate similarly named objects and to take on creative visual pursuits, like poetically describing the shape of a thing, or finding shapes in the clouds or stars. We use a new data collection method that elicits people's prioritized attention to shapes during visual photo inspection by asking them to trace important parts of the image under varying time constraints. Using data collected via crowdsourcing over a set of 187 photographs, we examine changes in patterns of visual attention across individuals, across image types, and across time constraints.

The Statistical Properties of Color and Shape of Objects in Visual Categorization

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Change of Body Representation in Symmetric Body Parts

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