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Categories can be learned through different strategies. Sometimes we may use abstract rules to categorize objects, and other times we may rely on the perceptual similarity among stimuli. The ability to categorize objects based on a common pattern develops since early childhood and exhibits systematic age differences. Numerous studies demonstrated that younger children rely on similarity-based processes, while older children employ rule-based categorization strategies (Miles &amp; Minda, 2009; Rabi &amp; Minda, 2014; Deng &amp; Sloutsky, 2016). We used a model-based approach to investigate individual differences in category learning in pre-school children (6 years old) and primary school children (6-8 and 10-11 years old). Our results suggest that older children were more likely to employ a rule-based categorization strategy and demonstrated better learning outcomes. Lastly, we employed several computational models of categorization to uncover the properties of the process that may best account for the obtained results.

CogSci 2021

Category learning in preschool and primary school children: The use of rule-based and similarity-based strategies

**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.

Categories can be learned through different strategies. Sometimes we may use abstract rules to categorize objects, and other times we may rely on the perceptual similarity among stimuli. The ability to categorize objects based on a common pattern develops since early childhood and exhibits systematic age differences. Numerous studies demonstrated that younger children rely on similarity-based processes, while older children employ rule-based categorization strategies (Miles & Minda, 2009; Rabi & Minda, 2014; Deng & Sloutsky, 2016). We used a model-based approach to investigate individual differences in category learning in pre-school children (6 years old) and primary school children (6-8 and 10-11 years old). Our results suggest that older children were more likely to employ a rule-based categorization strategy and demonstrated better learning outcomes. Lastly, we employed several computational models of categorization to uncover the properties of the process that may best account for the obtained results.

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**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.

Teachers often use drawings of the normal distribution to support explanations of related statistical concepts, assuming that the normal curve provides a common language for such discussions. However, we find that students may not understand the basic features of the normal curve. In Study 1, we showed that students who already have studied the normal distribution in a college-level class do not understand basic concepts associated with it. Then, in Study 2, we investigated whether a brief instructional, narrated video could improve students’ understanding of the normal probability distribution. Specifically, we compared three instructional formats: static slides, a video recording of a hand physically drawing those plots, and a screen recording of the hand-drawing. Despite the brevity of the intervention, we found significant improvements in students’ understanding of the normal probability distribution and related probability concepts. The findings are discussed in relation to the dynamic representation and embodied cognition literature.

Utilizing Dynamic and Embodied Visualization to Facilitate Understanding of Normal Probability Distributions

Mathematics, by its very nature, is rife with patterns. However, students frequently treat mathematics as a series of isolated and unlinked exercises. The current study examined whether experience with extending mathematical patterns affected adults’ ability to solve equations that involved patterns and/or to reason about mathematical relationships in new contexts. Participants who were given 13 trials of pattern extension experience then went on to demonstrate both more efficient problem-solving (i.e., faster response times) and more accurate problem-solving at posttest, relative to individuals who were given an equivalent amount of explicit instruction related to solving the equations. However, there was no difference between the groups in the ability to abstract the structure of the underlying mathematical relationships. These findings suggest that patterning tasks like those used in this study may be useful in supporting math performance.

Experience with Equations in Sequence Promotes Procedural Fluency

Planning and organization of one's actions are crucial for successfully performing everyday activities such as setting the table. While existing research has addressed planning for well-defined tasks and control of already established sequences, little is known about how such sequences are planned in ill-defined tasks such as everyday activities. Initial attempts suggest that planning may be opportunistic, based on a number of environmental factors to minimize cognitive and physical effort. We address two questions arising from the existing work: First, to what extent is variation in human everyday activity behavior captured by the proposed opportunistic consideration of environmental factors? We address this questions by employing machine learning baselines to gauge the proposed models explanatory scope. Second, to what extent are existing models of sequence control consistent with opportunistic action organization? We address this by investigating and discussing the implications opportunistic planning has for the mechanisms currently assumed for sequence control.

Planning and Action Organization in Ill-Defined Tasks: The Case of Everyday Activities

Visuospatial cognitive skills are increasingly recognized as critical in many areas of both formal and informal learning, but far less research has looked at their role in the workforce. We know, for example, that strong visuospatial skills in high school are predictive of STEM occupations, but we know less about how these skills are actually deployed in the workplace, and also the extent to which these skills might also be important in various non-STEM occupational pathways. Our review provides current context for these questions, including identifying occupations for which these research questions are actively being explored, as well as areas that urgently call for more research. We argue that systematic and rigorous cognitive research should be used to inform evidence-based best practices for addressing the unique workforce-related challenges of the 21st century.

Visuospatial Skills and the Workforce: A Brief Review

Knowing that something is unknown is an important part of human cognition. While Bayesian models of cognition have been successful in explaining many aspects of human learning, current explanations of how humans realise that they need to introduce a new concept are problematic. Bayesian models lack a principled way to describe ignorance, as doing so requires comparing the probabilities of concepts in the model with the probabilities of concepts not present in the model, which is by definition impossible. Formal definitions of uncertainty (e.g. Shannon-entropy) are commonly used as a substitute for ignorance, but we will show that these concepts are fundamentally distinct, and thus that something more is needed. Enhancing probability theory to allow Bayesian agents to conclude that they are ignorant would be an important advance for both cognitive engineering and cognitive science. In this research project, we formally analyse this challenge.

Certainly Strange: A Probabilistic Perspective on Ignorance

Reading comprehension research has identified sources of children’s difficulty with inference-making: lack of semantic/content knowledge and logical reasoning difficulty. NLP tools modeling semantic knowledge (e.g. BERT) can predict adult inference-making, but it is unclear whether they can predict children’s inference-making difficulty. In our ongoing study, we will examine whether our new inference difficulty metric can predict kindergarten students’ inference-making, using empirical data from a classroom intervention (ELCII). Students were given verbal information on a topic and multiple-choice questions, which require students to draw an inference from two given scaffolds. To develop this metric, we will train BERT on children’s books and ELCII content to compute an additive inference vector, the sum of the two vectorized scaffolds. The cosine distance between the additive and correct inferences may indicate inference difficulty. Results will indicate whether a probabilistic semantic space can model children’s inferences or if other components (e.g. logic) should be considered.

A metric of children’s inference-making difficulty during language comprehension

Co-speech gestures can enhance a listener’s understanding of a spoken message, yet children show a greater benefit of gesture than adults (Hostetter, 2011). We explore whether this effect may be driven by language proficiency, and in turn, by differences in how children visually attend to gesture when they are processing narratives in their stronger vs. weaker language. Bilingual children were shown narratives with scripted gesture in their stronger and weaker languages, while their visual attention was monitored. Memory for narratives was then assessed. Our findings suggest language proficiency does affect the degree to which children benefit from co-speech gesture – children showed a greater boost from co-speech gesture when processing narrative in their weaker language. Results also suggest that greater attention to gesture when processing ones’ weaker language may underlie this effect.

Language Proficiency Impacts the Benefits of Co-Speech Gesture for Narrative Understanding Through a Visual Attention Mechanism

Early vocabulary development is heavily influenced by children’s language environment. For example, higher SES children who have frequent and rich interactions with caregivers tend to have larger vocabulary size than lower SES children who have less stable home environment (e.g., Hoff & Naigles, 2002). Because of the recent pandemic, most Canadian children have experienced dramatic changes to their day-to-day life. In the current study, we ask whether the pandemic environment affects children’s vocabulary growth. We compare the MacArthur-Bates Communicative Development Inventory (CDI) scores of 11- to 34-month-olds collected before (N=1365) and after (N=301) the onset of COVID-19. Preliminary results show that CDI scores collected for toddlers after the pandemic onset are significantly lower than those collected before. We will continue to collect additional CDI data, and examine how factors such as screen time, reading time, SES, and number of people the child has regular interaction with mediate children’s vocabulary size.

Vocabulary Growth in Infancy and Toddlerhood: The Impact of COVID-19

Maintaining attention during instruction is challenging as children face various sources of distraction (peers, announcements, noise) as well as competition from the visual environment itself. Prior studies found decorated environments promote off-task behavior and reduce learning. Additionally, many classroom displays are not relevant to ongoing instruction. This raises the possibility that increasing alignment between displays and instructional content may afford opportunities for incidental learning, reducing the detrimental effects of environmental off-task behavior. To investigate this possibility, participants completed a lesson in which alignment between the lesson content and displays was manipulated (relevant, educational but irrelevant, or no displays). Attention to the lesson and learning gains for content presented in the lesson and/or displays were measured. Results suggest younger children’s learning can benefit from displays that reinforce the lesson content. However, there was no evidence of incidental learning from displays without additional lesson support. Implications for classroom design are discussed.

The Decorated Learning Environment: Simply Noise or an Opportunity for Incidental Learning?

Gesture during math instruction supports learning in children and adults. The mechanism by which gesture enhances learning across development is not known. One possibility is that instruction with gesture engages different cognitive abilities during learning than instruction without gesture. Our previous work showed a positive relationship between visuospatial working memory capacity and learning only when gesture was present, and a positive relationship between verbal working memory capacity and learning only when gesture was absent, suggesting that gesture may be processed using visuospatial working memory. The aim of the current experiment was to replicate and extend these prior findings with new instruction, random assignment to instructional condition, and improved measures of both learning and cognitive abilities. Participants observed video instruction in a novel mathematical system that either included speech and gesture or only speech. After instruction, participants completed a posttest to assess learning. Finally, participants completed tasks to assess verbal and visuospatial working memory capacity as well as fluid and crystallized intelligence. We found that gesture benefitted learning in adults. Contrary to previous findings, both learning with gesture and learning without gesture were supported by visuospatial working memory. These findings suggest that changing characteristics of instruction does not necessarily change the cognitive resources supporting learning in a novel math task.

Category learning in preschool and primary school children: The use of rule-based and similarity-based strategies

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Utilizing Dynamic and Embodied Visualization to Facilitate Understanding of Normal Probability Distributions

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