United States

Altering an individual’s identity during virtual tasks, such that it is incongruent with their own identity, can reduce unconscious biases and increase empathy and prosocial behavior. While such virtual embodiment has been investigated for race, age, and socioeconomic status, the question remains whether immersive perspective taking reduces implicit gender bias. Accordingly, we investigated the effect of gendered embodiment on gender bias related to the underrepresentation of women in science, technology, engineering, and math fields. Undergraduate students (N=65) undertook a simulated virtual interview task, which included the rating and selection of male and female candidates. Females were significantly more empathetic than males. Greater empathy predicted higher candidate ratings, except for competence of the female candidate. Preliminary findings establish foundations for future comparative studies and contribute to elucidating the nature of empathy during and after virtual perspective taking, such that it may have a greater impact on males for modulating gender bias.

CogSci 2021

Perspective Taking in Virtual Reality: Addressing Gender Bias in STEM

**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**

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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.

Preschool children vary in their numerical knowledge, and this variation predicts math achievement throughout elementary school. Can preschool interventions that exercise school-relevant numerical concepts support later school math learning, and if so, what numerical activities should be targeted to best foster this learning? Here we ask whether a game-based intervention targeting preschool children’s understanding of the base-10 compositional system of number words and symbols improves their school-relevant numerical concepts in the short term. Five- to six-year-old children who played a numerical board game at home with their parents for two-three weeks showed improved preschool numerical concepts, compared to children who played a game with similar materials and procedures but no numerical content. This finding takes a first step toward developing and evaluating a suite of game-based interventions, leveraging research in developmental cognitive science both to enhance children’s learning in school and to deepen understanding of how children learn.

Enhancing Preschool Readiness: Evidence from a Home-based Game to Improve 5-year-old Children’s Mastery of Symbolic Numbers and Concepts

The aim of this study was to identify the effects of perceptual (visual and olfactory) and emotional imageries of food names to word recognition memories. First, we asked Japanese healthy participants to imagine visual (Experiment 1), olfactory (Experiment 2), emotional (Experiment 3), or preferential features (Experiment 4) of presented words associated with foods and drinks (imagining condition), while we also asked them to passively read presented words (reading condition). Second, they judged whether each word was previously presented (word recognition memory task). Results showed that accuracy rates of the imagining condition were significantly higher than those of the reading condition in word recognition memory tasks of all the experiments. Our findings suggest that perceptual and emotional imageries of food names facilitate word recognition memories.

Effects of perceptual and emotional imageries of food names to word recognition memories: four behavioral experiments

Interrupted time series analysis (ITSA) is a statistical procedure that evaluates whether an intervention causes a change in the intercept and/or slope of the time series. However, very little research has accessed causal learning in interrupted time series situations. We systematically investigated whether people are able to learn causal influences from a process akin to ITSA, and compared four different presentation formats of stimuli. We found that participants' judgments agreed with ITSA in cases in which the pre-intervention slope is zero or in the same direction as the changes in intercept or slope. However, participants had considerable difficulty controlling for pre-intervention slope when it is in the opposite direction of the changes in intercept or slope. The presentation formats didn't affect judgments in most cases, but did in one. We discuss these results in terms of two potential heuristics that people might use aside from a process akin to ITSA.

Causal Learning With Interrupted Time Series

Human learning efficiency in reinforcement learning tasks decreases when the number of the presented stimuli increases, a finding known as the "set size effect". From the computational rationality perspective, this effect can be interpreted as the brain’s balancing task performance against rising cognitive costs. Still, it remains unclear how best to quantify cognitive cost in learning tasks. One candidate is policy complexity, defined in terms of information theory as the mutual information between the sensory input and behavioral response. However, using a published data set (Collins & Frank, 2012), we show that policy complexity alone cannot explain the set size effect because the optimal policy complexity does not necessarily increase with the set size. We therefore propose a computational model and conduct a model-based analysis to show the minimal constituents of cognitive cost are policy complexity and representation complexity---the information quantity conveyed from sensory inputs to internal representations.

A computationally rational model of human reinforcment learning

Relational reasoning is an important component of abstract thought that emerges early in development but shows substantial variation across contexts, with children in the US and China following distinct developmental trajectories in one paradigm between 18m and 4y. To understand the mechanisms through which variation in the learning environment influences the development of relational reasoning, we examine early relational reasoning in Punjabi speakers in India, who share some cultural and linguistic elements of their experience with children in both the US and China. In a causal relational match-to-sample task, we find that 3-year-olds in India exhibit performance that is intermediate to their high-performing peers in China and the relatively poor performance observed in the US at this age. These results suggest complexity and variability in the development of relational reasoning and lay a foundation for future research designed to tease apart the factors associated with early diversity in relational reasoning.

Characterizing the development of relational reasoning in India

Novel word disambiguation via mutual exclusivity is the tendency to discard familiar objects as referents of novel words. While mutual-exclusivity inferences have been widely documented in toddlers beyond 18 months of age, studies typically failed to show it in younger infants. Recently, Pomiechowska, Brody, Csibra, and Gliga (2021) showed that 12-month-old infants can use mutual exclusivity, but only if familiar objects are targeted by non-verbal communication (i.e., pointing) prior to labeling. This arguably triggers infants to represent these objects under a conceptual description, a representational format that is necessary for mutual exclusivity inferences but is not spontaneously generated in young infants. The present study is a direct online replication of the task used by Pomiechowska et al. with a wider age range (12-15 months) and adapted for Zoom-based testing.

Mutual exclusivity inferences in 12-to-15-month-olds: an online looking-while-listening study

In choice blindness (CB) experiments participants often accept a manipulated outcome as their actual choice. In a typical CB experiment the manual actions that participants perform are always correct (pointing, writing, etc.), while the outcome is mismatched. However, what would happen if an error was induced at the motor level, but the outcome nevertheless remained correct? We investigated this by having participants drag a mouse cursor across the screen to the face they found the most attractive, while we manipulated either the outcome (classic CB), or the cursor (forced motor deviation), or sometimes both. Interestingly, what we found was that when the cursor was manipulated but not the outcome, the motor ‘wrongness’ would override the goal ‘rightness’, and participants ended up rejecting the outcome they actually wanted. We will discuss the implications of this new reverse choice blindness effect for theories of self-monitoring, agency and preference change.

If you think your action was erroneous, you will reject the outcome you actually wanted: a case of reverse choice blindness

Hindsight bias is exhibited when knowledge of an outcome (i.e., an anchor) affects subsequent recollections of previous predictions (i.e., an estimate). Hindsight bias usually leads to estimates being remembered as closer to the anchor than they actually were. The exact amount of hindsight bias exhibited depends on the anchor value and the anchor plausibility, with experimental results showing that hindsight bias is elicited only when the anchor is perceived to be plausible. In this paper we present a Bayesian model that captures the relationship between hindsight bias and anchor plausibility. This model provides a rational account of hindsight bias by considering memory recall as a statistical problem, where the goal is to reconstruct the original estimate using the anchor as new evidence. Simulations show that the modeled trends align closely with previously published human data.

A Rational Account of Anchor Effects in Hindsight Bias

Though humans should defer to the superior judgement of AI in an increasing number of domains, certain biases prevent us from doing so. Understanding when and how these biases occur is a central challenge for human-computer interaction. A proposed source of such bias is the perceived subjectivity of tasks. We tested this hypothesis using one of the most subjective tasks possible: Evaluating joke funniness. Across two experiments, we addressed the following: Would people rate jokes as less funny if they believed an AI created them? When asked to rate jokes and guess their likeliest source, participants evaluated jokes attributed to humans as the funniest and those to AI as the least funny. However, when we explicitly framed these same jokes as either human or AI-created, there was no difference in performance-level ratings. These results challenge the notion that task subjectivity always biases users against AI if the source is transparent.

The Funny Thing About Algorithm Aversion: Investigating Bias Toward AI Humor

We explore the link between children’s gesture production and analogical reasoning. Specifically, we ask whether children who spontaneously gesture when completing a retelling task are more likely to engage in analogical transfer, compared to those who do not gesture. To test this, 85 5-7-year-olds listened to three superficially distinct stories that shared a common abstract problem and solution. After each of the first two exemplar stories, participants were asked to retell the story events to a naïve listener and their speech and spontaneous gesture(s) were coded. For the third story, participants were asked to generate the analogous solution themselves. Results indicate a significant relationship between children’s analogical transfer and gesture production. This preliminary study suggests that children’s spontaneous gestures may provide a window into their analogical processing. We discuss future directions aimed at further examining the mechanism underlying this relationship.

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Enhancing Preschool Readiness: Evidence from a Home-based Game to Improve 5-year-old Children’s Mastery of Symbolic Numbers and Concepts

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