United States

A stereotype is a generalization about people within a certain category––and category information is often used to make probabilistic predictions about people within a particular group. The current work examines whether stereotypes can be understood in terms of conditional probabilities as per Bayesian reasoning. For instance, the stereotype of Germans as efficient can be understood as the conditional probability of someone being efficient given that they are German. Whether such representations follow Bayes’ rule was tested in a replication and extension of McCauley and Stitt’s (1978) original studies. Across two experiments, we found that people&#39;s judgements of eight different social groups were appropriately Bayesian i.e., their direct posterior predictions were in line with what Bayes&#39; rule suggests they should be, given subjects’ priors and likelihood ratios. For any given social group, it was also the case that traits with a high calculated diagnostic ratio distinguished stereotypic from non-stereotypic traits.

CogSci 2021

Stereotypes as Bayesian Judgements of Social Groups

**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)

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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.

A stereotype is a generalization about people within a certain category––and category information is often used to make probabilistic predictions about people within a particular group. The current work examines whether stereotypes can be understood in terms of conditional probabilities as per Bayesian reasoning. For instance, the stereotype of Germans as efficient can be understood as the conditional probability of someone being efficient given that they are German. Whether such representations follow Bayes’ rule was tested in a replication and extension of McCauley and Stitt’s (1978) original studies. Across two experiments, we found that people's judgements of eight different social groups were appropriately Bayesian i.e., their direct posterior predictions were in line with what Bayes' rule suggests they should be, given subjects’ priors and likelihood ratios. For any given social group, it was also the case that traits with a high calculated diagnostic ratio distinguished stereotypic from non-stereotypic traits.

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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**

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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.

Previous studies have manifested a strong relationship between socio-economic status (SES) and cognitive abilities in young children, however, the effects of SES on developed cognitive architecture remain less explored. The current study examined whether and how cognitive processing differs as a function of socioeconomic status in adults. The performance measured individuals from lower and higher SES (29.9 ± 8.2 years, Male = 81, Female = 19) on tests of working memory (Digit Span), visuo-spatial memory (visual retention and recognition) and executive functioning (Stroop test and Koh’s block test). Lower SES adults had significantly lower test scores on all the measures as compared to the higher SES adults. These findings suggest that lower SES individuals exhibit lower cognitive abilities as compared to the individuals associated with higher SES. The current findings append to the previous literature by demonstrating a significant interaction between SES and cognitive processing in adults.

Impact of Socio-Economic Status on Cognitive Processing

The current study explores the dynamics of interpersonal collision avoidance during an everyday coordination task. The task was a full body adaptation of the collision avoidance arm-movement task employed by Richardson et al. (2015) and required pairs of co-actors to avoid colliding into one another as they moved back and forth within a collaborative workspace. Specifically, participants were required to serve drinks to humanoid customers in a virtual bar where they needed to move back and forth between drink service and refill stations. As expected, stable patterns of movement coordination emerged between co-actors, with one participant in a pair exhibiting a more circular movement trajectory between the service and refill locations, while the second participant in a pair exhibited a more straight-line trajectory between the two locations. Consistent with the results of Richardson et al., (2015), this pattern was observed across nearly all pairs, indicating that the same dynamical processes governing simple hand-arm coordination tasks also regulate more complex, full-body and naturalistic goal-directed interpersonal coordination tasks.

Behind the Bar: Coordinated Collision Avoidance in a Goal-Directed Joint Action Task

Biological motion is a crucial stimulus with social and survival 
value that can be processed incidentally. However, no study 
has examined the factors that would affect bottom-up 
processing of biological motion in depth. In this study, we 
investigated the effect of perceptual load and eccentricity on 
biological motion perception. Human subjects performed a 
letter search task at the center while biological motion in the 
form of point-light displays was displayed as a distractor in the 
periphery. We manipulated the perceptual load at the center as 
well as the eccentricity of the distractor stimuli in the 
periphery. Our results show that when the perceptual load is 
low, people are distracted more by biological motion at near 
eccentricities, whereas when the load is high, the position of 
the distractor does not have any effect. In sum, these results 
suggest that bottom-up perception of biological motion is 
influenced by perceptual load and eccentricity.

Visual Processing of Biological Motion in the Periphery under Attentional Load

Recognition of animacy is fundamental to human cognition, yet robots complicate this categorization, because they are non-living objects with human-like traits. We examined this categorization of robot animacy using speech balloons from comics, which require connecting to animate “stems” (speakers), or coercing inanimate objects to become animate (e.g., a talking toaster). Participants rated the text-image congruity of silhouettes of humans, inanimate objects, and robots paired with descriptive words placed in either a speech balloon or a label box. Overall, humans and object text-image pairs were rated as more congruent than those with robots. However, a positive correlation suggested human-looking robots with balloons were more congruent than less human-looking ones, but such a graded congruency did not appear with labels. This suggested that speech balloons select for an animate stem compared to labels, but also that intuitions for animacy in robots falls along a gradient depending on their human-like traits.

Categorization of robot animacy using implicit visual cues

According to prediction-based theories, prior learning creates expectations for subsequent learning events. As they learn words, individuals develop accurate and inaccurate expectations about word meaning. Existing research shows that people who shift their gaze to the referent of words more quickly have larger vocabularies. This shifting reflects the processing of accurate expectations about the referents of these words. Is the speed of processing inaccurate expectations also related to vocabulary size? To examine this question, adults learned eight novel word-object mappings during cross-situational word learning (CSWL). The mappings were either consistent or inconsistent with a prior familiarization phase. Early in CSWL, hearing inconsistent words violated expectations about the referent of those words. Shifting from a distractor to the target of an inconsistent word during CSWL was significantly associated with productive and receptive vocabulary. These findings are consistent with prediction-based theories, in which individuals use prediction errors to adjust their expectations.

Overcoming Error: Association between Attentional Reorientation and Vocabulary Size

Machine learning offers techniques for predicting behavior, but has limited capability to use behavior, context, and domain knowledge to infer intentions and predict behavior trajectory (i.e., sequence of directed actions towards reaching goal) of humans in real time to assist in strategic planning. Some existing models of human goal inference (Zhi-Xuan, Mann, Silver, Tenenbaum, & Manshinghka, 2020) possess some of these capabilities, however, these models are typically evaluated in toy worlds, which limits our understanding of how these approaches would generalize to real-world domains. Here, we introduce a novel integration of Bayesian goal inference and a deep learning model used for joint estimation (OpenPose) to address this generalizability problem and evaluate how this integrated approach can infer the goals of humans in real-world environments. This exploration provides an opportunity to evaluate capabilities required for real-time goal inference in real world environments and can highlight benefits and limitations informing future research.

Exploring Online Goal Inference in Real World Environments

A widely shared view on temporal representation suggests that people conceptualize time metaphorically as a spatial journey from a back (past) location to a front (future) location. This view predicts 1) shorter estimated distances to and better evaluations of front/future than back/past events (an asymmetry); 2) positive correlations between space, time, and evaluation; 3) negative correlations between responses to the front/future and the back/past. In the present study, participants performed a temporal distance task, a time discounting task, and a spatial distance task, all with back/past and front/future versions. Results showed that 1) there was not asymmetry between back/past and front/future in any task; 2) spatial and temporal tasks correlated positively, but they did not correlate with time discounting; and 3) responses toward the front/future and back/past correlated positively (and not negatively) in all three tasks. The results suggest the need to revise the "moving forward view of time".

How are Spatial Distance, Temporal Distance and Temporal Valuation Related?

Brainstorming suggests that generating ideas in groups can direct one's thoughts into areas that might be difficult to reach or consider, resulting in better performance than individuals working separately. However, while intuitively appealing, studies have repeatedly found no evidence for such synergy.
Here, we reinvestigate group synergy in a task with simulated agents. We use state-of-the-art language embeddings to generate close or far-off semantic associates to people's ideas. This design allows us to adopt a sampling for inference perspective, where individuals produce sample ideas from a generative process (Sanborn & Chater, 2016). We hypothesize that suggestions can allow participants to create samples from previously not considered concepts, and additionally, knowledge about the diversity of solutions can spur more broad idea generation. Our work helps inform broader questions about when and why groups can expect to produce better performance than individuals.

Sampling Associations with (Un)related Suggestions

The Response to Joint Attention (RJA) allows coordinating attention with a partner by following social cues as indicated by the gaze, head turn or gestures. One developmental hypothesis suggests that this ability may initially rely on the perception of head motion and refines until responding to the final gaze direction. The autism spectrum disorders (ASD) present reduced joint attention behaviors, the development of which is the goal of many therapeutic interventions and could benefit from a precise knowledge of how social cues trigger (or fail to trigger) the RJA.
In this study, we test the developmental hypothesis. We developed a computational model simulating gaze following tasks and explored the effect of differences in the amount of information and temporal dynamics of social and non-social cues. Our model is contrasted with previous empirical studies and it describes developmental trajectories of typical and atypical RJA.

Computational Analysis of Social Cues in the Response to Joint Attention, The More the Better

The study of mental representations of concepts has historically focused on the representations of the “average” person. Here, we shift away from this aggregate view and examine the principles of variability across people in conceptual representations. Using a database of millions of sketches by people worldwide, we ask what predicts whether people converge or diverge in their representations of a specific concept, and which kinds of concepts tend to be more or less variable. We find that larger and more dense populations tend to have less variable representations, and concepts high in valence and arousal tend to be less variable across people. Further, two countries tend to have people with more similar conceptual representations when they are linguistically, geographically, and culturally similar. Our work provides the first characterization of the principles of variability in shared meaning across a large, diverse sample of participants.

Stereotypes as Bayesian Judgements of Social Groups

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Impact of Socio-Economic Status on Cognitive Processing

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