United States

Bar charts are common visual tools used to convey statistical
information. Even though bar charts are effective in making
abstract concepts more accessible, poorly-designed bar charts
- whether designed intentionally or unintentionally - can easily
mislead the viewer. For example, a poorly-designed bar
chart may only present part of the effective range on the vertical
axis. This exaggerates the contrast among bars, leading
an unsuspecting graph viewer to wrong conclusions. More
broadly, misrepresentation in data visualization is becoming
an increasing societal problem contributing to daily misinformation.
This paper presents a computational and cognitive solution
to this problem. Our idea is to train viewers by showing
them a few dozen carefully designed bar charts that are misleading,
together with guidance on why these bar charts are
misleading. We then test whether the viewers identify similarly
misleading bar charts in the future. Importantly, we use
neural networks and cognitive models to optimize the training
(i.e., the design of those few dozen bar charts). Our experiment
shows that perceptual trainings can help viewers not be fooled
by similar misleading graphs in the future.

CogSci 2021

Digitally Training Graph Viewers against Misleading Bar Charts

**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.

A key question individuals face in any social learning environment is when to innovate alone and when to imitate others. Previous simulation results have found that the best performing groups exhibit an intermediate balance, yet it is still largely unknown how individuals collectively negotiate this balance. We use an immersive collective foraging experiment, implemented in the Minecraft game engine, facilitating unprecedented access to spatial trajectories and visual field data. The virtual environment imposes a limited field of view, creating a natural trade-off between allocating visual attention towards individual search or to look towards peers for social imitation. By analyzing foraging patterns, social interactions (visual and spatial), and social influence, we shine new light on how groups collectively adapt to the fluctuating demands of the environment through specialization and selective imitation, rather than homogeneity and indiscriminate copying of others.

Specialization and selective social attention establishes the balance between individual and social learning

Creativity is defined as the ability to produce an object/idea that is both original and appropriate to the context. How originality and appropriateness of production are integrated in creative cognitive processes is not understood. We propose that creativity involves an evaluative component based on individual preferences for appropriateness and originality of candidate ideas stemming from a generative component. Through behavioral experiments inspired by neuroeconomics and using computational modeling, we aimed to characterize this evaluative component. Participants had to generate creative word associations and then rate how much each of their responses was appropriate, original and how much they liked it. We found that the way individuals balanced appropriateness and originality to build a subjective value of their ideas was correlated with creative abilities. These findings provide new insights into how individual preferences can impact creativity.

The subjective value of creative outputs: appropriate or original?

Previous research indicates that Autism Spectrum Disorder (ASD) is associated with altered production of co-speech gestures. However, little research has examined whether ASD impacts the processes involved in interpreting observed gestures. We collected meaningfulness ratings and one-word interpretations for 165 video-recorded gestures of varying ambiguity from individuals with and without an ASD diagnosis. The resulting dataset contains insights into gesture processing in individuals with and without ASD, including the number and variability of interpretations assigned to gestures as well as tendencies to endorse ambiguous gestures as meaningful. We also used a subset of these stimuli to identify the neural mechanisms by which gestures enhance memory in neurotypical adults. This collection of videos, as well as the interpretations and ratings from each group, will be made available through the Open Science Framework for use in future research.

Interpretations of meaningful and ambiguous hand gestures from individuals with and without Autism Spectrum Disorder (ASD)

Eye contact serves as an important social signal and humans show a special sensitivity for detecting eyes. Here, we asked whether people’s sensitivity to eyes would enable them to overcome temporal limitations in visual attention. We used an "attentional blink" (AB) paradigm, in which the second of two visual stimuli presented in quick succession typically cannot be detected. Participants performed a rapid serial visual presentation (RSVP) task and were asked to identify, within a stream of symbols, a target and to detect whether the target was succeeded by a probe. The probe was either an image of an eye (with direct gaze) or of a star. As expected, participants' detection rate for the star was poor, demonstrating the typical attentional blink. Crucially, detection rate for the eye was significantly better. This reduced attentional blink suggests that people's sensitivity to eyes is strong enough to circumvent fundamental limitations in visuotemporal attention.

In the blink of an eye? Evidence for a reduced attentional blink for eyes

Many joint actions such as group dance and music performance require that partners take turns producing actions at specific temporal intervals. Here we assessed whether turn-taking partners can co-produce temporal intervals with the same levels of accuracy and precision as individuals. Participants learned to tap a piano key at the rate of a metronome cue, either alone (Individual) or in alternation with a partner (Joint). Findings revealed that partners did not achieve individual coordination levels: Temporal accuracy (deviation from the cued tapping rate) and precision were reduced in Joint relative to Individual sequences, though partners displayed learning across the experiment. Critically, partners appeared to group temporal intervals according to the turn-taking structure; no such grouping patterns were observed in Individual sequences. Together, co-production of temporal intervals with a turn-taking partner poses challenges to coordination and partners attempt to overcome this challenge by rhythmically grouping intervals according to the turn-taking structure.

Distinct rhythms of joint and individual action: Evidence from an auditory sequence production paradigm

This paper presents a theoretical framework for modeling human visual attention. The framework's core claim is that three mechanisms drive attention: selection, which picks out an item for further processing; engagement, which tags a selected item as relevant or irrelevant to the current task; and enhancement, which increases sensitivity to task-relevant items and decreases sensitivity to task-irrelevant items. Building on these mechanisms, the framework is able to explain human performance on attentionally demanding tasks like visual search and multiple object tracking, and it supports a broad range of predictions about the interactions between such tasks.

Selection, Engagement, & Enhancement: A Framework for Modeling Visual Attention

Learning to program is challenging, because it involves novel skills. In contrast to the majority of work focusing on code generation, we target the skill of code tracing. Code tracing involves simulating the high-level actions a computer takes when it executes the program, including the flow of execution through it and how variable values change as a result. Code tracing supports program comprehension and generation. However, many students do not code trace effectively. Our project investigates the utility of peer tutoring for the learning of code tracing. While this approach remains untested in this context, it has successfully been used in other domains to improve student learning. We will present qualitative data from a case study involving students tutoring each other to code trace. This work is the first step in the context of a broader project focused on identifying ways to help students learn to code.

Student collaboration during code tracing activities

Research provides evidence that infants infer what others can and cannot see from their differing perspectives, but do infants appreciate that their own perspective on an object can differ from that of a person who views the same object from a different direction? First, infants were shown two faces with screens in front of or behind them. Infants correctly inferred that a face that was visible to them was occluded to a person sitting across from them. Then two experiments presented infants with two unscreened faces: one upright and one inverted. Infants attributed their own perspective on those faces to the other person: They did not appreciate that faces that were upright to them were inverted to the other person. Thus, infants appreciate that others may see things that they do not, but they fail to grasp not that others may experience the same visible objects differently than they do.

Limits to Early Mental State Reasoning: Fourteen- to 15-Month-Old Infants Appreciate Whether Others Can See Objects, But Not Others’ Experiences of Objects

Children and adults use linguistic context to learn new words. For instance, in “She wears the dax,” “dax” likely refers to clothing. We asked whether learners retain these constraints across ambiguous word-learning exposures. Adults (n=139) learned 12 words. On Exposure 1, novel nouns were presented as the object of either Restrictive (e.g., “wears”) or Non-restrictive (“finds”) verbs. Participants selected which of two compatible referents the noun referred to. On Exposure 2, participants heard each noun again and chose between a distractor referent and the unselected referent from Exposure 1—now the only referent compatible with the previous Restrictive verb. If adults retain selectional restrictions, then Restrictive verbs should increase selection of the compatible referent. However, we found no difference between Restrictive (M=.74, SD=.26) and Non-restrictive (M=.76, SD=.24) conditions, p=.24. This suggests adults use verbs’ selectional restrictions to identify referents in the moment but do not retain these restrictions across exposures.

New exposure, no constraints: Semantic restrictions on novel nouns do not constrain adults’ subsequent referent selections

Some theories of abstract word representation posit a role for affective information; however, little has been done to delineate how this holds for the class of emotion concepts relative to other types of abstract concepts. Work using distributional semantic models has shown that emotion words tend to co-occur with other highly affective words, complicating the picture of how affective information contributes to the grounding of abstract concepts generally and emotion concepts specifically. Here, a novel set of data from 180 participants collected using a feature generation paradigm with emotion, non-emotion-related abstract, and concrete stimuli is leveraged to test how the distributional properties of emotion concepts and their associated features differs from those of other abstract concepts. Using co-occurrence statistics and normative ratings for affect, results show the importance of affect differs between emotion and non-emotion-related abstract words.

Digitally Training Graph Viewers against Misleading Bar Charts

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Specialization and selective social attention establishes the balance between individual and social learning

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