United States

The need to update our estimates of probabilities (e.g., the accuracy of a test) given new information is commonplace. Ideally, a new instance (e.g., a correct report) would just be added to the tally, but we are often uncertain whether a new instance has occurred. We present an experiment where participants receive conflicting reports from two early-warning cancer tests, where one has higher historical accuracy (HA). We present a model showing that while uncertain which test is correct, estimates of the accuracy of both tests should be reduced. However, among our participants, we find two dominant approaches: (1) participants increase the more HA test, reducing the other; (2) participants make no change to either. Based on mixed methods we argue that both approaches represent two sides of a ‘binary’ decision i.e., (1) update as if we have complete certainty which test is correct and (2) update as if we have no information.

CogSci 2021

Categorical Belief Updating Under Uncertainty

**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.

technical paper

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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,**
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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.

Over their first years of life, children learn not just the words of their native languages, but how to use them to communicate. Because manual annotation of communicative intent does not scale to large corpora, our understanding of communicative act development is limited to case studies of a few children at a few time points. We present an approach to automatic identification of communicative acts using a Hidden Topic Markov Model, applying it to the CHILDES database. We first describe qualitative changes in parent-child communication over development, and then use our method to demonstrate two large-scale features of communicative development: (1) children develop a parent-like repertoire of our model's communicative acts rapidly, their learning rate peaking around 14 months of age, and (2) this period of steep repertoire change coincides with the highest predictability between parents' acts and children's, suggesting that structured interactions play a role in learning to communicate.

Parent-Child Conversation About Negative Aspects of the Biological World

In this study (n=144) we investigated the perceptual processes that are the basis of the face inversion effect (better recognition for upright vs inverted faces). We evaluated the effects of disrupting configural information (i.e., the spatial relationships among the main facial features) and disrupting holistic information indexed by the face outline. We used scrambled faces which are characterized by a disruption of configural information and scrambled no-contour faces which in addition to disrupted configural information they also suffered of disruption of the face outline. Using an old/new recognition task we obtained a robust inversion effect for scrambled faces. No significant inversion effect was found for scrambled no-contour faces. Our results provide direct evidence that holistic information plays a significant role in the inversion effect. We also confirmed that it is possible to obtain a robust inversion effect when configural information is disrupted.

Perceptual Processes of Face Recognition: Single feature orientation and holistic information contribute to the face inversion effect

The pattern of implicatures of modified numeral `more than n' depends on the roundness of n. Cummins, Sauerland, and Solt (2012) present experimental evidence for the relation between roundness and implicature patterns, and propose a pragmatic account of the phenomenon. More recently, Hesse and Benz (2020) present more extensive evidence showing that implicatures also depend on the magnitude of n and propose a novel explanation based on the Approximate Number System (Dehaene 1999). Despite the wealth of experimental data, no formal account has yet been proposed to characterize the full posterior distribution over numbers of a listener after hearing `more than n'. We develop one such account within the Rational Speech Act framework, quantitatively reconstructing the pragmatic reasoning of a rational listener. We show that our pragmatic account correctly predicts various features of the experimental data.

The Shape of Modified Numerals

The Uniform Information Density hypothesis (UID) predicts that lexical choice between long and short word forms depends on the predictability of the referent in context, and recent studies have shown such an effect of predictability on lexical choice during online production. We here set out to test whether the UID predictions hold up in a related setting, but different language (German) and different phenomenon, namely the choice between compounds (e.g. Badewanne / bathtub) or their base forms (Wanne / tub). Our study is consistent with the UID: we find that participants choose the shorter base form more often in predictive contexts, showing an active tendency to be information-theoretically efficient.

A bathtub by any other name: the reduction of German compounds in predictive contexts

Studies of children's language use in the wild (e.g., in the context of child-caregiver social interaction) have been slowed by the time- and resource- consuming task of hand annotating utterances for communicative intents/speech acts. Existing studies have typically focused on investigating rather small samples of children, raising the question of how their findings generalize both to larger and more representative populations and to a richer set of interaction contexts. Here we propose a simple automatic model for speech act labeling in early childhood based on the INCA-A coding scheme (Ninio et al., 1994). After validating the model against ground truth labels, we automatically annotated the entire English-language data from the CHILDES corpus. The major theoretical result was that earlier findings generalize quite well at a large scale. Our model will be shared with the community so that researchers can use it with their data to investigate various question related to language use both in typical and atypical populations of children.

Embodied morality: Repetitive motor actions change moral decision-making

Speaking in a foreign accent has often been thought to carry many disadvantages. Here we probe the social evaluation of foreign-accented vs. native speakers using spoken utterances that either obey or violate the pragmatic principle of Informativeness. We show that listeners form different impressions of native and non-native speakers with identical pragmatic behavior: specifically, in contexts where violations of Informativeness can be detrimental to or misleading for the listener, people rated underinformative speakers more negatively on trustworthiness and interpersonal appeal compared to informative speakers, but this tendency was mitigated in some cases for speakers with foreign accents. Furthermore, this mitigating effect was strongest for less proficient non-native speakers who were presumably not fully responsible for their linguistic choices. Contrary to previous studies, we also find no consistent global bias against non-native speakers. Thus the fact that non-native speakers have imperfect control of the linguistic signal affects pragmatic inferences and social evaluation in ways that can lead to surprising social benefits.

Listeners evaluate native and non-native speakers differently (but not in the way you think)

Language is inherently flexible: people continually generalize over observed data to produce creative linguistic expressions. This process is constrained by a wide range of factors, whose interaction is not fully understood. We present a novel study of the creative use of verb constructions ``in the wild'', in a very large social media corpus. Our first experiment confirms on this large-scale data the important interaction of category variability and item similarity within creative extensions in actual language use. Our second experiment confirms the novel hypothesis that low-frequency exemplars may play a role in generalization by signaling the area of semantic space where creative coinages occur.

Coin it up: Generalization of creative constructions in the wild

One of the more surprising findings in visual cognition is the apparent sparsity of our scene percepts. Yet, scene perception also enables planning and navigation, which require a detailed, structured analysis of the scene geometry, including exit locations and the obstacles along the way. Here, we hypothesize that computation of navigational affordances (e.g., paths to an exit) is a “default” task in the mind, and that task induces selective analysis of the scene geometry most relevant to computing these affordances. In an indoor scene setting, we show that observers more readily detect changes if these changes impact shortest paths to visible exits. We show that behavioral detection rates are explained by a new model of attention that makes heterogeneous-precision inferences about the scene geometry, relative to how its different regions impact navigational affordance computation. This work provides a formal window into the contents of our scene percepts.

Automatic computation of navigational affordances explains selective processing of geometry in scene perception: behavioral and computational evidence

When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones--including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior. Full paper can be found at https://www.biorxiv.org/content/10.1101/2021.05.12.443806v1.

Perception of soft materials relies on physics-based object representations:Behavioral and computational evidence

Physics and mind are two major causes of motion. In a leash-chasing display, a disc (“sheep") is being chased by another disc (“wolf"), which is physically constrained by a leash attached to a third disc (“master"). A number of interesting motions can emerge from this simple system, such as a wolf being dragged away from its target. Therefore, it is important for vision to jointly infer a physics-mind combination that can best explain the motions. Here we reported two discoveries from studying this display to support this theory. First, an intuitive physical system like a leash can greatly lessen the detrimental effects of spatial deviation and the diminishing objecthood on perceived chasing, strengthening its robustness. Second, a mutual dependency exists between physics and mind, where disrupting one will inevitably result in an impaired perception on the other. These results collectively support a joint perception of physics and mind.

Categorical Belief Updating Under Uncertainty

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Categorical Belief Updating Under Uncertainty

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