United States

We asked whether categories expressed through lists of salient exemplars (e.g., car, truck, boat, etc.) convey the same meaning as categories expressed through conventional superordinate nouns (e.g., vehicles). We asked English speakers to list category members, with one group given superordinate labels like vehicles and the other group given only a list of salient exemplars. We found that the responses of the group given labels were more related, more typical, and less diverse than the responses of the group given exemplars. This result suggests that when people do not see a superordinate label, the categories that they infer are less well aligned across participants. In addition, categories inferred based on exemplars may be broader in general than categories given by superordinate labels.

CogSci 2021

Lions, tigers and bears: Conveying a superordinate category without a superordinate label

**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.

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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,**
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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.

A fundamental characteristic of human language is its combinatorial nature, which facilitates the communication of infinite meanings (i.e., words) built from finite sounds (i.e., phonemes). Investigating the selective drivers of this combinatorial feature represents a major challenge in the field of language evolution and has prospered into a rich interdisciplinary field. Here, we discuss the emergence of combinatorial structures in (human and non-human) communication systems from an information and coding theoretical perspective. We describe how “noise” (i.e., factors constraining communication processes) can hamper and distort signal transmission and perception, and how such noise-induced impairments can be circumvented through a combinatorial coding scheme that adds redundancy to a signalling system, in turn increasing its robustness and enhancing signal detection and discrimination. In doing so we argue that basic combinatoriality has emerged due to universal constraints imposed on signalling systems, and that human language-like productive combinatoriality builds upon this phenomenon.

An information and coding theoretical approach to combinatorial communication

Word learning models are typically evaluated as the problem of observing words together with sets of atomic objects and learn-ing an alignment between them. We use ADAM, a Python software platform for modeling grounded language acquisition, to evaluate a particular word learning model, Pursuit (Stevens, Gleitman, Trueswell, & Yang, 2017),under more realistic learning conditions (see e.g. Gleitman and Trueswell (2020) for review). In particular, we manipulate the degree of referential ambiguity and the salience of attentional cues available to the learner, and we present extensions to Pursuit which address the challenges of non-atomic meanings and exploiting attentional cues.

Grounding Word Learning Across Situations

Iconicity refers to instances in which the form of language resembles its meaning (Perniss et al., 2010). The most prominent example in spoken language is onomatopoeia (e.g., woosh, which sounds like a gust of wind). Here we tested whether iconicity makes language more vivid by depicting the sensorimotor experiences being referred to. In Experiment 1, 44 participants read ten short passages (five iconic, five non-iconic), and then rated their vividness on several scales. These passages differed on two key words, which were either iconic (e.g., screech) or non-iconic (e.g., yell). We found no evidence that iconic language was more vivid. In Experiment 2, 199 participants each rated one longer passage that was either iconic or non-iconic (differing on eight key words). We found only marginal evidence of iconic language being more vivid, on subscales related to felt vividness. These results suggest that iconicity does not make written language more vivid.

Is Iconic Language More Vivid?

Recent research suggests that an individual's disgust sensitivity affects language comprehension and correlates with political attitudes. Importantly, disgust sensitivity is not a stable measure and can be manipulated dynamically. We investigated the effect of the ongoing pandemic on language processing in a word rating and lexical decision study. Each participant was first exposed to either headlines portraying CoViD-19 as a serious disease or those downplaying it. The results showed an interaction between person-based factors, inherent word characteristics, and the participants’ responses. After reading headlines emphasizing the threat of CoViD-19, easily disgusted participants considered the least disgusting words more disgusting. Further, political views played a role. More liberal participants rated the words lower for disgust in the downplayed condition but higher in the severe condition than their more conservative peers. The results of this study shed new light on how the media's stance on the pandemic may affect the public’s response.

CoViDisgust: Language Comprehension at the Intersection of a Global Pandemic and Individual Disgust Sensitivity

Is “cow” more closely related to “grass” or “chicken”? Speakers of different languages judge similarity in this context differently, but why? One possibility is that cultures co-varying with these languages induce variation in conceptualizations of similarity. Specifically, East Asian cultures may promote reasoning about thematic similarity, by which cow and grass are more related whereas Western cultures may bias similarity judgements toward taxonomic relations, like cow-chicken. This difference in notions of similarity is the consensus interpretation for cross-cultural variation in this paradigm. We consider, and provide evidence for, an alternative possibility, by which notions of similarity are equivalent across contexts, but the statistics of the environment vary. On this account, similarity judgements are guided by co-occurrence in experience, and observing or hearing about cows and grass or cows and chickens more often could induce preferences for the relevant grouping, and account for apparent differences in notions of similarity across contexts.

Re-examining cross-cultural similarity judgements using lexical co-occurrence

While many formal frameworks distinguish between desires and intentions, and considerable empirical work shows that people interpret them differently, no studies examine how people reason about them. We extend Harner and Khemlani’s (2020) model-based theory of relations describing desire. The theory holds that people represent desires, as in, e.g., Pav wants to visit Angkor Wat, by pairing a factual representation of the negation of the desire (e.g., that Pav is not currently visiting Angkor Wat) with a future possibility where the desire is realized. We propose that intentions, which people express using verbs like plan, are represented as future actions that agents seek to perform. A particular individual’s plans must be consistent with one another, whereas desires can conflict with these plans. We show how the model theory distinguishes desires and intentions, namely that models can be coherent even when a desire and a plan are inconsistent with each other. The distinctions make predictions about how reasoners should assess the consistency of statements concerning desires and intentions, and we report on two experiments that validate them.

Desires can conflict with intentions; plans cannot

Questions are prevalent in everyday speech and they are often used to teach. When learners receive explicit cues that the intent of a question is pedagogical, they draw inferences that lead to superior learning. Although the ability to infer pedagogical intent is critical, very little is known about the mechanisms that support the inference that any particular statement is pedagogical or not. We tested the hypothesis that the prosody of speech marks the intent of pedagogical and information-seeking questions. In Studies 1 and 2, 256 naïve participants rated 100 pedagogical and information-seeking questions, spoken in child- or adult-directed speech. We found that naïve listeners can accurately infer pedagogical intent on the basis of prosody alone. In Study 3, we begin charting the acoustic features that differentiate pedagogical from information-seeking questions. These findings provide a window into the mechanisms that allow learners to infer pedagogical intent in otherwise ambiguous situations.

The Sound of Pedagogical Questions

Research suggests that people minimize the amount of effort used to generate natural language descriptions of visual scenes. In the case of visual scenes with multiple groups, recent work has found that people tend to generate quantitative descriptions that mention the number and cardinality of groups (e.g., ``two groups of three limes''), but omit the total quantity (e.g., ``six limes''). This finding suggests that people groupitize, that is, they more quickly determine the number of grouped items by rapid enumeration of subgroups, rather than slower item-by-item counting. %This hypothesis predicts that during description, people exert less effort to encode and report only the number and cardinality of groups. 
A recent proposal predicts that during description, people exert less effort by encoding and reporting only information that is readily available to perception.
In previously studied description tasks, people may have omitted the total quantity from their descriptions because of considerations of brevity and informativity. In this paper, we describe a study designed to test how individuals balance effort, brevity, and informativity when evaluating quantified descriptions. The experiment was designed to elicit more fine-grained preferences for descriptions using direct comparisons between two competing descriptive forms. The results suggest that perceptual effort plays a central role in how people describe grouped collections of items.

Preferences in the quantified description of visual groups

Scalar implicatures, one of the signatures of pragmatic reasoning, are believed to arise from competing alternative utterances, which the listener knows that the speaker could have used to express a strengthened meaning. But do scalar implicatures also arise in the presence of nonce objects, for which no alternative name is known? We conduct a series of experiments assessing the degree of scalar strengthening driven by familiar and nonce objects. We find that nonce objects can derive scalar implicatures as strongly as familiar objects in simple reference games. Our experiments also reveal an asymmetry in the relative strengths of familiar- and nonce-driven inferences: relative to the prior, participants preferentially interpret the name of a shared feature as referring to an object with an additional nonce feature over an object with an additional familiar feature, suggesting that familiar alternatives exert greater scalar pressure than nonce alternatives. We also present exploratory model simulations suggesting that our results may be explained by rationally reasoning about a high-cost description of the novel object. Our findings support the idea that novel lexical entries may be generated from one-shot encounters and spontaneously used in pragmatic inference.

Competition from novel features drives scalar inferences in reference games

Metaphor is an important part of everyday thought and language. Although we are often not aware of metaphor in everyday speech, on occasion, a particularly creative or novel use of metaphor will make us pay attention. It has been hypothesized that one of the driving cognitive factors behind the use of novel metaphor is a need to describe a new reality (as opposed to a preexisting reality) that would otherwise be difficult to convey using conventionalized metaphor. To this extent, novel metaphor use in everyday language may be more associated with episodic memory demands in contrast to conventional metaphor that is associated with semantic memory. To test this idea we analyzed novel metaphor use in the Hippocorpus --- a corpus of more than 5000 recalled and imagined stories about memorable life events in the first person perspective. In this dataset, recalled events have been shown to rely on episodic memory to a greater extent than descriptions of imagined events (i.e., narrating an event as if it happened to you but not describing an event that actually happened to you), which largely draw on semantic memory. We hypothesized that novel metaphor use during event narration should be modulated by the extent to which language users are able to draw on primary experience to describe events. We found that novel metaphor counts in recalled events were significantly higher than imagined events. Importantly, we found that factors that influence the extent to which language users are able to draw on primary experience during event narration (i.e., openness to experience, similarity to one's own experience, and how memorable or important an event was) modulated novel metaphor use in different ways in imagined compared to recalled events. The work paves the way for using large scale corpora to analyze underlying cognitive processes that modulate metaphorical language use.

Lions, tigers and bears: Conveying a superordinate category without a superordinate label

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An information and coding theoretical approach to combinatorial communication

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