United States

Corpus data suggests that frequent words have lower rates of replacement and regularization. It is not clear, however, whether this holds due to stronger selection against innovation among high-frequency words or due to weaker drift at high frequencies. Here, we report two experiments designed to probe this question. Participants were tasked with learning a simple miniature language consisting of two nouns and two plural markers. After exposing plural markers to drift and selection of varying strengths, we tracked noun regularization. Regularization was greater for low- than for high-frequency nouns, with no detectable effect of selection. Our results therefore suggest that lower rates of regularization of more frequent words may be due to drift alone.

CogSci 2021

Regularization of nouns due to drift, not selection: An artificial-language experiment

**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/)

Registration Is Required

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.

poster

### 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.

Research shows that language processing mechanisms are permeable to the speaker’s accent, but virtually no data exists on non-literal language. Our online rating study investigated whether accent-based biases could hinder making inferences from ironic speech. Ninety-six participants listened to dialogues between native and foreign-accented English speakers and rated them on several scales. We found that the ironic intent in the accented speech was missed significantly more often than in the native speech for all irony types. Importantly, participants’ individual differences significantly affected the ratings and interacted with both accent and irony-type. More conservative participants were worse at detecting irony than their liberal peers but this effect was stronger for accented speech and a rarer irony type. In contrast, high empathy facilitated irony detection. The results demonstrate that interpersonal variation in personality and moral values affects language comprehension and needs to be accounted for in models of language processing.

Oh, the Irony!: Interpersonal Variation in the Processing of Foreign-Accented and Native Irony

The role of semantics in inflectional morphology has long been debated (Huang & Pinker, 2010; Pinker & Prince, 1988; Ramscar, 2002) with most of the focus on the English past tense. This paper explores whether an effect of semantics can be found for German noun plural generalisation, a system as yet only poorly understood. German speakers were asked to first freely produce and then rate plural forms of 24 new wug words, presented in a semantically manipulated context. We expected that the German plural class ending in -n should be used more frequently with nouns presented as persons than as objects (Gaeta, 2008). While this hypothesis was not confirmed, the post-hoc discovery of other semantic influences prevents us from completely rejecting the original hypothesis. In light of these results we discuss possible sources of the observed pattern of plural classes and stress the importance of replicating wug studies with novel sets of wug words. We conclude that generalisation of the German plural system cannot easily be explained by phonological nor semantic influences.

Exploring the influence of semantics on the German plural system: a wug study

English clause-embedding verbs can be used in the evidential meaning (Simons, 2007; Murray, 2017), modulating the degree to which the speaker is committed to the truth of the
proposition in the embedded clause. For example, an utterance like “I think the movie starts at 4” can signal that the speaker is uncertain whether the proposition the movie starts at 4 is true, and would like to attenuate their claim. Previous research has provided detailed accounts of lexical and contextual features that give rise to evidential meanings — however, less is known about how widespread these uses are, whether they are part of the verb’s lexical meaning, or emerge under certain pragmatic conditions. In this study, we addressed these questions by conducting two large-scale acceptability judgment experiments. In line with the observations in literature (Simons, 2007), we found that non-factive clause-embedding verbs are the most acceptable in evidential contexts. We also found, however, that even highly factive verbs can be acceptable as evidentials under favorable pragmatic conditions.

Evidential meaning of English clause-embedding verbs

While lexical norm databases are being developed with a renewed emphasis on perceptual psycholinguistic features, verbs are often neglected. Research on nouns and adjectives utilizes vertical spatial localization ratings, providing an analogue for the inclusion of verbs via vertical directionality ratings. This study demonstrates the feasibility of collecting such ratings for 32 English verbs, as well as the possibility of assessing directionality ratings in other spatial dimensions. Further, ratings were analyzed using distributional semantic models. Results indicate that language statistics are strongly associated with human ratings, providing convergent validity for vertical directionality as a useful psycholinguistic measure. Additionally, a comparison of the predictive performance of LSA and Web 1T 5-gram models on human ratings revealed that the first-order 5-gram model accounted for significant unique variance in the norms, while LSA did not. It may be concluded that spatial verb associations are encoded linguistically by proximal, syntactic dependencies.

Vertical directionality ratings as lexical norms for English verbs

Child speech deviates from adult speech in predictable ways. Are listeners who routinely interact with children implicitly aware of these systematic deviations, and thereby better at comprehending children? In Experiment 1, we explore this possibility by testing three types of participants with variable experience interacting with children: undergraduates with minimal experience with children (N=48), mothers of young children (N=48), and early childcare educators (N=36). Participants transcribed single-word utterances produced by the same set of children at 2.5-, 4-, and 5.5-years-old. In Experiment 2, mothers (N=50) completed a similar transcription task that featured speech by their own, and another, 2.5-year-old child. Participants performed similarly regardless of their experience with children, while mothers demonstrated a Familiar Talker Advantage with their own child’s speech. Our findings suggest that while experience with children may not facilitate improved comprehension of child speech in general, it may lead to enhanced comprehension of those children in particular.

Do you speak 'kid'? The role of experience in comprehending child speech

Metaphors can both shape and reflect how we think about com- plex issues. Here, we explore the prevalence of WAR language in a large corpus (N = 1.63 million) of Tweets. We compare how different groups of people use the language of WAR to talk about different topics. In Study 1, we find that about 5% of Tweets about Covid-19 from the general public include WAR language, replicating prior work (Wicke & Bolognesi, 2020). In Studies 2 and 3, we find that politicians use WAR language much more often, while Reporters and Medical Experts use WAR language less often. The findings are relevant to current debates about the role of language in democracy, and to theo- ries of metaphor in communication.

War Language in Tweets of Politicians, Reporters, and Medical Experts: A Focus on Covid-19

Studies of bilingual language processing typically assign participants to groups based on their language proficiency and average across participants in order to compare the two groups. This approach loses much of the nuance and individual differences that could be important for furthering theories of bilingual language comprehension. In this study, we present a novel use of machine learning (ML) to develop a predictive model of language proficiency based on behavioral data collected in a priming task. The model achieved 75% accuracy in predicting which participants were proficient in both Spanish and English. Our results indicate that ML can be a useful tool for characterizing and studying individual differences.

Using Machine Learning to Predict Bilingual Language Proficiency from Reaction Time Priming Data

In bilinguals, certain concepts across languages come to be represented similarly—a semantic convergence effect that reflects interactivity between languages. The causal factors that affect semantic convergence are not fully understood; this gap may be due to limitations of the correlative methods used in extant work, which assesses the representations of real-world bilinguals. Here, we utilize an artificial language learning paradigm—inspired by the study of category learning—to elucidate causal influences on semantic convergence. We contrast simulated simultaneous bilingual learners with simulated sequential bilingual learners before assessing the representations of both. Bilingual groups are additionally compared to simulated monolingual controls from each language. We report on the pattern of semantic convergence and conclude with implications for theories of bilingual representation.

Now or Later: Representational Convergence in Simulated Simultaneous and Sequential Bilingual Learning Contexts

In English, Subject Relative Clauses are processed more quickly than Object Relative Clauses, but open questions remain about where in the clause slowdown occurs. The surprisal theory of incremental processing, under which processing difficulty corresponds to probabilistic expectations about upcoming material, predicts that slowdown should occur immediately on material that disambiguates the subject from object relative clause. However, evidence from eye tracking and self-paced reading studies suggests that slowdown occurs downstream of RC-disambiguating material, on the relative clause verb. These methods, however, suffer from well-known spillover effects which makes their results difficult to interpret. To address these issues, we introduce and deploy a novel variant of the Maze task for reading times (Forster, Guerrera, & Elliot, 2009), called the Interpolated Maze in two English web-based experiments. In Experiment 1, we find that the locus of reading-time differences between SRCs and ORCs falls on immediate disambiguating definite determiner. Experiment 2 provides a control, showing that ORCs are read more slowly than lexically-matching, non-anomalous material. These results provide new evidence for the locus of processing difficulty in relative clauses and support the surprisal theory of incremental processing.

Using the Interpolated Maze Task to Assess Incremental Processing in English Relative Clauses

Experimental investigation is fundamental to theory-building in cognitive science, but its value depends on the linking assumptions made by researchers about the mapping between empirical measurements and theoretical constructs. We argue that sufficient clarity and justification are often lacking for linking assumptions made in visual world eye-tracking, a widely used experimental method in psycholinguistic research. We test what we term the Referential Belief linking assumption: that the proportion of looks to a referent in a time window reflects participants’ degree of belief that the referent is the in- tended target in that time window. We do so by comparing eye-tracking data against explicit beliefs collected in an incremental decision task (Exp. 1), which replicates a scalar implicature processing study (Exp. 3 of Sun & Breheny, 2020). In Exp. 2, we replicate Sun and Breheny (2020) in a web-based eye-tracking paradigm using WebGazer.js. The results provide support for the Referential Belief link and cautious optimism for the prospect of conducting web-based eye-tracking. We discuss limitations on both fronts.

Regularization of nouns due to drift, not selection: An artificial-language experiment

Downloads

Next from CogSci 2021

Oh, the Irony!: Interpersonal Variation in the Processing of Foreign-Accented and Native Irony

Similar lecture

Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES