United States

Understanding language requires comprehenders to understand not only what speakers say, but what speakers might imply. Scalar items (e.g. some, numerals) often invite comprehenders to compute scalar implicatures, pragmatically strengthening the semantic meaning of scalar items by negating their stronger alternatives. Recent priming evidence suggests that scalar implicatures may share underlying mechanisms, priming both within and between implicature types. We report two experiments designed to extend these finding to or, which has an inclusive meaning that can be strengthened to an exclusive meaning, potentially via scalar implicature. Experiment 1 investigated or alongside some and numerals, holding the number of visual symbols constant. Experiment 2 reduced the visual complexity of Experiment 1. Both experiments found robust within-category priming, but failed to fully replicate or extend between-category priming effects. We discuss implications of these results with respect to visual manipulations and the potential fragility of priming across different categories of scalar implicature.

CogSci 2021

Do Scalar Implicatures Prime? The Case of Exclusive ‘or’

**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.
 
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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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**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! 

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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.

We investigated the emergence of register-like indexical associations, whereby linguistic forms that are associated with groups of speakers acquire novel associations with contextual features of those groups. We employed an artificial-language paradigm in which participants were exposed to an “alien” language spoken by two alien species wearing two different ceremonial outfits. The language varied with respect to plural suffixes, such that one suffix was associated reliably with one species and outfit in training. We then tested participants on what associations they had acquired. In two experiments we manipulated which aliens wore which outfits in the test phase. Regardless of condition or length of training, participants associated suffixes strongly with aliens rather than clothing. In a third experiment we introduced a new alien species in the test phase. For these aliens, which participants had not seen during training, participants made a clear association based on outfit. These results show clearly ranked indexical (or proto-indexical) associations on the part of participants and lay clear groundwork for the experimental investigation of the emergence of indexical social meaning in language.

The emergence of indexicality in an artificial language

Human cognition can tackle a wide range of problems, producing scalable results within a manageable timeframe. Many cognitive models can predict human behavior but lack such scalability, resulting in rapidly increasing processing times for more complex inputs. We present a task where participants mentally sort sequences of rectangles by size while we measure reaction times (RTs) and accuracy. By manipulating the size of the input and the presence of latent structure in the sequences, we investigate i) how mental sorting scales with input complexity, ii) how latent structure influences scaling, and iii) how mental computations can be captured by plausible cognitive models. Our results reveal RTs scale linearly with sequence length, and participants can learn and actively use latent structure to sort faster. This behavior is in line with a noisy sorting algorithm, which sequentially rules out potential hypotheses about the latent structure, thus reducing complexity while retaining accuracy.

How does mental sorting scale?

According to linguistic relativity, the structure of a language influences its speaker’s cognition. To examine the generalization to non-linguistic tasks, we compared Korean and German native speakers. In Korean, a grammatical distinction forces to discriminate between tight and loose spatial relations, while in German, this falls into one single semantic category. In visual priming and masking experiments, we found 1) direct and 2) indirect evidence for linguistic relativity. 1) Congruence effects in the discrimination of tight/loose spatial relations only for Korean speakers and 2) a reduced efficiency of metacontrast masking (which relies on spatial fit between stimulus and mask) for Korean speakers, as attention was automatically captured by varying distances, according to their grammar. With object-substitution masks (where tightness of fit does not play a role), Korean speakers no longer outperformed German speakers. In our study, the highly practiced characteristics of one’s mother tongue affected attention and, hence, perception.

Revising the Scope of Linguistic Relativity: Language Influences Perception in Non-linguistic Tasks

There is growing evidence that cognitive biases play a role in shaping language structure. We ask whether such biases contribute to the propensity of Zipfian word-frequency distributions, one of the striking commonalities between languages. Recent work suggests Zipfian distributions confer a learnability advantage, facilitating word learning and segmentation (e.g. Lavi-Rotbain & Arnon, 2019). However, it remains unclear whether this reflects the impact of prior linguistic experience with such distributions or a cognitive preference for them. Here, we use an iterated learning paradigm to see if learners change a uniform word distribution into a skewed one via cultural transmission. We exposed the first learner to a story where six nonce words appeared equally often, and asked them to re-tell it. Their output served as input for the next learner. Over time, word distributions became more skewed (lower entropy). The findings provide novel evidence for a cognitive bias for skewed distributions in language.

A cognitive bias for Zipfian distributions? Uniform distributions become more skewed via cultural transmission

Children struggle to derive scalar implicatures. Initially this was thought to relate to a lack of cognitive resources required for the computation. More recently however, there has been a shift towards the alternatives (what a speaker could have said but did not). The argument is that children struggle to make the scalar implicature associated with some because they are unaware of its relationship with the stronger alternative all. We present a priming study that investigates this. We show that children’s implicatures can be primed equally by alternatives in quantifier and ad hoc expressions. This suggests that children are aware of the scalar relationship between some and all, even if they choose not to derive the implicature.

Priming implicatures in young children

The present study explores learning vowel harmony with exceptions using artificial language learning paradigm. Participants were exposed to a back/round vowel harmony pattern in which one affix (prefix or suffix) alternated between /me/ and /mo/ depending on the phonetic feature of the stem vowels. In Experiment 1, participants were able to learn the behaviors of alternating and non-alternating affixes, but were more likely to generalize to novel affixes for non-alternating items than alternating items. In Experiment 2, participants were exposed to learning data that contains non-alternating affixes in prefix position while alternating affixes were all suffixes, or vice versa. Participants were able to extend the non-alternating affixes to novel items. Overall, the patterns of alternating affixes are harder to learn than patterns of exceptions, which aligns with previous results of non-alternation bias. Our study raises the question of how biases towards exceptionality and directionality interact in phonological learning.

Directionality Effects and Exceptions in Learning Phonological Alternations

Are morphosyntactically simpler languages easier to learn? In theory building and empirical work alike, researchers often assume so. While plausible, this assumption nevertheless requires empirical confirmation. To test it, we manipulate form-to-meaning transparency (FtMT) and morphophonemic cohesion (MC) in three large-scale artificial language learning experiments with English and Mandarin participants. We find that (a) FtMT doesn’t affect learnability for all semantic domains (for English speakers, the more transparent marking of plural number in a separate morpheme increases learnability with nouns, but not with pronouns); (b) MC similarly affects learnability in a domain dependent fashion (expressing subjects and verbs in two words, as opposed to one concatenated word, increases learnability with nouns, but not with pronouns); (c) the effects are L1 dependent (pronouns behave like English nouns in regards to plural marking for Mandarin speakers). Taken together, the results falsify the strong hypothesis that simpler languages are necessarily easier to learn.

It’s not so simple: morphosyntactically simpler languages are not always easier to learn

In two self-paced reading experiments, we investigated the integration of knowledge about cultural figures during language processing. Experiment 1 investigated how contextually-defined lifetime information (dead/alive) was integrated with temporal verb morphology (English Present Perfect and Past Simple). Experiment 2 investigated how long-term knowledge about a cultural figure, prompted by their picture, is integrated with two types of linguistic input in German: temporal phrases containing a year, and biographical information (e.g., Joaquin Phoenix: "In the year 2013 / *1960, I starred in the film ‘Her’ / *‘Psycho’"). Experiment 1 revealed longer reading times and higher rejection rates when life status mismatched tense (i.e., dead – Present Perfect, alive – Past Simple). In Experiment 2, shorter reading times and higher accuracy rates emerged in conditions containing lifetime-year mismatches. No effects were found for biographical information violations. Together, the two experiments provide cross-linguistic evidence that knowledge of a referent’s lifetime is integrated during language processing.

Effects of lifetime knowledge on language processing in German and English

Chunks allow us to use long-term knowledge to efficiently represent the world in working memory. Most views of chunking assume that when we use chunks, this results in the loss of specific perceptual details, since it is presumed the contents of chunks are decoded from long-term memory rather than reflecting the exact details of the item that was presented. However, in two experiments, we find that in situations where participants make use of chunks to improve visual working memory, access to instance-specific perceptual detail (that cannot be retrieved from long-term memory) increased, rather than decreased. This supports an alternative view: that chunks facilitate the encoding and retention into memory of perceptual details as part of structured, hierarchical memories, rather than serving as mere “content-free” pointers. It also provides a strong contrast to accounts in which working memory capacity is assumed to be exhaustively described by the number of chunks remembered.

Chunks are not “Content-Free”: Hierarchical Representations Preserve Perceptual Detail within Chunks

Compositionality is a core feature of human cognition and behavior. People readily decompose visual objects into parts and complex procedures into subtasks. Here we investigate how these two abilities interact to support learning in a block-tower assembly experiment. We measured the way participants segmented these towers based on shape information alone, and asked how well the resulting parts explained the procedures other participants used to build them. We found that people decomposed these shapes in consistent ways and the most common parts appeared especially frequently as subroutines in the assembly experiment. Moreover, we found that the subroutines participants used converged over time, reflecting shared biases toward certain ways of reconstructing each tower. More broadly, our findings suggest important similarities between the perceptual and procedural abstractions humans use to perceive and interact with objects in their environment.

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The emergence of indexicality in an artificial language

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