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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.

CogSci 2021

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

**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.

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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?

Reading comprehension research has identified sources of children’s difficulty with inference-making: lack of semantic/content knowledge and logical reasoning difficulty. NLP tools modeling semantic knowledge (e.g. BERT) can predict adult inference-making, but it is unclear whether they can predict children’s inference-making difficulty. In our ongoing study, we will examine whether our new inference difficulty metric can predict kindergarten students’ inference-making, using empirical data from a classroom intervention (ELCII). Students were given verbal information on a topic and multiple-choice questions, which require students to draw an inference from two given scaffolds. To develop this metric, we will train BERT on children’s books and ELCII content to compute an additive inference vector, the sum of the two vectorized scaffolds. The cosine distance between the additive and correct inferences may indicate inference difficulty. Results will indicate whether a probabilistic semantic space can model children’s inferences or if other components (e.g. logic) should be considered.

A metric of children’s inference-making difficulty during language comprehension

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

Symbolic representations of number are instrumental to mathematical reasoning and many aspects of social organization. What explains their emergence in human cultures? To understand how functional and cognitive constraints impact people’s communication about number, we used a drawing-based reference game to investigate how human dyads coordinated to form novel number systems. We found a systematic bias towards symbols exploiting 1-to-1 correspondence to objects in visual arrays, and that this strategy was contingent on the communicative relevance of number. Moreover, the meaning of these symbols was transparent to third party observers not present during their production. Finally, model-based analyses of these symbols' visual properties suggest that the ability to decode exact quantity from them may rely on perceptual processing mechanisms beyond those sufficient for object recognition. These findings contribute to our understanding of how both communicative need and capacity for visual abstraction constrain the emergence of iconic representations of exact number.

Improvised Numerals Rely on 1-to-1 Correspondence

Categories can be learned through different strategies. Sometimes we may use abstract rules to categorize objects, and other times we may rely on the perceptual similarity among stimuli. The ability to categorize objects based on a common pattern develops since early childhood and exhibits systematic age differences. Numerous studies demonstrated that younger children rely on similarity-based processes, while older children employ rule-based categorization strategies (Miles & Minda, 2009; Rabi & Minda, 2014; Deng & Sloutsky, 2016). We used a model-based approach to investigate individual differences in category learning in pre-school children (6 years old) and primary school children (6-8 and 10-11 years old). Our results suggest that older children were more likely to employ a rule-based categorization strategy and demonstrated better learning outcomes. Lastly, we employed several computational models of categorization to uncover the properties of the process that may best account for the obtained results.

Category learning in preschool and primary school children: The use of rule-based and similarity-based strategies

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

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

Data interpretation is crucial in modern society. One common data structure that people frequently encounter is 2 x 2 tables. Past work suggests that the nature of the variables affects how people interpret 2 x 2 tables. Specifically, people interpret tables with symmetric variables (present/present; e.g., treatment A vs. treatment B) more accurately than tables with asymmetric variables (present/absent; e.g., treatment vs. no treatment). This study tested whether interpreting tables with symmetric variables could scaffold later interpretation of tables with asymmetric variables. Undergraduates interpreted tables and rated the importance of each cell to their interpretations. Some participants interpreted tables with symmetric variables before tables with asymmetric variables; others interpreted only tables with asymmetric variables. Participants who first interpreted tables with symmetric variables later judged cells in the bottom row of asymmetric tables to be more important. Thus, experience with symmetric variables shifted participants’ views of tables with asymmetric variables.

Interpreting Data Tables: Can Variable Symmetry Scaffold Performance?

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.

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

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