United States

Studies of children&#39;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.

CogSci 2021

Large-scale study of speech acts&#39; development using automatic labelling

**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.
 
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 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.

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.

Large-scale study of speech acts' development using automatic labelling

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.

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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.

Recurrent neural network (RNN) language models that are trained on large text corpora have shown a remarkable ability to capture properties of human syntactic processing (Linzen & Baroni, 2021). For example, the fact that these models display human-like structural priming effects (Prasad, Van Schijndel, & Linzen, 2019; van Schijndel & Linzen, 2018) suggests that they develop implicit syntactic representations that may not be unlike those of the human language system. A rarely explored question is whether RNNs are also able to simulate aspects of human multilingual sentence processing (Frank, 2021) even though training RNNs on two or more languages simultaneously is technically unproblematic.

Cross-language structural priming in recurrent neural network language models

Three leading analogical reasoning paradigms: scene analogies and pictorial A:B::C:D analogies (both semantically-rich) and geometric analogies (semantically-lean) were solved by 251 participants. Pictorial analogies included four types of lures among the response options (perceptual, categorical, semantic, relational). Moreover, distractors related to both B and C were introduced. Additionally, a fluid intelligence test was applied to examine the relationship between the paradigms and general reasoning ability. Results indicated that: (a) objects semantically related to C yielded the strongest distraction in four-term analogies, categorical and relational distractors yielded moderate effects, perceptual distraction was negligible; (b) distractors related to B were relatively easy to ignore, suggesting that C is the primary object of reference during the response selection; (c) whilst the three tasks correlated significantly without control for fluid intelligence, only the semantically-rich tasks did after fluid intelligence was accounted for, suggesting a certain common mechanism, independent of fluid intelligence, underlying the two.

Distraction in Semantic Analogies and Their Relationship with Abstract Reasoning

In this paper, we test people’s causal judgments when the graphs have inhibitory causal relations. We find evidence that a particularly important class of errors known as Markov vio- lations extend to these settings. These Markov violations are important because they are incompatible with causal graphical models, a theoretical framework that is often used as a com- putational level account of causal cognition. In contrast, the systematic pattern of errors are in line with the predictions of a recently proposed rational process model that models peo- ple as reasoning about concrete cases (Davis & Rehder, 2020). These findings demonstrate that errors in causal reasoning ex- tend across a range of settings, and do so in line with the pre- dictions of a model that describes the process by which causal judgments are drawn.

Testing a Process Model of Causal Reasoning With Inhibitory Causal Links

The last few years have witnessed amazing breakthroughs in machine intelligence, using systems that rely on neural networks as advocated in Parallel Distributed Processing (Rumelhart, McClelland et al, 1986). Yet in this talk, I will argue, in agreement with Lake et al. (2017) and others, that we still have a long way to go before any machine has truly captured human like cognitive and learning abilities. Unlike Lake et al., I will argue that we should seek the reasons for many of the amazing achievements of human intelligence not in built in biases or special purpose start-up software, but in a fuller appreciation of the roles of culture and experience. I will argue for a central role for culturally constructed formal systems as powerful tools that extend human abilities beyond what can be achieved without these resources. I will also argue for a central role of language-based instruction and explanation.

Are people still smarter than machines? If so, why?

Tools are peculiar parts of our environment and tool manufacture remains one of the most prodigious achievements of humankind over the last million years. Chimpanzees, along with other non-primate species, also use and sometimes manufacture tools. In my research, I have investigated the cognitive, ecological, social and emotional factors influencing tool use in wild and captive apes, with a focus on Ugandan chimpanzees. In parallel, I have researched cognitive aspects of the evolution of emotional and intentional communication by studying primate, particularly great ape, vocalizations. Finally, in more recent years, I have investigated the same topics in children, to investigate the possible homologies with humans and our shared ancestry and specificities. My goal is to understand the evolutionary pressures that launched humans on the particular evolutionary pathway that have allowed them to become the ultimate culture-bearers. I am also interested in how other species, in turn, see the world. The research program I develop integrates these interests in a comparative, ecological, cognitive and socio-emotional approach to cultural knowledge in great apes and humans.

How does the Chimpanzee Mind Represent its Cultures?

The social brain hypothesis (Humphrey, 1976) poses that the intricacies of social life may have been a significant selection pressure for the evolution of mind. This evolution may act on competition between group members and strategies to outcompete others (Machiavellian intelligence hypothesis: Byrne & Whiten, 1988), or on cooperative tendencies between group-members that provide benefits that cannot be reached by a single individual (Vygotskian intelligence hypothesis: Moll & Tomasello, 2007). The latter hypothesis, however, creates an evolutionary conundrum, as cooperation is prone to free-riders, and with defection being an evolutionary stable system, the occurrence and complexity of cooperation in humans and other animals remains a puzzle. Several theoretical advances have been made to explain the evolution of cooperation nonetheless, with kin-selection (Hamilton, 1964) and reciprocal altruism (Trivers, 1971) being the most prominent ones. However, the proximate mechanisms that foster the strategies proposed in the Vygotskian intelligence hypothesis and the required cognition in nonhuman animals, remain a hotly debated topic (see Massen et al., 2019).

Studying the Evolution of Cooperation and Prosociality in Birds

There is a strong relation between children’s exposure to spatial terms and their later memory accuracy. In the current study, we tested whether the production of spatial terms by children themselves predicts memory accuracy and whether and how language modality of these encodings modulates memory accuracy differently. Hearing child speakers of Turkish and deaf child signers of Turkish Sign Language described pictures of objects in various spatial relations to each other and later tested for their memory accuracy of these pictures in a surprise memory task. We found that having described the spatial relation between the objects predicted better memory accuracy. However, the modality of these descriptions in sign, speech, or speech-plus-gesture did not reveal differences in memory accuracy. We discuss the implications of these findings for the relation between spatial language, memory, and the modality of encoding.

Spatial Language Use Predicts Spatial Memory of Children: Evidence from Sign, Speech, and Speech-plus-gesture

Unlike English, German contains single words for “the day after tomorrow” (übermorgen) and “the day before yesterday” (vorgestern). How might these cross-linguistic differences influence children’s acquisition of time words? Prior work shows that English-speaking preschoolers learn the deictic status of time words (e.g., yesterday was in the past) long before learning their precise temporal locations (e.g., yesterday was exactly one day ago). Here we ask whether the set of time words influences children’s understanding of proximal (yesterday/tomorrow) and distal (day before yesterday/day after tomorrow) terms. English- and German-speaking 3- to 7-year-olds (N = 253) marked the temporal location of each term relative to today on a calendar template. While children in both language groups demonstrated equal knowledge of deictic status, German speakers were more likely to have precise meanings for proximal and distal items, suggesting that having more alternative time words available may help narrow the scope of children’s meanings.

A crosslinguistic study of the acquisition of time words in English- and German-speaking children

Many students fail to develop adequate understanding of mathematical equivalence in early grades, which impacts later algebra learning. Work from McNeil and colleagues proposes that this failure is partly due to the format of traditional instruction and practice with highly similar problems, which encourages students to develop ineffective representations of problem types (McNeil, 2014, McNeil & Alibali, 2005). In the current study, we explore students’ learning trajectories in two matched equivalence interventions. We show that, relative to an active control, the principle-based treatment intervention gives rise to a greater number of successful learners, a designation that, in turn, leads to improved performance on distal transfer assessments. We further demonstrate a predictive relationship between students’ engagement with the intervention, via workbook completion, and likelihood of becoming a successful learner. Our findings have implications for early detection of learning and subsequent scaffolding for low-performing students.

Predicting Learning and Knowledge Transfer in Two Early Mathematical Equivalence Interventions

Understanding cause and effect relationships gives power to produce desired effects and avoid negative outcomes. Despite the power of causal explanations, people often lack full understanding of how causes relate to or produce their effects. In two experiments, we explored how people search for information to enrich their causal explanations of real-world phenomena when given the chance. Participants completed an information search task that provided a causal relationship where they could seek out mechanistic information at different steps between the cause and the effect. We measured where people searched in the causal chain of events that made the explanation. We found that when allowed to search freely (Experiment 1) or when instructed that they must search for information (Experiment 2) participants consistently sought out information closest to the root cause in the explanation. We discuss implications for how to improve the teaching of new explanations to maximize the informational desires of the learner.

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