United States

We present a neural network model of referent identification in a preferential looking task. The inputs are visual representations of pairs of objects concurrent with unfolding sequences of phonemes identifying the target object. The model is trained to output the semantic representation of the target object and to suppress the semantic representation of the distractor object. Referent identification is achieved in the model based only on bottom-up processing. The training set uses a lexicon of 200 words and their visual and semantic referents, reported by parents as typically known by toddlers. The phonological, visual and semantic representations are derived from real corpora.

The model successfully replicates experimental evidence that phonological, perceptual and categorical relationships between target and distractor modulate the temporal pattern of visual attention. In particular, the network captures early effects of phonological similarity, followed by later effects of semantic similarity on referent identification.

CogSci 2021

A neural network model of referent identification in the inter-modal preference looking task

**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! 

**Your Program Chairs,**
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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 subjective experience of loneliness during COVID-19 by analyzing social media postings from March 2020 to January 2021. We collected text data from loneliness-related subgroups of Reddit and sampled 12787 posts that were written in ten consecutive days from each month. The results suggest that when individuals express their loneliness, they show an internal focus of attention on their emotions, desires, and cognitive appraisals rather than an external focus of attention on situations or other people. Linguistic markers of emotions expressed by lonely individuals included depression, anxiety, anger, hate, helplessness, and sadness. Also, loneliness-related topics were generally about their internal states pertinent to various social relationships, interpersonal interaction deficits, and their own lives in broad time perspectives. COVID-19 related loneliness was associated with negative appraisal of one’s situation and reaching out for new relationships online.

Web-scraping the Expression of Loneliness during COVID-19

We propose a biologically constrained, large-scale neural network model that solves the Wisconsin Card Sorting Test (WCST). The WCST has been widely used in clinical and research settings to study cognitive flexibility and executive function. The model shows a good quantitative match with human responses across a number of WCST scoring indices, while consisting of neural networks that functionally and anatomically map to brain areas and structures implicated in the task, such as the prefrontal cortex and the cortico-basal ganglia-thalamus-cortical loop. We argue that the model provides a mechanistic account of WCST solving, and demonstrate its robustness by examining its performance across a range of biologically motivated parameter values.

Biologically Constrained Large-Scale Model of the Wisconsin Card Sorting Test

The meaning of a word is acquired using other words. The current study explores how Japanese native-speakers and adult second-language (L2) learners of Japanese apply the meanings of verbs that belong to the same semantic domain, focusing on the semantic domains of “kai (to open),” “guan (to close),” “kan (to see/to look at/to watch),” and “chuan (to wear)” in Chinese. A comparison of the results from native speakers and L2 learners revealed a marked dissimilarity between the patterns of their semantic domains. Native-speakers were able to use all of the words correctly, whereas L2 learners made errors that were heavily influenced by the word concepts of their native language. Our findings suggest two main points: first, it is possible that adult L2 learners have their own semantic domain influenced by their native language; second, it is important to systematically investigate words that belong to the same domain as a whole.

How do the Concepts of Native Language Influence Second Language Learning? : Evidence from the Reconstruction of Word Semantic Domain

This study examined dynamic attractor states in skin conductance activity during resting baselines and media viewing in order to determine if there are qualitatively distinct dynamics during information processing and whether those dynamics vary based on features of task stimuli. The results indicate that media viewing shifts one from a resting non-chaotic attractor to a chaotic attractor. Content valence (positive or negative) and the emotional context in which videos were delivered (presentation order) had significant impact on the probability of exhibiting a chaotic attractor. Using the nonlinear dynamic systems approach, this study provides novel understandings of emotional information processing, the electrodermal system, and the relationship between physiological and emotional experiences.

A Nonlinear Dynamical Systems Approach to Emotional Attractor States during Media Viewing

This study investigated how a group of twenty seven Wuhan citizens (coronavirus patients and their family member, medical staff, college students, social workers, teacher, journalist) employed metaphors to communicate about their experiences of COVID-19 pandemic through in-depth individual interviews. The analysis of metaphors captured the different kinds of emotional states and psychological conditions of the research participants, focusing on their mental imagery of COVID-19, extreme emotional experiences, and symbolic behaviors under the pandemic. The results show that multiple metaphors were used to construe emotionally-complex, isolating experiences of the COVID-19 pandemic. Most metaphorical narratives are grounded in embodied sensorimotor experiences such as body parts, batting, fighting, hit, weight, temperature, spatialization, motion, violence, light, and journey. Embodied metaphors were manifested in both verbal expressions and nonlinguistic behaviors (e.g., patients’ symptom of obsessive-compulsive behaviors). These results suggest that the bodily experiences of the pandemic, the environment, and the psychological factors combine to shape people’s metaphorical thinking processes.

Embodied Metaphor in Communication about Experiences of COVID-19 Pandemic

When you see a glass fall off the table, you can predict it will break without seeing it hit the ground. Similarly, if you hear the glass shatter, you can infer what happened without seeing anything at all. Our knowledge of the causal mechanisms structuring our world allows us to make impressively accurate inferences based on incomplete information spread across multiple sensory modalities. In this work, we study the cognitive processing that supports this remarkable behavior. We utilize the Plinko domain, an intuitive physics setup where marbles are dropped into a box from one of three holes, colliding with obstacles as they fall to the ground. Participants judge where they think the ball fell from based on visual and auditory evidence. We track participants' eye-gaze to gain deeper insight into their mental processes, and develop models that characterize the computational processes underlying participant behavior.

Eye-Tracking Multi-Modal Inference

This study investigates whether the ERPs of 4-years-olds in response to verbal overextensions reflect the encoding of actions through abstract categories. Participants were presented with images of actions (e.g. peeling an orange) while hearing a sentence containing a conventional verb (e.g. peeling), an approximative verb (e.g. undressing), a superficially related verb (e.g. pressing) or a pseudoverb (e.g. rauging). The N400 for approximative verbs significantly differed from the pseudoverb condition, but not from the conventional verb condition. In contrast, the N400 for superficially related verbs was significantly greater than for conventional verbs, but no significant difference was found with the pseudoverb condition. These results confirm our hypothesis that encoding mainly focuses on general categories (e.g. taking of an envelope). The implications of the findings are discussed regarding the conceptual organization of preschoolers and their analogical abilities.

Early Analogical Extensions: An ERP Study on Preschoolers' Semantic Approximations

An ongoing debate about the structure of conceptual space is based on two competing mathematical theories of similarity that make distinct predictions about the structure of mental representations and how to model the representational space they are stored in. These are known as metric (Shepard, 1962) and ultrametric (Tversky, 1977) theories, modeled by multidimensional scaling and additive trees respectively. Turning to the brain to resolve this conflict, we propose a computational framework to assess behavioral and neural data’s underlying structure and investigate whether the behaviorally known spaces for colors (metric) and letters (ultrametric) can be reproduced from neural data. Our results show that the metric color wheel can be reproduced from brain area V4, but that neural activations of the letters from extrastriate cortex (V2-V5) are also metric instead of being ultrametric. Finally, we discuss three possibilities for the brain’s similarity structure, including a potential metric bias.

Uncovering the Metricity of Representational Spaces in the Brain: Evidence from Colors and Letters

Learning verbs is challenging because it is difficult to infer the precise meaning of a verb when there are a multitude of relations that one can derive from a single event. To study this verb learning challenge, we used children's egocentric view collected from naturalistic toy-play interaction as learning materials and investigated how visual and linguistic information provided in individual naming moments as well as cross-situational information provided from multiple learning moments can help learners resolve this mapping problem using the Human Simulation Paradigm. Our results show that learners benefit from seeing children's egocentric views compared to third-person observations. In addition, linguistic information can help learners identify the correct verb meaning by eliminating possible meanings that do not belong to the linguistic category. Learners are also able to integrate visual and linguistic information both within and across learning situations to reduce the ambiguity in the space of possible verb meanings.

Human Learners Integrate Visual and Linguistic Information Cross-Situational Verb Learning

Human communication involves far more than words; speakers’ utterances are often accompanied by various kinds of emotional expressions. How do listeners represent and integrate these distinct sources of information to make communicative inferences? We first show that people, as listeners, integrate both verbal and emotional information when inferring true states of the world and others' communicative goals, and then present computational models that formalize these inferences by considering different ways in which these signals might be generated. Results suggest that while listeners understand that utterances and emotional expressions are generated by a balance of speakers’ informational and social goals, they additionally consider the possibility that emotional expressions are noncommunicative signals that directly reflect the speaker’s internal states. These results are consistent with the predictions of a probabilistic model that integrates goal inferences with linguistic and emotional signals, moving us towards a more complete formal theory of human communicative reasoning.

A neural network model of referent identification in the inter-modal preference looking task

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