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Learnability has been a topic of great interest in phonology. In particular is the question of the relative learnability of process interactions. In both historical and experimental domains, researchers have noted that certain kinds of interactions are harder to learn than others. In both domains, however, the results are seemingly in conflict. One potential source of the conflicting outcome is the types of processes involved. In this paper, we investigate the effect of process types on the learnability of different interaction types, using an ideal minimum-description-length learner (MDL). We find that the model indeed predicts different learnability outcomes for each interaction type; however, the asymmetry is largely independent of the process type. This computational model explains certain elements of both the historical as well as some experimental findings of the relative learnability of linguistics process interactions, while contradicting other behavioral findings.

CogSci 2021

Phonological Interactions, Process Types, and Minimum Description Length Principles

**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)

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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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**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.

Lay perceptions of medical conditions and treatments determine people’s health behaviors, guide biomedical research funding, and have important consequences for both individual and societal wellbeing. Yet it has been nearly impossible to quantitatively predict lay health perceptions for hundreds of everyday diseases due to the myriad psychological forces governing health-related attitudes and beliefs. Here we present a data-driven approach that uses text explanations on healthcare websites, combined with large-scale survey data, to train a machine learning model capable of predicting lay health perception. We use our model to analyze how language influences health perceptions, interpret the psychological underpinnings of health judgment, and quantify differences between different descriptions of disease states. Our model is accurate, cost-effective, and scalable, and offers researchers and practitioners a new tool for studying health-related attitudes and beliefs.

Machine Learning Models for Predicting, Understanding, and Influencing Health Perception

Previous studies have suggested testing and feature highlighting each facilitates category learning. We investigated whether the beneficial effects of interim testing change with the presence of feature highlighting in natural category learning. Participants learned various rock categories that were divided into two sections. They studied a series of rocks with or without feature descriptions highlighted on each image and either took an interim test or not between the two sections. On the final test, participants classified studied and new rock exemplars from both sections. The testing group outperformed the no-testing group for both sections, indicating both backward and forward effects of testing. Such beneficial effects of testing occurred regardless of whether the feature highlighting was provided or not. The feature highlighting, however, showed negative effects on learning of both sections, suggesting that provision of explicit instruction may impede learning when it is not appropriately embedded in the learning material.

Effects of interim testing and feature highlighting on natural category learning

We often organize objects around both visual and semantic boundaries in space. Across four experiments, we examined how semantically consistent partitions influenced memory for object locations. Participants learned the locations of items in a semantically partitioned display (where each partition contained objects from a single category), as well as a purely visually partitioned display (where each partition contained a random assortment of objects from different categories). While semantic partitions significantly improved location memory over the purely visually partitioned display, this advantage was significantly reduced when participants were cued to the correct partition during recall. Our results suggest that semantic category information benefits memory via strengthening the association between a given category and a spatial region delineated by a partition. Further, there was some indication that this benefit may come with the drawback of reducing memory precision for objects within a partitioned space.

The effect of semantic categorization on object location memory

Persuasion is hard. Then why do some consumers think that marketers can easily manipulate them? Three studies and an internal meta-analysis suggest that consumers’ beliefs about marketing manipulation are rooted in humans’ deeper and older psychology—motivation to understand life events. Consumers higher in motivation to make sense of their environments not only detect persuasion where it exists but also where none (make false-positive errors). Whereas consumers higher in sense-making motives believe that manipulations are more effective, objective sense-making abilities negatively predict false manipulation detection. We also tested how manipulation beliefs are related to personality traits, gender, and age. These findings help 1) understand the origins of lay (false) beliefs about the marketplace and persuasion in general, 2) consider marketing segmentation strategies to reach out to particularly skeptical consumers, and 3) understand ways to attenuate false-positive beliefs and foster accurate persuasion detection, which is critical in the era of infodemic.

Lay Theories of Manipulation: Do Consumers Believe They are Susceptible to Marketers’ Trickery?

Most machine learning based decision support systems are black box models that are not interpretable for humans. However, the demand for explainable models to create comprehensible and trustworthy systems is growing, particularly in complex domains involving risky decisions. In many domains, decision making is based on visual information. We argue that nevertheless, explanations need to be verbal to communicate the relevance of specific feature values and critical relations for a classification decision. To address that claim, we introduce a fictitious visual domain from archeology where aerial views of ancient grave sites must be classified. Trustworthiness among other factors relies on the perceived or assumed correctness of a system's decisions. Models learned by induction of data, in general, cannot have perfect predictive accuracy and one can assume that unexplained erroneous system decisions might reduce trust. In a 2×2 factorial online experiment with 190 participants, we investigated the effect of verbal explanations and information about system errors. Our results show that explanations increase comprehension of the factors on which classification of grave sites is based and that explanations increase the joint performance of human and system for new decision tasks. Furthermore, explanations result in more confidence in decision making and higher trust in the system.

Explaining Machine Learned Relational Concepts in Visual Domains - Effects of Perceived Accuracy on Joint Performance and Trust

In this study, we explicitly code and study the social, referential, and pragmatic features of gaze in human-human spontaneous dyadic interaction, providing novel observations that can be executed in a machine in order to improve multimodal human-agent dialogue. Gaze is an important non-verbal social signal that contains attentional cues about where to look and provides information about others' intentions and future actions. In this work, various types of gaze behaviour are annotated in detail along with speech to explore the meaning of temporal patterns in gaze cues and their co-relations. Considering that 80% of the total stimuli perceived by the brain is visual, gaze behaviour is complex and challenging; hence, implementing human-human gaze cues to an avatar/robots could improve human-agent interaction and make it more natural.

A Deep Gaze into Social and Referential Interaction

Text-generation algorithms like GPT-2 (Radford et al., 2019) and GPT-3 (Brown et al., 2020) produce documents which resemble coherent human writing. But no study has compared the discourse linguistics features of the artificial text with that of comparable human content. We used a sample of Reddit and news discourse as prompts to generate artificial text using fine-tuned GPT-2 (Grover; Zellers et al., 2019). Blind annotators identified clause-level discourse features (e.g., states and events; Smith, 2003), and coherence relations (e.g., contrast; Wolf and Gibson, 2005) in prompts and generated text. Comparing the >20000 clauses, Grover recreates human word co-occurrence patterns and clause types across discourse modes. However, its coherence relations have shorter length and lower quality, with many nonsensical instances. Therefore, annotators could perfectly guess the human/algorithmic source of documents. Using a corresponding GPT-3 sample, we discuss aspects of generation that have and have not improved since Grover.

Can computers tell a story? Discourse Structure in Computer-generated Text and Humans

In categorization tasks optimization of performance depends on attention to relevant stimulus features. However, studies show that adults and children seem to use different strategies when learning new categories. Deng & Sloutsky (2016) found that adults attend selectively, whereas children prefer to attend diffusely, even when the stimuli possess deterministic features (i.e. with 100% probability of belonging to a certain category). Most studies of attention in category learning rely on behavioral effects to infer attentional strategy, despite the availability of eye-tracking technology. In this study, we combine the use of category training and transfer paradigms (see Miles & Minda, 2009 and Deng & Sloutsky, 2016) and eye-tracking methods to investigate attentional strategies of children and adults during category learning. Preliminary results of our adult pilot (N = 14) confirm the prediction that adults optimize performance by attending increasingly to deterministic features of the stimulus once the categorical rule is found.

Attentional strategies during category learning: an eye-tracking study

Conditionals like 'If A, then C' can be used, among others, to convey important knowledge about rules, dependencies and causal relationships.
Much work has been devoted to the interpretation of conditional sentences, but much less is known about when speakers choose to use a conditional over another type of utterance in communication.
To fill this gap, we consider a recently proposed computational model from probabilistic pragmatics, adapted for modeling the use of conditionals in natural language, by comparing its predictions to experimental production data from a behavioral experiment.
In a novel experimental approach, we manipulate relevant causal beliefs that might influence whether utterances with conditional structure are preferred over utterances without conditional structure.
This is a step towards a systematic, quantitative investigation of the situations that do or do not elicit the natural use of conditionals.

Communicating uncertain beliefs with conditionals: Probabilistic modeling and experimental data

Despite recent progress in promoting gender equality, gender bias continues to post challenges to women's career advancement. Here, we use a statistically grounded framework to investigate how face-based social perception may contribute to gender biases in political and job application settings. By analyzing a large face dataset and performing a novel behavioral experiment, we find that: 1) female faces exhibit stronger anti-correlation between perceived trustworthiness and dominance, 2) this anti-correlation is due to distinct sets of facial features humans utilize to assess female and male faces for trustworthiness and dominance, 3) perceived dominance positively contributes to social decision preferences for female faces, contrary to prior suggestions that perceived dominance affects female candidates negatively, and 4) the anti-correlated perception of trustworthiness and dominance put females at a disadvantage in competitive environments. More generally, our findings reveal the important role of face-based trait perceptions underlying gender biases in social decision making.

Phonological Interactions, Process Types, and Minimum Description Length Principles

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