United States

Humans are social beings, and most of our decisions are influenced by considerations of how others will respond. Whether in poker or political negotiations, the riskiness of a decision is often determined by the variance of the other party&#39;s possible responses. Such socially-contingent decisions can be framed in terms of adversarial games, which differ from other risky situations such as lotteries because the risk arises from uncertainty about the opponent&#39;s decisions, and not some independent stochasticity in the world. 
We use chess as a lens through which we can study human risk-taking behavior in adversarial decision making. We develop a novel algorithm for calculating the riskiness of each move in a chess game, and apply it to data from over 1 billion online chess games. We find that players not only exhibit state-dependent risk preferences, but also change their risk-taking strategy depending on their opponent, and that this effect differs in experts and novices.

CogSci 2021

Risk-taking in adversarial games: What can 1 billion online chess games tell us?

**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.

Humans are social beings, and most of our decisions are influenced by considerations of how others will respond. Whether in poker or political negotiations, the riskiness of a decision is often determined by the variance of the other party's possible responses. Such socially-contingent decisions can be framed in terms of adversarial games, which differ from other risky situations such as lotteries because the risk arises from uncertainty about the opponent's decisions, and not some independent stochasticity in the world. 
We use chess as a lens through which we can study human risk-taking behavior in adversarial decision making. We develop a novel algorithm for calculating the riskiness of each move in a chess game, and apply it to data from over 1 billion online chess games. We find that players not only exhibit state-dependent risk preferences, but also change their risk-taking strategy depending on their opponent, and that this effect differs in experts and novices.

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.

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.

While humans routinely encode and retrieve memories in groups, the bulk of our knowledge of human memory comes from paradigms with individuals in isolation. The primary phenomenon of interest within the relatively new field of collaborative memory is collaborative inhibition: the tendency for collaborative groups to underperform in free recall tasks compared to nominal groups of the same size. This effect has been found in a variety of materials and group compositions (Rajaram & Pereira-Pasarin, 2010). However, the majority of research in this field is guided by verbal theories without formal computational models. In this paper we adapt the Search of Associative Memory (SAM; Raaijmakers & Shiffrin, 1981) model to collaborative free recall. We present a framework to scale SAM to collaborative paradigms with multiple SAM models working together. Our simulation results with the collaborative SAM model suggest that retrieval disruption, responsible for the part-set cuing effect in individuals, is also the cause of collaborative inhibition when multiple models are working together. Our work provides an existence proof that SAM can act as a unified theory to explain both individual and collaborative memory effects, and offers a framework for future predictions of scaling to increased group sizes, shared knowledge, and spread of false memories.

Towards a Cognitive Model of Collaborative Memory

Numerous studies in cognitive development have provided converging evidence that Joint Attention (JA) is crucial for children to learn about the world together with their parents. However, a closer look reveals that, in the literature, JA has been operationally defined in different ways. For example, some definitions require explicit signals of “awareness” of being in JA—such as gaze following, while others simply define JA as shared gaze to an object or activity. But what if “awareness” is possible without gaze following? The present study examines egocentric images collected via head-mounted eye-trackers during parent-child toy play. A Convolutional Neural Network model was used to process and learn to classify raw egocentric images as JA vs not JA. We demonstrate individual child and parent egocentric views can be classified as being part of a JA bout at above chance levels. This provides new evidence that an individual can be “aware” they are in JA based solely on the in-the-moment visual information. Moreover, both models trained on child views and those trained on parent views leveraged the visual properties associated with visual object holding to improve classification accuracy—suggesting a critical role for object handling in not only establishing JA, as shown in previous research, but also in inferring the social partner’s attentional state during JA.

Modeling joint attention from egocentric vision

The current study examined the extent to which the cohesion detected in readers’ constructed responses to multiple documents was predictive of persuasive, source-based essay quality. Participants (N=95) completed multiple-documents reading tasks wherein they were prompted to think-aloud, self-explain, or evaluate the sources while reading a set of four texts. They were then asked to write a source-based essay based on their reading. Natural Language Processing techniques were used to automatically analyze the cohesion of the constructed responses at both within- and across-documents levels. Results indicated that within-document cohesion was negatively related to essay quality, whereas across-documents cohesion was positively related to essay quality. Further, these relations differed by instructional condition such that strategic instructions to either self-explain or evaluate sources seemed to promote across-text integration, compared to thinking aloud. Overall, this study indicates that the cohesion of constructed responses to text can provide insights into the coherence of the mental representations readers construct while reading multiple documents.

Coherence-Building in Multiple Document Comprehension

Conversational interaction involves integrating the perspectives of multiple interlocutors with varying knowledge and beliefs. An issue that has received little attention in cognitive modeling of pragmatics is how speakers deal with the choice of words like come that are inherently perspectival. How do such lexical perspectival items fit into a speaker's overall integration of conversational perspective? We present new experimental results on production of perspectival words, in which speakers have varying degrees of certainty about their addressee's perspective. We show that the Multiple Perspectives Model closely fits the empirical data, lending support to the hypothesis that use of perspectival words can be naturally accommodated as a type of conversational perspective taking.

Come Together: Integrating Perspective Taking and Perspectival Expressions

Recent philosophical work has taken interest in the decision-theoretic problems posed by transformative experiences, or experiences that are epistemically revelatory and life-changing (like becoming a parent). The problem is roughly as follows: if we cannot know what it’s like to be a parent (its subjective value) before actually becoming one, then how are we to decide whether to become one? This topic has received recent empirical attention, some of which has challenged the central importance of subjective value for transformative decision-making. Here, we present empirical work that suggest these findings can be explained by the evaluability bias, in which people weigh decision criteria not based on their importance, but how easy they are to evaluate. Participants not only find subjective value important, but they report willing to pay a great deal of money to get this information. Furthermore, participants who were most uncertain about whether to undergo a transformative decision we most likely to report interest in seeking out information about subjective value. We conclude by considering the philosophical and empirical implications of this work.

Evaluating Transformative Decisions

Traditional investigations of quantifier-negation scope ambiguity (e.g., Everyone didn't go, meaning that no one went or not everyone went) have focused on universal quantifiers, and how ambiguity in interpretation preferences is due to the logical operators themselves and the syntactic relation between those operators. We investigate a broader range of quantifiers in combination with negation, observing differences in interpretation preferences both across quantifiers and also within the same quantifier (confirmed by corpus analysis). To explain this variation, we extend a computational cognitive model that incorporates pragmatic context-related factors, and which previously accounted for every-negation, to predict human interpretation preferences also for some and no. We evaluate the model's predictions against human judgments for quantifier-negation utterances, finding a strong qualitative and quantitative match when the listener has particular expectations about the world in which the utterance occurs. These results suggest that pragmatic factors can explain variation in interpretation preferences.

Pragmatic factors can explain variation in interpretation preferences for quantifier-negation utterances: A computational approach

We propose a computational model of social preference judgments that accounts for the degree of an agents’ uncertainty about the preferences of others. Underlying this model is the principle that, in the face of social uncertainty, people interpret social agents’ behavior under an assumption of expected utility maximization. We evaluate our model in two experiments which each test a different kind of social preference reasoning: predicting social choices given information about social preferences, and inferring social preferences after observing social choices. The results support our model and highlight how uncertainty influences our social judgments.

Reasoning about social attitudes with uncertain beliefs

Curiosity drives much of human behavior, but its open-ended nature makes it hard to study in the laboratory. Moreover, computational theories of curiosity -- models of how intrinsic motivation promotes complex behaviors -- have been challenging to test because of technical limits. To circumvent this problem, we develop a new way to assess intrinsic motivation for building: we assume people build what they find interesting, so we asked them to rate the "interestingness" of visual stimuli -- in this case, simple block towers.
Adults gave a range of ratings to towers built by children, with taller towers rated higher.
To probe interestingness further, we developed controlled tower stimuli in a simulated 3D environment. While tower height predicted much of the variation in ratings, people also favored more precarious towers, as inferred from geometric features and simulated dynamics. These ratings and features therefore give a clear target for computational accounts of curiosity to explain.

Measuring and predicting variation in the interestingness of physical structures

Systems of personal pronouns (e.g.,`you' and `I') vary widely across languages, but at the same time not all possible systems are attested. Linguistic theories have generally accounted for this in terms of strong grammatical constraints, but recent experimental work challenges this view. Here, we take a novel approach to understanding personal pronoun systems by invoking a recent information-theoretic framework for semantic systems that predicts that languages efficiently compress meanings into forms. We find that a test set of cross-linguistically attested personal pronoun systems achieves near-optimal compression, supporting the hypothesis that efficient compression shapes semantic systems. Further, our best-fitting model includes an egocentric bias that favors a salient speaker representation, accounting for a well-known typological generalization of person systems (`Zwicky's Generalization') without the need for a hard grammatical constraint.

Let's talk (efficiently) about us: Person systems achieve near-optimal compression

When two people disagree about matters of taste, neither is in the wrong: There is nothing contradictory in a dialog where one interlocutor says 'The rollercoaster was scary!' and the other responds 'No, it was not scary.' This contrasts with disagreements about objective facts. This phenomenon is known as faultless disagreement, and is central for theorizing about subjective expressions. Faultless disagreement is typically assumed to stem from subjective expressions having a special semantics. We present evidence that people’s judgments of faultless disagreement are sensitive not only to the lexical content of a sentence, but also to the broader discourse context (properties of the interlocutors in the dialog) and to extra-contextual factors (participants’ own attitudes about that particular domain). These results problematize arguments that faultless disagreement stems directly from the semantics of subjective lexical items.

Risk-taking in adversarial games: What can 1 billion online chess games tell us?

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Towards a Cognitive Model of Collaborative Memory

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Risk-taking in adversarial games: What can 1 billion online chess games tell us?

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Towards a Cognitive Model of Collaborative Memory

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Tolerating Untrustworthy Robots: Studying Human Vulnerability Experience within a Privacy Scenario for Trust in Robots

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