United States

People may engage in a joint activity (JA) to accrue material rewards or to help others. From a third-party perspective, the occurrence of JAs is thus ambiguous about the goals of the participating agents. We argue that the payoff structure of a JA (how costs and rewards are distributed) may help disambiguate these goals. Specifically, we hypothesize that an agent’s participation in a JA should be interpreted as prosocially motivated when its costs cannot be recouped by material rewards (disadvantageous payoff). We tested this hypothesis across three looking-time experiments with 12-month-olds. As predicted, infants expected a disadvantaged agent to behave altruistically towards her JA partner (Exp. 1). However, this expectation might be explained by a sensitivity to changes in overall reward distribution from familiarization to test (Exps. 2 &amp; 3). Our results call for a re-evaluation of the role that payoff information plays in early goal attribution within JA contexts.

CogSci 2021

Do infants infer prosocial goals from disadvantageous payoffs in joint action?

**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.

People may engage in a joint activity (JA) to accrue material rewards or to help others. From a third-party perspective, the occurrence of JAs is thus ambiguous about the goals of the participating agents. We argue that the payoff structure of a JA (how costs and rewards are distributed) may help disambiguate these goals. Specifically, we hypothesize that an agent’s participation in a JA should be interpreted as prosocially motivated when its costs cannot be recouped by material rewards (disadvantageous payoff). We tested this hypothesis across three looking-time experiments with 12-month-olds. As predicted, infants expected a disadvantaged agent to behave altruistically towards her JA partner (Exp. 1). However, this expectation might be explained by a sensitivity to changes in overall reward distribution from familiarization to test (Exps. 2 & 3). Our results call for a re-evaluation of the role that payoff information plays in early goal attribution within JA contexts.

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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.

Infants experience the world through their actions with objects and their interactions with other people, especially their parents. Prior research has shown that school-age children with hearing loss experience poorer quality interactions with typically hearing parents, and difficulties in controlling their visual attention. In the current study, we used mobile eye-tracking to investigate parent-child interactions in toddlers with and without hearing loss. Parents and toddlers engaged in a goal-directed, interactive task that involved inserting coins into a slot and required joint coordination between the parent and the child. We examined the visual behaviors of the toddlers and the scaffolding behaviors of the parents. In contrast to previous work, preliminary findings reveal a pattern of potential similarities between deaf and hearing toddlers or their parents.

Joint Action in Deaf and Hearing Toddlers: A Mobile Eye-Tracking Study

Delays between causes and effects are commonly found in cause-effect relationships in real life. However, previous studies have only investigated delays on the order of seconds. In the current study we tested whether people can learn a cause-effect relation with hour long delays. The delays between the cause and effect were either 0, 3, 9, or 21 hours, and the study lasted 16 days. Surprisingly, we found that participants were able to learn the causal relation about equally as well in all four conditions. These findings demonstrate a remarkable ability to accurately learn causal relations in a realistic timeframe that has never been tested before.

Causal Learning With Delays Up to 21 Hours

Cognitive control—the ability to execute goal-relevant responses in the presence of competing goal-irrelevant response alternatives—predicts academic achievement, delinquency, and occupational success. Assessing children's cognitive control is challenging due to the tedious nature of cognitive assessments and children’s low attention spans. This study examined whether a cognitive game-based assessment (GBA) may alleviate these challenges by investigating the construct and criterion validity of implementing game-based features into a traditional cognitive assessment, and the associations between GBA performance, academic achievement outcomes, and associated neural substrates in children ages 3-5. Performance on the GBA was significantly associated with performance on a traditional measure of cognitive control, functional brain connectivity, and mathematical and verbal test outcomes. Children also showed a stronger preference and higher ratings of enjoyment for the GBA compared to the traditional cognitive control assessment.

The Construct and Criterion Validity of a Cognitive Game-based Assessment: Cognitive Control, Academic Achievement, and Prefrontal Cortex Connectivity

Productive failure (PF) is a learning paradigm that reverses the standard order of instruction by asking students to solve problems prior to instruction. This paradigm has been shown to be effective for fostering student learning. To date, however, the role of student emotion in productive failure has not been investigated. In other paradigms, there is some evidence that failure elicits negative emotions and that these emotions can interfere with learning. This leads to a conundrum given productive failure’s positive effect on learning. To shed light on this, we report on results from a study (N = 48) in the productive failure paradigm. For the analysis, we used a mixed-methods approach to investigate the distribution of emotions in productive failure, how these changed across different instructional activities, and the relation between emotions and posttest performance.

Productive Failure and Student Emotions

The proliferation of the capability for producing and distributing deepfake videos threatens the integrity of systems of justice, democratic processes, and the general ability to critically assess evidence. This study sought to identify individual differences that predict one’s ability to detect these forgeries. It was hypothesized that measures of affect detection and political orientation would correlate with performance on a deepfake detection task. Within a sample (N = 173) of college undergraduates and participants from Amazon’s M-turk, affect detection ability was shown to correlate with deepfake detection ability, r(171) = .73, p < .001, and general orientation to the political left was shown to correlate with deepfake detection ability, r(171) = .42, p < .001. The results of this study serve to identify populations who are particularly susceptible to deception via deepfake video and to inform the development of interventions that may help defend against attempts to influence them.

Individual Differences in Deepfake Detection: Mindblindness and Political Orientation

Human-AI collaboration is an increasingly commonplace part of decision-making in real world applications. However, how humans behave when collaborating with AI is not well understood. We develop metacognitive bandits, a computational model of a human's advice-seeking behavior when working with an AI. The model describes a person's metacognitive process of deciding when to rely on their own judgment and when to solicit the advice of the AI. It also accounts for the difficulty of each trial in making the decision to solicit advice. We illustrate that the metacognitive bandit makes decisions similar to humans in a behavioral experiment. We also demonstrate that algorithm aversion, a widely reported bias, can be explained as the result of a quasi-optimal sequential decision-making process. Our model does not need to assume any prior biases towards AI to produce this behavior.

Explaining Algorithm Aversion with Metacognitive Bandits

Voice atypicalities are a characteristic feature of schizophrenia, often associated with core negative symptoms. A recent meta-analysis identified atypicalities in pitch, speech rate, and pauses. However, heterogeneity across studies was large and replications almost nonexistent. Further, it is not clear whether vocal patterns are directly related to the mechanisms underlying the disorder and could therefore be found across languages, or not.
In this study we implemented a more rigorously cumulative scientific approach by collecting and analyzing a large cross-linguistic corpus of voice recordings. We critically employed meta-analytic priors to systematically assess the replicability of previous findings, and modeled between-participants variability and cross-linguistic differences.
We replicate previous meta-analytic findings across all languages for reduced pitch variability, while increased pause duration and lower speech rate results were replicated only in some languages. Most atypical voice patterns, thus, seem not to be distinctive of schizophrenia in general, but more specifically situated in linguistic/cultural differences.

Vocal patterns in schizophrenia: toward a cumulative approach

Judging if a conclusion follows logically from a given set of
premises can depend much more on the believability than on
the logical validity of the conclusion. This so-called belief bias
effect has been replicated repeatedly for many decades now.
An interesting observation is, however, that process models for
deductive reasoning and models for the belief bias have not much
of an overlap - they have largely been developed independently.
Models for the belief bias often just implement first order logic
for the reasoning part, thereby neglecting a whole research field.
This paper aims to change that by presenting a first attempt at
substituting the first order logic components of two models for belief,
selective scrutiny and misinterpreted necessity, with two state
of the art approaches for modeling human syllogistic reasoning,
mReasoner and PHM. In addition, we propose an approach for
extending the traditionally dichotomous predictions to numerical
rating scales thereby enabling more detailed analysis. Evaluating
the models on a dataset published with a recent meta-analysis on
the belief bias effect, we demonstrate the general success of the
augmented models and discuss the implication of our extensions
in terms of the limitations of the current focus of research as well
as the potential for future investigation of human reasoning.

Unifying Models for Belief and Syllogistic Reasoning

When humans make sense of the world, they do not understand it as a cascade of observations; rather, from a cascade of observations, humans assemble a holistic narrative, connecting their observations using prior knowledge and inference. The final product of observations connected with prior knowledge and inference may be modeled as a knowledge graph. The process of sensemaking described above is one we seek to emulate in the realm of image understanding through a computational system. Starting from observed objects and relationships in a sequence of images (from Visual Genome Scene Graphs), the system we are building consults a commonsense knowledge network (ConceptNet), over-generates a set of hypothesized narrative-based connections between observations, and evaluates and trims its hypotheses through Multi-Objective Optimization to create a consistent set. The resultant knowledge graph reflects the system’s consistent speculations, beyond the directly observable, of what is happening in, and across, the images.

Understanding Image Sequences Via Narrative Sensemaking

In the development and analysis of interventions designed to improve student learning, it is important to consider potential influences of student behavior. Unproductive behaviors, such as “gaming the system,” have been studied for their potential impacts on the measurement and assessment of student knowledge within these interventions. Conversely, less attention has been given to factors that may influence gaming behavior. Gaming may be attributed to student-level traits, but could also be a temporary state brought on by systemic causes such as the perceived difficulty or presentation of a problem. We leverage prior research in this area to develop a data-driven measure of gaming and address whether this behavior occurs as a “state” or “trait” within a randomized controlled trial. We find that these factors are differential across the two study conditions, suggesting that context contributes to the underlying causes of gaming behavior.

Do infants infer prosocial goals from disadvantageous payoffs in joint action?

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Joint Action in Deaf and Hearing Toddlers: A Mobile Eye-Tracking Study

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