United States

Long perceptuo-motor sequences underlie skills from walking to language learning, and are often learned gradually and unconsciously in the face of noise. We used a non-parametric Bayesian n-gram model (Teh, 2006) to characterize the multi-day evolution of human subjects’ implicit representation of a serial reaction time task sequence with second-order contingencies. The reaction time for an element in the sequence depended on zero, one and more preceding elements at the same time, predicting frequency, repetition and higher-order learning effects. Our trial-level dynamic model captured these coexistent facilitation effects by seamlessly combining information from shorter and longer windows onto past events. We show how shifting their priors over window lengths allowed subjects to grow and refine their internal sequence representations week by week.

CogSci 2021

Tracking the Unknown: Modeling Long-Term Implicit Skill Acquisition as Non-Parametric Bayesian Sequence Learning

**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)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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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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### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**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,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

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.

Adults have been shown to favor stable causal relationships – those that hold robustly across background contexts – in their actions and causal/explanatory generalizations (Vasilyeva et al, 2018). Here we explore how this preference develops. We present results from one study with 141 4-7-year-olds investigating whether children pay attention to causal stability when they explain observations and design interventions in novel contexts. We report developmental shifts in reliance on causal stability in a range of inferential tasks, highlight the important role of perceived average causal strength in determining children’s causal preferences, and discuss the implications of our findings for theories of early causal learning. To our knowledge, this is the first study exploring the role of stability in children’s causal reasoning.

“It Depends”: How Children Reason about Stable and Unstable Causes

Students often receive encouragement ("You can do it!") to take on and stick with challenges, yet they don’t always listen. Past work suggests that domain knowledge underlies speaker credibility. We propose that students find encouragement more or less motivating based not only on the speaker’s domain knowledge (e.g., math), but also on their knowledge of the student’s abilities (e.g., math abilities). Adolescents (n=369; ages 11-19) said they would be more likely to seek out and listen to encouragement from a hypothetical person who has both knowledge of the domain and their abilities compared to someone who has knowledge of just one or neither. When asked to reason about real people in their lives (parents, teachers, peers), knowledge of domain and ability also significantly predicted whose encouragement participants would seek out and listen to. Ongoing work is experimentally testing this hypothesis with behavioral measures of persistence and challenge-seeking.

Who is motivating? Students evaluate encouragement based on speaker’s knowledge

In social psychology, a common finding is that people prefer confirmation-biased information. Although this confirmatory information seeking is commonly treated as an error in judgment, we note that biased sources of information can sometimes be more useful than more accurate sources. Such confirmatory sources will only advise someone to deviate from the policy they think is most useful when these sources are sure taking alternative action is correct. For this reason, these sources can allow people to avoid particularly costly errors. Avoiding such costly errors can sometimes be worth the price of inaccurate beliefs, even though these beliefs lead to more errors in total. In two studies, we find initial support for this idea. Within a Partially Observable Markov Decision process, we show that participants prefer optimistically-biased information when they would otherwise miss out on a particularly large reward, and pessimistically-biased information when they would otherwise face particularly strong punishment.

Deciding to be wrong: Optimism and pessimism in motivated information search

Having low confidence in a decision can justify the costly search for extra information. Rich literatures have separately modelled the metacognitive monitoring processes involved in confidence formation and the control processes guiding search, but these two processes have yet to be treated in unison. Here, we model the two as inference and action in a unified partially-observable Markov decision problem where decision confidence is generated by more sophisticated postdecisional or second-order models. Our work highlights how different metacognitive monitoring architectures generate diverse relationships between object- and meta-level accuracy as well as normative information collection in the face of costs. In particular, we demonstrate that decreased metacognitive efficiency prescribes both increased and decreased search, depending on the underlying model of metacognitive confidence. More broadly, our work shows how it is crucial to model interactions between metacognitive monitoring and control, whether in information search or beyond.

Confidence in control: Metacognitive computations for information search

Will people maintain their initial intuitive decision even when they know the rational answer is different? Some dual-process models assume that there is a process of detection and correction of initial intuitive biases. But do these models also account for patterns of behaviour that some people exhibit when they return to their initial intuitive response, even after acknowledging the rational response?

Over four studies we tested for acquiescence by asking participants to solve congruent and incongruent problems utilising a three-response decision paradigm, manipulating base-rate and ratios as well as measuring both Cognitive Reflection Test (CRT) and Rational-Experiential Inventory (REI) responses. 

Results suggest that for incongruent problems participants demonstrated acquiescence across all studies. In addition, although individuals appear to be more rational, and can explicitly recognise the rational response, in some case when moderated by task characteristics, and cognitive reflection, they are unable to supress their initial intuitive decision.

Moderators of Acquiescing to Intuition: Strength of Intuition, Task Characteristics and Individual Differences.

Attractiveness research is typically conducted using static photographs of human faces, often cropped or otherwise edited. We present a database of videos of faces, while talking with synchronized audio that will facilitate more ecologically valid, multimodal research into face and voice attractiveness. Our database contains videos, synchronized voice recordings, and photographs of 20 male and 20 female German speakers under different emotional conditions (neutral, sad, happy, angry, flirty). Recordings were simultaneously made from three different angles (frontal, profile, 3/4 view) in front of a green screen. We report the results of a comprehensive validation of this database to make it usable for multisensory empirical aesthetics research, but also for face or emotion research. An online study was conducted to collect ratings on several dimensions including general attractiveness, allowing us to uncover the effects of predictors for facial attractiveness in a natural setting.

Vienna Talking Faces: A multimodal database of synchronized videos (ViTaFa)

Corpus-based models of lexical strength have questioned the role of word frequency in lexical organization. Specifically, closer fits to lexical behavior data on single words have been obtained by measures of contextual diversity, which modifies frequency by ignoring word repetition in context, semantic diversity, which considers the semantic consistency of contextual word distribution, and socially-based semantic diversity, which encodes the communication patterns of individuals across discourses (Adelman, Brown & Quesada, 2006; Jones, Johns, & Recchia, 2012; Johns, in press). The present work aimed at determining if diversity drives lexical organization also at the level multiword units. Normative ratings of familiarity for 210 English idioms (Libben & Titone, 2008) were predicted from contextual, semantic and socially-based diversity measures computed from a 55-billion word corpus of Reddit comments. Results confirmed the superiority of diversity measures over word frequency, suggesting that multiword idiomatic phrases show similar lexical organization dynamics as single words.

Contextual Diversity and the Lexical Organization of Multiword Expressions

People can explain phenomena by appealing to temporal relations, e.g., you might explain a colleague’s absence at a meeting by inferring that their prior meeting did not end on time. Cognitive scientists have yet to investigate temporal explanations, and explanatory reasoning research tends to focus on how people assess causal explanations; it shows that reasoners often generate causal explanations to resolve conflicts. We posit that temporal explanations help reasoners resolve temporal conflicts, and describe three experiments that test the hypothesis. Experiment 1 provided participants with temporal information that was consistent or inconsistent and elicited their inferences about what followed. Participants spontaneously provided temporal explanations to resolve inconsistencies, and many of them also provided more conservative refutations. In Experiments 2 and 3, participants evaluated explanations and refutations in light of conflicting information. The studies showed that participants spontaneously generate temporal explanations, and in certain cases, they prefer temporal explanations when a more conservative refutation was available. The research is the first to examine patterns in temporal explanatory reasoning.

Temporal explanations help resolve temporal conflicts

Conventional metaphors such as early bird are interpreted rather fast and efficiently since they might be stored as lexicalized, non-compositional expressions. Pissani and de Almeida (2020, submitted) showed that, after reading metaphors (John is an early bird so he can…), participants took longer and were less accurate selecting the continuing word (attend) when it was paired with a literally related distractor (fly) rather than an unrelated one (cry) in a maze task. This suggests that the literal meaning of a conventional metaphor is available immediately afterward. But does the availability of the literal meaning remain further downstream? We examined whether this effect is replicated when there is a medium (6–8 words) and a large (11–13 words) distance between metaphor and word selection. Results indicated that the awakening effect persists but decreases significantly as word distance increases, suggesting that the literal meaning is still available downstream but fades rapidly.

Can early birds … fly? Awakening conventional metaphors further down the maze

Learning the generative model of the world based on limited data is an important problem faced by both human and artificial intelligence. How should one choose among multiple generative models that all well fit the existing data? Occam's razor suggests that one should select the simplest one. Here we asked whether Occam’s razor applies to human learning of probability distribution models. On each trial, participants saw 20~160 samples of spatial locations from a Gaussian mixture model and were asked to choose among four different Gaussian mixture models the one that had generated the samples. In three experiments, we found participants did not, as Occam’s razor would suggest, prefer the one-cluster option that appears to be simplest, other things being the same. Instead, they showed preference for options with two or three clusters. Such violation of Occam’s razor sheds light on distinction between complexity-based and experience-based priors for model selection.

Tracking the Unknown: Modeling Long-Term Implicit Skill Acquisition as Non-Parametric Bayesian Sequence Learning

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“It Depends”: How Children Reason about Stable and Unstable Causes

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