Canada

The ancient Greek hero Ulysses chose to bind himself to resist the temptation of Sirens, highlighting the fact that humans may voluntarily sacrifice their freedom of choice to achieve committed goals. In this work, we propose a computational model for such commitment under the framework of Bayesian Theory of Mind. The model is based on the idea that even when alone, humans act to better demonstrate their intentions to an imagined third-party observer (ITO) censoring their actions. Our model successfully captures the Ulysses-constraint of freedom, as the freedom confuses the ITO’s inference of their intention. We further show that, trajectories generated both by human actors and actors modeled with ITO censorship are easy to interpret both in the eyes of an actual human ob- server and an ITO. The results demonstrate that under conflict- ing desires, humans achieve commitment by spontaneously censoring their actions with an internalized theory of mind.

CogSci 2022

Intentional commitment through an internalized theory of mind: Acting in the eyes of an imagined observer

**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/)

Registration Is Required

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.

poster

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
 [**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)

This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

[Register to Attend](https://cognitivesciencesociety.org/registration/)

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





People collaborate daily to achieve joint goals, negotiating sidewalks, preparing meals, constructing furniture. Here we analyzed how strangers coordinate a complex novel task, assembling a TV cart. Before beginning and before major actions, partners used a photo of the completed cart, speech, and gesture to establish a joint model of the structure of the completed cart. Most coordination was implicit, a conversation of actions that could be understood in the context of the current state of the object and the shared mental representation. Actions served a dual purpose, both to communicate and to advance the task. The basic unit of coordination was the dyad, not the individual.

Explicit and Implicit Coordination of Joint Action

Although pointing is sparse, overloaded, and indirect, it allows humans to effectively decode shared information, (ex)change their minds, and plan accordingly. Pointing is an invitation to jointly attend to an object, which triggers the mutual inference between agents of each other’s mind. Relevance is a fundamental assumption underlying all human communication, including pointing. We define relevance as how much a signaler’s belief can make a positive difference to its receiver’s well being. We build a Theory of Mind (ToM) model to test our definition of relevance and use pointing as a case study. In two experiments, we test our relevance model in a classic artificial intelligence (AI) task, the Wumpus world, with the key difference that there is a guide that points to help a hunter. Agents with our relevance model gain significantly higher rewards than agents who ignore signals from the guide. Agents with our model also achieve better performance than agents who receive an additional observation of the environment. The results show that the power of pointing comes from the ToM inference of relevance, rather than providing more precise individual perception.

What Is the point? a Theory of Mind Model of Relevance

Testing on previously learned materials can enhance the learning of new materials studied after the test (the forward effect of testing). We examined how the timing of interim testing influenced subsequent learning. Participants studied trivia facts in two learning sections (A & B) and provided a mental effort rating for each section. Between the two sections, participants either restudied, took an immediate test, or a 15-min delayed test on Section A. They then proceeded to Section B and took a final test on both sections. The results demonstrated that both the immediate and delayed testing groups outperformed the restudy group on Section B, indicating that the forward effect of testing was independent of the test timing. Furthermore, the testing groups showed a bigger increase in their mental effort from Section A to B than the restudy group, suggesting that the forward testing effect occurred probably due to more mental effort.

Does delayed interim testing enhance subsequent learning?

Computational cognitive theories of Autism Spectrum Disorder have received renewed attention in recent years. Consistent with the predictive processing framework, ASD has
been re-conceptualized as a disorder of aberrant prediction and learning-rate estimation involving multiple levels of a putative cognitive computational hierarchy. Specifically, behavioral symptoms of individuals with ASD might manifest due to an aberrant overestimation of the volatility of environmental contingencies (i.e. tendency of change in cue-outcome probabilities) which in turn might induce a dysfunctional setting of learning rates. In this work, we attempted to conceptually replicate computational modeling analyses of an impactful study of the recent ASD modeling literature in an independent
sample of subjects. We were not able to replicate some prior reported effects likely due to differences in model architecture and cognitive task setup. We found statistical trends in similar directions.

Modeling aberrant volatility estimates in Autism Spectrum Disorder

While problem-solving is central in our daily life, its underlying mechanisms remain elusive. The dominant theory states that one must restructure a problem (i.e., reorganize problem-related representations) to solve it. As empirical evidence supporting this mechanism is scarce, we used network science methodology to demonstrate the key role of restructuring in problem-solving. Individual semantic memory networks were estimated before and after participants attempted to solve a riddle. These networks represent the organization of solution-relevant and irrelevant words as nodes, with edges representing the strength of the relationship between them based on the participants’ relatedness judgments. Restructuring was quantified as the difference in semantic network metrics between pre- and post-solving phases. Successful problem-solving was predicted by local semantic network restructuring, only in edges and nodes assessed as helpful to solve the riddle. These results shed new light on the mental restructuring in problem-solving and provide a new method to quantify it.

Restructuring problem-related semantic associations promotes solving success

Logico-semantic theories have long noted parallels between the linguistic representation of temporal entities (events) and spatial entities (objects): bounded (or telic) predicates such as fix a car resemble count nouns such as sandcastle because they are “atoms” with well-defined boundaries. By contrast, unbounded (or atelic) phrases such as drive a car resemble mass nouns such as sand in that they are unspecified for atomic features. Here, we show for the first time that there are similarities in the perceptual-cognitive representation of events and objects in non-linguistic tasks. Specifically, after viewers form a bounded or an unbounded event category, they can extend the category to objects or substances respectively (Experiment 1). Furthermore, viewers can intuitively make event-to-object mappings that respect atomicity (Experiment 2). These striking similarities between the mental representation of events and objects have implications for current theories of event cognition, as well as the relationship between language and thought.

Events and Objects Are Similar Cognitive Entities

Events are considered as temporal segments with a beginning and an endpoint. Philosophical and linguistic literature on events distinguishes between bounded events that include distinct temporal stages leading to culmination (e.g., fix a car) and unbounded events that include largely undifferentiated stages and lack an inherent endpoint (e.g., drive a car). The present study shows that event viewers spontaneously compute this distinction through an interruption detection task. People watched videos of bounded or unbounded events with a visual interruption lasting .03s at the midpoint or close to the endpoint of the event stimulus. People indicated whether they saw an interruption after each video (Experiments 1) or responded as soon as possible during each video (Experiment 2). In both cases, the endpoint-midpoint difference depended on whether participants were watching a bounded or an unbounded event. As people perceive dynamic events, they spontaneously track boundedness, or the internal temporal structure of events.

Viewers Spontaneously Represent Event Temporal Structure

Motion lines are the drawn lines trailing behind an object to show movement. Prior work has shown that motion lines aid comprehension by clarifying the direction of motion, but no experimental research has examined how they interact with path segmentation, like a visual sequence in comics. In Experiment 1, participants understood panels with motion lines better than without lines. With motion lines, omitting the path’s source was harder to comprehend than showing the source but having it disappear. Moreover, less experienced comic readers benefited more from showing a whole path with lines in single panels than fluent ones. In Experiment 2, reversed-direction lines were harder to process than normal or omitted lines, but segmentation did not matter. Overall, motion event comprehension can be modulated by path segmentation of lines and comics reading experience.

Motion Event Segmentation in Visual Narratives

Previous research demonstrated an asymmetry between Sources and Goals in people’s linguistic and non-linguistic encoding of motion events: when describing events such as a fairy going from a tree to a flower, people mention the Goal (“to a flower”) more often than the Source (“from a tree”) and are better at detecting Goal changes in a Same-different memory test. Many take these findings as evidence for a homology between linguistic and conceptual representations: an unmentioned event component is also conceptually less robust. Here, we show that the nonlinguistic Source-Goal asymmetry disappears when memory is probed with a Forced-choice task instead of a Same-different task. We argue that, despite frequent absence from linguistic descriptions, Sources are robust in event memory, but not attended to during Same-different tests due to people’s task-relevance assumption. This result bears on the nature of the Source-Goal asymmetry and calls for a finer-grained account for language-cognition homology.

The Source-Goal Asymmetry in Motion Events: Sources Are Robustly Encoded in Memory but Overlooked at Test

Human social interactions require understanding and predict- ing other people’s behavior. A growing body of work has found that these inferences are structured around an assumption that agents act rationally and efficiently in space. While powerful, this view treats action understanding in a vacuum, ignoring that much social inference happens in the context of familiar, hierarchically structured events (e.g.: buying groceries, ordering in a restaurant). We propose that social and world knowledge is critical for efficiently interpreting behavior and test this idea through a simple block-building paradigm, where participants infer an agent’s sub-task (study 1a), next action (study 1b), and higher-level goal (study 1c), from very sparse observations. We compare these inferences against a Bayesian model of goal inference that exploits task structure to interpret agents’ actions. This model fit participant judgments with high quantitative accuracy, highlighting how world knowledge may help support social inferences in a rich and powerful way.
Keywords: Computational modeling; Social cognition

Premium content

Downloads

Next from CogSci 2022

Explicit and Implicit Coordination of Joint Action

Similar lecture

AI bias in Human-Robot Interaction: An evaluation of the Risk in Gender Biased Robots