United States

This study explores the human evaluation of a robot&#39;s behavior when it makes a mistake during a collaborative task, particularly concerning the success or failure of the individual. We conducted an experiment where an interactive robot collaborated with participants to estimate and report the number of balls and rods in 2D/3D models built using magnetic balls and rod toys. The participants were also required to provide their estimates, and occasionally both made mistakes because of time constraints and parts that were invisible from different views. The experimental results showed that participants evaluated the robot&#39;s failures more favorably when they failed than when they succeeded. This suggests that the participants&#39; outcomes may have influenced their perception of the robot&#39;s behavior. These findings contribute to the development of a robot that fosters better relationships with humans and deepens our understanding of the psychological effects involved in evaluations within social interactions.

CogSci 2025

Do you like a robot that makes mistake? Preliminary study on changing evaluations of a robot that makes mistake in collaborative task

intelligent agents

human factors

human-computer interaction

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

IEEE RO-MAN 2021

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

This study explores the human evaluation of a robot's behavior when it makes a mistake during a collaborative task, particularly concerning the success or failure of the individual. We conducted an experiment where an interactive robot collaborated with participants to estimate and report the number of balls and rods in 2D/3D models built using magnetic balls and rod toys. The participants were also required to provide their estimates, and occasionally both made mistakes because of time constraints and parts that were invisible from different views. The experimental results showed that participants evaluated the robot's failures more favorably when they failed than when they succeeded. This suggests that the participants' outcomes may have influenced their perception of the robot's behavior. These findings contribute to the development of a robot that fosters better relationships with humans and deepens our understanding of the psychological effects involved in evaluations within social interactions.

poster

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

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.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
<br><br>

#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Humans recognize social interactions effortlessly, even when presented with minimal visual information in unfamiliar displays. While force dynamics has been proposed as latent representations for perceiving social interactions, most research has approached this topic from a linguistic perspective based on conceptual knowledge, leaving open the central question of how latent force representations arise from visual inputs. The present study developed a force model that represents social interactions through two types of compositional forces: interactive forces, driven by interactions between agents; and self-propelled forces, driven by intentions of individual agents. Each force was formulated using a physics function to capture the dynamics of repulsive and attractive forces. We conducted two human experiments to measure human similarity judgments across a range of interaction animations and to evaluate recognition performance using generated animations in which the forces applied to individual agents were systematically manipulated. We found that the force model provides a parsimonious account for human judgments in both experiments. These findings suggest that mid-level representations based on compositional forces driven by different goals play an important role in social perception. We conjecture that the development of social perception may be grounded in perceptual mechanisms that support intuitive physics.

The Emergence of Latent Force Representation in Human Perception of Social Interactions

This study examined how classifier-noun conventions and classifier semantic compatibility influence the selection of classifiers in Mandarin- speaking children aged five to seven. Results indicated that children’s classifier use was shaped not only by conventional associations between classifiers and noun categories but also by semantic congruence, particularly in the absence of explicit noun labels. Older children demonstrated greater sensitivity to labels in guiding classifier selection than younger children. Furthermore, explicit noun labels most strongly boosted children’s choice of conventional classifiers for non‑prototypical stimuli, while having a much smaller impact on prototypical stimuli. Overall, these results highlight the interplay between memorization and semantic compatibility in classifier acquisition and underscore the importance of semantic and perceptual features in shaping language learning.

Balancing Conventional Pairings and Semantic Fit: Classifier Production in Mandarin-Speaking Children

Prior work has shown that novices’ intuitive conception of frictional force is often different from that of formal physics theory. These discrepancies can lead to challenges in physics education. The current paper focuses on a common misconception: “frictional force always resists motion.” Though frictional force is linked to relative motion (motion of one object compared to another), it is not always determined by absolute motion. We collected think-aloud protocols from participants, who had varying levels of prior knowledge, on physics problems about motion, relative motion, and frictional force. We analyzed how participants selected properties, made comparisons, and drew inferences. We found that regardless of prior knowledge, participants were able to extract relevant information from the descriptions. When collapsed across the three problem types, frequencies of comparisons did not differ. However, participants with high prior knowledge were more likely to compare objects when making inferences about force and motion. In contrast, participants with low prior knowledge were more likely to rely on insufficient information like motion of a single object. Participants with high prior knowledge were also more likely to leverage experience with other problems, by comparing across processes or constructing hypothetical scenarios.

Problem-solving Strategies in Frictional Force Problems: Evidence from Think-aloud Protocols

Relational reasoning develops slowly. Children's difficulty may stem from the difficulty of inhibiting object attention, but the role of object attention in relational reasoning remains unclear.
Experiment 1 tested the hypothesized trade-off between processing relational and object information using a novel match-then-recognize paradigm. The relational-mapping task required participants to match ordinal positions of objects, followed by an object memory test.
In adults, there was a clear trade-off: object recognition was negatively correlated with relational mapping. Children's object recognition was lower than adults' but positively correlated with relational matching. Experiment 2 further tested the role of inhibition by occluding object matches. Surprisingly, older children and adults actively removed occluders, to their own detriment, whereas the youngest children neither removed occluders nor were helped by them. 
Findings suggest that selective attention can be crucial for relational reasoning, but ignoring object information may be less important for success than total allocated attention.

The Role of Object Attention in Relational Mapping Changes Over Development

When people witness a harmful event, they can judge who is responsible and to what degree. Such judgments depend on multiple factors, including what happened, whether the agent could have prevented the outcome, and whether they were aware that their behavior could cause harm. This information is either observed or inferred from perceptual inputs, often based on details that are present in naturalistic videos but are difficult to convey in word descriptions (e.g. if a pedestrian crossing the road was hit by a car, how suddenly did the pedestrian start crossing?). This study investigates how humans generate rich representations of responsibility from visual input using a controlled set of naturalistic traffic videos sampled to achieve wide variation along relevant dimensions. From these videos, we extract a variety of event features describing entities and their relations, studying the extent to which different features contribute to responsibility judgments in realistic settings.

From Observation to Attribution: How Event Features Shape Responsibility Judgments in Real-World Traffic Videos

When we give feedback, we face a delicate balancing act – we want to convey accurate information, but we also don’t want to hurt someone’s feelings. While computational pragmatic models have elegantly shown how politeness emerges from these principles, they’ve mainly focused on choices from limited predefined responses. Large language models (LLMs) enable the study of open-ended politeness strategies, but their ability to balance informational and social goals like humans remains uncertain. First, replicate previous work using restricted utterance sets, finding that sufficiently large LLMs (≥70B parameters) capture key human politeness patterns, particularly the strategic use of negation. We then extend this investigation to open-ended contexts, collecting and evaluating naturalistic feedback from both humans and LLMs. Surprisingly, human evaluators preferred LLM responses, which demonstrated sophisticated goal sensitivity and diverse politeness tactics. These findings suggest remarkable pragmatic competence in LLMs’ polite language generation while raising questions about the underlying mechanisms.

Polite Speech Generation in Humans and Language Models

Resting-state neural oscillations have been linked with attention and language processing, yet their specific role in reading skills remains unclear. We studied the associations between resting-state EEG and reading skills among 83 German-speaking first to third graders. Our analyses focused on power spectrum density across four frequency bands (delta, theta, alpha, beta) and cluster-based connectivity, in children with varying reading abilities, controlling for age. Spectral analysis showed no significant associations between reading skills and frequency bands. Functional connectivity analysis revealed a negative association with delta-band activity in the central-parietal and with theta-, beta- and delta-band activity in the central-occipital network. Pseudoword reading and reading comprehension were negatively associated with theta-band activity in the left central-occipital and delta-band activity in the right central-occipital network, respectively. Our findings provide insights into the complex relationship between neural oscillations and early reading and suggest resting-state functional connectivity as a neural marker of reading skills.

 Resting-state EEG and Reading Skills in German-speaking Children

Analogy is a mainstay in STEM education. It benefits learning by highlighting important commonalities between concepts and promoting transfer of knowledge. However, students often fail to process analogies deeply, and thus miss the potential benefits. This research aims to equip students with a domain-general strategy for understanding analogies. We created an Analogy Template that guides students through an explicit analysis of the relational matches and object correspondences.  To test its effects, we gave undergraduates a series of science analogies. The Training group used the template to analyze the analogies. The Control group explained the same analogies without the template. Then both groups were asked to explain four novel science analogies. Raters blind to condition judged the explanations. Students who successfully completed the template training showed better understanding of the analogies than those in the control group. These results provide initial evidence that analogical training can contribute to science understanding.

Scaffolding the Understanding of Scientific Analogies 

Infants’ attachment to their caregivers is a central feature of their early social and emotional development. Attachment Theory posits that these relationships vary systematically across distinct styles, though there has been debate about the extent to which these differences reflect features of caregivers’ responsiveness vs. infants’ own temperament. We develop a simple reinforcement learning model of infant exploration that allows us to vary the characteristics of simulated infants and caregivers and analyze the resulting patterns of model behavior. A set of equilibria reliably emerges that corresponds qualitatively to canonical attachment styles; particular agents’ equilibria are controlled by both caregiver and infant parameters. These simulations point the way towards a quantitative synthesis of prior theoretical debates about the nature of attachment.

Simulating variation in infant-caregiver attachment using reinforcement learning

As Large Language Models (LLMs) are increasingly deployed in real-world settings, understanding the knowledge they implicitly use when making decisions is critical. One way to capture this knowledge is in the form of Bayesian prior distributions. We develop a prompt-based workflow for eliciting prior distributions from LLMs. Our approach is based on iterated learning, a method that has been used to explore implicit knowledge in human decision-makers in which successive inferences are chained together to converge to the prior distribution. We validated our method in settings where iterated learning has previously been used to estimate the priors of  human participants -- causal learning, proportion estimation, and predicting everyday quantities. We found that priors elicited from GPT-4 qualitatively align with human priors in these settings. We then used the same method to elicit priors from GPT-4 for a variety of speculative events, such as the timing of the development of superhuman AI.

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The Emergence of Latent Force Representation in Human Perception of Social Interactions

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