United States

Forgiveness plays a significant role in our everyday social life, and, because of that, it has received an increasing amount of attention in academic research. However, philosophers and psychologists are equally worried by the fact that we still lack an empirically adequate characterization of forgiveness. In this paper, we present two preregistered study in which we explore what ordinary people believe a speaker does when he or she perform the speech act of forgiving by uttering the phrase, “I forgive you”. Experiment 1 uses a vignette-based stimulus to examine what participants believe to change after the victim granted forgiveness to their wrong-doer. In Experiment 2, we apply a linguistic test, the cancellability test, to determine whether participants consider it compatible to forgive the wrong-doer but to still blame them.

CogSci 2023

“I forgive you!” — Exploring the Impact of Forgiveness on Negative Emotions and Blame

### 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!<br><br>

**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/).


CogSci 2021

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.

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
<br><br>

#### **CogSci 2023 Program Book**
<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/tzgk/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>
<br>

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Social biases can negatively impact our sense of belonging, achievement, and social relationships. However, it is unclear what inferential processes underlie how people detect biases. We propose that people infer social biases by positing prior beliefs to account for potential gaps between what someone observed (e.g., seeing you succeed on a challenging task) and how they responded (e.g., recommending you try something easier). We present a computational model formalizing this process, and validate it with two experiments. We find a strong quantitative fit between model predictions and participant judgments across a range of inferences, namely which prior bias the coach held, and even the player's emotional reactions to the coach's actions (i.e., how offended or flattered the player feels). This work bridges computational methods with social psychological research on social biases, by showing how mental state inferences contribute to our ability to rapidly detect biases.

Detecting social biases using mental state inference

People have a remarkable capacity to infer other people’s goals and intentions based on how they behave. Yet, humans are also motivated to ensure that others can infer their mental states easily and accurately. Past work has argued that people achieve this by introducing inefficiencies to their behavior, which reveal its underlying goal (e.g., exaggerating one’s movements so as to make their purpose obvious). We hypothesized that inefficiency is not a constitutive feature of signaling, and that people will often signal their goals and intentions solely through efficient action. We test this idea in a signal-design experiment where participants need to reach a goal while also making their behavior as inferable as possible. We find that people shape their behavior to increase inferability without jeopardizing efficiency. We then test how well these efficient signals reveal their goals using a computational model to simulate observer inferences. Our results show that humans not only exploit opportunities to reveal their goals without deviating from efficient action, but that these signals are shaped to allow observers to more quickly understand the intention underpinning the movement.

Guiding Inference: Signaling intentions using efficient action

People engage in intentional misunderstandings to get around direct non-compliance. In other words, they use loopholes. Previous work showed that adults and children consider loophole behavior to be less costly than direct noncompliance (Bridgers, Schulz, & Ullman, 2021), and suggested this is a primary reason for their use: loopholes will land you in less trouble than defiance. However, we propose that this difference between loopholes and defiance will not hold for a specific context: moral violations. We replicate the finding that loopholes are less costly in a neutral context, but find that engaging in loopholes in a moral context is as bad as non-compliance (Experiment 1, N=360). We then use a direct comparison between loopholes and non-compliance (N=150), to investigate whether in some contexts loopholes will be seen as even worse than non-compliance. We discuss possible extensions and limitations, and consider why loopholes in moral violations may be uniquely unacceptable.

Skirting the Sacred: Moral Violations make Intentional Misunderstandings Worse

We provide an empirical test for shared reality theory, which predicts individuals seek to share similar impressions about the world with others in order to connect and reduce uncertainty. We built an online platform permitting synchronous communication between groups of participants time-locked to video stimuli. We first presented participants with the same introduction to a reality show and then seamlessly showed different dyads within a group separate video segments depicting different social interactions between pairs of contestants. People reported feeling more connected to group members who underwent the same video experience compared to those who unknowingly watched a different video. The nature of the interactions also appeared to impact relationships as participants reported feeling more connected to partners who responded to their messages more quickly during the group chat. Together, these results highlight the importance of communication during shared experiences in aligning shared interpretations of reality and facilitating social connection.

Shared experiences strengthen social connectedness through shared impression formation and communication behavior

The question of how children develop multimodal coordination skills to engage in meaningful face-to-face conversations is crucial for our broader understanding of children's healthy socio-cognitive development. Here we focus on investigating the ability of school-age children to coordinate turns with their interlocutors, especially regarding when to take the floor (i.e., the main channel of the conversation) and when to provide attentive listening signals via the back channel. Using data of child-caregiver naturalistic conversations and data-driven research tools, we found that children aged 6 to 12 years old already show adult-like behavior both in terms of reacting to the relevant channel-specific cues and in terms of providing reliable, multimodal inviting cues to help their interlocutor select the most appropriate channel of the conversation.

Development of Multimodal Turn Coordination in Conversations: Evidence for Adult-like behavior in Middle Childhood

Social media is largely used as a tool to promote social connection among people and groups; yet that does not imply social media users are not lonely. Here we explore whether we can detect loneliness in individuals from their language and social indicators on Twitter. We define a sample of self-declared lonely Twitter users and compare their language to a matching random control sample. For each user we create a text em-
bedding for the content of their online posts, excluding terms and expressions related to loneliness. Using a principal component analysis of the resulting embeddings we position each user in a two-dimensional space spanned by the first two components. Lonely individuals are spatially separated from the control sample, indicating that lonely individuals exhibit distinct language patterns related to the expression of internalizing thoughts focused on the self, e.g. “I should” and “but I”. Indicators of online social relations, such as the number of online friends, favorites, mentions, indicate that lonely individuals have fewer social relations, while a sentiment analysis indicates that their posts have lower valence. Our results provide insights into the lexical, social, and affective markers that characterize loneliness online, providing a starting point for the development of diagnostics and prevention.

Quantifying the Digital Phenotype of Loneliness on Twitter

Languages describe spatial relations in different manners. It is however hypothesized that highly frequent ways of categorizing spatial relations across languages correspond to the natural ways humans conceptualize them. In this study, we explore the use of machine translation to gather data in semantic typology to address whether different languages show similarities in how they carve up space. We collected spatial descriptions in English, translated them using machine translation, and subsequently extracted spatial terms automatically. Our results suggest that most spatial descriptions are accurately translated. Despite limitations in our extraction of spatial terms, we obtain meaningful patterns of spatial relation categorization across languages. We discuss translation limits for semantic typology and possible future directions.

Typology of topological relations using machine translation

Classifiers are a noun categorization device found in many East and Southeast Asian languages. Their syntactic status is debated: some theories treat them as forming a constituent with the noun, others with the numeral. Typological data on how classifiers, numerals, and nouns are ordered largely support the classifier-with-numeral hypothesis: orders in which the noun intervenes between the numeral and the classifier are unattested. However, typological evidence is problematic in this case, because the distribution of classifiers is highly geographically constrained, and ordering tendencies may reflect language contact. In this study, we look for evidence of the syntactic status of classifiers using artificial language learning experiments. We test whether learners’ preference among patterns are predicted by one of these two accounts. Results suggest that, if anything, learners in fact prefer orders that group classifiers with the numeral.

Cognitive biases for word order between numeral, classifier and noun

Automated Writing Evaluation (AWE) tools are steadily gaining prominence in educational settings given the ease and scale in which they can be deployed. Nevertheless, despite producing a similar performance as human raters, the accuracy of automated systems is often met with skepticism. In the current study we explore whether such skepticism extends to writing feedback believed to be generated by human tutors in training or an AI tutor under development. When both sources are fallible, are critical judgments mitigated? Participants (N=477) judged the accuracy of feedback on writing samples given by human or AI tutors where the feedback was normed to be accurate, inaccurate, and ambiguous. Results showed participants were still more likely to deem identical feedback provided by an AI, as compared to human tutors, as less accurate, and they were more confident in these evaluations. It appears that “novice” AWE systems are not completely immune to negative bias.

Fallible feedback: Judgments of "novice" automated and human writing tutors

The concept of a good life is usually assumed by philosophers to be equivalent to that of well-being, or perhaps of a morally good life, and hence has received little attention as a potentially distinct subject matter. In a series of experiments participants were presented with vignettes involving socially sanctioned wrongdoing toward outgroup members. Findings indicated that, for a large majority, judgments of bad character strongly reduce ascriptions of the good life, while having no impact at all on ascriptions of happiness or well-being. Taken together with earlier findings these results suggest that the lay concept of a good life is clearly distinct from those of happiness, well-being, or morality, likely encompassing both morality and well-being, and perhaps other values as well: whatever matters in a person’s life. Importantly, morality appears not to play a fundamental role in either happiness or well-being among the folk.



Premium content

Downloads

Next from CogSci 2023

Detecting social biases using mental state inference

Similar lecture

Who went fishing? Inferences from social evaluations