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Research suggests that perspective-taking in children is associated with greater cognitive and emotion understanding as well as parent use of mental state language—words concerning inner mental states or feelings. However, prior research shows conflicting results on which categories of mental state language—cognition (e.g., think, know), emotion (e.g., happy), or desire (e.g., want)—facilitate children’s perspective-taking. The current study examines the relationship between parent mental state language and children’s perspective-taking.

We are coding parent mental state language categories from a wordless book task with two-year-old children and assessing children’s perspective-taking using a card task (Flavell et al., 1981). Preliminary results (N=7, planned 41) show that, on average, parents use more cognition words per session than emotion and desire words and, on average, children score at chance on the perspective-taking task. Differences between mental state categories on children’s perspective-taking skills as well as individual variability will be analyzed.

CogSci 2023

The association between types of caregiver mental state language and toddler perspective-taking

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

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.

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

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.

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The association between types of caregiver mental state language and toddler perspective-taking

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

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