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In our global and technical world, there is an increasing need for humans to be able to solve problems that are complex, nontransparent and self-dynamic. There are vast differences between humans in terms of their capability to solve such problems that go beyond classical intelligence and having an analytical mind. Factors such as personality, knowledge, and the motivation to engage in effortful cognitive problems can contribute to success. Only a few studies investigated the relationship between these `soft&#39; factors and complex problem-solving (CPS). In the following, we will investigate these factors in the CPS framework Tailorshop, a computer-based scenario with the goal of increasing the company value by manipulating several variables. Results indicate that personality traits and need for cognition are no successful predictors. Overall, the present study points out the tendency of relevant personality traits as CPS predictors.

CogSci 2023

The Impact of Personality for Solving Complex Problems

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

In our global and technical world, there is an increasing need for humans to be able to solve problems that are complex, nontransparent and self-dynamic. There are vast differences between humans in terms of their capability to solve such problems that go beyond classical intelligence and having an analytical mind. Factors such as personality, knowledge, and the motivation to engage in effortful cognitive problems can contribute to success. Only a few studies investigated the relationship between these `soft' factors and complex problem-solving (CPS). In the following, we will investigate these factors in the CPS framework Tailorshop, a computer-based scenario with the goal of increasing the company value by manipulating several variables. Results indicate that personality traits and need for cognition are no successful predictors. Overall, the present study points out the tendency of relevant personality traits as CPS predictors.

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. 
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!

 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
 [**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
 

#### **CogSci 2023 Program Book**
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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.

Vaccines are among the best tools to limit the spread of preventable diseases. yet, recent years have seen a rise in anti-vaccination sentiments for vaccines against COVID-19, the MMR, and more. It is critical to understand the factors that influence whether a person will accept or reject a vaccine for a given disease. 
This paper tests a Bayesian model to predict attitudes toward vaccines. with five factors (subjective beliefs concerning danger of illness, safety of the vaccine, prevalence of the disease, perceived social norm, and governmental recommendation). 
To parameterize the model, Study 1 elicits the full conditional probability table while Study 2 tests model predictions by eliciting people’s priors for COVID-19 and the common flu. We find a good fit between predictions and observations, accounting for 53% and 44% of the variance. This suggests the usefulness of a formal model to capture people’s beliefs about vaccination.


Estimating attitudes toward vaccination: A Bayesian framework

Both the logical form and content of assertions can have an impact on the conclusions humans derive. Despite this, research in that domain has often singled out one aspect: e.g., in syllogistic reasoning research about the logical form dominates (with some exceptions), while in conditional reasoning the role of different contents is more prominent. A core result in the latter domain focuses on the suppression effect, namely that alternatives or disablers can make a conclusion less likely. An effect that is very likely in everyday reasoning, where rarely a statement is exclusively true or false. So far, however, it has not been studied whether this is an effect specific to the conditional domain, or whether it transfers to the domain of syllogisms. This paper systematically investigates the impact of assertions that allows for disablers and alternatives in syllogistic reasoning. The response distribution for both conditions demonstrate on the aggregate level a shift for the case of non valid conclusions to preferred patterns. Using methods from machine learning we develop information based models that represent the gathered data with an accuracy of up to 83%. We discuss the need to bridge the gap between concepts and phenomena found in different reasoning domains.

Towards Bridging the Gap Between Conditional and Syllogistic Reasoning

What does it mean to deny the conditional statement, if you steal an apple, you go to jail? One theory argues that, because conditionals are probabilistic, their denials are too. And so the conditional probability, P(not-jail | steal-apple), best describes a conditional denial. Another theory argues that conditional denials concern possibilities, i.e., they activate imagined situations in which you cheat on your taxes but don’t go to jail. The two accounts make diverging predictions: only the latter predicts that people should assess conditionals and their denial as mutually inconsistent. Two experiments corroborate the possibility-based account: the studies show that both in implicit and explicit evaluations of consistency, conditional denials conflict with the conditionals they deny.

Conditionals conflict with their denials

Gestures play a critical role in human-human and human-robot interaction. In task-based contexts, deictic gestures like pointing are particularly important for directing attention to task-relevant entities. While most work on task-based human-human and human-robot dialogue focuses on closed-world domains where all task-relevant entities are known of a priori, recent research has begun to consider open-world tasks, where task-relevant objects may not be known of to the hearer a priori. In open-world tasks, we argue that a more nuanced consideration of gesture is necessary, as interactants are likely to gestures that uniquely bridge traditional gesture categories in order to navigate the open-world dimensions of their task environment. In this work, we explore the types of gestures used in open-world task contexts, and their frequencies of use. Our results suggest a need to rethink the way that gesture analysis is approached in the study of human-human and human-robot interaction.


What was and what will be: What gestures are used in open-world task-based referential communication?

Languages vary in the way they categorize semantic domains. Incidentally certain semantic systems appear more often than others across the world. Recent research has shown that the attested variability can be explained as the result of languages being a plurality of optimal solutions to efficiency constraints. However, the question of the prevalence remains open. Assuming that languages are a form of culturally transmitted cognitive technology, the Typological Prevalence Hypothesis proposes that the prevalence of a linguistic system is explained by how cognitively natural it is to learn and use. We aim to formalize and test this hypothesis by proposing an information-theoretic measure of communicative and developmental naturalness applied to color typology. While controlling for phylogenetic relatedness, we find that communicative naturalness is a better predictor of typological prevalence than developmental naturalness.

A Colorful Formalization the Typological Prevalence Hypothesis

By the preschool age, children develop a strong understanding of norms, fairness, and inequality. However, little is known about the social mechanisms underlying these developments; a gap we hope to bridge. We explored how parenting styles and beliefs in agency affect children's beliefs about inequality. Four-to-eight-year-old children (n=45; target n=80) were exposed to two trials in which they watched a character be disadvantaged either due to poor performance (merit) or a bad decision (self-chosen) and were asked to make a fairness judgment and resource reallocation. We also assessed parenting style and children’s beliefs in free choice/agency. We found that children with authoritative parents gave more stickers to disadvantaged characters and were more likely to think of both inequalities as fair. Additionally, greater belief in free choice correlated with belief in the fairness of both inequalities. Results show how parenting and beliefs in choice impact children’s social cognition.

Authoritative Parenting and Beliefs in Agency Impact Children's Views on Fairness and Inequality

Infant language development is facilitated by social environments and predicted by their parents’ timely responses. However, the increasing uptake in digital technologies raises important questions about how digitally mediated interactions influence parents’ responsiveness. In the present study, the quantity (total numbers of words) and quality (unique words) of parents’ utterances to their infants were examined in contingent (less than 2 seconds) and non-contingent segments across two digital conditions (face-to-face with a tablet present vs. video-chat) during a peekaboo play (N = 67, infants’ age: 18 – 26 months-old). We assessed the lexical variability using a natural language processing (NLP) approach with size-matched random sampling simulation. Preliminary results indicate (1) less lexical diversity was used in non-contingent video-chat and (2) the lexical variability was similar between contingent video-chat and non-contingent while playing peekaboo with a tablet. These findings suggest parents’ timely inputs play a more critical role in video-chat environment.

Lexical diversity in parents’ responses to infants’ vocalisations during peekaboo play across digital media contexts

Biases and stereotypes about social groups emerge from a young age. This study examines whether counterevidence that is statistically representative of the entire group can change children’s attitudes and beliefs about minimally defined social groups. We found that 5- to 6-year-olds and 9- to 10-year-olds’ initial attitudes and beliefs about minimal groups were positive overall, and more positive for the ingroup than the outgroup. Observing a 90% mean sample randomly drawn from the group, or a 10% mean sample in some cases, reduced this positivity for both the ingroup and the outgroup. This work has important implications for combating biases and stereotypes in the real world.

Changing Children’s Intergroup Biases and Social Stereotypes Through Statistical Counterevidence

Over-engaged parenting is on the rise and detrimental to children. However, it is unclear why parents choose to take over and complete developmentally appropriate tasks for their children. Across two preregistered studies, we test the novel hypothesis that parents’ decisions to take over are guided by their beliefs about children's learning. In line with this hypothesis, parents of 4-5-year-olds (N = 77) reported taking over more on tasks from which they believe their child learns less, which was more often the case for non-academic (versus academic) tasks (Study 1). Study 2 found causal evidence for this hypothesis: Increasing parents' (N = 60) beliefs about what their child could learn from doing a novel, non-academic task reduced their taking over actions by half. This work shows that parents take over less when they think that their young child is learning and opens new avenues for interventions to reduce over-engaged parenting.


Parents take over less when they think their young child is learning

During social interactions, we adapt our behavior in anticipation and response to the actions of our partners. This project explores whether anticipating that we are going to do something together also affects how we perceive objects in the world. In 4 Experiments, children and adults estimated the weight of objects when they anticipated that they will be lifted alone and when they thought they would have help. Results showed that the estimated weight of objects varied for adults and children depending on whether they thought they would lift alone or together. Children reliably perceived objects as lighter when they expected help from an adult parent compared to lifting them alone or with the help of a sibling. Parents, in turn, estimated objects would be heavier when they were helping their child. Overall, results suggest that we take into account the costs and benefits of helping another person when acting together.

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Estimating attitudes toward vaccination: A Bayesian framework

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