United States

Experimental pragmatics tells us that a metaphor conveys salient features of a vehicle and that highly typical features tend to be salient. But can highly atypical features also be salient? When asking if John is loyal and hearing “John is a fox”, will the hearer conclude that John is disloyal because loyalty is saliently atypical for a fox? This prediction follows from our RSA-based model of metaphor understanding which relies on gradient salience. Our behavioral experiments corroborate the model&#39;s predictions, providing evidence that high and low typicality are salient and result in high interpretation confidence and agreement, while average typicality is not salient and makes a metaphor confusing. Our model implements the idea that other features of a vehicle, along with possible alternative vehicles, influence metaphor interpretation. It produces a significantly better fit compared to an existing RSA model of metaphor understanding, supporting our predictions about the factors at play.

CogSci 2021

Pragmatics of Metaphor Revisited: Formalizing the Role of Typicality and Alternative Utterances in Metaphor Understanding

### 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/).


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.

Experimental pragmatics tells us that a metaphor conveys salient features of a vehicle and that highly typical features tend to be salient. But can highly atypical features also be salient? When asking if John is loyal and hearing “John is a fox”, will the hearer conclude that John is disloyal because loyalty is saliently atypical for a fox? This prediction follows from our RSA-based model of metaphor understanding which relies on gradient salience. Our behavioral experiments corroborate the model's predictions, providing evidence that high and low typicality are salient and result in high interpretation confidence and agreement, while average typicality is not salient and makes a metaphor confusing. Our model implements the idea that other features of a vehicle, along with possible alternative vehicles, influence metaphor interpretation. It produces a significantly better fit compared to an existing RSA model of metaphor understanding, supporting our predictions about the factors at play.

poster

The testing effect was used to teach the testing effect to increase feelings of self-efficacy in test taking and reduce test anxiety in 10-to11- year-olds. The impact of this intervention was measured with a new Self-efficacy in Test Taking measure and an adapted thoughts' subscale from the Children’s Test Anxiety Scale in pre-test, post-test conditions and with a control group. The intervention was designed to target the self-knowledge beliefs ‘layer’ from the S-REF (Self-referent Executive Function) model of test anxiety. The intervention was delivered primary classrooms, over six weeks and aimed to teach children to believe in test taking abilities, that testing routes were ‘well-oiled’, to improve feelings of self-efficacy about taking high stakes’ tests. There was a significant increase in self-efficacy in test taking for students, who were high in test anxiety, η²p = 0.07 when measured with a subscale from the Self-efficacy in Test Taking new measure.

Can Retrieval Practice of The Testing Effect Increase Self-efficacy in Tests and Reduce Test Anxiety, in 10- to 11-Year-olds?

People are capable of learning diverse functional relationships from data; nevertheless, they are most accurate when learning linear relationships, and deviate further from estimating the true relationship when presented with non-linear functions. We investigate whether, when given the opportunity to learn actively, people choose samples in an efficient fashion, and whether better sampling policies improve their ability to learn linear and non-linear functions. We find that, across multiple different function families, people make informative sampling choices consistent with a simple, low-effort policy that minimizes uncertainty at extreme values without requiring adaptation to evidence. While participants were most accurate at learning linear functions, those who more closely adhered to the simple sampling strategy also made better predictions across all non-linear functions. We discuss how the use of this heuristic might reflect rational allocation of limited cognitive resources.

Sampling Heuristics for Active Function Learning

Recently, several behavioral studies have demonstrated 4-10 Hz rhythmic fluctuations in attention. So far, this attentional sampling has only been demonstrated with regards to external stimuli. Attention, however, is often directed towards internal working memory representations. We conducted a human behavioral dense-sampling experiment on whether simultaneously held representations of two distinct feature dimensions (color and orientation) also exhibit a rhythmic temporal profile. We found an oscillatory component at 9.4 Hz in the joint time-courses of both representations, presumably reflecting a common early perceptual sampling process in the alpha-frequency range. Further, we observed an oscillatory component at 3.5 Hz with a significant phase-difference between feature dimensions. This likely corresponds to a later attentional sampling process, indicating that internal representations of distinct features are activated in alteration. In summary, we demonstrate the cyclic reactivation of internal representations, as well as the co-occurrence of perceptual- and attentional rhythmic fluctuations at distinct frequencies.

Cyclic reactivation of internal working memory representations of distinct feature dimensions

Research shows that anxiety can disrupt learning processes, but few studies have examined anxiety’s relationships to online learning behaviors. This study considers the interplay between students’ anxiety about science and behavior within an online system designed to support self-regulated science inquiry. Using the searching, monitoring, assessing, rehearsing, and translating (SMART) classification schema for self-regulated learning (SRL), we leverage microanalysis of self-regulated behaviors to better understand how science anxiety inhibits (or supports) different learning operations. Specifically, we show that while science anxiety is positively associated with searching behaviors, it is negatively associated with monitoring behaviors, suggesting that anxious students may avoid evaluation, opting instead to compensate with information-seeking. These findings help us to better understand SRL processes and may also help us support anxious students in developing SRL strategies.

Who’s Stopping You? – Using Microanalysis to Explore the Impact of Science Anxiety on Self-Regulated Learning Operations

Most psychological studies focused on how people reason about generative causation, in which a cause produces an effect. We study the prevention of effects both on the general and singular level. A general prevention query might ask how strongly a vaccine is expected to reduce the risk of contracting COVID-19, whereas a singular prevention query might ask whether the absence of COVID-19 in a vaccinated person actually resulted from this person's vaccination. We propose a computational model answering how knowledge about the general strength of a preventive cause can be used to assess whether a preventive link is instantiated in a singular case. We also discuss how psychological models of causal strength learning relate to mathematical models of vaccination efficacy used in medical research. The results of an experiment suggest that many, but not all people differentiate between preventive strength and singular prevention queries, in line with the formal model.

Evaluating General versus Singular Causal Prevention

This study examined relations between teacher-child closeness and conflict in preschool and children’s behavior problems, social skills, and executive function (EF) in kindergarten, and explored if these relations are moderated by parental education. The study also sought to examine the relation between teacher-child closeness and conflict and the subscales of children’s behavior problems and social skills. The sample consisted of 126 preschool children (M = 56.70 months, SD = 3.89). Regression analyses revealed that teacher-child conflict predicted children’s social skills, specifically assertion, engagement, and cooperation. Parental education moderated the association between teacher-child conflict and EF, and also emerged as a marginally significant moderator of teacher-child closeness and behavior problems. The findings thereby indicated differential relations between teacher-child closeness and conflict and children’s outcomes. With regard to future research, it may be important to consider other aspects of the teacher-child relationship and classroom environment as well.

The association between preschool teacher-child relationship and children’s kindergarten outcomes

Computational models are greatly useful in cognitive science in revealing the mechanisms of learning and decision making. However, it is hard to know whether all meaningful variance in behavior has been account for by the best-fit model selected through model comparison. In this work, we propose to use recurrent neural networks (RNNs) to assess the limits of predictability afforded by a model of behavior, and reveal what (if anything) is missing in the cognitive models. We apply this approach in a complex reward-learning task with a large choice space and rich individual variability. The RNN models outperform the best known cognitive model through the entire learning phase. By analyzing and comparing model predictions, we show that the RNN models are more accurate at capturing the temporal dependency between subsequent choices, and better at identifying the subspace in the space of choices where participants' behavior is more likely to reside. The RNNs can also capture individual differences across participants by utilizing an embedding. The usefulness of this approach suggests promising applications of using RNNs to predict human behavior in complex cognitive tasks, in order to reveal cognitive mechanisms and their variability.

Using Recurrent Neural Networks to Understand Human Reward Learning

On the road to a more fair and just world, we must recognize ubiquitous disparities in our society, but awareness alone is not enough: Observed disparities between groups often get wrongly attributed to inherent traits (e.g., African Americans are disproportionately arrested because they are more prone to crime), creating a self-perpetuating feedback loop. As shown in a past study (Meng & Xu, 2020), such reasoning can result from the Naive Utility Calculus (Jara-Ettinger et al., 2016): If an agent knows a target trait's "hit rate'' in every group and avoids unnecessary sampling, it is rational to infer that groups sampled from more often have higher hit rates. The previous study used non-social categories (robot chickens) as stimuli, which raises the question of whether the results generalize to the social domain. In the current study, we replicated past findings using novel social groups (aliens): Overall, people were more likely to check groups examined more often by the agent but when observed hit rates did not support the agent's sampling behavior, people incorporated both information sources to infer group hit rates. This work brought NUC-based models one step closer towards tackling disparities in the real world consisted of social groups.

Naive Utility Calculus underlies the reproduction of disparities in social groups

The physical world is rich with social information that people readily detect and extract, such as inferring that someone was present when we encounter a stack of rocks in the woods. How do people recognize that a physical scene contains social information? Research in developmental psychology has argued that this capacity is supported by a sensitivity to violations of randomness. Here we present a computational model of this idea and test its explanatory power in a quantitative manner. Our model infers agency by estimating the likelihood that a scene would arise naturally, as determined by human intuitive physics instantiated as a physics engine. Our results suggest that people's ability to detect agency in a physical scene is sensitive not only to the superficial visual properties, but also to the underlying physical generative process. Our results highlight how people use intuitive physics to decide when to engage in nuanced social reasoning.

Detecting the involvement of agents through physical reasoning

Verbs such as ‘know’ and ‘think’ help people describe mental states, and reasoners without any training in logic can make epistemic inferences about mental states. For instance, verbs such as ‘know’ are factive, i.e., they describe true propositions, and the statement Ora knows that it’s sunny licenses the inference that it’s sunny. Logicians have accordingly developed epistemic logics capable of characterizing valid and invalid epistemic inferences based on operators that serve as analogs to verbs such as ‘know’ and ‘think’. Recent work suggests that no existing logical system can capture the inferences that naïve individuals tend to make. This paper describes a new theory of epistemic reasoning that operates on the assumption that reasoners represent epistemic relations as spatial models. The theory accords with recent theoretical advances, existing data, as well as two novel experiments that show how reasoners cope with nested epistemic verbs, e.g., Ami knows that Ora thinks it’s sunny.

Pragmatics of Metaphor Revisited: Formalizing the Role of Typicality and Alternative Utterances in Metaphor Understanding

Downloads

Next from CogSci 2021

Can Retrieval Practice of The Testing Effect Increase Self-efficacy in Tests and Reduce Test Anxiety, in 10- to 11-Year-olds?

Similar lecture

Native perception of non-native speech: Speaker accent mitigates penalization for language errors in non-native speech unless the listener is conscientious

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES