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A mental model is an internal representation that explains how something works. Mental model construction is facilitated by self-explanation, the active generation of explanations for oneself. The overarching goal of this research is to empirically investigate the utility of self-explanation for developing mental models when learning to program. Programming is notoriously challenging and, despite evidence of the importance of mental models for learning, little work has focused on mental models of students learning how to program. They need correct mental models of the notional machine, an abstraction of the steps taken by a computer as it processes a program. Because students do not spontaneously self-explain, we are using a user-centered approach to design a computer tutor to prompt for self-explanation about the notional machine. Here, we present qualitative results on students’ interactions with an initial version of the tutor, including the form of their self-explanations and corresponding mental models.

CogSci 2021

Investigating the Utility of Prompting Novice Programmers for Self-Explanations to Improve Mental Models

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**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**

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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.

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Lake 2015 presented the Omniglot dataset to study how people generate and classify characters. They proposed a model for one-shot-learning of new concepts based on inferring compositionally structured generative models, and could transfer from familiar concepts to new ones. Their Bayesian-Programming-Language model was able to both classify and generate characters similar to adults. However, adults have years of experience, would a similar model apply to children without this prior knowledge? We introduce a new dataset called Omniglot_Jr., composed of Omniglot letters generated by children aged 3-6. Our results of training BPL with children's data in classification and generation find that BPL achieves higher classification accuracy and generates more adult looking letters. We propose the challenge of reproducing children's distinctive pattern of mistakes. Skills such as character recognition depend on child-like learning; this challenge should help us understand how that learning is possible, and how to simulate it in computational systems.

The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification?

Preparing the next generation for an ever-changing environment is of utmost importance in Higher Education. Though cultural diversity is highly prevalent in modern societies, (implicit) biases, stereotypes and racism maintain a persistent yet mostly overlooked part of everyday life. Many students are not aware of their biases. Hence, it is imperative for them to undergo a critical self-reflection process about their beliefs and (unconscious) biases. To induce this process, raise awareness and confront them with potential (unconscious) biases and stereotypes, 404 university students in teacher training and vocational education completed an Implicit Association Test (IAT) on skin color before watching a lecture discussing biases and stereotypes. The preliminary results show a variety of reactions after taking the test, ranging from denial and anger to discomfort and surprise. We will discuss possibilities to support students in leveraging the resulting cognitive dissonance as an opportunity to begin their individual self-reflection process.

Unveiling unconscious biases and stereotypes in students: The necessity of self-reflection in Higher Education

Numbers and variables often follow the same principles of arithmetic operations, yet numbers can be computed to a value whereas variables cannot. We examined the effect of symbols—numbers versus variables—on middle school students’ problem-solving behaviors in a dynamic algebra notation system by presenting problems in numbers (e.g., 3+5−3) or variables (e.g., x+y−x). We found that compared to problems presented in numbers, students attempted the problems more times and took more total steps when the problems were presented in variables. We did not find differences in pre-solving pause time or strategy efficiency on the two types of problems, indicating that students might notice problem structure in both types of problems. The results have implications for research on cognitive processes of symbols as well as the design of educational technologies.

Numbers vs. Variables: The Effect of Symbols on Students’ Math Problem-Solving

The present study investigated whether judgments of learning (JOLs) and judgments of inference (JOIs) enhance the retention and transfer of previously studied text (backward effect) and newly studied text (forward effect). Participants read two different passages through Section A and B. After studying Section A, participants made either JOLs or JOIs on Section A, while the control group read texts without any interim judgments. Then all groups studied Section B and took a final test for Sections A and B. For Section A, there were no significant performance differences among the groups, showing no backward effect of metacognitive judgments on text learning (neither JOLs nor JOIs). In contrast, for Section B, the JOI group outperformed both the JOL and control groups on the transfer test, indicating a forward effect of JOIs on text learning. Metacognitive judgments with a higher-order learning goal appear to help subsequent learning of new material.

Do Judgments of Learning and Judgments of Inference Enhance Text Learning?

We rely heavily on information from the social world to inform our real-world beliefs. How is this social information used, and when is it most influential? We assess the role of one kind of social information, the prevalence of a belief, in belief updating. Using real-world pseudoscientific and conspiratorial claims, we show that increases in people’s estimates of the prevalence of a belief led to increases in their endorsement of said belief. Prevalence information elicited the strongest belief change when people were most uncertain of their initial belief, suggesting that people weigh social information rationally according to the strength of their initial evidence. We discuss the implications of our results in the context of the present misinformation epidemic.

Beliefs are most swayed by social prevalence under uncertainty

There are numerous studies that show that the more learner actively participate in the learning process, the more they learn. Although the use of active learning to increase learning outcomes has been recently introduced in a variety of methods, empirical experiments are lacking. In this study, we introduce two frameworks of human active learning and then conducted two experiments to determine how these frameworks can be used as leaning tools. In experiment 1, we compared three types of active learning and passive learning in order to empirically confirm the effect of active learning. In experiment 2, based on the results of experiment 1, we explored through simulation on machine learning with the frameworks that the more active the learners are, the better outcomes can be obtained. Both experiments showed that active learning both also effective in human and machine learning. Therefore, our analyses of the two experiments fit within the taxonomy and classification of the frameworks of active learning. This result is further significant in that it gives practical implications on human and machine learning methods.

Passive Versus Active: Frameworks of Active Learning for Linking Humans to Machines

Communication in groups allows social learners to influence one another and change their beliefs over time. Though some of the same heuristics that guide learners’ trust in individual informants can be applied to groups, variation in how individual beliefs are aggregated into a collective judgement can radically alter the accuracy of collective judgement. How do observers evaluate collective judgements? We present two experiments testing the impact of affective signals on observer trust. In each experiment, one faction “converts” group members from an opposing faction, or is converted by them. When the focal faction is surprised at the opposing view, observer trust in the focal faction’s belief rises or falls as consensus increases or decreases. When the focal faction is angry, observer trust falls when consensus decreases, but does not rise even when the “consensus” approaches unanimity. Affective signals in group interactions may help naive learners evaluate collective accuracy.

You can’t trust an angry group: asymmetric evaluations of angry and surprised rhetoric affect confidence in trending opinions

Block-building is an early-developing spatial skill in which multiple spatial actions unfold over time. The processes underlying block-building have eluded our understanding, but existing data show that children’s and adults’ construction paths are highly systematic and selective. In this paper, we investigate whether manipulating the stability of block models to be built affects the character of construction paths. We asked participants to build models with either strong or weak support. We measured the step-by-step actions taken and used eye-tracking to assess differences between adults and children in how they collect and use visual information while building. We find that children and adults are highly selective in their construction paths, but models with weak support are more difficult to build, reflected in the specifics of their paths and the eye movements made during building. These results suggest that the stability of a structure drives information-gathering and building strategies in block construction.

The importance of stability in children’s and adults’ block-building

With an increased reliance on technology to exchange social communication, social media platforms have become vehicles for humorous content — often taking the form of short internet videos (Vine or TikTok). Dynel (2016) hypothesizes that short internet videos may be effective because they capitalize on contrast to promote humor — i.e., consistent with incongruity theory (see Morreal, 2012). Specifically, incongruity theory assumes that humor arises from a contrast between an expectation and a perceived deviation from that expectation. To test the effects of incongruity in short internet videos, we manipulated linguistic and paralinguistic content in common short internet videos from the defunct social media platform - Vine. Results indicated that incongruence is extremely important for humor perception of short internet videos, as more linguistic and paralinguistic incongruity promoted higher social judgments of humor. Findings support that incongruity is an extremely effective tool for short internet videos to communicate humor.

Humorous Judgments of Incongruity in Short Internet Videos

Comparison settings (i.e. several stimuli introduced simultaneously) favor novel word learning and generalization. This study investigates the temporal dynamics of 6-year-olds solving strategies in a relational noun (e.g. “x is the dax for y”) comparison and generalization task with eye tracking data. We manipulated conceptual distance between the task’s items and recorded children’s performances and eye tracking data. We analyze and interpret solving strategies following the predictions made by two hypotheses, the Projection-First and Alignment-first. Eye tracking data clearly revealed that children, first, extract the relation from comparisons of items within a pair and search for a match for the extracted relation, which confirms the predictions of the projection-first hypothesis. Further analyses on error and correct trials suggest that errors occurred in the late, choice, phase of a trial.

Investigating the Utility of Prompting Novice Programmers for Self-Explanations to Improve Mental Models

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The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification?

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