Netherlands

We demonstrate an application for learning multiplication problems with an adaptive algorithm that is based on a computational cognitive model of the learner&#39;s memory. The application helps learners automatise multiplications through repeated practice over three levels of difficulty. In a naturalistic setting involving more than 500 primary school students (ages 6-10) who together recorded over 200,000 responses, we observed that performance improved as learners using the application progressed through the levels. A model-based analysis of performance revealed that learners&#39; estimated speed of forgetting decreased from the second to the third level. This is consistent with a shift towards stronger declarative knowledge and/or more efficient computation procedures. The model also identified consistent differences in the difficulty of individual multiplication facts that persisted across levels. This study demonstrates the feasibility of using an adaptive fact learning application to help young learners automatise multiplication, an essential mathematical skill.

**Authors:**

Stefania D. Iancu: MemoryLab; Myrthe Braam: MemoryLab; Nathan Mccabe: MemoryLab; Thomas Jan Wilschut: University of Groningen; Hedderik van Rijn: University of Groningen; Maarten van der Velde: MemoryLab

CogSci 2024

An Adaptive Learning System for Stepwise Automatisation of Multiplication Facts in Primary Education

The 46th Annual Meeting of the Cognitive Science Society was an in-person meeting held in Rotterdam, The Netherlands at the Postillion Hotel & Conference Centre.

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**Thursday, July 25** 
0900: [Keynote Speaker Morgan Barense on Enhancing real-world event memory](https://underline.io/events/465/sessions/17997/lecture/99084-dynamics-between-minds-and-the-environment) 
1000: [Gleitman Award Winner Isabelle Dautriche on Language Foundations: Insights from acquisition, communication, cognition, and more](https://underline.io/events/465/sessions/18000/lecture/99085-gleitman-talk) 
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture) 
1700: [Rumelhart Prize Presentation Speaker Alison Gopnik on Exploit, explore, empower: Three ages and three intelligences](https://underline.io/events/465/sessions/18028/lecture/99161-rp1-rumelhart-prize-presentation) 

**Friday, July 26** 
0900: [C.L. de Carvalho-Heineken Prize Keynote Speaker Kia Nobre on Focusing in memory](https://underline.io/events/465/sessions/18035/lecture/99162-hp1-c-l-de-carvalho-heineken-prize-keynote-address) 
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration) 
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture) 
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture) 
1745: [Keynote Speaker Andrea E. Martin on Neural dynamics encode the structure and statistics of language](https://underline.io/events/465/sessions/18077/lecture/99271-neural-dynamics-encode-the-structure-and-statistics-of-language) 

**Saturday, July 27** 
0900: [Keynote Speaker Gregor Schöner on How higher cognition emerges from the dynamics of strongly interacting neural populations](https://underline.io/events/465/sessions/18083/lecture/99273-dynamics-within-the-mind) 
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture) 
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture) 


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#### **Letter from the President**

Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
their experience inspires them to join our future meetings for many years to come.

I want to thank this year’s conference co-chairs, Larissa K Samuelson, Stefan Frank, Mariya Toneva, Allyson Mackey, and Eliot Hazeltine, for putting together an amazing series of invited talks and symposia around the theme of Dynamics of Cognition. The co-chairs also deserve credit for handling a record-breaking number of submissions and producing a truly exciting conference program 

Beyond the invited talks and symposia dedicated to this year’s conference theme, I would like to highlight the presentations and events honoring the 
Rumelhart, Elman, Gleitman and C .L . de Carvalho Heineken prize winners, including the Rumelhart reception on Thursday. As you may know, we are recording all of the invited talks and symposia, and all prize-winners’ talks, and will be making this content available after the conference. 

The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
Society is deeply grateful to Rick Dale and Andrea Bender, who serve as editors of the two Society journals, Cognitive Science and Topics in Cognitive Science respectively. We especially acknowledge our editors’ generosity in donating their stipends each year to D&I and outreach efforts. 

This year’s Annual Meeting depends critically on the support of Marischal De Armond and his team at Podium Conference Specialists: Sharon Zwack, Cendrine De Vis, Sarah-Kate Burke, and Rachel Waller worked tirelessly to secure the venue and handled a multitude of logistical and organizational issues, from arranging coffee breaks and receptions to making sure that a huge number of posters fits comfortably into the available space. Finally, and importantly, I would like to acknowledge the Cognitive Science Society Executive Officer, Erica Wojcik, for managing the complex and ever expanding sphere of our Society’s activities.

I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

![](https://assets.underline.io/markdown_image/1/image/3c66d0e21fff30e204df1138ffd15173.jpeg)

**Anna Papafragou** 
President, Cognitive Science 
Society 2023-2024

**CODE OF CONDUCT** 
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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

We demonstrate an application for learning multiplication problems with an adaptive algorithm that is based on a computational cognitive model of the learner's memory. The application helps learners automatise multiplications through repeated practice over three levels of difficulty. In a naturalistic setting involving more than 500 primary school students (ages 6-10) who together recorded over 200,000 responses, we observed that performance improved as learners using the application progressed through the levels. A model-based analysis of performance revealed that learners' estimated speed of forgetting decreased from the second to the third level. This is consistent with a shift towards stronger declarative knowledge and/or more efficient computation procedures. The model also identified consistent differences in the difficulty of individual multiplication facts that persisted across levels. This study demonstrates the feasibility of using an adaptive fact learning application to help young learners automatise multiplication, an essential mathematical skill.

**Authors:**

Stefania D. Iancu: MemoryLab; Myrthe Braam: MemoryLab; Nathan Mccabe: MemoryLab; Thomas Jan Wilschut: University of Groningen; Hedderik van Rijn: University of Groningen; Maarten van der Velde: MemoryLab

poster

Prior research has shown that many elementary school students hold misconceptions about mathematical equivalence, interpreting the equal sign operationally as an indicator to give an answer or the total. They often fail to correctly solve missing operand problems such as 1+5= ___ + 2. The present study extends the research on mathematical equivalence to examine pre-service teachers’ performance on equivalence tasks. Results show that some participants failed to correctly solve missing operand problems and chose an operational definition of the equal sign over the correct relational definition. Many participants failed to recognize statements that violate equality and failed to correctly identify equations and operations. These findings suggest that misconceptions of mathematical equivalence can involve confusion about the definition of equation and the meaning of mathematical operation.

**Authors:**

Jennifer Kaminski: Wright State University

Preservice teachers’ understanding of mathematical equivalence

People can distinguish circular explanations from an early age. However, they begin to encounter difficulties when circular explanations mimic the structure of a formal explanation (e.g., "Alcohol promotes cancer because it is a carcinogen"). We explored the possibility of enhancing the ability to detect circular formal explanations by pre-activating participants' prior knowledge of definitions for relevant terms. In Experiment 1, we posed questions about definitions (e.g., What is the best definition for "carcinogen?") immediately before asking participants to evaluate the satisfiability of the explanation. In Experiments 2, we directly provided definitions of the terms. Across both experiments, the effect proved robust, with participants consistently rating such explanations as more satisfactory compared to explicitly circular explanations (e.g., "Alcohol promotes cancer because it is a substance that causes cancerous tumors"). Furthermore, Experiment 1 demonstrated that the effect is not dependent on individuals' ability to select the correct definition of a term.

**Authors:**

Ivan Aslanov: Universidad de Chile; Ernesto Guerra: Universidad de Chile

Tautological formal explanations are satisfactory regardless of prior knowledge

Expectations play a critical role in children's learning. Prior studies suggest that children selectively focus on and better remember details of expectation-violating events (Stahl & Feigenson, 2017; 2019). Yet, it remains unclear whether this enhanced memory persists across varying degrees (e.g., somewhat vs very surprising) and types of expectation violations (core-knowledge vs. schema-based violations). Adapting a surprise storybook paradigm from Foster and Keane (2019), we measure children’s (5-8 years; N=20) surprise and recognition memory for six stories that span different expectation-related domains and contain outcomes that are expectation-congruent, somewhat expectation-violating, or completely violating. While preliminary data revealed no significant difference in recognition accuracy by level of surprise, a trend towards better memory for violations of well-entrenched versus schema-based expectations was observed. This preliminary work points to potential differences in how varying types of expectations influence memory in young children and has important implications for learning.

**Authors:**

Carla Macias: RUTGERS UNIVERSITY-NEWARK; Kimele Persaud: RUTGERS UNIVERSITY-NEWARK

The Role of Surprise in Memory: Assessing the Impact of Levels of Surprise on Children's Episodic Memory

Learners are often unaware of effective learning strategies, hindering their actual utilization. We investigated intervention methods to increase the utilization of effective learning strategies in self-regulated learning settings, specifically focusing on the interleaving strategy in category learning. Undergraduate students were either informed or not about effective learning methods and studied painting styles of various artists in a self-paced order. In Experiment 1, participants who received instructions about specific goals required for successful category learning at the beginning were more likely to recognize the importance of identifying between-artists differences, but did not necessarily increase interleaving strategies during their study. In Experiment 2, however, participants provided with procedural and conditional metacognitive knowledge (i.e., why interleaving is effective and how to interleave exemplars) in the middle of their study significantly increased interleaved practices. Our findings suggest that enhancing metacognitive knowledge can help encouraging the use of effective learning strategies.

**Authors:**

Yundeok Kim: Yonsei University; Hee Seung Lee: Yonsei University

How can we increase the use of interleaved study in self-regulated learning?

This study aimed to test the benefit of note-taking on pauses and learning in online educational videos. Participants (N = 81) were randomly assigned to one of two note-taking conditions (allowed or not) with the possibility to take pauses during a 10-minute online instructional video on the autonomic nervous system. The results did not reveal a significant correlation between note-taking and the number of pauses. Moreover, we observed no significant effect of note-taking on learning performance. However, prior knowledge and age were found to contribute significantly to the model and affected the relationship between pauses, note-taking and learning performance. We discuss the importance of prior knowledge and age as moderating factors for future research.

**Authors:**

Mathieu Pinelli: LaRAC, Univ. Grenoble Alpes; Salomé Cojean: Univ. Grenoble Alpes

Do learners make more pauses in instructional videos when taking notes?

The present study examines how natural language aptitude subskills predict individual differences in learning Python and Java. Past work has demonstrated that overall performance on the Modern Language Aptitude Test (MLAT), a standardized measure of language aptitude, is a strong predictor of both the speed and accuracy with which individuals learn Python. However, language aptitude is a broad multidimensional construct made up of individual subskills. In the present study, we examine how two of these subskills - sensitivity to form and meaning mapping - relate to programming outcomes in both Python and Java. Results indicate that both sensitivity to form (MLAT IV) and meaning mapping (MLAT V) are related to programming acquisition in both languages. We also examined how programming skills tied to semantics and syntax related between Python and Java in a subset of learners who learned both languages. These results demonstrated that proficiency in Python predicted individual differences in both syntactic and semantic knowledge in Java. Taken together, these results further elucidate the role of natural language aptitude in programming learning and suggest that semantic and syntactic content may transfer across programming languages.

**Authors:**

Malayka Mottarella: University of Washington; Katherine Mortimore: Florida Institute for Human and Machine Cognition; Chantel Prat: University of Washington

Exploring Programming Aptitude: Comparing the Predictive Utility of Language Aptitude Subskills for Python and Java Learning

The present study examined the role of detecting the initial causal system model followed by engaging in active vs. passive structural alignment in recognizing the key causal principles in subsequent novel examples. The results echo prior research on the benefit of analogical comparison in learning relational categories: participants who were prompted to compare outperformed participants in a baseline condition. Moreover, while the accurate representation of the causal system predicted noticing the relational structure in novel examples, making more accurate relational mappings made participants more likely to notice the structure above and beyond having an accurate representation. These findings offer insight into the role of active vs. passive analogical comparison and have implications for conditions that might support learning of relational categories.

**Authors:**

Margarita V. Pavlova: New Bulgarian University

Building Abstraction: The Role of Representation and Structural Alignment in Learning Causal System Categories

In personalized-schedule learning, previous research has shown the benefit of initial attempted retrieval of study-items on short-term retention and later test performance. As a way of prior-knowledge identification, initial attempted retrieval may help to optimize learning and long-term performance further, through the removal (or ‘drop’) of items from the learning set that are answered correctly on the first attempt. This study sought to support this hypothesis through a real-world, within-subjects experiment, comparing vocabulary test performance of Dutch middle school students after the use of a drop- and non-drop adaptive learning algorithm. The results show that short- and long-term item retention was higher for material studied using the drop-algorithm, while dropping items did not lead to worse retention compared to items that were kept upon initial correct responses. This suggests that initially-known items are correctly identified as ‘mastered’, and that their removal from the learning material allows students to focus their efforts on unknown items, leading to increased learning gains.

**Authors:**

Malte Krambeer: University of Groningen; Maarten van der Velde: MemoryLab; Hedderik van Rijn: SlimStampen BV

Prior Knowledge Adaptation Through Item-Removal in Adaptive Learning Increases Short- and Long-Term Learning Benefits

Crossmodal correspondences are associations between perceptual features from different senses that aid in crossmodal binding. The semantic coding of these correspondences is expected to capture and mediate the emergence of perceptual crossmodal correspondences. However, the cross-cultural nature of such semantic coding has not been thoroughly studied. This research worked with five languages in three different linguistic families (English, Dutch, Turkish, Chinese and Italian). Using distributional semantics, modality exclusivity norms and emotional lexicons, we constructed networks to represent semantic crossmodal correspondences and assessed its relationship with Valence, Arousal and Dominance. Results indicate that emotion, particularly Valence and Dominance, play pivotal roles in shaping the structure of semantic crossmodal correspondences networks across languages. Moreover, the findings reveal that some types of semantic crossmodal correspondences might be shared among different languages in different language families, suggesting shared cognitive processes. This support the significance of emotions as fundamental components in semantic crossmodal correspondences. Additionally, the study provides evidence supporting shared crossmodal correspondences among languages and cultures.

**Authors:**

Jorge Alvarado: Pontificia Universidad Javeriana

Emotional significance in Cross-Cultural Semantic Crossmodal Correspondences

We compare storytelling in GPT-3.5, a recent large language model, with human storytelling. Although GPT models are capable of solving novel and challenging tasks and matching human-level performance, it is not well understood if GPT processes information similarly as humans. We hypothesized that GPT differs from humans in the kind of memories it possesses, and thus could perform differently on tasks influenced by memory, such as storytelling. Storytelling is an important task for comparison as GPT becomes an increasingly popular writing and narrative tool. We used an existing dataset of human stories, either recalled or imagined (Sap et al., 2022), and generated GPT stories with prompts designed to align with human instructions. We found that GPT's stories followed a common narrative flow of the story prompt (analogous to semantic memory in humans) more than details occurring in the specific context of the event (analogous to episodic memory in humans). Furthermore, despite lacking episodic details, GPT-generated stories exhibited language with greater word affect (valence, arousal, and dominance). When provided with examples of human stories (through few-shot prompting), GPT was not able to align its stories' narrative flow with human recalled stories, nor did it match its affective aspects with either human imagined or recalled stories. We discuss these results in relation to GPT's training data as well as the way it was trained.

**Authors:**

Charlotte Cornell: Rutgers University--New Brunswick; Shuning Jin: Rutgers University; Qiong Zhang: Rutgers University - New Brunswick

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