Netherlands

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

CogSci 2024

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

## 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.

poster

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.

**ON-DEMAND PROGRAM ACCESS AFTER THE CONFERENCE** 
Recordings of the invited program are now available. You can access them by clicking on the 'Schedule' icon on the left. Select view by week and navigate to the day and time slot of the recording you wish to view. Click on the time slot to access the recording.

Recordings available (click on each recording to view):

**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) 


#### **CogSci 2024 Program Book**
<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/cble/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>


**The Program Book can be downloaded [here](https://drive.google.com/file/d/1yRVp1cBqRnAbdYi4GXWV_pZeyWNsRquv/view?usp=sharing).**

#### **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** 
By attending the CogSci 2024 Conference, you are required to adhere to the society’s [Code of Conduct](https://drive.google.com/file/d/16-6KkptF0Gn3ZYGJDlpqpwTdImw45Ng0/view?usp=drive_link).

**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
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.

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

It looks like you are not registered for this event. 

To access the site please register [**here**](https://cognitivesciencesociety.org/registration/). 

The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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

The Role of Episodic Memory in Storytelling: Comparing Large Language Models with Humans

People have strong intuitions about which colors do and do not match particular pieces of music --- a phenomenon often conceptualized through semantic mediation. We further explored the specific strategies people employ to navigate these color-music associations which offers crucial insights to identifying the cognitive mechanisms that enable such cross-domain associations. We show that while some people rely more on intuition, other people actively seek justification of association by consulting common linguistic descriptors, emotional contents, and environmental cues. We found that more spontaneous strategies lessen the role of semantic mediation, while more evaluative strategies, especially those involving the use of language, amplify it. Notably, the use of evaluative strategies introduced an asymmetrical effect for matching and mismatching colors. Additionally, individuals employing similar strategies associated a given music excerpt with more similar colors, suggesting that strategy alignment enhances the consistency of color-music associations. Interestingly, this pattern of convergence was not observed among individuals who predominantly relied on guessing.

**Authors:**

Qiawen Liu: University of Wisconsin-Madison; Huang Ham: Department of Psychology; Kesong Cao: University of Wisconsin-Madison; Gary Lupyan: University of Wisconsin - Madison

Why is Bach blue instead of red? Different strategies moderate people’s color-music associations

Index systems are central to our everyday and intellectual lives. Their ubiquity and diversity make them an im-portant class of cognitive artifacts, the study of which has implications for our understanding of representational systems in general. This paper builds schema-theoretic network models of the nature of the memory structures, that underpin the interpretation of indexing systems. We identify four common classes of index systems. Using Representation Interpretation Structure Theory, we explain how the four basic classes can be responsible for the sub-stantial diversity among index systems.

**Authors:**

Peter Cheng: University of Sussex; Grecia Garcia Garcia: University of Sussex; Daniel Raggi: University of Cambridge; Mateja Jamnik: University of Cambridge

Index Systems: Enumerating Their Forms and Explaining Their Diversity With Representational Interpretive Structure Theory

The ability to flexibly interpret signals in context is at the core of human communication, as even the most conventionalized linguistic signals are necessarily ambiguous and subject to inter-individual variability. We introduce a novel experimental semiotic game (the Pizzini game) requiring pairs of participants to exchange linguistic signals that can only be successfully interpreted when using contextual information freshly generated by each pair. By allowing this common ground between once-strangers to be developed interactively in the lab, we are able to characterize the pair-specific contextual information available to participants when inferring intended meanings. We present preliminary data testing the predictions that (1) interactants align on an abstracted conceptual representation of a set of stimuli during the context-building portion of the task and (2) that the characteristics of this pair-specific conceptual representation predict the dynamics of how participants later resolve context-specific references to the same stimuli.

**Authors:**

Miriam Greidanus Romaneli: Radboud University Nijmegen; Jesper Sinneker: Radboud University Nijmegen; Jana Bašnáková: Radboud University Nijmegen; Bruno Galantucci: Yeshiva University; Ivan Toni: Radboud University

Investigating contextual effects in referential communication: an experimental semiotic game

Various methods for social learning have been proposed within the reinforcement learning framework. These methods involve the social transmission of information within specific representational formats like policies, value, or world models. However, transmission of higher-level, model-based representations typically require costly inference (i.e., mentalizing) to ``unpack'' observable actions into putative mental states (e.g., with inverse reinforcement learning). Here, we investigate cheaper, non-mentalizing alternatives to social transmission of model-based representations that bias the statistics of experience to ``hijack'' asocial mechanisms for learning of environments. We simulate a spatial foraging task where a naïve learner learns alone or through observing a pre-trained expert. We test model-free vs. model-based learning together with simple non-mentalizing social learning strategies. Through analysis of generalization when the expert can no longer be observed and through correspondence between expert and learner representations, we show how simple social learning mechanisms can give rise to complex forms of cultural transmission.

**Authors:**

Miriam Bautista: University of Tübingen; Ryutaro Uchiyama: Tübingen AI Center; Claudio Tennie: University of Tübingen; Charley M Wu: University of Tübingen

Emergent social transmission of model-based representations without inference

Alongside the advancement of large language models (LLMs), attention towards their limitations and potential risks has also increased. One common issue is hallucination, which occurs when LLMs generate inaccurate or irrelevant answers, especially for complex sentences. To address this issue, we propose a novel question preprocessing method inspired by how young children comprehend complex sentences. Our method consists of two modules: (1) hierarchical clause annotation (HCA)-based sentence decomposition, which breaks down complex sentences into one-verb-centered clauses, and (2) abstract meaning representation (AMR)-based clause rewriting, which reformulates the clauses based on AMR into the child-comprehensible subject-verb-object (SVO) structure. We evaluate our method on the question-answering dataset, TruthfulQA, and show that it can improve the truthfulness and informativeness of widely-used LLMs, LLaMA-7B, and LLaMA-2-7B-chat, preventing from generating hallucinated answers. Moreover, our method is highly efficient, as it does not require any pre-training, fine-tuning, or invoking larger-scale models.

**Authors:**

Yunlong Fan: Southeast University; Bin Li: Southeast University; Zhiqiang Gao: Southeast University

Mitigating Hallucinations in Large Language Models by Preprocessing Questions into Child-Comprehensible

This study investigates whether fixation behaviour during scene viewing can offer insights into sequentialisation in verbal scene description production. We explored the correlation between visual and linguistic attention on naturalistic scenes using scene descriptions and eye movement measures. Results demonstrate an overlap in object prioritization during scene viewing and describing. Our additional analysis of scene descriptions reveals a tendency towards selecting and prioritizing category-specific objects.

**Authors:**

Pelin Celikkol: University of Potsdam; David Schlangen: University of Potsdam; Jochen Laubrock: University of Potsdam

Premium content

Downloads

Next from CogSci 2024

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

Similar lecture

Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES