Netherlands

Researchers have long been interested in understanding and closing the “reading gap” that exists between White and racially marginalized students. This study explored whether group differences in inference strategies (i.e., bridging and elaboration) and comprehension performance existed among college readers. Three hundred college participants who self-identified as White, Black, or Hispanic completed a think-aloud task along with measures of reading proficiency and comprehension. Results from hierarchical regression models indicated that group differences in elaborative strategies were present, but differences in bridging strategies and comprehension performance disappeared when foundational skills in reading were included in the models. The results are explained in terms of inequities in educational experiences prior to entering college.

**Authors:**

Daniel P. Feller: University of Memphis; Ann Cale Kruger: Georgia State University; Joe P Magliano Magliano: Georgia State University.

CogSci 2024

Bridging (and Elaborating on) the Achievement Gap

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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.

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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.

In recent years, with the widespread popularity of location-based social network platforms, the data generated by users on social networks has grown exponentially. There has been a growing focus on the problem of POI (Point-of-Interest) recommendations. Unlike traditional sequence recommendation that primarily considers the temporal dimension, POI recommendation needs to account for the influence of geographical information to a large extent. However, previous works in the graph construction process often only consider the places users have visited, neglecting those they haven't been to. To address this, we propose a Dual Weighted Graph Convolutional Network for POI recommendation called DualPOI. Specifically, we first leverage graph neural networks and attention mechanisms to capture users' local trajectory preferences for visited POIs. A delicately designed spatiotemporal encoder is conducted to model users' local spatiotemporal preferences. Subsequently, using a dual graph convolutional approach, we transfer the user's local preference information to a global scope, thereby modeling novel preferences for unvisited locations. Extensive experiments on four real-world datasets validate the effectiveness of our proposed method in enhancing the accuracy of POI recommendations. Comprehensive ablation studies and parameter analysis further confirm the efficacy of the proposed modules.

**Authors:**

Zhi Liu: Zhejiang University of Technology; Deju Zhang: College of Computer Science and Technology, Zhejiang University of Technology; Junhui Deng: College of Computer Science and Technology; Guojiang Shen: Zhejiang University of Technology; Xiangjie Kong: Zhejiang University of Technology

Dual Weighted Graph Convolutional Network for POI Recommendation

Next point-of-interest (POI) recommendation is a challenging task that aims to recommend the next location that a user may be interested in based on their check-in trajectories. Since users travel not only with long-term stable preferences but also with short-term dynamic interests, there is often a potential dependency between long-term and short-term preferences. Most existing works tend to mine the dependencies between long-term and short-term trajectories by contrastive learning but always ignore the negative impact of the learned dependencies on the accuracy of short-term trajectory modeling. Moreover, they often only utilize the context information of the user's trajectory, while neglecting the spatiotemporal dependencies between user trajectories. To address these issues, we proposed a novel dual contrastive learning framework DCLS. Specifically, we designed a novel dual contrastive learning scheme, for which we built two views: the first view is between the user's own long-term and short-term trajectories, and the second view is between the short-term trajectories of different users. We performed contrastive learning on both views, to learn the dependency between long-term and short-term trajectories, and improve the accuracy of trajectory modeling. We also designed a multi-class attention fusion module, which integrates the spatiotemporal influence of trajectory dependencies on user mobility, enhancing the recommendation performance. We conducted extensive experiments on three real-world datasets, which demonstrated that our model achieves advanced performance in the next POI recommendation.

**Authors:**

Zhi Liu: Zhejiang University of Technology; Junhui Deng: College of Computer Science and Technology; Deju Zhang: College of Computer Science and Technology, Zhejiang University of Technology; zhiyu chen: Computer Science and Technology; Guojiang Shen: Zhejiang University of Technology; Xiangjie Kong: Zhejiang University of Technology

Dual Contrastive Learning for Next POI Recommendation with Long and Short-Term Trajectory Modeling

This study explores how L1 and L2 Chinese speakers use world knowledge and classifier information to predict fine-grained referent features. In two visual-world-paradigm eye-tracking experiments, participants were presented with two visual objects that were denoted by the same noun in Chinese but matched different shape classifiers. Meanwhile, they heard sentences containing world knowledge triggering context and classifiers. The effect of world knowledge has been differentiated from word-level associations. Native speakers generated anticipations about the shape/state features of the referents at an early processing stage and quickly integrated linguistic information with world knowledge upon hearing the classifiers. In contrast, L2 speakers show delayed, reduced anticipation based on world knowledge and minimal use of classifier cues. The findings reveal different cue-weighting strategies in L1 and L2 processing. Specifically, L2 speakers, whose first languages lack obligatory classifiers, do not timely employ classifier cues, even though the semantic meanings of shape classifiers are accessible to them. No evidence supports over-reliance on world knowledge in L2 processing. This study contributes to the understanding of L2 real-time processing, particularly in L2 speakers’ utility of linguistic and non-linguistic information in anticipating fine-grained referent features.

**Authors:**

Baorui Xu: Peking University; Shuo Feng: Peking University; Yipu Wei: Peking University

Anticipating object shapes using world knowledge and classifier information: Evidence from eve-movements in L1 and L2 processing

Eye movement detection plays a crucial role in various fields, including eye tracking applications and understanding human perception and cognitive states. Existing detection methods typically rely on gaze positions predicted by gaze estimation algorithms, which may introduce cumulative errors. While certain video-based methods, directly classifying behaviours from videos, have been introduced to address this issue, they often have limitations as they primarily focus on detecting blinks. In this paper, we propose a video-based two-stream framework designed to detect four eye movement behaviours—fixations, saccades, smooth pursuits, and blinks—from infrared near-eye videos. To explicitly capture motion information, we introduce optical flow as the input for one stream. Additionally, we propose a spatio-temporal feature fusion module to combine information from the two streams. The framework is evaluated on a large-scale eye movement dataset and performs excellent results.

**Authors:**

Ziheng Wang: Defense Innovation Institute, Academy of Military Sciences; Xiaowei Bai: Defense Innovation Institute, Academy of Military Sciences; Xiaodong Wang: Defense Innovation Institute, Academy of Military Sciences; Zhenyu Fang: Northwestern Polytechnical University; Liang Xie: Defense Innovation Institute, Academy of Military Sciences; Ye Yan: Defense Innovation Institute, Academy of Military Sciences; Erwei Yin: Defense Innovation Institute, Academy of Military Sciences

Two-Stream Vision Swin Transformer for Video-based Eye Movement Detection

This study explores the way discourse structure information is used during encoding of linguistic representations, using the distinction between main and subordinate information as a case study. We use the two contrasting constructions: (a) “The singers\textsubscript{MAIN} who admired the violinists\textsubscript{MAIN} invited their mentors to the party”; (b) “The singers\textsubscript{MAIN}, who admired the violinists\textsubscript{SUBORDINATE}, invited their mentors to the party.” While both contain discourse-main information, (b) includes discourse-subordinate information in the clause ``who admired \textit{the violinists}.” Importantly, \textit{the singers} and \textit{the violinists} are both plausible antecedents for \textit{their}, but the overlap in discourse-information between the two NPs differs: (a) overlap ({MAIN, MAIN}); (b) no overlap ({MAIN, SUBORDINATE}). Through two web-based eye-tracking experiments using a visual world paradigm, we find that this overlap leads to competition between the two NPs, evidenced by eye-gaze differences, (a) < (b). We also find that this effect manifests early, even before retrieval, i.e., before pronoun resolution.

**Authors:**

Sanghee J. Kim: University of Chicago; Ming Xiang: University of Chicago

Encoding discourse structure information during language comprehension: Evidence from web-based visual world paradigm experiments

Outrage has often been interpreted as a shorthand for “moral outrage,” anger upon a moral standard being violated (Batson et al., 2007). We ask whether a violation of a moral standard is necessary for producing outrage or whether other variables can also produce it. By presenting participants with a series of potentially outrage-inducing scenarios and measuring their emotional responses, we seek to identify the predictors of outrage. We find that anger and disgust are the strongest predictors of level of outrage compared to sense of threat, level of surprise, level of uncomfortableness, severity of the moral violation, and how much one values the moral being violated. Mediation analyses suggest that moral violations do not mediate the effects of anger and disgust on outrage. However, anger and disgust do mediate the effect of moral violations on outrage. Our findings suggest that moral violations elicit anger and disgust, which in turn produce feelings of outrage.

**Authors:**

Amelia Day Jessop: Brown University ; Hyoseok Kim: Southern Connecticut State University; Steven Sloman: Brown University

Violations of Moral Standards versus Emotional Reactions: How is Outrage Generated?

Emotion recognition based on electroencephalogram (EEG) signals is a current focus in brain-computer interface research. However, due to the individual differences, how to build a simple and effective model and quickly adapt to the target subject are significant challenges in cross-subject emotion recognition. In this study, we proposed an approach by combining the Dual-Attention network and Meta-Transfer Learning (MTL) strategy based on k-means clustering for meta-task sampling. The Dual-Attention network extracts EEG features through a channel attention block and a temporal attention block. The MTL strategy trains the model to learn both common and individual features among subjects. The meta-task sampling method based on k-means clustering adaptively groups the source domain samples, sampling support and query sets for meta-tasks from Different Groups(DG sampler). The DG sampler allows the model to ”grow in diversity”, further enhancing its generalization capabilities. Binary classification experiments were conducted on the DEAP dataset, achieving accuracies of 72.35% and 71.77% in the arousal and valence dimensions, respectively. The results have reached the state-of-the art level and demonstrated significant performance enhancement in cross-subject EEG-based emotion recognition.

**Authors:**

Qian Zhang: Xidian university; Liying Yang: XiDian University; gang cao: XiDian University; qiang wang: Xidian University

Cross-Subject Emotion Classification based on Dual-Attention Mechanism and Meta-Transfer Learning

Chain-of-Thought (CoT) has brought a fresh perspective to improve the reasoning ability of large language models (LLMs). To relieve the burden of manual design in CoT, Zero-shot CoT has pioneered a direct interaction with LLMs. Based on it, researchers attempt to optimize reasoning paths through various prompting approaches like reflection, selection, and planning. However, few studies have focused on the possibility of combining all these strategies through a cognitive theory. Inspired by experiential learning, this paper proposes a new zero-shot prompting method based on Kolb's reflective cycle, named Self-Hint prompting. Specifically, Self-Hint prompting introduces an automated iterative interaction approach to simulate the conscious reflection process, which uses intermediate observations as hints to guide LLMs. We have conducted comprehensive experiments on various math reasoning benchmarks. The empirical results on GPT models demonstrate the effectiveness of our method. Proposed Self-Hint prompting consistently outperforms other zero-shot baselines.

**Authors:**

Jindou Chen: Shanghai Jiao Tong University; Jidong Tian: Shanghai Jiao Tong University; Yaohui Jin: Artificial Intelligence Institute

Self-Hint Prompting Improves Zero-shot Reasoning in Large Language Models via Reflective Cycle

Drawing on the principles of associative learning theory and positing a statistical foundation for language acquisition, this paper investigates the independent contributions of the predictive validities of verbal and constructional cues in English native and non-native speakers’ mental representations of the English as-predicative construction. This is examined through two experiments: a sentence completion task targeting constructional outcome retrieval (Experiment 1), and a gap-fill schema task with a focus on verb retrieval (Experiment 2). The results demonstrate that both cues are integrated in parallel when eliciting a constructional outcome (Experiment 1), but only construction cue validity plays a role in eliciting verbal outcomes (Experiment 2). Verb frequency and voice additionally contribute to the retrieval of verbal and constructional information in distinct manners. The present study raises discussions about distributional cue integration in forward versus backward retrieval of linguistic information, in addition to emphasizing the importance of considering cross-linguistic factors in future research.

**Authors:**

Ivana Domazetoska: University of Melbourne; Helen Zhao: University of Melbourne

Native and Non-Native Speakers’ Cue Integration in the Processing of the English As-Predicate Construction

A large body of work in psycholinguistics has focused on the idea that online language comprehension can be shallow or `good enough': given constraints on time or available computation, comprehenders may form interpretations of their input that are plausible but inaccurate. However, this idea has not yet been linked with formal theories of computation under resource constraints. Here we use information theory to formulate a model of language comprehension as an optimal trade-off between accuracy and processing depth, formalized as bits of information extracted from the input, which increases with processing time. The model provides a measure of processing effort as the change in processing depth, which we link to EEG signals and reading times. We validate our theory against a large-scale dataset of garden path sentence reading times, and EEG experiments featuring N400, P600 and biphasic ERP effects. By quantifying the timecourse of language processing as it proceeds from shallow to deep, our model provides a unified framework to explain behavioral and neural signatures of language comprehension.

**Authors:**

Jiaxuan Li: University of California Irvine; Richard Futrell: UC Irvine

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