Netherlands

In the field of medical decision-making, understanding the treatment intentions of clinicians is crucial for effective treatment strategies. However, these intentions are often implicit and challenging to quantify. In this paper, we propose a novel two-module model to infer and implement clinicians&#39; treatment intentions through treatment records. We construct the InfCTI module, which infers intentions and quantifies them numerically, and the ImpCTI module, which generates treatment strategies based on inferred intentions. Our experiments demonstrate that the treatment strategies obtained by ImpCTI reflect clinicians&#39; intentions and the intention values obtained by InfCTI are reasonable. This model has the potential to improve the quality of care provided to patients.

**Authors:**

Jinsheng Shi: Beijing University of Posts and Telecommunications; Yuyu Yuan: Beijing University of Posts and Telecommunications; Yuang Cai: Beijing University of Posts and Telecommunications; Rui Han: Beijing University of Posts and Telecommunications; Zhenyu Zhao: Beijing University of Post and Telecommunications ; Zijie Shi: Beijing University of Posts and Telecommunications

CogSci 2024

InfCTI-ImpCTI: Inferring and Implementing Clinicians&#39; Treatment Intentions

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**
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**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** 
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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 the field of medical decision-making, understanding the treatment intentions of clinicians is crucial for effective treatment strategies. However, these intentions are often implicit and challenging to quantify. In this paper, we propose a novel two-module model to infer and implement clinicians' treatment intentions through treatment records. We construct the InfCTI module, which infers intentions and quantifies them numerically, and the ImpCTI module, which generates treatment strategies based on inferred intentions. Our experiments demonstrate that the treatment strategies obtained by ImpCTI reflect clinicians' intentions and the intention values obtained by InfCTI are reasonable. This model has the potential to improve the quality of care provided to patients.

**Authors:**

Jinsheng Shi: Beijing University of Posts and Telecommunications; Yuyu Yuan: Beijing University of Posts and Telecommunications; Yuang Cai: Beijing University of Posts and Telecommunications; Rui Han: Beijing University of Posts and Telecommunications; Zhenyu Zhao: Beijing University of Post and Telecommunications ; Zijie Shi: Beijing University of Posts and Telecommunications

InfCTI-ImpCTI: Inferring and Implementing Clinicians' Treatment Intentions

poster

Behavioral studies and computer simulations of analogical retrieval suggest that the availability of surface matches in long-term memory (LTM) hinders the spontaneous retrieval of purely structural analogs. We investigated whether this competition effect still holds during hypothesis-generation, a goal-driven activity that entails a more profound and sustained consideration of the target situation. In two experiments, we obtained that the availability of a less isomorphic but more superficially similar item did not complicate retrieving a structural analog, thus suggesting that goal-driven activities such as hypothesis generation aid participants in overcoming the activation of a structurally suboptimal analog in working memory, as compared to pragmatically impoverished activities such as reading the target situation. However, the activation of the surface match hindered the successful application of structural matches that were successfully retrieved. Results render a more nuanced picture of the role of surface similarities in analogical thinking, traditionally restricted to the retrieval stage.

**Authors:**

Sabina Leon: Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT); Leandro E. Rivas: University of Comahue; Maximo Trench: University of Comahue

Analogical Reasoning During Hypothesis Generation: The Role of Surface Competition During Access and Transfer

Social media has affected how we relate to our body image. Digital makeovers have both reinforced existing beauty ideals and created new ones. This project investigated whether young adults’ detection of image filters was biased by internalized beauty ideals and gender. Participants completed a visual detection task (forced choice paradigm) where contrast filter correction was assessed for images of male and female bodies that were thin, average, and curvaceous/muscular. Results showed that people can detect filters and that accuracy is higher when filters are applied to bodies that represent the historical beauty ideals: thin female bodies and muscular male bodies. These findings suggest that the perception of low-level image features is biased to fit internalized beliefs about beauty

**Authors:**

Ivonnia M Flores Bravo: National Scientific and Technological Council of Argentina; Trinidad Belén Speranza: Centro de Investigaciones en Psicología y Psicopedagogía. National Scientific Research and Technology Council; Gaston Saux: Centro de Investigaciones en Psicología y Psicopedagogía, Pontificia Universidad Católica Argentina; Verónica Ramenzoni: Centro de Investigaciones en Psicología y Psicopedagogía. National Scientific Research and Technology Council

Detection of Image Filters is Biased by Gender and Internalized Beauty Ideals

Human cognition significantly influences expressed behavior and is intrinsically tied to authentic personality traits. Personality assessment plays a pivotal role in various fields, including psychology, education, social media, etc. However, traditional self-report questionnaires can only provide data based on what individuals are willing and able to disclose, thereby lacking objective. Moreover, automated measurements and peer assessments demand significant human effort and resources. In this paper, given the advantages of the Virtual Reality (VR) technique, we develop a VR simulator --- PersonalityScanner, to stimulate cognitive processes and simulate daily behaviors based on an immersive and interactive simulation environment, in which participants carry out a battery of engaging tasks that formulate a natural story of first-day at work. Through this simulator, we collect a synchronous multi-modal dataset with ten modalities, including first/third-person video, audio, text, eye tracking, facial microexpression, pose, depth data, log, and inertial measurement unit. By systematically examining the contributions of different modalities on revealing personality, we demonstrate the superior performance and effectiveness of PersonalityScanner.

**Authors:**

Xintong Zhang: Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University; Di Lu: Peking University; Huiqi Hu: Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University; Nan Jiang: Peking University; Xianhao Yu: Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University; Jinan Xu: Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University; Yujia Peng: Peking University; Qing Li: National Key Laboratory of Beijing Institute for General Artificial Intelligence; Wenjuan Han: Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University

PersonalityScanner: Exploring the Validity of Personality Assessment Based on Multimodal Signals in Virtual Reality

Adult language learners have difficulty segmenting words from continuous speech when the phonology is unfamiliar. Since speaker variability is known to improve acquisition of novel language structures, it could be processed in ways that bootstrap phonological patterns and enhance learners’ ability to segment words. To test this, the present experiment examined adult participants’ learning of a stream of statistically determined tri-syllabic words that were spoken by one or multiple speakers. Syllables were constructed with either English phonology or non-English phonology. Two tasks (target detection and two-alternative forced choice) assessed the extent of listeners’ sensitivity to language patterns and word segmentation. Results suggest speaker variability negatively impacted learners’ ability to track the underlying statistics. 2AFC word segmentation performance was poor—independent of speaker number; it is hypothesized that attentional demands of the target detection task conflicted with statistical word segmentation mechanisms.

**Authors:**

Larissa Melville: University of British Columbia; Alexis K. Black: The University of British Columbia

Beyond Noise: The Role of Speaker Variability on Statistical Learning

Studies have shown that readers who do not always experience an inner reading voice (less-IRV readers) move their eyes more freely and do more efficient silent reading than those who always experience IRV (full-IRV readers). This conclusion suggests that less-IRV readers may not be suited for studying with vocalization. In this study, forty students were assigned to full- and less-IRV reading groups. The main task in the experiment was to read short stories and answer comprehension tests. The reading materials comprised 12 stories, the same as those used by Morita and Takahashi (2019). Participants read them with audio assistance and answered three comprehension tests after reading each story. While reading the stories, the readers' eye movements were recorded. The results of the eye-movement index showed no difference in eye movement patterns (fixation, fixation time, saccade size, regression) and comprehension between the two kinds of readers. We found no relationship between inner reading voice styles and eye movements in audio-assisted reading.

**Authors:**

Yefei Liang: Hiroshima University; Takumi Arima: Hiroshima University; Aiko Morita: Hiroshima University

Inner reading voice styles and eye movements during audio-assisted reading

Online mental health support plays a crucial role in addressing the mental health issues faced by modern individuals. However, delivering high-quality online mental health support presents a significant challenge. In response to this challenge, we introduce MentalBlend, a framework that leverages psychological counseling theories, including Cognitive-Behavioral Therapy, Dialectical Behavior Therapy, Person-Centered Therapy, and Reality Therapy, to guide large language models (LLMs) in offering professional online mental health support to individuals seeking assistance. Experimental evidence validates that the collaboration between LLMs and the MentalBlend framework results in the generation of responses that align with professional standards for online mental health support. Overall, our research aims to contribute to advancing the capabilities of LLMs in understanding the emotional backgrounds of help-seekers and delivering professional mental health support effectively.

**Authors:**

Ziyin Gu: University of Chinese Academy of Sciences; Qingmeng Zhu: University of Chinese Academy of Sciences

MentalBlend: Enhancing Online Mental Health Support through the Integration of LLMs with Psychological Counseling Theories

Although decision-making processes are typically studied with isolated individuals in the laboratory to control external factors, we mostly make decisions in a social environment in the presence of other individuals. The aim of the current study was to investigate the effects of social conditions on individuals’ decision-making performance in economic and moral contexts. Forty-four pairs of participants of the same gender constituted the sample for this study. Each pair was required to complete both economic and moral tasks under three types of social conditions, namely, “individual,” “joint,” and “joint with gaze-cueing.” Furthermore, eye- and mouse-tracking technologies were utilized to record the participants’ responses to the decision tasks. We hypothesized that even a minimal social context would influence people’s decisions, as manifested in their gaze and mouse responses. The results revealed that the minimalist social condition in which participants do not communicate or interact with each other affected their decision-making performance.

**Authors:**

Pardis Aydin: Graduate School of Informatics, Middle East Technical University; Murat Perit Cakir: Middle East Technical University

Decision-Making Behaviour and Minimal Social Conditions: Economic versus Moral Choices

When individuals are pressed to make decisions quickly, their accuracy tends to decline, which is termed as speed-accuracy trade-off. But does this phenomenon extend to perceptual rating? In other words, do rapid judgments result in more extreme outcomes? To address this question, the study analyzed a global dataset covering 11,481 adult participants’ ratings of 120 targets across 45 countries. The hypothesis posited that the rating became more extreme if it took less time. The study firstly identified response time as a significant predictor in extremity of social judgments through a machine learning algorithm, XGBoost, with cultural variables emerging as the second most important predictor. Given the importance of response time, the study employed hierarchical general linear models to investigate whether faster decision-making correlates with more extreme ratings and how this effect varies across diverse cultural contexts. The findings revealed a significant global level effect, also showing considerable variance across eleven regions. This observed phenomenon is termed as the “speed-extremity trade-off,” and is strongest in the Middle East and weakest in East Southeast Asia and Scandinavia.

**Authors:**

Nan Wang: the Chinese University of Hong Kong

Quicker, extremer: a computational modeling of reaction time and rating in social evaluation of faces

This study presents ADViRDS, an innovative scale and dataset specifically developed for examining the psychological traits of domestic violence (DV) perpetrators. Recognizing the critical need to understand the psychological dynamics of perpetrators, our research shifts the focus from the experiences of DV victims to the characteristics of the perpetrators. Our approach involves a six-dimensional scale designed to detect the psychological traits of DV perpetrators, formulated with insights from established DV research and psychologists. To complement this scale, we constructed a detailed dataset containing 591 individual entries from the Chinese social media platform "Zhihu." Each entry was carefully annotated by experienced professionals, ensuring a high degree of accuracy and relevance. We conducted a comprehensive analysis using a range of models, including Zero-Shot classification, GPT series, and fine-tuned pre-trained models, to evaluate their effectiveness in identifying individuals with psychological predispositions to DV. The findings reveal significant insights into the models' capabilities, highlighting the nuances in detecting DV tendencies through psychological profiling. Our research offers a new paradigm in DV studies, focusing on the psychological traits of perpetrators for a comprehensive understanding of DV dynamics and prevention.

**Authors:**

Chengwei Tong: University of Science and Technology of China; Mengzhuo Guo: University of Science and Technology of China; Yao Tian: University of Science and Technology of China; Mengzhu Zhang: Anhui Medical University; Yangyang Li: Academy of Cyber; Chunyan Zhu: Department of Psychology, The Second Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.; Jie Bao: University of Science and Technology of China; Rongrong Sheng: Anhui Medical University; Qianqian Li: Department of Psychology and Sleep Medicine, the Second Affiliated Hospital of Anhui Medical University; Yong Liao: University of Science and Technology of China

ADViRDS: Assessment of Domestic Violence Risk Dataset and Scale on Social Media

The digital environment has a significant impact on our everyday lives, but there is a lack of studies on how it affects cognitive processes like attention and working memory (WM). This study aims to compare attention and WM in digital and non-digital environments. In Experiment 1, we compared attention and working memory under paper and computer-based environment tasks. The findings showed that under non-digital condition attentional sustainability and visual working memory were better. In Experiment 2, we examined attentional shifting and sustainability at different levels of digital saturation (the presence of perceptual feedback on a website). Attentional sustainability was better in a saturated condition, but attentional shifting was not affected. Thus, the real environment is suggested to be superior due to lower saturation and higher motor-visual coherence. Digital saturation, along with the ACD idea, can guide attention. These results have applications for enhancing the user experience with interfaces.

**Authors:**

Anastasia Anufrieva: HSE University

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