Large Language Models (LLMs) have demonstrated their capabilities across various tasks, from language translation to complex reasoning. Understanding and predicting human behavior and biases are crucial for artificial intelligence (AI) assisted systems to provide useful assistance, yet it remains an open question whether these models can achieve this. This paper addresses this gap by leveraging the reasoning and generative capabilities of the LLMs to predict human behavior in two sequential decision-making tasks. These tasks involve balancing between exploitative and exploratory actions and handling delayed feedback, both essential for simulating real-life decision processes. We compare the performance of LLMs with a cognitive instance-based learning (IBL) model, which imitates human experiential decision-making. Our findings indicate that LLMs excel at rapidly incorporating feedback to enhance prediction accuracy. In contrast, the cognitive IBL model better accounts for human exploratory behaviors and effectively captures loss aversion bias, i.e., the tendency to choose a sub-optimal goal with fewer step-cost penalties rather than exploring to find the optimal choice, even with limited experience. The results highlight the benefits of integrating LLMs with cognitive architectures, suggesting that this synergy could enhance the modeling and understanding of complex human decision-making patterns.
CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease
Department of Mechanical, Industrial & Manufacturing Eng.（机械、工业与制造工程系）；The University of Toledo（托莱多大学）；Department of Industrial Eng. and Mngt. Systems（工业工程与管理系统系）；University of Central Florida（中央佛罗里达大学）；Department of Statistics and Data Science（统计与数据科学系）；Department of Internal Medicine（内科医学系）
Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Faculty of Computer Science Dalhousie University（达尔豪斯大学计算机科学学院）；Department of Computer Science University of Waterloo（滑铁卢大学计算机科学系）；Computer Science and Artificial Intelligence Laboratory Massachussetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Department of Computer Science Cornell University（康奈尔大学计算机科学系）
Peri-AIIMS: Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes
University of Florida（佛罗里达大学）；UF Health（佛罗里达大学健康医疗中心）；Rowan University（罗文大学）
Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels using Variational Auto-Encoder
多示例学习是一种弱监督学习。它处理数据是一组袋子的任务，每个袋子是一组实例。仅观察到袋标签，而实例的标签未知。多实例学习的一个重要优点是，通过将对象表示为一组实例，它能够保持对象各部分之间的固有依赖关系。不幸的是，大多数现有的算法假设所有实例都是\textit{identically and independently distributed}，这违反了现实世界的场景，因为包中的实例很少是独立的。在这项工作中，我们提出了多实例变分自动编码器（MIVAE）算法，该算法显式地建模预测袋标签和实例标签的实例之间的依赖关系。在多个多实例基准测试和端到端医学成像数据集上的实验结果表明，MIVAE在实例标签和袋子标签预测任务上的表现优于最先进的算法。
Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling
Goldsmiths, University of London（伦敦大学金史密斯学院）；School of Health Sciences, The University of Manchester（曼彻斯特大学健康科学学院）；Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences（法兰克福应用科学大学计算机科学与工程学院）；Institute of Psychiatry, Psychology and Neuroscience King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
University of Notre Dame（圣母大学）；University of Colorado, Boulder（科罗拉多大学博尔德分校）；University of California, Irvine（加州大学欧文分校）；Dartmouth College（达特茅斯学院）；Georgia Institute of Technology（佐治亚理工学院）；University of Washington（华盛顿大学）
DeepRepViz: Identifying Confounders in Deep Learning Model Predictions
Humboldt-Universität zu Berlin（柏林洪堡大学）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Einstein Center for Neurosciences Berlin（柏林爱因斯坦神经科学中心）；Freie Universität Berlin（柏林自由大学）
