主持人: Wanke Pan(潘晚坷)
朱睿达
中山大学心理学系
朱睿达，中山大学心理学系副教授，主要从事道德情绪与决策的认知神经机制研究。以第一作者或通讯作者（含共同）身份在 PLOS Biology、eLife、Neuroscience & Biobehavioral Reviews、Behavioral and Brain Sciences、NeuroImage 等学术期刊发表多篇论文。主持国家自然科学基金青年科学基金项目、广东省哲学社会科学规划项目各1项。任Collabra: Psychology期刊副编辑（Associate Editor）。
While humans resemble other primates in terms of motor and perceptual abilities, our behavior is uniquely characterized by morality—the ability to choose our actions based on specific ethical values. The evolutionary origins and neurobiological basis of moral behavior are debated intensely, with opposing proposals about whether moral decisions rely on domain-general decision-making neural circuitry (neural common currency hypothesis) or on unique neural processes that were shaped by evolutionary pressures associated with life in groups (social brain hypothesis). Here we examine the potential overlap between moral and perceptual decision-making, by combining functional magnetic resonance imaging with computational modeling of behavior in a novel task requiring human participants to make alternating moral and perceptual choices on identical stimuli. At the algorithmic level, we found that moral and perceptual choices were well fit by the same sequential sampling model, which allowed us to quantify and directly compare conflicts between the choice alternatives for both types of decision-making. At the implementational level, moral and perceptual choice conflicts were represented by distinct neural activity patterns; in fact, neural signals in some brain areas (e.g., temporoparietal junction) encoded the strength of the two types of conflict in opposite directions. Our findings suggest that while similar computational decision mechanisms underlie both moral and perceptual choices, their neural implementation is fundamentally different, supporting the social brain hypothesis.
北京时间 [GMT+8] 2026年10月20日 19:00-20:00
会议信息： ZOOM 会议号：863 0404 9478
报告语言： 中文
主持人： Wanke Pan(潘晚坷)
DDMJC 主页: https://ddmjournalclub.github.io
DDM Bilibili 主页: https://space.bilibili.com/3461571806235136
]]>主持人: Wanke Pan(潘晚坷)
朱广予
澳大利亚国立大学（ANU）心理学博士
国际数学心理学学会会员，国际判断与决策学会会员，澳大利亚数学心理学学会会员，澳大利亚实验心理学会会员。主要从事决策科学、心理测量学与认知建模研究，重点关注个体在不确定性情境下的决策机制，并结合因果发现与计算建模方法，构建可解释的个体与群体决策过程的数学模型。相关成果发表于 Behavior Research Methods 及 Personality and Social Psychology Bulletin。
杨光
悉尼大学（USYD）心理学博士候选人
美国视觉科学学会会员，澳大利亚数学心理学学会会员，澳大利亚实验心理学会会员，关注心理学与计算机领域的交叉方向，如视觉感知过程的计算建模及方法学研究。相关成果展示于美国视觉科学学会、澳大利亚实验心理学会、及亚太视觉会议。负责基于模拟的证据积累建模 R 语言开源包 eam package 的高性能实现。
Evidence accumulation models are widely used to model choices and reaction times in cognitive tasks. Despite their success in binary and multi-alternative decision-making, their application to sequential multi-response tasks, such as free recall and verbal fluency, has remained limited. The primary challenge in modeling multiple responses within a single trial lies in the absence of tractable likelihood functions. To address this limitation, we introduce simulation-based inference into the evidence accumulation framework and develop an R package that integrates simulation engines for five representative models (the diffusion decision model, leaky competing accumulator model, linear ballistic accumulator model, racing diffusion model, and Lévy flight model), extending them to the multi-response contexts. Across three simulation studies, the package demonstrated robust parameter recovery across representative models, their multi-response extensions, and covariate-dependent variants. An empirical application to an aging-related free recall dataset further demonstrated the package’s ability to uncover computational mechanisms underlying age-related differences in memory retrieval. Taken together, this package provides a foundation for building customized evidence accumulation models and expands the range of cognitive processes that can be studied within the evidence accumulation framework.
北京时间 [GMT+8] 2026年06月05日 20:00~21:00
会议信息： ZOOM 会议号：863 0404 9478
报告语言： 中文
主持人： Wanke Pan(潘晚坷)
DDMJC 主页: https://ddmjournalclub.github.io
DDM Bilibili 主页: https://space.bilibili.com/3461571806235136
]]>分享嘉宾： Haomin Chen
单位： University of Melbourne
A major target for evidence accumulation models of decision-making has been the development of a joint account of confidence, accuracy, and latency. While successful extensions to confidence have been made, there remains a lack of consensus as to how confidence is generated and there have been few comparisons between existing models. In this work, we developed and compared three different mechanisms for confidence generation in the linear ballistic accumulator framework (Brown & Heathcote, 2008). These take the form of a.) multi-alternative decisions among all of the confidence options as a competitive race, b.) the balance of evidence between the response alternatives (the multiple threshold race; Reynolds et al., in revision), and c.) confidence being inversely proportional to the amount of subjective time that has elapsed in the decision, which is measured as a separate accumulation process. Each theory was tested against two experiments that manipulated the number of confidence options in a decision (Experiment 1) or the amount of time pressure (Experiment 2). All theories cleared the empirical hurdles, but there were also subtle differences between each of the theories in their ability to address the data.
北京时间 [GMT+8] 2026年3月18日 (周三) 14:00~15:00
会议信息： ZOOM 会议号：863 0404 9478
报告语言： 英文
主持人： 胡传鹏
Chen, H., Heathcote, A., & Osth, A. F. (2026). Linear ballistic accumulator models of confidence and response time. PsyArXiv. https://osf.io/preprints/psyarxiv/jsqx5_v1/
Brown, S. D., & Heathcote, A. (2008). The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57, 153–178.
Dr. Noah van Dongen
Amsterdam School of Law, University of Amsterdam
Formal modelling is increasingly prevalent in psychological research, though not easily accessible to most researchers, for educational and technical reasons. Our project aims at creating a database that facilitates sharing, (re)using, and extending computational implementations of formal models of psychological processes.
PsychoModels (https://www.psychomodels.org/) aims for a collection of mathematical and computational models that spans all of psychology. It contains models that are annotated and presented in a similar way, making it easier to skim a model and grasp its content than currently existing repositories allow for. Furthermore, this similar presentation facilitates a common language around modelling that facilitates communication between researchers. Also, indexing model characteristics allows for search options across the model database, facilitates model review efforts, and promotes the reuse of the models in the database. Lastly, easing access to existing models and connecting educational materials to the database will allow researchers with less experience in formalizing their research to learn from best practice.
The PsychoModels database is still under development, but our beta version is already accessible. During the presentation, I will talk about its development, its current features, our plans for the future, and how you can contribute.
北京时间 [GMT+8] 2025年7月7日 (周一) 16:00~17:00
会议信息： ZOOM 会议号：863 0404 9478
报告语言： English
主持人： 刘逸康
Nak, J., Volz, L., Verbrugh, V., Borsboom, D., & van Dongen, N. N. N. (2025, May 19). The PsychoModels database: Facilitating the exchange of formal models. https://doi.org/10.31234/osf.io/ptzdg_v1
分享嘉宾： Zhanbin 占斌 辽宁师范大学 24年12月博士毕业于中国科学院心理研究所，现任辽宁师范大学脑与认知神经科学研究中心讲师，主要采用心理物理法、EEG、核磁、眼动追踪以及计算建模等技术探究人类视知觉、决策与社会认知。
As social creatures, we are naturally swayed by the opinions of others, which largely shape our attitudes and preferences. However, whether social influence can directly impact our visual perceptual experience remains debated. We designed a two-phase dyadic training paradigm where participants first made a visual categorization judgment and then were informed of an alleged social partner’s choice on the same stimulus. Results demonstrated that social influence significantly modified participants’ subsequent visual categorizations, even when they had been well-trained prior to the dyadic training. This effect persisted for an extended period of up to six weeks. Diffusion model analysis revealed that this effect stemmed from perceptual processing more than mere response bias, and its strength was inversely related to the participants’ confidence and autistic-like tendencies. These findings offer compelling evidence that our perceptual experiences are deeply influenced by social factors, with individual confidence and personality traits playing significant roles.
北京时间 [GMT+8] 2025年4月17日 20:00~21:00
主持人： 潘晚坷
参考文献：Zhan, B., Chen, Y., Wang, R., & Jiang, Y. (2025). Prolonged visual perceptual changes induced by short-term dyadic training: The roles of confidence and autistic traits in social learning. iScience, 28(2), 111716. https://doi.org/10.1016/j.isci.2024.111716
分享嘉宾： 卢炜文
单位： 北京师范大学 认知神经科学与学习国家重点实验室
Decision making under uncertainty entails selecting optimal actions from noisy evidence. Currently influential models, such as drift-diffusion and attractor frameworks, posit decisions as bottom-up stochastic evidence accumulation from sensory inputs but ignore critical interactions between decision, commitment, and metacognition. While these models explain basic choice behaviors and accompanying confidence, they fail to reconcile many empirical findings, including motor-area encoding of decision variables and time-dependent urgency signals.
We present a closed-loop neural network model unifying three modules: decision module accumulating evidence, a motor module implementing action thresholds, and a metacognition module regulating deliberation through dual feedback pathways to suppress noise-driven errors and accelerate decision commitment under time constraints, respectively. This architecture can account for crucial characteristics of decision-making and metacognition that were empirically observed. By integrating decision, motor, and metacognitive dynamics, our model provides a biologically grounded framework for optimal decision making, offering testable predictions for neural and behavioral studies.
北京时间 [GMT+8] 2025年4月8日 (周二) 20:00~21:00
会议信息： ZOOM 会议号：827 9274 9402
报告语言： 中文
主持人： 胡啸
Lu, W., & Wan, X. (2025). Closing the loop: A dynamic neural network model integrating decision making and metacognition. bioRxiv: The Preprint Server for Biology. https://doi.org/10.1101/2025.03.06.641797
An Inherent Limitation: We Cannot Perfectly Switch Perception of Information in Flexible Perceptual Decision-Making
（报告简介待补充）
北京时间 [GMT+8] 2025年1月23日 20:00~21:00
主持人： 潘晚坷
分享嘉宾： Yufei Wu (吴雨霏)
单位： 鲁汶大学 (KU Leuven)
Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models, which are statistical models representing cognitive processes. In this study, we test and improve the robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks. To this end, we conduct systematic analyses on a toy example and analyze both synthetic and real data using a popular cognitive model, the Drift Diffusion Models (DDM). First, we study the sensitivity of ABI to contaminants with tools from robust statistics: the empirical influence function and the breakdown point. Next, we propose a data augmentation or noise injection approach that incorporates contamination distribution into the data-generating process during training. We examine several candidate distributions and evaluate their performance and cost in terms of accuracy and efficiency loss relative to a standard estimator. Introducing contaminants from a Cauchy distribution during training considerably increases the robustness of the neural density estimator as measured by bounded influence functions and a much higher breakdown point.
北京时间 [GMT+8] 2025年1月16日 (周四) 20:30~21:30
会议信息： ZOOM 会议号：待确认
报告语言： 英文
主持人： 潘晚坷
Wu, Y., Radev, S., & Tuerlinckx, F. (2024). Testing and improving the robustness of amortized bayesian inference for cognitive models. arXiv. https://doi.org/10.48550/arXiv.2412.20586
Commonality and Diversity of Impairments in Inhibitory Tasks After Total Sleep Deprivation: Evidence from Drift Diffusion Model
曹艺轩
华南师范大学心理学院博士研究生，导师为马宁教授。主要研究方向：睡眠缺失对认知功能的影响。
This study investigated the effects of total sleep deprivation on two key sub-components of inhibitory control—interference inhibition and response inhibition—and examined whether sleep deprivation affects these components through shared or distinct cognitive mechanisms.
北京时间 [GMT+8] 2024年11月19日 (周三) 20:00~21:00
会议信息： ZOOM 会议号：863 0404 9478
报告语言： 中文
主持人： 刘逸康
Cao, Y., Luo, J., Li, J., Wang, L., & Ma, N. (2025, in press). Commonality and diversity of impairments in inhibitory tasks after total sleep deprivation: Evidence from drift diffusion model. SLEEP. https://doi.org/10.1093/sleep/zsaf355
分享嘉宾： Jiaqi Huang (黄家麒)
单位： Indiana University Bloomington
Humans can reason with probabilities, yet not in a perfect way. My research aims to bridge the gap between quantitative probabilistic accounts of human cognition, and empirical violations of probability theory in human judgments. The main topic of my presentation is my recent psychological review paper of the quantum sequential sampler model, a novel approach aimed at bridging this gap. I will commence by summarizing key past advancements in models of human probability judgments. Following this, I will present a conceptual overview of the quantum sequential sampler model, elucidating its qualitative and quantitative superiority over other existing models like the Bayesian sampler. To conclude, I will highlight open problems within the domain and outline the future directions of my research aimed at narrowing this gap.
北京时间 [GMT+8] 2024年10月10日 (周四) 21:30~22:30
会议信息： ZOOM 会议号：896 0645 5649
报告语言： 中文
主持人： 潘晚坷
Huang, J., Busemeyer, J., Ebelt, Z., & Pothos, E. (2024). Bridging the gap between subjective probability and probability judgments: the Quantum Sequential Sampler. Psychological Review. https://doi.org/10.1037/rev0000418