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Bayes Factors for Mixed Models: a Discussion 混合模型的贝叶斯因子探讨
Pub Date : 2023-02-16 DOI: 10.1007/s42113-022-00160-3
Johnny van Doorn, J. Haaf, A. Stefan, E. Wagenmakers, Gregory E. Cox, C. Davis-Stober, A. Heathcote, D. Heck, M. Kalish, David Kellen, D. Matzke, R. Morey, Bruno Nicenboim, D. van Ravenzwaaij, Jeffrey N. Rouder, D. Schad, R. Shiffrin, H. Singmann, S. Vasishth, J. Veríssimo, F. Bockting, Suyog H. Chandramouli, J. Dunn, Q. Gronau, M. Linde, Sara D McMullin, Danielle Navarro, Martin Schnuerch, Himanshu Yadav, F. Aust
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引用次数: 1
Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects 卷积神经网络训练识别单词提供了一个令人惊讶的良好的视觉形式启动效应的说明
Pub Date : 2023-02-08 DOI: 10.1007/s42113-023-00172-7
Dong Yin, Valerio Biscione, J. Bowers
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引用次数: 1
Stimulus Selection in a Q-learning Model Using Fisher Information and Monte Carlo Simulation 基于Fisher信息和蒙特卡罗模拟的q -学习模型中的刺激选择
Pub Date : 2023-01-30 DOI: 10.1007/s42113-022-00163-0
Kazuya Fujita, Kensuke Okada, K. Katahira
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引用次数: 0
Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For? 感觉运动变异性的时间结构:稳定的特质,但有什么用?
Pub Date : 2023-01-03 DOI: 10.1007/s42113-022-00162-1
Marlou Nadine Perquin, Marieke K van Vugt, Craig Hedge, Aline Bompas

Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies. Furthermore, their potential benefit for studying individual differences in healthy and clinical populations remains unclear. Here, we gather new and archival datasets featuring 11 sensorimotor and cognitive tasks across 526 participants, to examine individual differences in temporal structures. We first investigate intra-individual repeatability of the most common measures of temporal structures - to test their potential for capturing stable individual differences. Secondly, we examine inter-individual differences in these measures using: (1) task performance assessed from the same data, (2) meta-cognitive ratings of on-taskness from thought probes occasionally presented throughout the task, and (3) self-assessed attention-deficit related traits. Across all datasets, autocorrelation at lag 1 and Power Spectra Density slope showed high intra-individual repeatability across sessions and correlated with task performance. The Detrended Fluctuation Analysis slope showed the same pattern, but less reliably. The long-term component (d) of the ARFIMA(1,d,1) model showed poor repeatability and no correlation to performance. Overall, these measures failed to show external validity when correlated with either mean subjective attentional state or self-assessed traits between participants. Thus, some measures of serial dependencies may be stable individual traits, but their usefulness in capturing individual differences in other constructs typically associated with variability in performance seems limited. We conclude with comprehensive recommendations for researchers.

Supplementary information: The online version contains supplementary material available at 10.1007/s42113-022-00162-1.

人类的表现会随着时间的推移而产生巨大的内生变异,这种变异是个体差异的有力标志。心理学家越来越感兴趣的是,人们意识到变异性并非完全随机,而是经常表现出时间依赖性。然而,对它们的测量和解释却存在一些争议。此外,它们对研究健康和临床人群个体差异的潜在益处仍不明确。在此,我们收集了526名参与者的11项感觉运动和认知任务的新数据集和档案数据集,以研究时间结构的个体差异。我们首先研究了最常见的时间结构测量的个体内可重复性,以测试它们捕捉稳定个体差异的潜力。其次,我们利用以下数据研究这些测量指标的个体间差异:(1) 来自相同数据的任务表现评估;(2) 来自整个任务过程中偶尔出现的思维探究的开动性元认知评级;(3) 自我评估的注意力缺陷相关特征。在所有数据集中,滞后期 1 的自相关性和功率谱密度斜率显示出个体内部在不同阶段的高重复性,并与任务表现相关。去趋势波动分析斜率显示了相同的模式,但可靠性较低。ARFIMA(1,d,1)模型的长期分量(d)显示出较低的可重复性,并且与成绩没有相关性。总体而言,当这些测量指标与参与者的平均主观注意状态或自我评估特征相关时,均未能显示出外部有效性。因此,某些序列依赖性的测量方法可能是稳定的个体特质,但它们在捕捉通常与成绩变异相关的其他构造的个体差异方面的作用似乎有限。最后,我们向研究人员提出了全面的建议:在线版本包含补充材料,可查阅 10.1007/s42113-022-00162-1。
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引用次数: 0
Bayes Factors for Mixed Models: Perspective on Responses. 混合模型的贝叶斯因子:反应视角
Pub Date : 2023-01-01 Epub Date: 2023-02-14 DOI: 10.1007/s42113-022-00158-x
Johnny van Doorn, Frederik Aust, Julia M Haaf, Angelika M Stefan, Eric-Jan Wagenmakers

In van Doorn et al. (2021), we outlined a series of open questions concerning Bayes factors for mixed effects model comparison, with an emphasis on the impact of aggregation, the effect of measurement error, the choice of prior distributions, and the detection of interactions. Seven expert commentaries (partially) addressed these initial questions. Surprisingly perhaps, the experts disagreed (often strongly) on what is best practice-a testament to the intricacy of conducting a mixed effect model comparison. Here, we provide our perspective on these comments and highlight topics that warrant further discussion. In general, we agree with many of the commentaries that in order to take full advantage of Bayesian mixed model comparison, it is important to be aware of the specific assumptions that underlie the to-be-compared models.

在 van Doorn 等人(2021 年)的文章中,我们概述了有关混合效应模型比较的贝叶斯因子的一系列开放性问题,重点是聚合的影响、测量误差的影响、先验分布的选择以及交互作用的检测。七份专家评论(部分)涉及了这些初步问题。出人意料的是,专家们对最佳做法的意见并不一致(通常是强烈的意见不一致),这证明了进行混合效应模型比较的复杂性。在此,我们将对这些意见提出自己的看法,并强调值得进一步讨论的话题。总的来说,我们同意许多评论的观点,即要充分利用贝叶斯混合模型比较的优势,就必须了解作为待比较模型基础的具体假设。
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引用次数: 0
What Happens After a Fast Versus Slow Error, and How Does It Relate to Evidence Accumulation? 快速和慢速错误发生后会发生什么,它与证据积累有何关系?
Pub Date : 2022-11-23 DOI: 10.1007/s42113-022-00137-2
K. Damaso, Paul G. Williams, A. Heathcote
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引用次数: 0
Discovering Common Hidden Causes in Sequences of Events 发现事件序列中常见的隐藏原因
Pub Date : 2022-11-11 DOI: 10.1007/s42113-022-00156-z
Simon Valentin, Neil R. Bramley, Christopher G. Lucas
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引用次数: 2
AI-Assisted Decision-making: a Cognitive Modeling Approach to Infer Latent Reliance Strategies 人工智能辅助决策:一种推断潜在依赖策略的认知建模方法
Pub Date : 2022-10-19 DOI: 10.1007/s42113-022-00157-y
Heliodoro Tejeda, Aakriti Kumar, Padhraic Smyth, M. Steyvers
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引用次数: 8
Measure-Theoretic Musings Cannot Salvage the Full Bayesian Significance Test as a Measure of Evidence 测度论的思考不能挽救作为证据测度的贝叶斯显著性检验
Pub Date : 2022-09-28 DOI: 10.1007/s42113-022-00154-1
A. Ly, E. Wagenmakers
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引用次数: 0
Traveling Salesperson Problem with Simple Obstacles: The Role of Multidimensional Scaling and the Role of Clustering 具有简单障碍的旅行推销员问题:多维尺度的作用和聚类的作用
Pub Date : 2022-09-21 DOI: 10.1007/s42113-022-00155-0
Jacob VanDrunen, Kevin Nam, Mark Beers, Z. Pizlo
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引用次数: 1
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Computational brain & behavior
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