Suppr超能文献

局部语境和全局偏差对统计学习的相对重要性。

The relative importance of local contingencies and global biases for statistical learning.

机构信息

Department of Psychology, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada.

Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, ON, Canada.

出版信息

Atten Percept Psychophys. 2023 May;85(4):961-967. doi: 10.3758/s13414-023-02692-7. Epub 2023 Mar 17.

Abstract

Effective behavior requires adapting to the changing regularities evident in the world. Analogous to the global and local processing distinction for perception, these statistical regularities may be evident in global biases (i.e., some events are more likely) or local contingencies (i.e., subsequent events depend on preceding events). To explore whether mental model updating unfolds in distinct ways according to global and local statistical properties, we had healthy individuals perform two variations of an updating task in which both global and local statistical properties changed over time. Participants predicted whether the next triangle in a sequence of triangles would point up or down. The probability of pointing up or down was fixed for epochs of trials (i.e., global probability) and correlated with the colors of elements in the display. In addition, we made the triangle's apex direction on trial n+1 depend on the pointing direction of the prior trial (i.e., local probability). For both experiments, it was the local contingencies that dominated participant choices. When global and local statistical cues of equal magnitude are available, we conclude that healthy individuals are biased towards using the local statistical properties.

摘要

有效的行为需要适应世界上明显变化的规律。类似于感知的全局和局部处理区别,这些统计规律可能表现在全局偏差(即某些事件更有可能发生)或局部偶然性(即后续事件取决于先前事件)中。为了探索心理模型更新是否根据全局和局部统计特性以不同的方式展开,我们让健康个体在两个更新任务变体中进行操作,其中全局和局部统计特性随时间变化。参与者预测序列中的下一个三角形将指向向上还是向下。对于试验的时间段(即全局概率),指向向上或向下的概率是固定的,并且与显示元素的颜色相关。此外,我们使第 n+1 个试验的三角形顶点方向取决于前一个试验的指向方向(即局部概率)。对于这两个实验,都是局部偶然性主导了参与者的选择。当具有同等大小的全局和局部统计线索时,我们得出结论,健康个体偏向于使用局部统计特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2f6/10022545/dc685464857b/13414_2023_2692_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验