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Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?亚马逊土耳其机器人:一种新的廉价、高质量数据来源?
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Disentangling decision models: from independence to competition.解析决策模型:从独立到竞争。
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Neural mechanisms underlying paradoxical performance for monetary incentives are driven by loss aversion.金钱激励反常表现的神经机制是由损失规避驱动的。
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Unfolding visual lexical decision in time.时间展开的视觉词汇判断。
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Reinforcement-based decision making in corticostriatal circuits: mutual constraints by neurocomputational and diffusion models.基于强化的皮质纹状体回路决策:神经计算和扩散模型的相互约束。
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Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain.神经预测误差揭示了人类大脑中风险敏感的强化学习过程。
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Transformation of stimulus value signals into motor commands during simple choice.简单选择过程中刺激值信号向运动指令的转换。
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Changes of mind in an attractor network of decision-making.决策吸引子网络中的思维变化。
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决策中的局部动态:决策内和决策间偏好的演变。

Local dynamics in decision making: The evolution of preference within and across decisions.

机构信息

School of Psychology, National University of Ireland, Galway, University Road, Galway, Ireland.

出版信息

Sci Rep. 2013;3:2210. doi: 10.1038/srep02210.

DOI:10.1038/srep02210
PMID:23860466
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3713532/
Abstract

Within decisions, perceived alternatives compete until one is preferred. Across decisions, the playing field on which these alternatives compete evolves to favor certain alternatives. Mouse cursor trajectories provide rich continuous information related to such cognitive processes during decision making. In three experiments, participants learned to choose symbols to earn points in a discrimination learning paradigm and the cursor trajectories of their responses were recorded. Decisions between two choices that earned equally high-point rewards exhibited far less competition than decisions between choices that earned equally low-point rewards. Using positional coordinates in the trajectories, it was possible to infer a potential field in which the choice locations occupied areas of minimal potential. These decision spaces evolved through the experiments, as participants learned which options to choose. This visualisation approach provides a potential framework for the analysis of local dynamics in decision-making that could help mitigate both theoretical disputes and disparate empirical results.

摘要

在决策中,感知到的备选方案相互竞争,直到其中一个被选中。在不同的决策中,这些备选方案竞争的舞台不断演变,从而有利于某些备选方案。鼠标光标轨迹提供了与决策过程中此类认知过程相关的丰富连续信息。在三个实验中,参与者学习在判别学习范式中选择符号以赚取积分,并且记录了他们的响应的光标轨迹。在获得相同高分数奖励的两个选择之间进行决策时,竞争程度远低于在获得相同低分数奖励的选择之间进行决策时的竞争程度。使用轨迹中的位置坐标,可以推断出一个潜在的力场,其中选择位置占据最小势能的区域。随着参与者学习选择哪些选项,这些决策空间在实验中不断发展。这种可视化方法为分析决策中的局部动态提供了一个潜在的框架,这可能有助于缓解理论争议和不同的经验结果。