Suppr超能文献

知觉决策中的内部和外部变异性源。

Internal and external sources of variability in perceptual decision-making.

机构信息

Department of Psychology, The Ohio State University.

出版信息

Psychol Rev. 2018 Jan;125(1):33-46. doi: 10.1037/rev0000080. Epub 2017 Oct 16.

Abstract

It is important to identify sources of variability in processing to understand decision-making in perception and cognition. There is a distinction between internal and external variability in processing, and double-pass experiments have been used to estimate their relative contributions. In these and our experiments, exact perceptual stimuli are repeated later in testing, and agreement on the 2 trials is examined to see if it is greater than chance. In recent research in modeling decision processes, some models implement only (internal) variability in the decision process whereas others explicitly represent multiple sources of variability. We describe 5 perceptual double-pass experiments that show greater than chance agreement, which is inconsistent with models that assume internal variability alone. Estimates of total trial-to-trial variability in the evidence accumulation (drift) rate (the decision-relevant stimulus information) were estimated from fits of the standard diffusion decision-making model to the data. The double-pass procedure provided estimates of how much of this total variability was systematic and dependent on the stimulus. These results provide the first behavioral evidence independent of model fits for trial-to-trial variability in drift rate in tasks used in examining perceptual decision-making. (PsycINFO Database Record

摘要

识别处理过程中的变异性来源对于理解感知和认知中的决策非常重要。处理过程中的变异性有内部变异性和外部变异性之分,双通实验被用来估计它们的相对贡献。在这些和我们的实验中,精确的感知刺激在测试中稍后重复,然后检查两次试验的一致性是否大于偶然。在最近关于决策过程建模的研究中,一些模型仅在决策过程中实现(内部)变异性,而另一些模型则明确表示存在多种变异性来源。我们描述了 5 个感知双通实验,这些实验显示出大于偶然的一致性,这与仅假设内部变异性的模型不一致。从标准扩散决策模型对数据的拟合中,估计了证据积累(漂移)率(与决策相关的刺激信息)在总试验间变异性中的估计值。双通程序提供了关于这种总变异性中有多少是系统的和依赖于刺激的估计。这些结果提供了第一个行为证据,独立于模型拟合,证明了在用于检验感知决策的任务中漂移率的试验间变异性。

相似文献

1
Internal and external sources of variability in perceptual decision-making.
Psychol Rev. 2018 Jan;125(1):33-46. doi: 10.1037/rev0000080. Epub 2017 Oct 16.
2
A Note on Decomposition of Sources of Variability in Perceptual Decision-making.
J Math Psychol. 2020 Sep;98. doi: 10.1016/j.jmp.2020.102431. Epub 2020 Aug 10.
4
Causal role of dorsolateral prefrontal cortex in human perceptual decision making.
Curr Biol. 2011 Jun 7;21(11):980-3. doi: 10.1016/j.cub.2011.04.034. Epub 2011 May 27.
5
Internal signal correlates neural populations and biases perceptual decision reports.
Proc Natl Acad Sci U S A. 2012 Nov 13;109(46):18938-43. doi: 10.1073/pnas.1216799109. Epub 2012 Oct 29.
9
Perceptual and categorical decision making: goal-relevant representation of two domains at different levels of abstraction.
J Neurophysiol. 2017 Jun 1;117(6):2088-2103. doi: 10.1152/jn.00512.2016. Epub 2017 Mar 1.
10
A neurocognitive model of perceptual decision-making on emotional signals.
Hum Brain Mapp. 2020 Apr 15;41(6):1532-1556. doi: 10.1002/hbm.24893. Epub 2019 Dec 23.

引用本文的文献

1
Behavior engineering using quantitative reinforcement learning models.
Nat Commun. 2025 May 2;16(1):4109. doi: 10.1038/s41467-025-58888-y.
2
Disentangling sources of variability in decision-making.
Nat Rev Neurosci. 2025 May;26(5):247-262. doi: 10.1038/s41583-025-00916-3. Epub 2025 Mar 20.
3
Noise in Cognition: Bug or Feature?
Perspect Psychol Sci. 2025 May;20(3):572-589. doi: 10.1177/17456916241258951. Epub 2025 Mar 4.
4
Featural Representation and Internal Noise Underlie the Eccentricity Effect in Contrast Sensitivity.
J Neurosci. 2024 Jan 17;44(3):e0743232023. doi: 10.1523/JNEUROSCI.0743-23.2023.
5
A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data.
Psychometrika. 2023 Sep;88(3):940-974. doi: 10.1007/s11336-023-09902-z. Epub 2023 May 12.
6
Prior experience modifies acquisition trajectories via response-strategy sampling.
Anim Cogn. 2023 Jul;26(4):1217-1239. doi: 10.1007/s10071-023-01769-y. Epub 2023 Apr 10.
7
Stimulus-response congruency effects depend on quality of perceptual evidence: A diffusion model account.
Atten Percept Psychophys. 2023 May;85(4):1335-1354. doi: 10.3758/s13414-022-02642-9. Epub 2023 Feb 1.
9
Integrated diffusion models for distance effects in number memory.
Cogn Psychol. 2022 Nov;138:101516. doi: 10.1016/j.cogpsych.2022.101516. Epub 2022 Sep 14.
10
An initial 'snapshot' of sensory information biases the likelihood and speed of subsequent changes of mind.
PLoS Comput Biol. 2022 Jan 13;18(1):e1009738. doi: 10.1371/journal.pcbi.1009738. eCollection 2022 Jan.

本文引用的文献

1
Comparing fixed and collapsing boundary versions of the diffusion model.
J Math Psychol. 2016 Aug;73:59-79. doi: 10.1016/j.jmp.2016.04.008. Epub 2016 May 24.
2
The drift diffusion model as the choice rule in reinforcement learning.
Psychon Bull Rev. 2017 Aug;24(4):1234-1251. doi: 10.3758/s13423-016-1199-y.
3
A single trial analysis of EEG in recognition memory: Tracking the neural correlates of memory strength.
Neuropsychologia. 2016 Dec;93(Pt A):128-141. doi: 10.1016/j.neuropsychologia.2016.09.026. Epub 2016 Sep 29.
4
Diffusion Decision Model: Current Issues and History.
Trends Cogn Sci. 2016 Apr;20(4):260-281. doi: 10.1016/j.tics.2016.01.007. Epub 2016 Mar 5.
7
Separating decision and encoding noise in signal detection tasks.
Psychol Rev. 2015 Jul;122(3):429-60. doi: 10.1037/a0039348.
8
Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making.
J Neurosci. 2015 Feb 11;35(6):2476-84. doi: 10.1523/JNEUROSCI.2410-14.2015.
9
A neural implementation of Wald's sequential probability ratio test.
Neuron. 2015 Feb 18;85(4):861-73. doi: 10.1016/j.neuron.2015.01.007. Epub 2015 Feb 5.
10
Modeling individual differences in response time and accuracy in numeracy.
Cognition. 2015 Apr;137:115-136. doi: 10.1016/j.cognition.2014.12.004. Epub 2015 Jan 29.

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