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本文引用的文献

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Stan: A Probabilistic Programming Language.斯坦:一种概率编程语言。
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2
Reward associations do not explain transitive inference performance in monkeys.奖励关联不能解释猴子的传递性推理表现。
Sci Adv. 2019 Jul 31;5(7):eaaw2089. doi: 10.1126/sciadv.aaw2089. eCollection 2019 Jul.
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Discovering Implied Serial Order Through Model-Free and Model-Based Learning.通过无模型学习和基于模型的学习发现隐含的序列顺序。
Front Neurosci. 2019 Aug 20;13:878. doi: 10.3389/fnins.2019.00878. eCollection 2019.
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Inferential Learning of Serial Order of Perceptual Categories by Rhesus Monkeys ().恒河猴对感知类别序列顺序的推理学习()
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Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.内隐价值更新解释传递性推理表现:贝塔排序模型。
PLoS Comput Biol. 2015 Sep 25;11(9):e1004523. doi: 10.1371/journal.pcbi.1004523. eCollection 2015.
6
Links Between Single-Trial Changes and Learning Rate in Eyelid Conditioning.眼睑条件反射中单次试验变化与学习率之间的联系
Cerebellum. 2016 Apr;15(2):112-21. doi: 10.1007/s12311-015-0690-8.
7
Transitive inference in two lemur species (Eulemur macaco and Eulemur fulvus).两种狐猴(黑美狐猴和褐美狐猴)的传递性推理
Am J Primatol. 2015 Mar;77(3):338-45. doi: 10.1002/ajp.22349. Epub 2014 Oct 18.
8
Pigeon transitive inference: Tests of simple accounts of a complex performance.鸽子传递性推理:对复杂行为简单解释的测试。
Behav Processes. 1997 Jan;39(1):95-112. doi: 10.1016/s0376-6357(96)00048-4.
9
Time to rethink the neural mechanisms of learning and memory.是时候重新思考学习和记忆的神经机制了。
Neurobiol Learn Mem. 2014 Feb;108:136-44. doi: 10.1016/j.nlm.2013.11.019. Epub 2013 Dec 3.
10
Transfer of a serial representation between two distinct tasks by rhesus macaques.恒河猴在两个不同任务之间的串行表示的转移。
PLoS One. 2013 Jul 31;8(7):e70285. doi: 10.1371/journal.pone.0070285. Print 2013.

恒河猴(Macaca mulatta)经过最少训练后的传递推理。

Transitive inference after minimal training in rhesus macaques (Macaca mulatta).

机构信息

Zuckerman Mind Brain Behavior Institute.

Department of Psychology.

出版信息

J Exp Psychol Anim Learn Cogn. 2021 Oct;47(4):464-475. doi: 10.1037/xan0000298.

DOI:10.1037/xan0000298
PMID:34855434
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8647760/
Abstract

Rhesus macaques, when trained for several hundred trials on adjacent items in an ordered list (e.g., A > B, B > C, C > D), are able to make accurate transitive inferences (TI) about previously untrained pairs (e.g., A > C, B > D). How that learning unfolds during training, however, is not well understood. We sought to measure the relationship between the amount of TI training and the resulting response accuracy in 4 rhesus macaques using seven-item lists. The training conditions included the absolute minimal case of presenting each of the six adjacent pairs only once prior to testing. We also tested transfer to nonadjacent pairs with 24 and 114 training trials. Because performance during and after small amounts of training is expected to be near chance levels, we developed a descriptive statistical model to estimate potentially subtle learning effects in the presence of much larger random response variability and systematic bias. These results suggest that subjects learned serial order in an incremental fashion. Thus, rather than performing transitive inference by a logical process, serial learning in rhesus macaques proceeds in a manner more akin to a statistical inference, with an initial uncertainty about list position that gradually becomes more accurate as evidence accumulates. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

摘要

恒河猴在经过数百次针对有序列表中相邻项目的训练后(例如,A>B,B>C,C>D),能够对之前未经训练的项目进行准确的传递性推理(TI)(例如,A>C,B>D)。然而,这种学习是如何展开的还不太清楚。我们使用七项列表,试图在四只恒河猴身上测量 TI 训练量与由此产生的反应准确率之间的关系。训练条件包括在测试前仅对每个相邻的六个项目各呈现一次的绝对最小案例。我们还测试了有 24 次和 114 次训练的非相邻对的转移。因为在训练量较少的情况下的表现预计接近随机水平,所以我们开发了一个描述性统计模型来估计在存在更大的随机反应变异性和系统偏差的情况下可能存在的微妙学习效果。这些结果表明,被试以增量的方式学习了序列顺序。因此,恒河猴的序列学习不是通过逻辑过程进行传递性推理,而是更类似于统计推理,最初对列表位置的不确定性随着证据的积累而逐渐变得更加准确。