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知觉学习的多时程机制研究
Alternative TitleThe Multi-component Mechanism of Perceptual Learning
杨 佳
Contributor黄昌兵
2021-06
Abstract知觉学习指通过持续性训练,人们在知觉任务上的表现得到稳定、持久的提高。这种提高可以维持一年甚至更长时间,但是反复训练如何逐渐优化行为表现这一问题依然有待回答。为了回答学习效应如何发生与累积这一基本问题,本论文首先提出了一种综合考虑长时程和短时程效应的多时程知觉学习理论模型,该模型的基本假设包括:1)知觉学习不仅包括一般性的长时程学习,还涉及到训练期间遗忘(between-session forgetting)、训练期间增益(between-session gain)、训练期间内适应(between-session adaptation)和训练期内再学习(between-session relearning)等四种短时程效应;2)不同任务涉及的效应成分及各成分的幅度可能有所差异,导致了任务特异的学习模式;3)粗糙的分析粒度(如基于session的分析,通常包含几百甚至超过一千个试次)可能掩盖了部分短时效应,更精细粒度的分析,如组块(block,一般由几十个试次的训练组成),可以揭示更完整的学习过程。 研究二在多时程模型的理论框架下,分别考察睡眠、训练量、学习经验、任务难度、反馈和混合训练范式等任务设定因素调控学习效应的多时程机制,包括五个实验。实验二发现睡眠组和清醒组在游标任务上的学习没有差异,表明睡眠不能诱发游标任务的session 间增益或者减少session 间遗忘。实验三结果则表明低强度训练(5 个试次)不足以引起声音频率辨别任务的再学习效应,遗忘幅度随session间间隔时间延长而增加。实验四通过分析研究一中七种任务连续学习过程中的顺序效应,结果发现,学习经验可以通过影响一般性学习的起始值、学习速度和/或session 间效应的强度,使得后学习的较低级知觉任务的起始值变差或学习速度变慢,而对相对高级的工作记忆任务而言,虽然起始值变差,但是学习速度加快,表现出任务特异性顺序效应;实验四通过双任务顺序学习(游标和形状搜索任务)的控制实验证实,排除了实验安排的影响后,较低级的游标任务依然受学习顺序影响。实验五考察任务难度和内、外部反馈对学习时程的影响。结果发现:困难任务较一致地表现出session间遗忘,而简单任务普遍存在session内适应;内部和完整外部反馈可以降低困难任务的起始值,外部反馈还可以调节session间效应或改变一般性学习速度。实验六混合训练两种不同难度水平的(困难35%,简单70%)面孔角度辨别和Gabor 朝向辨别任务,共4 种任务/难度组合。结果发现,相比于混合相同难度的两个任务,混合不同难度进行训练会导致一般性学习的起始值变差,或者使得困难朝向任务的学习出现session 内适应现象,导致每个session 的表现变差,但学习速度不受影响。 本研究构建了较为完备的知觉学习多时程机制的理论框架,结合行为实验论证了该模型的合理性,进一步揭示不同实验条件影响知觉学习效应的多时程机制。该模型为理解知觉学习效应的发生和累积这一知觉学习领域的基本问题,综合比较不同任务设定下的学习特性差异和机制提供了新的视角和分析框架,对建立知觉学习的优化应用途径也有指导意义。
Other AbstractPerceptual learning refers to steady and stable performance improvement or sensitivity enhancement as a result of repetitive training. Perceptual learning is usually long-lasting but how repetitive practice leads to long-term improvement remains largely unknown. To answer how learning cumulates as training proceeds, one of the fundamental questions in perceptual learning, the current thesis proposed a multi-component framework that covered three hypotheses: (1) improved perceptual task performance through perceptual learning may reflect cumulative effects of both long- (e.g. general learning) and short- term processes (e.g.between-session gain, between-session forgetting, within-session relearning and within-session adaptation), (2) learning different tasks may engage different longand short-term processes, and (3) coarse-grain analysis may have obscured some short-term processes while fine-grained analysis of the learning curve at the block level (e.g. tens of trials) may reveal some important long- and short-term processes. We conducted two series of studies to examined our model and apply the model to investigate learning mechanisms under different experimental settings. In Study 1, we validated the potential of understanding a set of learning curves with the proposed multi-component model. We trained 49 subjects to learn 7 tasks sequentially in 35 days (sessions) and fitted learning curves in both coarse (e.g.session) and fine (e.g. block) grains. The session-wise analysis revealed a significant learning effect across sessions. Insterestingly, block-wise analysis not only confirmed block-by-block general learning effects but also indicated within-session relearning for all the seven tasks. In addition, between-session forgetting was identified in learning the Vernier offset discrimination, face view discrimination, and auditory frequency discrimination tasks, between-session off-line gain in the visual shape search and contrast detection tasks, and within-session adaptation in the contrast detection task. These results provided compelling evidence for our hypotheses and the rationality of our multi-component model of perceptual learning. In Study 2, we conducted 5 experiments and applied the multi-component model to investigate how training settings, including between-session sleep, the amount of training, experience, task difficulty, external feedback, and mixed training paradigm, modulated long- and/or short-term processes. In Experiment 2, we found no difference in the magnitude of general learning, between-session forgetting, and within-session relearning between sleep and wake groups, indicating of little influence of sleep on the between-session effect in Vernier task. Experiment 3 showed that, compared to conventional daily training group, 5-trial relearning group showed greater between-session forgetting, greater within-session relearning amplitude, and slower relearning rate in auditory frequency discrimination task, indicating that relearning process also required a critical amount of training and the amplitude of forgetting increased as between-session delay prolongs. In Experiment 4, we compared learning curves in each task between subjects who learned earlier (1st, 2nd, 3rd) and those who learned later (5th, 6th, 7th) from Study 1. The results showed that training experience affected initial performance and learning rate of general learning process and the magnitude of between-session effects. For subjects who were trained later, they exhibited worse initial performances in contrast detection, Vernier offset discrimination, motion direction discrimination, auditory frequency discrimination tasks, and slower learning rate in Vernier and face view discrimination tasks; worse initial performance but faster learning rate in N-back task. The findings were confirmed by an additional control experiment that involved only shape and Vernier tasks. Our results indicated that training experience mainly imposed a negative influence on primary perceptual tasks (e.g. contrast detection, Vernier, motion direction, and auditory frequency discrimination tasks) while high-level tasks were more immune to training sequence (e.g. shape search task). Experiment 5 investigated the role of task difficulty, internal and external feedback in perceptual learning in a 4-Alternative Forced-Choice (4-AFC) Gabor orientation discrimination task. We found that difficult tasks mainly showed between-session forgetting while easy tasks were more subject to within-session adaptation. Adding internal feedback induced by easy task (i.e., 70% correct) or complete external feedback improved initial performance while providing external feedback speeded up general learning rate, introduced relearning process and modulated between-session effects. In Experiment 6, we co-trained two different tasks, e.g. face view discrimination and Gabor orientation discrimination, with two different performance levels (e.g. 35% and 70%) in four groups. Compared with mixed training of two tasks with same accuracies (e.g., 35% face + 35% orientation), mixed training with different accuracies (35% face + 70% orientation) led to worse initial performance or introduced within-session adaptation, but spared learning rate. The current study proposed a comprehensive framework for the multi-component mechanism of perceptual learning. Applying the model to analyze a series of behavioral data demonstrated the rationality of our three hypotheses and identified learning mechanisms underlying different experimental settings. Our model provides a new perspective to understand the fundamental question of how behavioral benefits accumulated as training proceeds and an analytical tool to accommodate possible memory effects involved in learning across a variety of tasks and experimental conditions. In addition, the current research provides strong implications for a flexible and hybrid learning mechanisms and shows the great potential of utilizing the multi-component framework to optimize learning outcomes in real-world applications.
Keyword视知觉学习 学习时程 可塑性 遗忘 再学习
Subtype博士
Language中文
Degree Name理学博士
Degree Discipline基础心理学
Degree Grantor中国科学院心理研究所
Place of Conferral中国科学院心理研究所
Document Type学位论文
Identifierhttp://ir.psych.ac.cn/handle/311026/39546
Collection认知与发展心理学研究室
Recommended Citation
GB/T 7714
杨 佳. 知觉学习的多时程机制研究[D]. 中国科学院心理研究所. 中国科学院心理研究所,2021.
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