反馈对知觉类别学习的影响及其认知神经生理机制

知觉类别学习是一种人类对知觉刺激进行分类、习得类别的过程, 反馈是进行知觉类别学习不可或缺的重要部分。研究者通过操纵反馈的不同特征, 如反馈延迟(即时、延迟)、反馈性质(积极、消极)、反馈类型(丰富、简单)等, 对反馈如何影响知觉类别学习进行了广泛的研究, 并试图从神经生理学方面给出合理的解释。但是目前反馈对知觉类别学习影响的研究还存在许多不足, 特别在反馈延迟时间的细化、延迟反馈时间临界点、无关因素掩蔽、反馈试次等影响方面的研究较少涉及, 现有的反馈机制尚需进一步研究。     

学习方式和抑制能力对概率类别学习的影响

采用经典的色词Stroop任务和天气预报任务范式,探讨反馈学习和观察学习两种方式以及不同抑制能力对概率类别学习的影响。实验采用2(学习方式)x2(抑制能力)被试间设计,随机选取204名大学生被试。结果显示:反馈学习的成绩显著高于观察学习;高抑制能力者的成绩显著高于低抑制能力者;反馈学习的成绩与意识知识出现分离。这表明,学习方式影响概率类别学习,反馈学习的效果优于观察学习;抑制能力影响概率类别学习,高抑制能力者的学习效果更佳;概率类别学习中同时存在内隐和外显两种学习系统。     

反馈延迟与掩蔽类型对知觉类别学习的影响

通过两个实验探讨反馈延迟与掩蔽类型对知觉类别学习的影响。实验1光栅掩蔽条件下,采用2（类别结构：基于规则vs.信息整合）×2（延迟反馈：500vs.3000ms）被试间实验设计,实验2采用同样的实验设计,保持延迟反馈时间不变,但改掩蔽类型为黑屏掩蔽。结果发现：（1）光栅掩蔽条件下,反馈延迟削弱信息整合类别结构的成绩,不影响基于规则类别学习,内隐外显类别结构之间存在分离效应。（2）黑屏掩蔽条件下,信息整合与基于规则类别结构的成绩均不受到延迟反馈的影响,分离效应消失。实验说明“无关因素”掩蔽影响知觉类别学习,知觉噪音与标准噪音在反馈延迟影响知觉类别学习中起到重要作用,COVIS模型理论关于延迟反馈时间影响知觉类别学习的解释具有局限性。     

非监控类别学习的分类策略与表征

类别学习是人类对不同类别加以归类的过程。类别信息的表征、分类策略运用的特点一直是类别学习研究的重点。非监控类别学习可分为直接的非监控类别学习和间接的非监控类别学习。直接的非监控类别学习(非限制任务, 限制任务)中被试的分类策略具有分类“单维度倾向”策略特点,类别变异程度会影响类别表征; 间接的非监控类别学习更倾向形成相似性表征, 直接的非监控类别学习则为基于规则表征。现有的非监控类别学习的理论对分类策略和表征的解释仍显薄弱, 不同学习任务下类别迁移和知识效应的研究还存在不足, 未来研究还需要进一步验证知识效应对非监控类别学习的认知加工过程的影响、探索影响类别表征形成的因素等问题。     

类别标签与典型性程度对类别特征推理的影响

本研究采用学习-测试二阶段实验范式,探讨了类别标签及典型性程度对类别特征推理任务的共同影响,结果表明,类别标签及典型性程度都会影响类别特征推理任务,而且,类别标签及典型性程度之间存在交互作用,典型性程度为高条件下类别标签对类别特征推理任务的影响要高于典型性程度为低条件,类别标签匹配条件下,典型性程度对类别特征推理任务的影响要高于类别标签不匹配条件。     

反馈延迟对信息整合类别学习的影响：视觉项目精准性的调节作用

研究旨在探究更精确的视觉项目表征能否消除延迟反馈对信息整合学习的损害。实验采用2（视觉项目表征精确性：精确/非精确）×2（反馈延迟：即时反馈/延迟反馈）组间设计,结果发现无论有无延迟反馈,精确的项目表征条件下类别学习成绩都显著高于非精确条件,精确性与反馈延迟存在交互作用。采用状态痕迹分析进一步证明精确性显著影响信息整合类别学习的稳定性。在反馈延迟条件下,精确的视觉项目表征能提高信息整合类别学习的成绩。     

线索呈现位置对概率类别学习的影响

当前概率类别学习中主要存在多系统和单系统两种观点之争, 而持不同观点的研究者在其实验中分别采用了不同的线索位置呈现方式, 因此本研究采用经典的天气预测任务通过操纵线索位置的呈现方式来进一步探讨其对概率类别学习的影响。本研究包括2个实验：实验1考察所有线索位置固定和随机对概率类别学习系统的影响; 实验2通过单线索模式下线索位置固定考察概率类别学习的策略。结果发现, 当所有线索的呈现位置固定时, 概率类别学习是外显学习; 而当所有线索的呈现位置随机时, 概率类别学习是内隐学习; 而当只在线索单独出现时固定其位置, 概率类别学习仍是内隐学习。结果表明, 线索位置的不同呈现方式会影响概率类别学习中外显和内隐学习系统的竞争, 研究支持了多系统观点, 且概率类别学习的主要策略可能是多线索策略而不是单模式策略。     

学习程度和关系复杂性对个人关系类别间接性学习的影响

采用个人关系类别间接性学习研究范式,设计关系复杂性不断增加的三种关系类别学习材料,通过功能预测任务和维度选择任务,探查了学习程度和关系复杂性对个人关系类别间接性学习的影响。结果发现:两种任务中关系复杂性影响效应不显著;维度选择任务中成绩组影响效应显著,且在选择性注意整体水平、指向性水平和集中性水平上表现不同。     

概念和类别研究中基于解释的观点

该文介绍了概念和类别研究中基于解释的观点。和原型观、样例观不同,基于解释的观点强调了三个方面的影响因素,一是知识背景的影响;二是不同的学习任务的影响;三是类别知识的运用的再影响。该文从时问的先后描述了解释观的主要观点及其典型的实验研究。     

样例呈现方式对概念训练类别表征的影响

以往研究发现训练形式会影响类别学习的表征方式。实验采用学习-迁移范式,探究概念训练中样例的呈现方式对类别表征的影响。实验中被试通过不同呈现方式来学习类别知识,在学习3个block之后对其进行测验。实验结果表明:(1)学习单样例和同一类别比较学习的被试在测验阶段的成绩与学习阶段之间没有差异;(2)学习不同类别比较学习的被试在测验阶段的成绩大幅度下降。因此得出结论,在概念训练中,不同类别比较学习导致被试形成类别间信息的表征。     

Feedback interference and dissociations of classification: Evidence against the multiple-learning-systems hypothesis

Researchers have argued that different categorization problems are learned by separate and distinct cognitive systems. They propose that an explicit system is responsible for learning rule-based categories and that a separate implicit system learns information-integration categories. One source of supporting evidence involves experiments in which observers perform a concurrent memory-scanning task that interferes with the processing of feedback. Researchers have reported a dissociation in which this manipulation impairs learning of a rule-based category but not an information-integration category. In the present research, we test the hypothesis that the dissociation was the result of lowered perceptual discriminability in the rule-based structure in comparison with the information-integration one. We demonstrate an example of an alternate rule-based category with easy-to-discriminate stimuli in which performance is unaffected by the interfering memory-scanning task. Furthermore, we demonstrate that learning of an information-integration category with low perceptual discriminability is impaired by the memory-scanning task. These demonstrations of the reverse dissociation challenge the interpretation that rule-based and information-integration category structures are learned by separate cognitive systems.     

Task and distribution sampling affect auditory category learning

There is substantial evidence that two distinct learning systems are engaged in category learning. One is principally engaged when learning requires selective attention to a single dimension (rule-based), and the other is drawn online by categories requiring integration across two or more dimensions (information-integration). This distinction has largely been drawn from studies of visual categories learned via overt category decisions and explicit feedback. Recent research has extended this model to auditory categories, the nature of which introduces new questions for research. With the present experiment, we addressed the influences of incidental versus overt training and category distribution sampling on learning information-integration and rule-based auditory categories. The results demonstrate that the training task influences category learning, with overt feedback generally outperforming incidental feedback. Additionally, distribution sampling (probabilistic or deterministic) and category type (information-integration or rule-based) both affect how well participants are able to learn. Specifically, rule-based categories are learned equivalently, regardless of distribution sampling, whereas information-integration categories are learned better with deterministic than with probabilistic sampling. The interactions of distribution sampling, category type, and kind of feedback impacted category-learning performance, but these interactions have not yet been integrated into existing category-learning models. These results suggest new dimensions for understanding category learning, inspired by the real-world properties of auditory categories.     

Procedural memory effects in categorization: Evidence for multiple systems or task complexity?

According to an influential multiple-systems model of category learning, an implicit procedural system governs the learning of information-integration category structures, whereas a rule-based system governs the learning of explicit rule-based categories. Support for this idea has come in part from demonstrations that motor interference, in the form of inconsistent mapping between response location and category labels, results in observed deficits, but only for learning information-integration category structures. In this article, we argue that this response location manipulation results in a potentially more cognitively complex task in which the feedback is difficult to interpret. In one experiment, we attempted to attenuate the cognitive complexity by providing more information in the feedback, and demonstrated that this eliminates the observed performance deficit for information-integration category structures. In a second experiment, we demonstrated similar interference of the inconsistent mapping manipulation in a rule-based category structure. We claim that task complexity, and not separate systems, might be the source of the original dissociation between performance on rule-based and information-integration tasks.     

The effects of category overlap on information-integration and rule-based category learning

In three experiments, we investigated whether the amount of category overlap constrains the decision strategies used in category learning, and whether such constraints depend on the type of category structures used. Experiments 1 and 2 used a category-learning task requiring perceptual integration of information from multiple dimensions (an information-integration task) and Experiment 3 used a task requiring the application of an explicit strategy (a rule-based task). In the information-integration task, participants used perceptual-integration strategies at moderate levels of category overlap, but explicit strategies at extreme levels of overlap--even when such strategies were suboptimal. In contrast, in the rule-based task, participants used explicit strategies, regardless of the level of category overlap. These data are consistent with a multiple systems view of category learning, and suggest that categorization strategy depends on the type of task that is used, and on the degree to which each stimulus is probabilistically associated with the contrasting categories.     

嵌套范式下视空工作记忆对基于规则类别学习

基于COVIS模型与认知加工阶段假设,通过2个实验探讨嵌套范式下, 视空工作记忆对基于规则类别学习的影响。实验1采用类别学习中嵌套视空工作记忆的范式,结果发现视空工作记忆削弱基于规则类别学习成绩,与COVIS模型预测相一致。实验2则采用视空工作记忆中嵌套类别学习任务的范式,结果却发现视空工作记忆对基于规则类别学习的影响消失。实验结果表明嵌套范式下视空工作记忆的位置影响基于规则类别学习,初步验证了类别学习存在多个认知加工阶段的假设,视空工作记忆主要影响基于规则类别学习中规则的发现和检验阶段。     

Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning

W. T. Maddox, F. G. Ashby, and C. J. Bohil (2003) found that delayed feedback adversely affects information-integration but not rule-based category learning in support of a multiple-systems approach to category learning. However, differences in the number of stimulus dimensions relevant to solving the task and perceptual similarity failed to rule out 2 single-system interpretations. The authors conducted an experiment that remedied these problems and replicated W. T. Maddox et al.'s findings. The experiment revealed a strong performance decrement for information-integration but not rule-based category learning under delayed feedback that was due to an increase in the number of observers using hypothesis-testing strategies to solve the information-integration task, and lower accuracy rates for the few observers using information-integration strategies.     

When more is less: feedback effects in perceptual category learning

Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rule-based learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed.     

Posterror slowing predicts rule-based but not information-integration category learning

We examined whether error monitoring, operationalized as the degree to which individuals slow down after committing an error (i.e., posterror slowing), is differentially important in the learning of rule-based versus information-integration category structures. Rule-based categories are most efficiently solved through the application of an explicit verbal strategy (e.g., “sort by color”). In contrast, information-integration categories are believed to be learned in a trial-by-trial, associative manner. Our results indicated that posterror slowing predicts enhanced rule-based but not information-integration category learning. Implications for multiple category-learning systems are discussed.