维度的结合与分离对归类不确定性预测的影响

探讨在归类不确定的情境下目标与预测特征两个维度的结合或分离对被试特征预测的影响。共包括 3个实验 :实验 1在Murphy和Ross的研究的基础上进一步提高非靶类型中目标及预测特征的基本概率 ,考察被试的特征预测是否会受非靶类型信息的影响。实验 2探讨非靶类型的目标与预测特征结合与否是否会影响被试预测特征时对非靶类型信息的使用。实验 3探讨提高靶类型中目标与预测特征结合的比例是否影响被试对特征的预测。结果表明 :当非靶类型中目标与关键特征处于分离的状态时 ,被试在进行特征预测时没有利用非靶类型的信息 ,符合单类说的假设 ;而当非靶类型中目标与关键特征结合时 ,被试在进行特征预测时则会利用非靶类型的信息 ,符合Bayesian规则 ;靶类型中的目标与关键特征结合的比例提高 ,被试对特征预测的概率也随之提高。据此 ,本研究将目标与预测特征结合比例这个变量加入Bayesian规则的计算公式 ,对该预测模型进行了修正     

不同维度特征的共存对归类不确定性特征推理的影响

探讨在归类不确定的情境下,目标特征和预测特征的共存对特征推理的影响。共包括了三个实验,其中实验1、2考察了非靶类别中目标特征和预测特征的共存性对特征推理的影响,实验3考察了靶类别中目标特征和预测特征的共存性对特征推理的影响。三个实验五个分实验的结果一致支持了修正后的Bayesian规则,排除了“单类说”、“综合条件概率模型”的假设,并进一步修正了Bayesian规则为：     

归类不确定情景下特征推理的综合条件概率模型

用大学生被试,通过三个实验探讨在集中呈现类别成员样本信息的归类不确定情景下的特征推理。实验结果表明,单纯的归类确定性程度和靶类别靶特征的代表性并不直接影响被试的特征推理,而是预测特征相对于目标特征的综合条件概率直接影响被试的特征推理;特征推理不是基于类别中介的间接推理,而是基于特征关联综合条件概率的直接推理。实验结果支持作者提出的预测特征综合条件概率模型。     

类别特征相似性与竞争性的线性变化对特征推理的影响

探讨在双类别情境中,新项目与类别成员的关系对特征推理的影响.共包括两个实验,被试是62名大一年级学生.实验1与实验2分别探讨在有无类别标签的情况下,诊断特征数量的增减对新项目预测特征推理的影响,即探讨类别特征相似性与竞争性的线性变化对特征推理的影响.结果表明:无论有无类别标签,诊断特征数量的增减对新项目预测特征的推理影响是相同的,即相似性与竞争性的线性变化对特征推理的影响是相同的.     

类别使用的特征诊断效应

运用预备实验评定过的鱼轮廓图,考察了类别判断和特征预测这两种类别使用方式下的特征诊断效应。被试是48名大学生,学习材料是两类鱼的类别成员,在测验阶段,实验1判断新项目类别标签,实验2预测新项目的缺失特征。结果表明,类别判断条件下,单个特征诊断力加强和诊断性特征数量增加都对类别判断有促进作用;特征预测条件下,只有单个特征诊断力加强可以促进特征预测,诊断性特征数量增加无助于特征预测     

类别成员跨维度特征关系对类别学习的影响

探讨类别成员具有多维度相同特征与多维度匹配特征条件下对人们类别学习的影响。被试来自华南师范大学一年级本科生,要求他们学习不同特征结构的类别材料,然后进行测试,以学习阶段的单元数与错误率、测试阶段的反应时与错误率为指标。共包括3个实验,实验1探讨了类别成员具有双维度相同特征、双维度匹配特征与非匹配特征条件下进行类别学习的成绩。实验2探讨类别成员在空间位置分离的两个维度上具有相同特征对类别学习的影响。实验3进一步探讨类别间成员具有双维度相同特征对类别学习的影响。结果表明：（1）类别成员具有双维度相同特征时,人们在归类学习中会将这两个维度看成是一个维度,从而促进类别学习;（2）类别成员具有双维度匹配特征,或者在两个不相邻维度具有相同特征时,对于类别学习没有起到这种特定的促进作用;（3）如果类别间成员具有双维度相同特征,则对类别学习产生消极影响。研究结果表明,在类别学习过程中,特征相似性状况对维度的确立也会产生影响。     

多类别情境中类别特征的相似性与竞争性对特征推理的影响

探讨在多类别情境中新项目与类别成员的关系对特征推理的影响。共包括两个实验,被试是212名大一学生。实验1与实验2分别探讨类别特征的竞争性与相似性对特征推理的影响。结果表明:无论当非靶类别中目标与关键特征维度是否结合及是否预先归类时,类别特征的相似性与竞争性影响特征推理,验证了类别学习的特征概率模型。     

预测特征竞争范式下的特征推理方式

当面临归类不确定的情景时,人们的特征推理是基于类别进行,还是基于特征联结进行,一直是类别研究中的重要内容。本研究采用预测特征竞争范式对这一问题进行了探究。共两个实验：实验1采用预测特征竞争范式探讨推理前预先归类时,人们的推理方式是基于类别还是基于特征联结。实验2采用预测特征竞争范式探讨推理前不预先归类时,人们的推理是基于类别还是基于特征联结。实验结果表明,推理前预先归类时,被试倾向于基于类别进行推理。推理前不预先归类时,被试倾向于根据特征联结进行推理。     

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。     

不同学习方式下归类不确定时的特征推理

采用学习-迁移模式, 探讨了同时学习和继时学习两种方式下归类不确定时的特征推理。共包括2个实验, 其中实验1探讨了固定学习轮次的情况, 实验2探讨了固定学习正确率的情况。实验结果表明：同时呈现类别要素的同时学习方式下, 被试习得序列式的单类别表征(原型表征), 在归类不确定时的特征推理中按照“单类的Bayesian规则”进行特征推理, 即P(j\F) =P(k\F)·P(j\k); 相继呈现类别要素的继时学习方式下, 被试习得并列式的多类别表征, 在归类不确定时的特征推理中按照“理性模型”进行推理, 即 P(j\F) =Σk P(k\F)·P(j\k)。     

Noncategorical approaches to feature prediction with uncertain categories

In four experiments, we investigated how people make feature predictions about objects whose category membership is uncertain. Artificial visual categories were presented and remained in view while a novel instance with a known feature, but uncertain category membership was presented. All four experiments showed that feature predictions about the test instance were most often based on feature correlations (referred to as feature conjunction reasoning). Experiment 1 showed that feature conjunction reasoning was generally preferred to category-based induction in a feature prediction task. Experiment 2 showed that people used all available exemplars to make feature conjunction predictions. Experiments 3 and 4 showed that the preference for predictions based on feature conjunction persisted even when category-level information was made more salient and inferences involving a larger number of categories were required. Little evidence of reasoning based on the consideration of multiple categories (e.g., Anderson, (Psychological Review, 98:409–429, 1991)) or the single, most probable category (e.g., Murphy & Ross, (Cognitive Psychology, 27:148–193, 1994)) was found.     

The emotional meaning of harmonic intervals

The present study reexamines the hypothesis that there exist emotional attributions specific to simple musical elements. In Experiment 1, groups of participants, with varying musical expertise, rated the emotional meaning of four natural intervals heard as two harmonic sine waves. In Experiment 2, the higher tone was kept constant at an octave above the low tone used in Experiment 1, while the lower tone was constant. Attributions for each interval were positively correlated from one experimental session to another; despite the intervals differed in terms of their component pitches. Musicians gave the most reliable choices of meaning. In a third experiment, participants rated the emotional meaning of various unfamiliar ethnic melodies with expressions describing the intervals’ meaning based on the results of Experiment 1 and 2. There were distinct profiles of emotional meanings for each melody and these coincided with the meaning of intervals that constituted the surface and deep structure of each melody. The intervallic structures (i.e., the main intervals of the tunes) and respective chords for each melody were also presented aurally and participants’ ratings showed similar emotional profiles for these when compared to those of the melodies themselves.     

类别不确定下的特征推理是基于类别还是基于特征联结

共有3个实验探讨归类不确定情况下的特征推理是基于类别进行还是基于特征联结进行。实验1在中文条件下重复了Verde等人2005的实验,得出了与之相符的结果,这个结果用基于类别的理性模型的设想或者是用基于特征联结的设想都可以解释。实验2考察被试在靶类别的类别特征频次并且特征结合出现频次高低不同的条件下特征推理的情况,实验2的结果表明,高集中与低集中两种条件下特征推理没有显著差异,不符合特征推理是基于类别进行的设想,而与特征推理是基于特征联结进行的设想吻合。实验3进一步考察被试在特征结合出现的总频次并且靶类别中特征结合出现的总频次高低不同的条件下特征推理的情况,结果表明,在高结合条件下进行特征推理要优于在低结合条件,支持了在归类不确定情况下的特征推理是基于特征联结进行的设想。据此可以认为,人们的特征推理是基于特征之间联结的频次进行,而不是基于类别进行     

Elemental versus configurai models of categorization in children and adults

In experiment 1, 7-and 8-year-old children learned to classify six features, A–F, as belonging to one of two artificial categories, or to neither category. Feature A and the compound BC were designated as members of Category 1, the compounds DE and EF were members of category 2, while D alone and the compound AB, belonged to neither category. Following successful learning, the participants were asked to rate two groups of test features, ABC and DEF, as likely members of their respective categories. Participants' certainty ratings of the categorization of the compound DEF were greater than for the compound ABC. Experiment 2 replicated the results of experiment 1 with adult participants. These data are at odds with predictions from an elemental associative theory, that suggested by Rescorla and Wagner (1972), which assumes that category judgements are made on the basis of associations between individual features and a category.     

Elemental versus configurai models of categorization in children and adults

In experiment 1, 7-and 8-year-old children learned to classify six features, A–F, as belonging to one of two artificial categories, or to neither category. Feature A and the compound BC were designated as members of Category 1, the compounds DE and EF were members of category 2, while D alone and the compound AB, belonged to neither category. Following successful learning, the participants were asked to rate two groups of test features, ABC and DEF, as likely members of their respective categories. Participants' certainty ratings of the categorization of the compound DEF were greater than for the compound ABC. Experiment 2 replicated the results of experiment 1 with adult participants. These data are at odds with predictions from an elemental associative theory, that suggested by Rescorla and Wagner (1972), which assumes that category judgements are made on the basis of associations between individual features and a category.     

Decision processes in verifying category membership statements: Implications for models of semantic memory

Current models of semantic memory assume that natural categories are well-defined. Specific predictions of two such models, the Smith, Shoben, and Rips (1974a) two-stage feature comparison model and the Glass and Holyoak (1974/75) ordered search model, were tested and disconfirmed in Experiment I. We propose an alternative model postulating fuzzy categories represented as sets of characteristic properties. This model, combined with a Bayesian decision process, accounts for the results of three additional experiments, as well as for the major findings in the semantic memory literature. We argue that people verify category membership statements by assessing similarity relations between concepts rather than by using information which logically specifies the truth value of the sentence. Our data also imply that natural categories are fuzzy rather than well-defined.     

The empirical case for role-governed categories

Most theories of categorization posit feature-based representations. Markman and Stilwell (2001) argued that many natural categories name roles in relational systems and therefore they are role-governed categories. There is little extant empirical evidence to support the existence of role-governed categories. Three experiments examine predictions for ways that role-governed categories should differ from feature-based categories. Experiment 1 shows that our knowledge of role-governed categories, in contrast to feature-based categories, is largely about properties extrinsic to category members. Experiment 2 shows that role-governed categories have more prominent ideals than feature-based categories. Experiment 3 demonstrates that novel role-governed categories are licensed by the instantiation of novel relational structures. We then discuss broader implications for the study of categories and concepts.