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：     

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)。     

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     

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。     

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。     

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。     

归类不确定时人们的推理方式研究

该研究采用集中呈现样例的研究范式,探讨了归类不确定情况下,人们的推理方式。实验1控制特征联结频次,考察人们的推理方式是否是基于类别的推理。实验2控制类别中特征的基本概率,考察人们推理方式是否是基于特征联结的推理。实验结果表明,特征推理前不预先归类直接推理时,人们的推理根据目标特征与预测特征联结的频次进行,是基于特征联结的推理;特征推理前预先要求被试归类时,人们的特征推理是基于类别的推理。     

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     

归类不确定情境下特征预测的理性模型研究

探讨了Bayesian规则计算公式的具体含义,进而考察了归类不确定情境下的特征预测是否符合Bayesian规则.包括2个实验:实验1探讨保持类别内目标成员中预测特征的比例不变,而改变类别内所有成员中预测特征的比例是否影响特征预测.实验2探讨保持类别内目标成员中预测特征的比例不变,进一步扩大类别内所有成员中预测特征比例的差异是否会影响特征预测.结果表明: 类别内目标成员中预测特征的比例影响特征预测,而类别内所有成员中预测特征的比例不影响特征预测.据此,本研究认为,Murphy对Bayesian规则计算公式的含义理解有偏差,其由此提出的"单类说"值得商榷.     

归类及推理研究的几个数学理论模型

归类是认知心理学的一个重要研究领域 ,而在归类基础上的特征推理及预测则更为重要。该文对经典的归类研究作一简略的述评 ,同时概述包含有复杂推理过程的归类学习研究的新进展 ,重点介绍三种有关样例推理及归类预测的数学模型———情境模型、多阶情境模型和理性模型 ,以及它们之间的内在联系。     

Induction with uncertain categories: When do people consider the category alternatives?

These three experiments examined how people make property inferences about exemplars whose category membership is uncertain. Participants were shown two categories and a novel exemplar with a feature that indicated that the exemplar was more likely to belong to one category (target) than to the other (nontarget). Participants then made categorization decisions and property inferences about the novel exemplar. In some conditions, property inferences could be made only by considering both target and nontarget categories. In other conditions, predictions could be based on both categories or on the target category alone. Consistent with previous studies (e.g., Murphy & Ross, 1994, 2005), we found that many people made predictions based only on consideration of the target category. However, the prevalence of such single-category reasoning was greatly reduced by highlighting the costs of neglecting nontarget alternatives and by asking for inferences before categorization decisions. The results suggest that previous work may have exaggerated the prevalence of single-category reasoning and that people may be more flexible in their use of multiple categories in property inference than has been previously recognized.     

Where to look first for an explanation of induction with uncertain categories

Recent research has examined how people predict unobserved features of an object when its category membership is ambiguous. The debate has focused on whether predictions are based solely on information from the most likely category, or whether information from other possible categories is also used. In the present experiment, we compared these category-based approaches with feature conjunction reasoning, where predictions are based on a comparison among exemplars (rather than categories) that share features with a target object. Reasoning strategies were assessed by examining patterns of feature prediction and by using an eye gaze measure of attention during induction. The main findings were (1) the majority of participants used feature conjunction rather than categorical strategies, (2) people predominantly gazed at the exemplars that were most similar to the target object, and (3) although people gazed most at the most probable category to which an object could belong, they also attended to other plausible category alternatives during induction. These findings question the extent to which category-based reasoning is used for induction when category membership is uncertain.     

Bogus concerns about the false prototype enhancement effect

Two experiments addressed the mechanism responsible for the false prototype effect, the phenomenon in which a prototype gradient can be obtained in the absence of learning. Previous demonstrations of this effect have occurred solely in a single-category paradigm in which transfer patterns are assigned or not to the learning category. We tested the hypothesis that any extraneous variable potentially responsible for this effect, such as compactness varying with pattern distortion (Zaki & Nosofsky, 2004), may be functional in the single-category paradigm but not when multiple categories are available at the time of transfer. In the present study, subjects received a bogus or a real category learning phase, followed by a transfer test that required assignment into 1 or 3 prototype categories. The results showed that a minimal prototype gradient was obtained in the bogus conditions, with performance approaching chance levels when classification into 3 categories was required. In contrast, a substantial prototype gradient effect was found following learning. We conclude that the prototype gradient typically obtained following multiple-category learning is primarily driven by real learning and that the false prototype effect is itself an artifact of the single-category paradigm.     

Reasoning with uncertain categories

Five experiments investigated how people use categories to make inductions about objects whose categorisation is uncertain. Normatively, they should consider all the categories the object might be in and use a weighted combination of information from all the categories: bet-hedging. The experiments presented people with simple, artificial categories and asked them to make an induction about a new object that was most likely in one category but possibly in another. The results showed that the majority of people focused on the most likely category in making inductions, although there was a group of consistently normative responders who used information from both categories (about 25% of our college population). Across experiments the overall pattern of results suggests that performance in the task is improved not by understanding the underlying principles of bet-hedging but by increasing the likelihood that multiple categories are in working memory at the time of the induction. We discuss implications for improving everyday inductions.     

Further attempts to clarify the importance of category variability for categorisation

The issue of how category variability affects classification of novel instances is an important one for assessing theories of categorisation, yet previous research cannot provide a compelling conclusion. In five experiments we reexamine some of the factors thought to affect participant performance. In Experiments 1 and 2, participants almost always classified the test item as belonging to the high variability category. By contrast, in Experiment 3 we employed an alternative experimental paradigm, where the difference in variability of the two categories was less salient. In that case, participants tended to classify a test item as belonging to the low variability category. Two additional experiments (4 and 5) explored in detail the differences between Experiments 1, 2 on the one hand, and 3 on the other. Some insight into the underlying psychological processes can be provided by computational models of categorisation, and we focus on the continuous version of Anderson's (1991) Rational Model, which has not been explored before in this context. The model predicts that test instances exactly halfway between the prototypes of two categories should be classified into the more variable category, consistent with the bulk of empirical findings. We also provided a comparison with a slightly reduced version of the Generalised Context Model (GCM) to show that its predictions are consistent with those from the Rational Model, for our stimulus sets.     

Acquiring ownership and the attribution of responsibility

How is ownership established over non-owned things? We suggest that people may view ownership as a kind of credit given to agents responsible for making possession of a non-owned object possible. On this view, judgments about the establishment of ownership depend on attributions of responsibility. We report three experiments showing that people’s judgments about the establishment of ownership are influenced by an agent’s intent and control in bringing about an outcome, factors that also affect attributions of responsibility. These findings demonstrate that people do not just consider who was first to possess an object in judging who owns it, and are broadly consistent with the view that ownership is acquired through labor. The findings also suggest that rather than exclusively being the product of social conventions, judgments about the establishment of ownership over non-owned things also depend on the psychological processes underlying the attribution of responsibility.     

Intuitive physics: the straight-down belief and its origin

This study examines the nature and origin of a common misconception about moving objects. We first show through the use of pencil-and-paper problems that many people erroneously believe that an object that is carried by another moving object (e.g., a ball carried by a walking person) will, if dropped, fall to the ground in a straight vertical line. (In fact, such an object will fall forward in a parabolic arc.) We then demonstrate that this "straight-down belief" turns up not only on pencil-and-paper problems but also on a problem presented in a concrete, dynamic fashion (Experiment 1) and in a situation in which a subject drops a ball while walking (Experiment 2). We next consider the origin of the straight-down belief and propose that the belief may stem from a perceptual illusion. Specifically, we suggest that objects dropped from a moving carrier may be perceived as falling straight down or even backward, when in fact they move forward as they fall. Experiment 3, in which subjects view computer-generated displays simulating situations in which a carried object is dropped, and Experiment 4, in which subjects view a videotape of a walking person dropping an object, provide data consistent with this "seeing is believing" hypothesis.