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.     

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.     

归纳推理的抽样理论

作者提出归纳推理的抽样理论,认为归纳推理实质是根据与归纳特征有关的抽样样本情况来推断结论类别具有归纳特征的可能性,其中的抽样可分为类别抽样和特征抽样两种。以大学生为被试的两个实验结果一致支持抽样理论而不是别的归纳推理理论。对于归纳推理的主要现象,抽样理论比其它的相似性解释具有更大的解释范围,特别是能够解释其它理论严格不能解释的归纳推理非对称性现象     

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     

When similarity and causality compete in category-based property generalization

Five experiments were performed to investigate the category-based generalization of nonblank properties, properties that were novel but that were attributed to existing category features with causal explanations. Experiments 1-3 tested how such explanations interact with the well-known effects of similarity on such generalizations. The results showed that when the causal explanations were used, standard effects of typicality (Experiment 1), diversity (Experiment 2), or similarity itself (Experiment 3) were almost completely eliminated. Experiments 4 and 5 demonstrated that category-based generalizations exhibit some of the standard properties of causal reasoning; for example, an effect (i.e., a novel category property) is judged to be more prevalent when its cause (i.e., an existing category feature) is also prevalent. These findings suggest that category-based property generalization is often an instance of causal inference.     

Expertise and category-based induction

The authors examined inductive reasoning among experts in a domain. Three types of tree experts (landscapers, taxonomists, and parks maintenance personnel) completed 3 reasoning tasks. In Experiment 1, participants inferred which of 2 novel diseases would affect "more other kinds of trees" and provided justifications for their choices. In Experiment 2, the authors used modified instructions and asked which disease would be more likely to affect "all trees." In Experiment 3, the conclusion category was eliminated altogether, and participants were asked to generate a list of other affected trees. Among these populations, typicality and diversity effects were weak to nonexistent. Instead, experts' reasoning was influenced by "local" coverage (extension of the property to members of the same folk family) and causal-ecological factors. The authors concluded that domain knowledge leads to the use of a variety of reasoning strategies not captured by current models of category-based induction.     

Speeded induction under uncertainty: The influence of multiple categories and feature conjunctions

When people are uncertain about the category membership of an item (e.g., Is it a dog or a dingo?), research shows that they tend to rely only on the dominant or most likely category when making inductions (e.g., How likely is it to befriend me?). An exception has been reported using speeded induction judgments where participants appeared to use information from multiple categories to make inductions (Verde, Murphy, &; Ross, 2005). In two speeded induction studies, we found that participants tended to rely on the frequency with which features co-occurred when making feature predictions, independently of category membership. This pattern held whether categories were considered implicitly (Experiment 1) or explicitly (Experiment 2) prior to feature induction. The results converge with other recent work suggesting that people often rely on feature conjunction information, rather than category boundaries, when making inductions under uncertainty.     

Food rejection and the development of food category-based induction in 2–6 years old children

We studied children’s inductive inferences within the domain of food categories. There has so far been little research on inductive reasoning about food among children, despite the theoretical and practical importance of knowing what knowledge children bring to the table and how they use it. We tested the hypotheses that children’s food category-based induction performances and their food rejection are negatively correlated, and that these performances are influenced by the colour typicality of the food items. We recruited 126 children aged 2–6 years, and administered a category-based induction task. Participants were successively shown 8 sets of three pictures containing one target picture (a vegetable) and two test pictures (a vegetable dissimilar in colour to the target picture and a fruit similar in colour to the target picture). For each set, participants were told a novel property about the target picture and asked to generalise this property to one of the two test pictures. Additionally, the parents of each child filled out a questionnaire about his or her food rejection tendencies. Results on accuracy (i.e. if participants generalised the properties according to category membership, not perceptual similarity) provided the first empirical evidence in favour of a negative relationship between children’s food rejection and food category-based induction.     

How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning

A robust finding in category-based induction tasks is for positive observations to raise the willingness to generalize to other categories while negative observations lower the willingness to generalize. This pattern is referred to as monotonic generalization. Across three experiments we find systematic non-monotonicity effects, in which negative observations raise the willingness to generalize. Experiments 1 and 2 show that this effect emerges in hierarchically structured domains when a negative observation from a different category is added to a positive observation. They also demonstrate that this is related to a specific kind of shift in the reasoner’s hypothesis space. Experiment 3 shows that the effect depends on the assumptions that the reasoner makes about how inductive arguments are constructed. Non-monotonic reasoning occurs when people believe the facts were put together by a helpful communicator, but monotonicity is restored when they believe the observations were sampled randomly from the environment.     

特征相似性、类别标签与因果关系在归纳推理中的作用

采用人工材料,比较类别标签、特征相似性与因果关系对归纳推理强度的影响。包括两个实验,实验1比较类别标签与特征相似性对归纳推理的影响,结果表明,当类别标签对归纳推理的影响显著强于特征相似性时,不能将类别标签等同于一个相似性特征。实验2进一步探讨类别标签与因果关系对归纳推理的作用,结果表明,因果关系作用明显强于类别标签的作用。综合两个实验的结果并整合前人相关研究,提出了不同关系影响归纳推理的强度假想。     

When learning to classify by relations is easier than by features

Relational reasoning is often considered more resource intensive than feature-based reasoning. This view implies that learning categories defined by relational regularities should be more difficult than learning categories defined by featural regularities. Unfortunately previous studies do not ground featural and relational information in a common perceptual substrate. After addressing this concern, a series of experiments compare learning performance for relation- and feature-based categories. Under certain circumstances we find faster learning for relation-based categories. The results suggest that mechanisms rooted in relational processes (e.g., relative stimulus judgement, analogical comparison) facilitate or hinder learning depending on whether the relational processes highlight or obscure the underlying category structure. Conversely, category learning affects relational processes by promoting relational comparisons that increase the coherence of acquired categories. In contrast to the largely independent research efforts in category learning and analogy research, our findings suggest that learning and comparison processes are deeply intertwined.     

特征归纳中关联强度与关联相似性的交互作用效应

根据特征归纳的关联相似性模型分析和三个因素实验研究发现,在基于关联相似性而进行的特征归纳中,关联强度和关联相似性两因素对归纳强度的影响存在交互作用效应。关联相似性变化对归纳强度的影响依赖于关联强度,随关联强度的增加而增加。关联强度变化对归纳强度的影响依赖于关联相似性水平：在高的关联相似性条件下,归纳强度随关联强度增加而增加;在中等关联相似性条件下,归纳强度不受关联强度变化的影响;而在低的关联相似性条件下,归纳强度随关联强度增加而减小     

Category-based induction: An effect of conclusion typicality

Category-based induction involves the willingness of a thinker to project some newly learned property of one or more classes of objects to another class on the basis of their shared membership in a common superordinate category. Previous research has established that the perceived strength of arguments of the form "Class A has Property P; therefore, Class B has Property P" is influenced by the similarity of A to B and by the typicality or representativeness of A in a shared category, superordinate to both A and B. (The nature of P is also crucial, but we do not examine it in this study.) There is, however, no prior evidence that the relation between B and the category is influential. Three experiments were designed to test whether the typicality of B in the superordinate category also has an effect on inductive argument strength. By using multiple regression (Experiment 1) and an experimental design (Experiment 3), an effect of conclusion typicality was found, so that people are more willing to project properties to more typical conclusions. Experiment 2 ruled out conclusion familiarity as a potential confounding variable. The results are interpreted in the light of current models of category-based induction.     

How many processes underlie category-based induction? Effects of conclusion specificity and cognitive ability

Two studies investigated participants' sensitivity to the amount and diversity of the evidence when reasoning inductively about categories. Both showed that participants are more sensitive to characteristics of the evidence for arguments with general rather than specific conclusions. Both showed an association between cognitive ability and sensitivity to these evidence characteristics, particularly when the conclusion category was general. These results suggest that a simple associative process may not be sufficient to capture somekey phenomena of category-based induction. They also support the claim that the need to generate a superordinate category is a complicating factor in category-based reasoning and that adults' tendency to generate such categories while reasoning has been overestimated.     

Conceptual influences on induction: A case for a late onset

This research examines the mechanism of early induction, the development of induction, and the ways attentional and conceptual factors contribute to induction across development. Different theoretical views offer different answers to these questions. Six experiments with 4- and 5-year-olds, 7-year-olds and adults (N = 208) test these competing theories by teaching categories for which category membership and perceptual similarity are in conflict, and varying orthogonally conceptual and attentional factors that may potentially affect inductive inference. The results suggest that early induction is similarity-based; conceptual information plays a negligible role in early induction, but its role increases gradually, with the 7-year-olds being a transitional group. And finally, there is substantial contribution of attention to the development of induction: only adults, but not children, could perform category-based induction without attentional support. Therefore, category-based induction exhibits protracted development, with attentional factors contributing early in development and conceptual factors contributing later in development. These results are discussed in relation to existing theories of development of inductive inference and broader theoretical views on cognitive development.     

Category structure affects the developmental trajectory of children's inductive inferences for both natural kinds and artefacts

Inductive reasoning is fundamental to human cognition, yet it remains unclear how we develop this ability and what might influence our inductive choices. We created novel categories in which crucial factors such as domain and category structure were manipulated orthogonally. We trained 403 4–9-year-old children to categorise well-matched natural kind and artefact stimuli with either featural or relational category structure, followed by induction tasks. This wide age range allowed for the first full exploration of the developmental trajectory of inductive reasoning in both domains. We found a gradual transition from perceptual to categorical induction with age. This pattern was stable across domains, but interestingly, children showed a category bias one year later for relational categories. We hypothesise that the ability to use category information in inductive reasoning develops gradually, but is delayed when children need to process and apply more complex category structures.     

特征归纳的关联相似性模型

作者提出特征归纳的关联相似性模型,用以描述以归纳特征的关联特征相似性为基础的归纳推理,把归纳推理中相似性和关联知识统一整合起来。该模型认为归纳强度主要取决于归纳特征与关联特征的关联强度和关联特征的相似性程度的乘积,归纳信心主要取决于关联强度,从而分离归纳信心和归纳强度。以大学生为被试的两个实验的主要结果支持关联相似性模型的主要预测。关联相似性模型能够描述解释以关联特征相似性为基础的归纳推理现象,比以往的归纳理论具有更大的解释能力和解释范围