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.     

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.     

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)。     

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.     

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：     

The downside of categories

One of the primary uses of categories is to draw inferences about novel objects based on their category membership. In a recent study, Lagnado and Shanks show that people make different inferences about an object depending on whether they first categorize the object at a general or specific level. Indeed, their inference changes even though they have been given no information about the object. This finding reveals limitations of category-based induction.     

Feature-based versus category-based induction with uncertain categories

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is known. The present experiments examined the conditions that drive feature-based and category-based strategies in induction under category uncertainty. Specifically, 2 experiments investigated whether reliance on feature-based inductive strategies is a product of the lack of coherence in the categories used in previous research or is due to the use of a decision-only induction procedure. Experiment 1 found that feature-based reasoning remained the preferred strategy even when categories with relatively high internal coherence were used. Experiment 2 found a shift toward category-based reasoning when participants were trained to classify category members prior to feature induction. Together, these results suggest that an appropriate conceptual representation must be formed through experience with a category before it is likely to be used as a basis for feature induction.     

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.     

The role of category coherence in experience-based prediction

Both real-world category knowledge and instance-based sample data are often available as sources of inductive inference. In three experiments using natural social categories, we test the influence of general category knowledge on the use of category instances to make property inductions both to other category members and to others in the population. We find that a category’s coherence—the extent to which its features are interrelated through prior knowledge (Murphy & Medin, 1985)—influences inductions positively to new category members and negatively to the population. This effect of coherence is strongest with small as compared with large samples of instances. The results are interpreted from both similarity and explanation-based perspectives.     

Labeling bias and categorical induction: generative aspects of category information

When a person is characterized categorically with a label (e.g., Linda is a feminist), people tend to think that the attributes associated with that person are central and long lasting (S. Gelman & G. D. Heyman, 1999). This bias, which is related to category-based induction and stereotyping, has been thought to arise because a category label (e.g., feminist) activates the dominant properties associated with the representation of the category. This explanation implies that categorical information influences inferential processes mainly by conjuring up main attributes or instances represented in the category. However, the present experiments reveal that this attribute-based explanation of induction does not provide a complete picture of inferential processes. The results from 3 experiments suggest that category information can affect inferences of attributes that are not directly related to the category, suggesting that categories not only activate likely attributes but also help integrate unlikely or even unrelated attributes.     

Induction with cross-classified categories

One of the main functions of categories is to allow inferences about new objects. However, most objects are cross-classified, and it is not known whether and how people combine information from these different categories in making inferences. In six experiments, food categories, which are strongly crossclassified (e.g., a bagel is both a bread and a breakfast food), were studied. For each food, the subjects were told fictitious facts (e.g., 75% of breads are subject to spoilage from Aspergillus molds) about two of the categories to which it belonged and then were asked to make an inference about the food (e.g., how likely is a bagel to be subject to spoilage from Aspergillus molds?). We found no more use of multiple categories in these cases of cross-classification than in ambiguous classification, in which it is uncertain to which category an item belongs. However, some procedural manipulations did markedly increase the use of both categories in inferences, primarily those that focused the subjects’ attention on the critical feature in both categories.     

The two faces of typicality in category-based induction

Studies of category-based induction using different methods have found somewhat contradictory results for whether typical items are a stronger basis for induction. Typical category items are generally more similar to other category items than are atypical ones, and they are also more likely to be categorized into the category in question. We propose that the first aspect (representativeness) influences induction, but the second (uncertainty about the correct category) does not. Two experiments using artificial categories found support for this prediction. Two further experiments manipulated pictures of objects and also found that representativeness in the category influenced the strength of induction, but uncertainty of classification did not. Thus, the two aspects of typicality have different effects on category-based induction.     

The impact of training sequence and between-category similarity on unsupervised induction

Studies of supervised categorization often show better learning when examples are presented in random alternation rather than massed by category, but such interleaving impairs learning in unsupervised tasks. The exemplar comparison hypothesis explains this result by assuming that people in unsupervised tasks discover generalizations about categories by comparing individual examples, and that interleaving increases the difficulty of such within-category comparisons. The category invention hypothesis explains the interleaving effect by assuming that people are more likely to merge or aggregate potentially separable categories when they are interleaved, and this initial failure to recognize separate categories then acts as an effective barrier to further learning. The present experiments show that the interleaving effect depends on the similarity or alignability of the presented categories. This result provides evidence in favour of the category invention hypothesis, which expects that highly dissimilar (nonalignable) categories will resist aggregation and hence will not be affected by interleaving. The nonmonotonic pattern of learning, and the interaction between sequence and similarity, observed in the alignable conditions of Experiment 3 were also consistent with category invention, but not with exemplar comparison. Implications are discussed for real-world learning, especially the relationship between exposure and learning and between supervised and unsupervised learning.     

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     

Will any doll do? 12-month-olds' reasoning about goal objects

Infants as young as 5 months of age view familiar actions such as reaching as goal-directed (Woodward, 1998), but how do they construe the goal of an actor's reach? Six experiments investigated whether 12-month-old infants represent reaching actions as directed to a particular individual object, to a narrowly defined object category (e.g., an orange dump truck), or to a more broadly defined object category (e.g., any truck, vehicle, artifact, or inanimate object). The experiments provide evidence that infants are predisposed to represent reaching actions as directed to categories of objects at least as broad as the basic level, both when the objects represent artifacts (trucks) and when they represent people (dolls). Infants do not use either narrower category information or spatiotemporal information to specify goal objects. Because spatiotemporal information is central to infants' representations of inanimate object motions and interactions, the findings are discussed in relation to the development of object knowledge and action representations.     

Children's evaluation of the certainty of another person's inductive inferences and guesses

In three studies, 5–10-year-old children and an adult comparison group judged another's certainty in making inductive inferences and guesses. Participants observed a puppet make strong inductions, weak inductions, and guesses. Participants either had no information about the correctness of the puppet's conclusion, knew that the puppet was correct, or knew that the puppet was incorrect. Children of all ages (but not adults) rated the puppet as more certain about statements the child knew to be correct than statements the child knew to be incorrect. When assessing another's certainty, children have difficulty inhibiting their own knowledge and focusing on the other's perspective. Children were more likely to differentiate between inductions and guesses when the puppet made an Incorrect Statement, but even the oldest children did not differentiate consistently. The distinction between induction and guessing appears to be only acquired gradually but is important as a contributor to more advanced forms of reasoning and epistemological understanding.     

On the relative effectiveness of affect regulation strategies: A meta-analysis

To examine the effectiveness of various affect regulation strategies and categories of affect regulation strategies, a meta-analysis was conducted. Results generally indicate that reappraisal (d=0.65) and distraction (d=0.46 for all studies; d=0.95 for studies with a negative or no affect induction) are the most effective regulation/repair strategies, producing the largest hedonic shift in affect. The effectiveness of different categories of regulation/repair strategies depended on the valence of the preceding affect induction. Results also indicate that stronger affect inductions and the use of bivariate affect measures will provide a richer understanding of affect regulation. Additionally, not all specific strategies or categories of strategies have been researched and the impact of individual differences on affect regulation has received relatively little attention. Finally, results indicate that control conditions in affect regulation research may not provide a valid point for comparison, as they facilitate effective affect repair.     

Information about the logical structure of a category affects generalization

This paper considers whether information about the logical structure of a category affects how people generalize. We carried out three experiments with the following structure: participants were first presented with a set of training items, and were subsequently asked to decide whether new items belonged to the same category as the training items. Each experiment had two conditions that differed only in terms of the category label provided for the training items; different category labels conveyed different information about the logical structure of the category to which the training items were supposed to belong. In all cases, participants' generalization was greatly affected by such information. Our results suggest that people make the default assumption that category labels correspond to groupings of highly similar objects.