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.     

The importance of being nonalignable: a critical test of the structural alignment theory of similarity

The structural alignment theory of similarity distinguishes 2 types of difference that may occur between stimuli: Alignable differences are those related to a commonality, whereas nonalignable differences are not related to a commonality. Alignment theory predicts that alignable differences should be more heavily weighted than nonalignable differences in similarity judgment. Experiments 1 and 2 demonstrate that, contrary to this prediction, nonalignable differences exerted a greater impact than alignable differences in similarity and difference judgments of geometric stimuli. Experiment 3 revealed that the relative weight accorded a given difference was also affected by contextual constraints. Thus, although the experiments supported the validity of the distinction between alignable and nonalignable differences, results were discordant with the specific prediction of structural alignment theory.     

Processing Product Unique Features: Alignability and Involvement in Preference Construction

Consumers often form preferences based on the presented attributes of choice options. Previous research has suggested that consumers tend to form their preferences using the attributes that are related to common aspects between the options (i.e., alignable differences) and ignore attributes that are unique to individual options (i.e., nonalignable differences). We combine cognitive psychology and motivation research from social psychology and propose that consumers’ preference formation can be systematically affected by whether they focus more on alignable differences or more on nonalignable differences of the options as a function of consumers’ involvement with the task. The results of 3 experiments show that preference judgment favors alignable differences over nonalignable differences in a low‐motivation condition. In contrast, high motivation to process information enables consumers to increase their use of nonalignable differences in preference formation, which can result in a preference reversal relative to the low‐motivation condition. The results suggest that a preference reversal occurs when the nonalignable differences of the target option are superior to both the nonalignable differences and the alignable differences of the reference option. However, the reversal does not occur when the nonalignable differences of the target option are superior only to the nonalignable differences of the reference option. Theoretical and managerial implications relating to differentiation, positioning, and communication strategies for innovative brands are discussed.     

3～5岁儿童概念比较过程研究

本研究以两个实验探讨了儿童的一个认知过程:人优先考虑共同性,其次才是组合差异性与非组合差异性。3~5岁三个年龄组儿童分别对日常生活的基本概念进行类似性判断与差异性判断。实验1的结果表明,3岁年龄组的儿童还没有表现出组合差异性,4岁年龄组的儿童初步表现出明显的组合差异性,5岁年龄组的儿童表现出明显的组合差异性。他们首先考虑共同性,其次才是组合差异性与非组合差异性。实验2的结果表明,5岁儿童比4岁儿童更多地表现了组合差异性的判断能力。最后以结构组合的观点讨论了这些结果。     

THE EFFECTS OF ALIGNABILITY ON MEMORY

Abstract— According to structure-mapping theory, the process of comparison in one of alignment and mapping between representational structures. This process induces a focus on commonalities and alignable differences (i.e., those related to the commonalities). Non-alignable differences (i.e., those not related to the commonalities) are held to be neglected. The theory thus predicts increased focus on the corresponding information, Whether these are commonalities or differences. In this article, we explore the implications of this claim for memory: Specifically, we test the prediction that alignable differences are more likely to be processed and stored than nonalignable differences.
We present a study in which people made similarity comparisons between pairs of pictures and then were probed for recall. The recall probes were figures taken from the pictures and were either alignable or nonalignable differences between the pairs. The alignable differences were better memory probes than the nonalignable differences, suggesting that people were more likely to encode and store the corresponding information than the noncorresponding information.     

Commonalities and differences in similarity comparisons

Similarity is a central component of many cognitive processes. Current research suggests that similarity is well characterized as a comparison of structured representations. This process yields commonalities, differences related to the commonalities (alignable differences), and differences not related to the commonalities (nonalignable differences). In the first study, further evidence for this tripartite distinction is provided in a commonality and difference listing study involving pairs of pictures. This study indicates that alignable differences rather than nonalignable differences are central to the comparison process by virtue of their connection to the commonalities. The second study further demonstrates that alignable differences count more against the similarity of a pair than do nonalignable differences. We end by discussing implications of the distinction between alignable and nonalignable differences for other cognitive processes involving comparisons.     

What makes a man similar to a tie? Stimulus compatibility with comparison and integration

We argue and show that different properties of stimuli are compatible with different types of processing. Specifically, object pairs from the same taxonomic category (e.g., chair-bed) tend to be alignable and thus compatible with comparison, whereas object pairs that play different roles in thematic relations (e.g., chair-carpenter) tend to be nonalignable and compatible with integration. Using object pairs that varied orthogonally in alignability and thematic relatedness, we demonstrated that stimulus compatibility modulates processing and affects the outcomes of tasks that are currently believed to involve only comparison (similarity ratings, Experiment 1; listing commonalities and differences, Experiment 2) or only integration (thematic relatedness ratings, Experiment 3). Our findings and others that we have reviewed suggest that: (1) many cognitive tasks involve both comparison and integration, and (2) the relative influence of each process is modulated by an interplay between the task-appropriate and the stimulus-compatible process. We believe that single-process models should be extended to take this interplay into account.     

Choice-Process Satisfaction: The Influence of Attribute Alignability and Option Limitation

This research investigates how choice-process satisfaction is influenced by limitation of choice option and by the types of features used to represent the options. Studies of choice satisfaction have focused on how satisfied the decision maker feels about the choice that has been made and have overlooked the importance of the process through which the decision maker makes a choice, i.e., choice-process satisfaction. We show that the comparability of choice options through alignable features increases choice-process satisfaction, whereas option limitation (i.e., making one option unavailable from a set of equally attractive options) decreases choice-process satisfaction. Further, this decrease in satisfaction, relative to all options being available, occurs for people who are given a set of options in which the difference features are alignable (i.e., differences of a corresponding dimension) but not for people who are given a set of options in which the difference features are nonalignable (i.e., differences of unique dimensions). We propose that alignable differences are easier to compare and have more weight in people's attribute processing, and thus give rise to a perception of a greater amount of information about the option set that is relevant for choice. Making an option unavailable in this case would have a bigger impact than in a situation in which all options have nonalignable differences. Nonalignable differences are difficult to process and are less likely to make people aware that there is very much information about the options for decision making. This explanation and the interaction effect between option limitation and feature alignability are tested in four experiments.     

Dual-task interference in perceptual category learning

The effect of a working-memory-demanding dual task on perceptual category learning was investigated. In Experiment 1, participants learned unidimensional rule-based or information integration category structures. In Experiment 2, participants learned a conjunctive rule-based category structure. In Experiment 1, unidimensional rule-based category learning was disrupted more by the dual working memory task than was information integration category learning. In addition, rule-based category learning differed qualitatively from information integration category learning in yielding a bimodal, rather than a normal, distribution of scores. Experiment 2 showed that rule-based learning can be disrupted by a dual working memory task even when both dimensions are relevant for optimal categorization. The results support the notion of at least two systems of category learning a hypothesis-testing system that seeks verbalizable rules and relies on working memory and selective attention, and an implicit system that is procedural-learning based and is essentially automatic.     

空间距离与产品属性对消费者选择偏好的影响*

通过两个实验检验空间距离如何影响消费者选择偏好。实验一中,产品与消费者之间的空间距离不影响他们对不可匹配属性更优产品的偏好。实验二中,两种产品之间的空间距离与产品属性交互作用影响了消费者的选择偏好,使他们在两产品之间空间距离远时比近时更偏好不可匹配属性更优产品。两种不同类型的空间距离对消费者选择偏好的影响是不同的。强调两产品之间的空间距离远,会影响消费者在选择过程中对不可匹配属性的使用。     

Alignable and nonalignable differences in causal explanations

Prior research indicates that people may base their causal explanations on distinctive features between an event and a contrasting background instance in which the event did not occur. Research on similarity judgments suggests that there are two types of distinctive features: alignable differences, which are corresponding characteristics of a pair, and nonalignable differences, which are characteristics of one item for which there are no corresponding characteristics in the other. In three experiments, the hypothesis that people's evaluations of causal explanations vary as a function of feature alignment was examined. The results suggest that people will rate explanations differently on the basis of alignable or nonalignable differences, depending on the type of the event, and that alignability depends on the relational structure among the features of the event.     

Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning

Research has identified multiple category-learning systems with each being “tuned” for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus–response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning.     

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。     

On the necessity of alignment: reply to Costello and Keane (2001)

In their article, "Testing two theories of conceptual combination: Alignment versus diagnosticity in the comprehension and production of combined concepts," F. J. Costello and M. T. Keane (2001) evaluate the role of alignment in the interpretation of noun-noun combinations. They found that participants were not strongly biased to prefer and produce interpretations with alignable differences. Instead, participants sometimes preferred and produced interpretations with nonalignable differences. These results are surprising given that most research has found advantages of alignable differences over nonalignable differences. Costello and Keane also found that feature diagnosticity better predicted their results, and they concluded that alignment does not play an important role in conceptual combination. However, drawing on recent work, the author of the present article gives an alternative interpretation of Costello and Keane's results, showing that alignment is crucial in conceptual combination. The author also shows that the dual-process model accounts for their results.     

Observed attention allocation processes in category learning

In two empirical studies of attention allocation during category learning, we investigate the idea that category learners learn to allocate attention optimally across stimulus dimensions. We argue that “optimal” patterns of attention allocation are model or process specific, that human learners do not always optimize attention, and that one reason they fail to do so is that under certain conditions the cost of information retrieval or use may affect the attentional strategy adopted by learners. We empirically investigate these issues using a computer interface incorporating an “information-board” display that collects detailed information on participants' patterns of attention allocation and information search during learning trials. Experiment 1 investigated the effects on attention allocation of distributing perfectly diagnostic features across stimulus dimensions versus within one dimension. The overall pattern of viewing times supported the optimal attention allocation hypothesis, but a more detailed analysis produced evidence of instance- or category-specific attention allocation, a phenomenon not predicted by prominent computational models of category learning. Experiment 2 investigated the strategies adopted by category learners encountering redundant perfectly predictive cues. Here, the majority of participants learned to distribute attention optimally in a cost–benefit sense, allocating attention primarily to only one of the two perfectly predictive dimensions. These results suggest that learners may take situational costs and benefits into account, and they present challenges for computational models of learning that allocate attention by weighting stimulus dimensions.     

Observed attention allocation processes in category learning

In two empirical studies of attention allocation during category learning, we investigate the idea that category learners learn to allocate attention optimally across stimulus dimensions. We argue that "optimal" patterns of attention allocation are model or process specific, that human learners do not always optimize attention, and that one reason they fail to do so is that under certain conditions the cost of information retrieval or use may affect the attentional strategy adopted by learners. We empirically investigate these issues using a computer interface incorporating an "information-board" display that collects detailed information on participants' patterns of attention allocation and information search during learning trials. Experiment 1 investigated the effects on attention allocation of distributing perfectly diagnostic features across stimulus dimensions versus within one dimension. The overall pattern of viewing times supported the optimal attention allocation hypothesis, but a more detailed analysis produced evidence of instance- or category-specific attention allocation, a phenomenon not predicted by prominent computational models of category learning. Experiment 2 investigated the strategies adopted by category learners encountering redundant perfectly predictive cues. Here, the majority of participants learned to distribute attention optimally in a cost-benefit sense, allocating attention primarily to only one of the two perfectly predictive dimensions. These results suggest that learners may take situational costs and benefits into account, and they present challenges for computational models of learning that allocate attention by weighting stimulus dimensions.     

Testing two theories of conceptual combination: alignment versus diagnosticity in the comprehension and production of combined concepts

People often interpret novel noun-noun combinations by transferring a property from one constituent concept of the combination to the other. Two theories make different predictions about these "property" interpretations. Dual-process theory predicts that properties transferred will be alignable differences of the concepts being combined. Constraint theory predicts that properties transferred will be diagnostic properties of the concepts in which they originate. An experimental study tested these contrasting predictions in interpretation comprehension and interpretation production tasks. The results showed that participants reliably preferred diagnostic property interpretations, whether alignable or nonalignable, in both tasks. There was no reliable preference for alignable interpretations in either task. This confirms constraint theory's predictions about property interpretations and goes against the predictions of dual-process theory.     

Connectedness and part-relation integration in shape category learning

We investigated the role of connectedness in the use of part-relation conjunctions for object category learning. Participants learned categories of two-part objects defined by the shape of one part and its location relative to the other (part-relation conjunctions). The topological relationship between the parts (connected, separated, or embedded) varied between participants but was invariant for any given participant. In Experiment 1, category learning was faster and more accurate when an object’s parts were connected than when they were either separated or embedded. Subsequent experiments showed that this effect is not due to conscious strategies, differences in the salience of the individual attributes, or differences in the integrality/separability of dimensions across stimuli. The results suggest that connectedness affects the integration of parts with their relations in object category learning.     

The effect of category learning on the representation of shape: dimensions can be biased but not differentiated

Recent studies have suggested a profound influence of category learning on visual perception, resulting in independent processing of previously integral dimensions. The authors reinvestigate this issue for shape dimensions. They first extend previous findings that some shape dimensions (aspect ratio and curvature) are processed in a separable way, whereas others (radial frequency components) are not. They then show that a category-learning phase improved the discrimination of a relevant with respect to an irrelevant dimension, but only for separable dimensions. No similar effect was found on the relative sensitivity for integral shape dimensions. Thus, category learning is capable of biasing separable shape dimensions but does not alter the status of dimensions in the visual system as either separable or integral.     

Learning about the internal structure of categories through classification and feature inference

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a “partial inference” condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.