Contrastive Constraints Guide Explanation‐Based Category Learning

This paper provides evidence for a contrastive account of explanation that is motivated by pragmatic theories that recognize the contribution that context makes to the interpretation of a prompt for explanation. This study replicates the primary findings of previous work in explanation‐based category learning (Williams & Lombrozo, 2010), extending that work by illustrating the critical role of the context in this type of learning. Participants interacted with items from two categories either by describing the items or explaining their category membership. We manipulated the feature overlap between the categories and examined both the explanations generated and acquired knowledge of the categories. Explanations for membership in a given category were influenced by the unprompted contrast category, indicating an important role for contrastive processing in the generation of explanations. The influence of the contrast category was similarly seen in the transfer performance of the participants.     

Categories of Large Numbers in Line Estimation

How do people stretch their understanding of magnitude from the experiential range to the very large quantities and ranges important in science, geopolitics, and mathematics? This paper empirically evaluates how and whether people make use of numerical categories when estimating relative magnitudes of numbers across many orders of magnitude. We hypothesize that people use scale words—thousand, million, billion—to carve the large number line into categories, stretching linear responses across items within each category. If so, discontinuities in position and response time are expected near the boundaries between categories. In contrast to previous work (Landy, Silbert, & Goldin, 2013) that suggested only that a minority of college undergraduates employed categorical boundaries, we find that discontinuities near category boundaries occur in most or all participants, but that accurate and inaccurate participants respond in opposite ways to category boundaries. Accurate participants highlight contrasts within a category, whereas inaccurate participants adjust their responses toward category centers.     

Dealing with Big Numbers: Representation and Understanding of Magnitudes Outside of Human Experience

Being able to estimate quantity is important in everyday life and for success in the STEM disciplines. However, people have difficulty reasoning about magnitudes outside of human perception (e.g., nanoseconds, geologic time). This study examines patterns of estimation errors across temporal and spatial magnitudes at large scales. We evaluated the effectiveness of hierarchical alignment in improving estimations, and transfer across dimensions. The activity was successful in increasing accuracy for temporal and spatial magnitudes, and learning transferred to the estimation of numeric magnitudes associated with events and objects. However, there were also a number of informative differences in performance on temporal, spatial, and numeric magnitude measures, suggesting that participants possess different categorical information for these scales. Educational implications are discussed.     

Changing the affective valence of the stimulus items influences the IAT by re-defining the category labels

The Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) is a computer-based categorization task that measures concept association strengths. Greenwald et al. (1998) demonstrated that participants completed the categorizations more quickly when pleasant and flower shared a response key than when pleasant and insect shared a response key, and when pleasant and White shared a response key than when pleasant and Black shared a response key. In Study 1, we reversed the typical IAT effect for flowers and insects, and eliminated the typical IAT effect for White and Black, by changing the affective valence of the stimulus items. In Study 2, we replicated the reversibility effect for an animal and plant IAT, and supported a category re-definition hypothesis. Our results have implications for understanding the IAT, and suggest that the IAT not only measures stereotypic responses, but can also be influenced by individuating information of the stimulus items.     

Contrasting rule-based and similarity-based category learning: The effects of mood and prior knowledge on ambiguous categorization

This study adopts a dual-system view of category learning. The findings suggest that consumers who learn a dominant feature as a verbal rule for a product category will classify a new ambiguous product according to that feature even if it more closely resembles a different product category. The findings also demonstrate that dominant features can bias categorization toward a less prototypical category in the event that the new product breaks the rule. We refer to this phenomenon as criterial inferencing. Lastly, we offer unique empirical evidence to suggest that mood influences category learning and thus attenuates the criterial inferencing bias.     

Restructuring partitioned knowledge: the role of recoordination in category learning

Knowledge restructuring refers to changes in the strategy with which people solve a given problem. Two types of knowledge restructuring are supported by existing category learning models. The first is a relearning process, which involves incremental updating of knowledge as learning progresses. The second is a recoordination process, which involves novel changes in the way existing knowledge is applied to the task. Whereas relearning is supported by both single- and multiple-module models of category learning, only multiple-module models support recoordination. To date, only relearning has been directly supported empirically. We report two category learning experiments that provide direct evidence of recoordination. People can fluidly alternate between different categorization strategies, and moreover, can reinstate an old strategy even after prolonged use of an alternative. The knowledge restructuring data are not well fit by a single-module model (ALCOVE). By contrast, a multiple-module model (ATRIUM) quantitatively accounts for recoordination. Low-level changes in the distribution of dimensional attention are shown to subsequently affect how ATRIUM coordinates its modular knowledge. We argue that learning about complex tasks occurs at the level of the partial knowledge elements used to generate a response strategy.     

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。     

Nature and facts about natural and artifactual categories: Sex differences in the semantic priming paradigm

There is abundant evidence from behavioral and neurophysiological experiments for the distinction of natural versus artifactual categories and a gender-specific difference: women’s performances in cognitive tasks increase when natural categories are used, whereas men’s performances increase with artifactual categories. Here, we used the semantic priming paradigm to study retrieval processes by presenting category labels as primes and exemplars as targets. Overall, in two experiments we found larger priming effects for natural than for artifactual categories. In addition, females showed positive priming effects for natural but negative effects for artifactual categories, whereas males showed positive priming effects for both categories. This pattern matches with that from other tasks and can be interpreted as evidence that the findings from these other tasks are, at least partially, indeed due to different representations or processing modes for males and females and not (exclusively) due to—for example—different familiarity with a category. In a further experiment, we showed that the found pattern for females can be manipulated by focusing on perceptual vs. functional features. The results can be interpreted as first evidence that there are (eventually in addition to different “crystallized” semantic structures) specific default processing modes that differ for males and females.     

Is ZF a hack?: Comparing the complexity of some (formalist interpretations of) foundational systems for mathematics

This paper presents Automath encodings (which are also valid in LF/λP) of various kinds of foundations of mathematics. Then it compares these encodings according to their size, to find out which foundation is the simplest.

The systems analyzed in this way are two kinds of set theory (ZFC and NF), two systems based on Church's higher order logic (Isabelle/Pure and HOL), three kinds of type theory (the calculus of constructions, Luo's extended calculus of constructions, and Martin-Löf's predicative type theory) and one foundation based on category theory.

The conclusions of this paper are that the simplest system is type theory (the calculus of constructions), but that type theories that know about serious mathematics are not simple at all. In that case the set theories are the simplest. If one looks at the number of concepts needed to explain such a system, then higher order logic is the simplest, with twenty-five concepts. On the other side of the scale, category theory is relatively complex, as is Martin-Löf's type theory.

(The full Automath sources of the contexts described in this paper are one the web at http://www.cs.ru.nl/~freek/zfc-etc/.)     

Beyond common features: the role of roles in determining similarity

Historically, accounts of object representation and perceived similarity have focused on intrinsic features. Although more recent accounts have explored how objects, scenes, and situations containing common relational structures come to be perceived as similar, less is known about how the perceived similarity of parts or objects embedded within these relational systems is affected. The current studies test the hypothesis that objects situated in common relational systems come to be perceived as more similar. Similarity increases most for objects playing the same role within a relation (e.g., predator), but also increases for objects playing different roles within the same relation (e.g., the predator or prey role in the hunts relation) regardless of whether the objects participate in the same instance of the relation. This pattern of results can be captured by extending existing models that extract meaning from text corpora so that they are sensitive to the verb-specific thematic roles that objects fill. Alternative explanations based on analogical and inferential processes are also considered, as well as the implications of the current findings to research in language processing, decision making, and category learning.