Classification as diagnostic reasoning

Rehder, Bob; Kim, ShinWoo

doi:10.3758/mc.37.6.715

Cited by 26 publications

(25 citation statements)

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“…Consistent with this interpretation, Heit and Bott found that learners classified a feature as "Doe" even if it was never observed during training so long as their prior knowledge indicated that it was typical of churches. Other studies provide evidence of the inferences in service of classification that knowledge supports (Rehder & Kim, 2009;Rehder & Ross, 2001). Indeed, recall Murphy and Medin's (1985) example of classifying a partygoer who jumps into a pool as drunk-one reasons from aberrant behavior to its underlying cause even if one has never before observed drunken swimming.…”

Section: Discussionmentioning

confidence: 99%

How prior knowledge affects selective attention during category learning: An eyetracking study

Kim

Rehder

2010

Mem Cogn

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Research has shown that category learning is affected by (a) attention, which selects which aspects of stimuli are available for further processing, and (b) the existing semantic knowledge that learners bring to the task. However, little is known about how knowledge affects what is attended. Using eyetracking, we found that (a) knowledge indeed changes what features are attended, with knowledge-relevant features being fixated more often than irrelevant ones, (b) this effect was not due to an initial attentional bias toward relevant dimensions but rather emerged gradually as a result of observing category members, and (c) this effect grew even after a learning criterion was reached, that is, despite the absence of negative feedback. We argue that models of knowledgebased learning will remain incomplete until they specify mechanisms that dynamically select prior knowledge in response to observed category members and which then directs attention to knowledge-relevant dimensions and away from irrelevant ones.

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Section: Discussionmentioning

confidence: 99%

How prior knowledge affects selective attention during category learning: An eyetracking study

Kim

Rehder

2010

Mem Cogn

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“…A second basis for predicting a larger causal status effect in the Essentialized-Chain-80 condition stems from the fact that we expected that participants might construe the classification test as a causal reasoning task. For example, Rehder and Kim (2009a) found that a feature was more diagnostic of category membership when it was caused by an underlying essential feature because participants reasoned backwards from the observed to the essential feature and decided category membership on that basis. This implies a stronger causal status effect in the present EssentializedChain-80 condition because only feature X provides direct inferential support for E, contributing further to X's greater classification weight relative to Y and Z.…”

Section: Methodsmentioning

confidence: 99%

“…Objects that are likely to have been generated by a category's causal model are considered to be good category members, and those unlikely to be generated are poor category members. Quantitative predictions for the generative model can be derived assuming a particular representation of causal relations first introduced by Cheng (1997) and later applied to a variety of categorybased tasks (Rehder, 2003a(Rehder, , 2003bRehder, 2009;Rehder & Burnett, 2005;Rehder & Hastie, 2001;Rehder & Kim, 2006, 2009a. For example, for the causal model in Figure 1, assume that the causal mechanism relating feature j and its parent i operates (i.e., produces j) with probability m ij when i is present and that any other potential background causes of j collectively operate with probability b j .…”

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confidence: 99%

Causal status and coherence in causal-based categorization

Rehder¹,

Kim²

2009

PsycEXTRA Dataset

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Research has documented two effects of interfeature causal knowledge on classification. A causal status effect occurs when features that are causes are more important to category membership than their effects. A coherence effect occurs when combinations of features that are consistent with causal laws provide additional evidence of category membership. In this study, we found that stronger causal relations led to a weaker causal status effect and a stronger coherence effect (Experiment 1), that weaker alternative causes led to stronger causal status and coherence effects (Experiment 2), and that "essentialized" categories led to a stronger causal status effect (Experiment 3), albeit only for probabilistic causal links (Experiment 4). In addition, the causal status effect was mediated by features' subjective category validity, the probability they occur in category members. These findings were consistent with a generative model of categorization but inconsistent with an alternative model. Keywords: categorization, causal knowledge, conceptual representation, causal model theory, generative modelSupplemental materials: http://dx.doi.org/10.1037/a0019765.supp An important goal in the psychology of concepts is to identify how both empirical information-information that people directly observe-and theoretical beliefs involving explanatory and causal knowledge contribute to how we represent and use categories. Although early research into concepts focused on the effect of empirical information, subsequent research has shown that theoretical beliefs influence many types of category-based judgments, including learning, induction, and categorization (see Murphy, 2002, for a review). In this article, we are concerned with how one type of theoretical knowledge, namely, causal knowledge that relates features of categories, affects one type of category-based judgment, classification.The presence of causal relationships between category features is pervasive. For example, we know that having claws enables tigers to catch prey, having gills enables fish to breathe, and having a fan causes an automobile's engine to remain cool. Not surprisingly then, numerous studies have investigated how this knowledge affects classification. Some studies have tested naturally occurring categories and the real-world causal knowledge that people already possess about those categories (e.g., Ahn, 1998;Kim & Ahn, 2002;Sloman, Love, & Ahn, 1998). However, to conduct precise tests of alternative models, investigators have turned to artificial categories that are subject to greater experimental control. In these studies, participants are instructed on new types of objects and their features and then are taught causal relations among those features. To assess the effect of those causal relations, participants are then asked to judge the category membership of items displaying various combinations of features. Note that although the categories are artificial, they are intended to be plausible, that is, to consist of features and interfeature causal re...

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“…Their conclusion that complexity facilitates learning when the dimensions are organized in a coherent manner may arise because the intercorrelated complex generates a prototype-like representation or integrated unit that better maintains memory for later judgments that tap these relations. The introduction of shaping variables (Homa, 1984), such as category size, pattern variance, and so forth, and the manipulation of the types of associated features, such as causal links (Rehder & Kim, 2009), when combined with categorical structure variables as in the present study, might further elucidate conditions that foster the category label as a special feature. A theoretically productive line of research might be to build in feature correlations, as in the present study, modified (perhaps by instructions) such that some, but not all, features were causal.…”

Section: Test Label Test Featurementioning

confidence: 95%

Category inference as a function of correlational structure, category discriminability, and number of available cues

2012

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Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.Keywords Categorization . Concepts . Inductive reasoning . Expertise . MemoryThe experimental study of human categories has an extensive history, beginning with the seminal study of Hull (1920) and continuing today into the identification of variables critical to the shaping of concepts and the development of formal, quantitative models of classification.Hull introduced the classification paradigm that dominates most current research today. In this paradigm, the subject initially assigns a number of patterns into designated categories, followed by a transfer test containing old and new instances. By manipulation of variables in the learning phase and then evaluating transfer performance, Hull was able to draw a number of conclusions about the learning and representation of concepts; for example, concepts were learned more rapidly in the order from simple to complex rather than the reverse, transfer was better following learning of many patterns shown infrequently rather than a few patterns presented numerous times, and so forth.However, categories provide functions above and beyond classification. Bruner, Goodnow, and Austin (1966) summarized a number of additional utilities of categories: Once learned, they permit generalization to novel instances, thereby reducing the need for new learning; they simplify the incredible complexity of the environment into a manageable set of units, thereby facilitating a host of cognitive functions, including logical reasoning and communication; they are adaptive so that harmful or threatening stimuli can be responded to appropriately; and they permit i...

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Classification as diagnostic reasoning

Cited by 26 publications

References 64 publications

How prior knowledge affects selective attention during category learning: An eyetracking study

How prior knowledge affects selective attention during category learning: An eyetracking study

Causal status and coherence in causal-based categorization

Category inference as a function of correlational structure, category discriminability, and number of available cues

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