Classification as diagnostic reasoning

An ongoing goal in the field of categorization has been to determine how objects’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed.     

Branded Into Submission: Brand Attributes and Hierarchization Behavior in Same-Sex and Mixed-Sex Dyads

In 2 experiments, the role of brand attributes in the process of nonconscious hierarchization in dyadic interactions was examined. Experiment 1 showed that in same-sex dyads, brands that are associated with an agent and that are rated high on the brand personality dimension of competence ( Aaker, 1997 ) induced patterns of nonverbal submissive behavior on the part of the interaction partner. This effect was more pronounced for individuals with a low intrinsic tendency for dominant behavior. Experiment 2 extended these findings to mixed-sex dyads and brands that varied in overall status. Moreover, participant gender moderated the effects, such that the impact of brand status on hierarchization behavior and on agent likability was more pronounced for women than for men.     

Influence of object size on baseline identification, priming, and explicit memory

We investigated the influence of size on identification, priming, and explicit memory for color photos of common objects. Participants studied objects displayed in small, medium, and large sizes and memory was assessed with both implicit identification and explicit recognition tests. Overall, large objects were easier to identify than small objects and study-to-test changes in object size impeded performance on explicit but not implicit memory tests. In contrast to previous findings with line-drawings of objects but consistent with predictions from the distance-as-filtering hypothesis, we found that study-test size manipulations had large effects on old/new recognition memory test for objects displayed in large size at test but not for objects displayed small or medium at test. Our findings add to the growing body of literature showing that the findings obtained using line-drawings of objects do not necessarily generalize to color photos of common objects. We discuss implications of our findings for theories of object perception, memory, and eyewitness identification accuracy for objects.     

A simple electrode to record fetal EEG for optimal signal analysis

A simple EEG electrode for Intrauterine use In the human fetus is described. The stainless steel screw electrode possesses low noise characteristics, is constructed from commercially available material, and is easily attached to the fetal head during labor. Principles of optical signal analysis as an improved method of spectral analysis of the fetal EEG are also presented. The optical system provides visual display and hard copy output of the fetal EEG.     

A causal-model theory of conceptual representation and categorization

This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.     

A knowledge-resonance (KRES) model of category learning

This article introduces a connectionist model of category learning that takes into account the prior knowledge that people bring to new learning situations. In contrast to connectionist learning models that assume a feedforward network and learn by the delta rule or backpropagation, this model, the knowledge-resonance model, or KRES, employs a recurrent network with bidirectional symmetric connection whose weights are updated according to a contrastive Hebbian learning rule. We demonstrate that when prior knowledge is represented in the network, KRES accounts for a considerable range of empirical results regarding the effects of prior knowledge on category learning, including (1) the accelerated learning that occurs in the presence of knowledge, (2) the better learning in the presence of knowledge of category features that are not related to prior knowledge, (3) the reinterpretation of features with ambiguous interpretations in light of error-corrective feedback, and (4) the unlearning of prior knowledge when that knowledge is inappropriate in the context of a particular category.     

Essence and definition by abstraction

We may define words or concepts, and we may also, as Aristotle and others have thought, define the things for which words stand and of which concepts are concepts. Definitions of words or concepts may be explicit or implicit, and may seek to report preexisting synonymies, as Quine put it, but they may instead be wholly or partly stipulative. Definition by abstraction, of which Hume’s principle is a much discussed example, seek to define a term-forming operator, such as the number operator, by fixing the truth-conditions of identity-statements featuring terms formed by means of that operator. Such definitions are a species of implicit definition. They are typically at least partly stipulative. Definitions of things, or real definitions, are, by contrast, typically conceived as true or false statements about the nature or essence of their definienda, and so not stipulative. There thus appears to be an obvious and head-on clash between taking Hume’s principle as an implicit and at least partly stipultative definition of the number operator and taking it as a real definition, stating the nature or essence of cardinal numbers. This paper argues that this apparent tension can be resolved, and that resolving it sheds light on part of the epistemology or essence and necessity, showing how some of our knowledge of essence and necessity can be a priori.

     

Probabilistic constraint satisfaction at the lexical/phonetic interface: evidence for gradient effects of within-category VOT on lexical access

Research in speech perception has been dominated by a search for invariant properties of the signal that correlate with lexical and sublexical categories. We argue that this search for invariance has led researchers to ignore the perceptual consequences of systematic variation within such categories and that sensitivity to this variation may provide an important source of information for integrating information over time in speech perception. Data from a study manipulating VOT continua in words using an eye-movement paradigm indicate that lexical access shows graded sensitivity to within-category variation in VOT and that this sensitivity has a duration sufficient to be useful for information integration. These data support a model in which the perceptual system integrates information from multiple sources and from the surrounding temporal context using probabilistic cue-weighting mechanisms.     

How causal knowledge affects classification: A generative theory of categorization

Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes.