Biomedical subjects

Christian C Luhmann

Publications and source records attributed to Christian C Luhmann.

3 recordsLinked to original sources

PubMed2006

Theory-based categorization under speeded conditions.

It is widely accepted that similarity influences rapid categorization, whereas theories can influence only more leisurely category judgments. In contrast, we argue that it is not the type of knowledge used that determines categorization speed, but rather the complexity of the categorization processes. In two experiments, participants learned four categories of items, each consisting of three causally related features. Participants gave more weight to cause features than to effect features, even under speeded response conditions. Furthermore, the time required to make judgments was equivalent, regardless of whether participants were using causal knowledge or base-rate information. We argue that both causal knowledge and base-rate information, once precompiled during learning, can be used at roughly the same speeds during categorization, thus demonstrating an important parallel between these two types of knowledge.

Association Learning↗

PubMed2005

The meaning and computation of causal power: comment on Cheng (1997) and Novick and Cheng (2004).

D. Hume (1739/1987) argued that causality is not observable. P. W. Cheng (1997) claimed to present "a theoretical solution to the problem of causal induction first posed by Hume more than two and a half centuries ago" (p. 398) in the form of the power PC theory (L. R. Novick & P. W. Cheng, 2004). This theory claims that people's goal in causal induction is to estimate causal powers from observable covariation and outlines how this can be done in specific conditions. The authors first demonstrate that if the necessary assumptions were ever met, causal powers would be self-evident to a reasoner--they are either 0 or 1--making the theory unnecessary. The authors further argue that the assumptions the power PC theory requires to compute causal power are unobtainable in the real world and, furthermore, people are aware that requisite assumptions are violated. Therefore, the authors argue that people do not attempt to compute causal power.

Cognition↗

PubMed2002

Effect of theory-based feature correlations on typicality judgments.

In the present study, we examine what types of feature correlations are salient in our conceptual representations. It was hypothesized that of all possible feature pairs, those that are explicitly recognized as correlated (i.e., explicit pairs) and affect typicality judgments are the ones that are more likely theory based than are those that are not explicitly recognized (i.e., implicit pairs). Real-world categories and their properties, taken from Malt and Smith (1984), were examined. We found that explicit pairs had a greater number of asymmetric dependency relations (i.e., one feature depends on the other feature, but not vice versa) and stronger dependency relations than did implicit pairs, which were statistically correlated in the environment but were not recognized as such. In addition, people more often provided specific relation labels for explicit pairs than for implicit pairs; these labels were most often causal relations. Finally, typicality judgments were more affected when explicit correlations were broken than when implicit correlations were broken. It is concluded that in natural categories, feature correlations that are explicitly represented and affect typicality judgments are the ones about which people have theories.

Adult↗