Knowledge that Transforms

In probabilistic categorization tasks, the correct category is determined only probabilistically by the stimulus pattern Data from such experiments have been successfully accounted for by a simple network model, but have posed difficulties for exemplar models In the present article, we consider an exemplar model, CLEM (concept learning by exemplar memorization), which differs from previously tested exemplar models in that exemplar traces are assumed to be stored only when the subject has guessed or made a classification error Fits of CLEM to both learning and test data were comparable to those of the network model, and better than those obtained for a version of CLEM in which encoding was independent of the subject's response The implications of these results for the processes underlying classification decisions are discussed

Theories in cognitive science have debated whether visual selective attention is a space-based or object-based process To investigate this issue, we applied a new experimental paradigm that permits the simultaneous measurement of both space-based and object-based attention to a split-brain patient with disconnected cerebral hemispheres The data demonstrate both space-based and object-based components to the allocation of attention, and reveal that the two processes have different neural substrates These findings are related to previous research on split-brain and unilateral parietal patients

The “flash of insight” sometimes observed in problem solving and in scientific discovery has been thought to be due to a sudden cognitive restructuring of the problem situation Direct confirmation of restructuring has been difficult without an independent procedure for determining cognitive structure Graph structures were derived from judgments of concept relatedness made by subjects who had an insight and by several groups who either did not or could not have the insight The graphs of the solvers differed from the graphs of subjects who tried and failed, those who listened to the solvers, and those who were given the solution When other subjects in a subsequent experiment repeatedly judged similarity of pairs of concepts, there was evidence that those connections critical to the new cognitive order were targeted long before there was the breathtaking cognitive reorganization

We introduce a new, readily computed statistic, the counternull value of an obtained effect size, which is the nonnull magnitude of effect size that is supported by exactly the same amount of evidence as supports the null value of the effect size In other words, if the counternull value were taken as the null hypothesis, the resulting p value would be the same as the obtained p value for the actual null hypothesis Reporting the counternull, in addition to the p value, virtually eliminates two common errors (a) equating failure to reject the null with the estimation of the effect size as equal to zero and (b) taking the rejection of a null hypothesis on the basis of a significant p value to imply a scientifically important finding In many common situations with a one-degree-of-freedom effect size, the value of the counternull is simply twice the magnitude of the obtained effect size, but the counternull is defined in general, even with multi-degree-of-freedom effect sizes, and therefore can be applied when a confidence interval cannot be The use of the counter-null can be especially useful in meta-analyses when evaluating the scientific importance of summary effect sizes