Considerations Relating to the Study of Group Differences in Intelligence

ABSTRACT— There are signs that the debate over racial and gender differences in intelligence is about to begin again. In this article we will be concerned primarily with racial differences but will make remarks about gender differences where applicable. Previously there have been bitter arguments over whether or not races exist, over whether it is either important or proper to study racial and gender differences in intelligence, and over the conclusions that have been drawn about environmental and genetic causes as determinants of these differences. We argue that races do, indeed, exist and that studying differences in cognitive competence between groups is a reasonable thing to do. We also point out that past research on both racial and gender differences in intelligence has been marked by methodological errors and overgeneralizations by researchers on all sides of the issue. We propose ten principles of design, analysis, and reporting that ought to be considered carefully when doing or evaluating research in this area.     

Evil and theistic minimalism

International Journal for Philosophy of Religion -     

Statistical learning in a serial reaction time task: access to separable statistical cues by individual learners.

The ability of adult learners to exploit the joint and conditional probabilities in a serial reaction time task containing both deterministic and probabilistic information was investigated. Learners used the statistical information embedded in a continuous input stream to improve their performance for certain transitions by simultaneously exploiting differences in the predictability of 2 or more underlying statistics. Analysis of individual learners revealed that although most acquired the underlying statistical structure veridically, others used an alternate strategy that was partially predictive of the sequences. The findings show that learners possess a robust learning device well suited to exploiting the relative predictability of more than I source of statistical information at the same time. This work expands on previous studies of statistical learning, as well as studies of artificial grammar learning and implicit sequence learning.