Mathematical transcription of the 'time-based resource sharing' theory of working memory

Working memory complex span tasks and fluid intelligence: Does the positional structure of the task matter?

The complex span task used to evaluate working memory (WM) capacity has been considered to be the most predictive task of fluid intelligence. However, the structure of the complex span tasks varies from one study to another, and it has not been questioned yet whether these variants could influence the predictive power of these tasks. Previous studies have typically used either structures based on alternating processing-storage patterns or alternating storage-processing patterns. We present one experiment in which the participants were submitted to both the processing-storage vs. storage-processing types. After completing both types of complex span tasks, the participants performed a reasoning test (Matrix Reasoning of the Wechsler Adult Intelligence Scale - WAIS-IV). The results showed a significant difference in the WM spans between the two conditions, with higher spans observed in the processing-storage alternating structure, and different serial position curves. However, the correlations showed that both types of tasks remained equally predictive of performance in the reasoning test. These results are discussed in regard to the time-based resource-sharing model.

Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.