Strategic behavior plays a key role in fluid intelligence tasks like Raven’s matrices. Some participants solve items using the strategy of mentally constructing the answer (constructive matching), which is effective but costly for complex problems; other participants rely on the less accurate strategy of discarding potential answers (response elimination). While this process is relatively well-known, past research hints that intraindividual changes in strategy use may also take place as the task becomes increasingly difficult; however, intraindividual variability in Raven’s matrices is poorly understood. The present study aimed to (a) test the hypothesis that participants dynamically shift between strategies during the course of Raven’s matrices, as predicted by the literature, and (b) investigate the possibility that these shifts are moderated by individual differences in both ability and motivation. Two samples of 100 participants each completed Raven’s advanced progressive matrices, and measures of working memory and need for cognition. The results confirmed that participants tended to turn to the less costly response elimination strategy as the difficulty of the task increased; this variability in strategy use predicted 78% of item-to-item variance in accuracy. Working memory capacity and need for cognition predicted strategy use, and working memory capacity additionally moderated the shift toward response elimination, so that only participants with both high working memory capacity and high need for cognition continued using constructive matching in the later part of the task.

In this study, we tested the hypothesis that action planning is impaired in children with neurofibromatosis type 1 (NF1). Thirty-six children with NF1 were pair-matched to 36 healthy controls (HC) on age (range, 7-12 years), sex, and parental education level, and both groups were administered three action-planning tasks. To examine the relation of task performance to attention deficit hyperactivity disorder (ADHD), the NF1 group was divided into subsets of children who met or did not meet criteria for ADHD. Children with NF1 performed less well than HC on all planning tasks, and differences remained when controlling for IQ or a measure of visuospatial skill. Both the NF1 with ADHD subset and NF1 without ADHD subset performed more poorly than HC on two of the tasks, whereas only the NF1 with ADHD subset performed worse than HC on the third planning task. The results underscore the importance of evaluating executive function in children with NF1 and suggest that deficits in this domain may be only partially related to ADHD. Planning deficits in children with NF1 may be part of their cognitive phenotype. Identifying these deficits is relevant in determining factors contributing to learning problems and in developing appropriate interventions.

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T2-weighted hyperintensities, as they are currently measured, cannot be used as a strong indicator of executive dysfunction in children with NF1. Based on the available NF1 cognitive impairment pathogenesis models, a critical discussion on anatomical-functional relationships between hyperintensities and neuropsychological profile is proposed, especially the executive dysfunction.