In recent years, theoretical perspectives on posterior parietal function have evolved beyond the traditional visuospatial processing models to include more diverse cognitive operations, such as long-term and working memory. However, definitive neuropsychological evidence supporting the superior parietal lobule's purported role in working memory has been lacking. Here, we studied human brain lesion patients to determine whether the superior parietal lobule is indeed necessary for working memory. We assessed a wide range of memory functions in three participant groups: superior parietal lesions (n= 19), lesions not involving superior parietal cortex (n= 146), and no brain lesions (n= 55). Superior parietal damage was reliably associated with deficits on tests involving the manipulation and rearrangement of information in working memory, but not on working memory tests requiring only rehearsal and retrieval processes, nor on tests of long-term memory. These results indicate that superior parietal cortex is critically important for the manipulation of information in working memory.

Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani, Barbey, & Johnson-Laird, 2014) and the causal model theory (Sloman, Barbey, & Hotaling, 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people’s ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed.

Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g.,N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. This Opinion article surveys recent neuroscience evidence to elucidate how general intelligence, g, emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g, represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation.