The speed of moving stimuli can bias duration perception. Here, we investigated whether words describing different speeds influence subjective duration estimation in a temporal bisection task. Duration estimations of two different types of speed words (fast– vs. slow–speed words) were compared. We found that the time bisection point was significantly lower for fast-speed words than for slow-speed words, suggesting that the durations of fast-speed words were overestimated compared to the slow-speed words. In contrast, fast- and slow-speed words did not significantly differ in just noticeable differences and Weber fractions, indicating that the types of speed words did not influence the sensitivity of duration estimation. These results provide new evidence to support the theory of embodied cognition in the context of implicit meaning of a speed word.

The triple network model that consists of the default-mode network (DMN), central-executive network (CEN), and salience network (SN) has been suggested as a powerful paradigm for investigation of network mechanisms underlying various cognitive functions and brain disorders. A crucial hypothesis in this model is that the fronto-insular cortex (FIC) in the SN plays centrally in mediating interactions between the networks. Using a machine learning approach based on independent component analysis and Bayesian network (BN), this study characterizes the directed connectivity architecture of the triple network and examines the role of FIC in connectivity of the model. Data-driven exploration shows that the FIC initiates influential connections to all other regions to globally control the functional dynamics of the triple network. Moreover, stronger BN connectivity between the FIC and regions of the DMN and the CEN, as well as the increased outflow connections from the FIC are found to predict individual performance in memory and executive tasks. In addition, the posterior cingulate cortex in the DMN was also confirmed as an inflow hub that integrates information converging from other areas. Collectively, the results highlight the central role of FIC in mediating the activity of large-scale networks, which is crucial for individual cognitive function.

Increasing evidence suggests that functional brain connectivity is an important determinant of cognitive aging. However, the fundamental concept of inter-individual variations in functional connectivity in older individuals is not yet completely understood. It is essential to evaluate the extent to which inter-individual variability in connectivity impacts cognitive performance at an older age. In the current study, we aimed to characterize individual variability of functional connectivity in the elderly and to examine its significance to individual cognition. We mapped inter-individual variability of functional connectivity by analyzing whole-brain functional connectivity magnetic resonance imaging data obtained from a large sample of cognitively normal older adults. Our results demonstrated a gradual increase in variability in primary regions of the visual, sensorimotor, and auditory networks to specific subcortical structures, particularly the hippocampal formation, and the prefrontal and parietal cortices, which largely constitute the default mode and fronto-parietal networks, to the cerebellum. Further, the inter-individual variability of the functional connectivity correlated significantly with the degree of cognitive relevance. Regions with greater connectivity variability demonstrated more connections that correlated with cognitive performance. These results also underscored the crucial function of the long-range and inter-network connections in individual cognition. Thus, individual connectivity–cognition variability mapping findings may provide important information for future research on cognitive aging and neurocognitive diseases.

No abstract