Greater response variability in adolescents is associated with increased white matter development.

Adolescence is a period of learning, exploration, and continuous adaptation to fluctuating environments. Response variability during adolescence is an important, understudied, and developmentally appropriate behavior. The purpose of this study was to identify the association between performance on a dynamic risky decision making task and white matter microstructure in a sample of 48 adolescents (14-16 years). Individuals with the greatest response variability on the task obtained the widest range of experience with potential outcomes to risky choice. When compared with their more behaviorally consistent peers, adolescents with greater response variability rated real-world examples of risk taking behaviors as less risky via self-report. Tract-Based Spatial Statistics (TBSS) were used to examine fractional anisotropy (FA) and mean diffusivity (MD). Greater FA in long-range, late-maturing tracts was associated with higher response variability. Greater FA and lower MD were associated with lower riskiness ratings of real-world risky behaviors. Results suggest that response variability and lower perceived risk attitudes of real-world risk are supported by neural maturation in adolescents

Network constraints on learnability of probabilistic motor sequences

Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.Comment: 29 pages, 4 figure

Selection-Based Learning: The Coevolution Of Internal And External Selection In High-Velocity Environments

To understand the effects of selection on firm-level learning, this study synthesizes two contrasting views of evolution. Internal selection theorists view managers in multiproduct firms as the primary agents of evolutionary change because they decide whether individual products and technologies are retained or eliminated. In contrast, external selection theorists contend that the environment drives evolution because it determines whether entire firms live or die. Though these theories differ, they describe tightly interwoven processes. In assessing the coevolution of internal and external selection among personal computer manufacturers across a 20-year period, we found that (1) firms learned cumulatively and adaptively from internal and partial external selection, the latter occurring when the environment killed part but not all of a firm; (2) internal and partial external selection coevolved, as each affected the other's future rate and the odds of firm failure; (3) partial external selection had a greater effect on future outcomes than internal selection; and (4) the lessons gleaned from prior selection were reflected in a firm's ability to develop new products, making that an important mediator between past and future selection events.Managemen

HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations

Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of the task matures during training. We propose a novel HYperbolic Self-Paced model (HYSP) for learning skeleton-based action representations. HYSP adopts self-supervision: it uses data augmentations to generate two views of the same sample, and it learns by matching one (named online) to the other (the target). We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace. Hyperbolic uncertainty is a by-product of the adopted hyperbolic neural networks, it matures during training and it comes with no extra cost, compared to the established Euclidean SSL framework counterparts. When tested on three established skeleton-based action recognition datasets, HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3 downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive pairs and bypasses therefore the complex and computationally-demanding mining procedures required for the negatives in contrastive techniques. Code is available at https://github.com/paolomandica/HYSP.Comment: Accepted at ICLR 202

The influence of serial carbohydrate mouth rinsing on power output during a cycle sprint

The objective of the study was to investigate the influence of serial administration of a carbohydrate (CHO) mouth rinse on performance, metabolic and perceptual responses during a cycle sprint. Twelve physically active males (mean (± SD) age: 23.1 (3.0) years, height: 1.83 (0.07) m, body mass (BM): 86.3 (13.5) kg) completed the following mouth rinse trials in a randomized, counterbalanced, double-blind fashion; 1. 8 x 5 second rinses with a 25 ml CHO (6% w/v maltodextrin) solution, 2. 8 x 5 second rinses with a 25 ml placebo (PLA) solution. Following mouth rinse administration, participants completed a 30 second sprint on a cycle ergometer against a 0.075 g·kg-1 BM resistance. Eight participants achieved a greater peak power output (PPO) in the CHO trial, resulting in a significantly greater PPO compared with PLA (13.51 ± 2.19 vs. 13.20 ± 2. 14 W·kg-1, p < 0.05). Magnitude inference analysis reported a likely benefit (81% likelihood) of the CHO mouth rinse on PPO. In the CHO trial, mean power output (MPO) showed a trend for being greater in the first 5 seconds of the sprint and lower for the remainder of the sprint compared with the PLA trial (p > 0.05). No significant between-trials difference was reported for fatigue index, perceived exertion, arousal and nausea levels, or blood lactate and glucose concentrations. Serial administration of a CHO mouth rinse may significantly improve PPO during a cycle sprint. This improvement appears confined to the first 5 seconds of the sprint, and may come at a greater relative cost for the remainder of the sprint