Simple deterministic and complex probabilistic regularities underlying risky choices are acquired in a changing decision context

Beliefs about positive and negative outcome probabilities have been frequently investigated in experience-based risky decision making. However, it has not been clarified how these beliefs emerge and whether they remain persistent if the predictability and complexity of outcome probabilities change across decision contexts. Hence, the present study manipulated these two factors in a variant of the Balloon Analogue Risk Task performed by healthy young adults. In the first and final task phases, outcomes (rewards or balloon bursts) were predictable because of the presence of an underlying regularity. In the middle phase, outcomes were unpredictable because the regularity was absent. The complexity of the regularity differed across the deterministic, probabilistic, and hybrid experimental conditions. In the simple deterministic condition, a repeating sequence of three deterministic regularities perfectly predicted balloon bursts. In the more complex probabilistic condition, a single probabilistic regularity ensured that the probability of balloon bursts increased with each successive pump. In the most complex hybrid condition, a repeating sequence of three different probabilistic regularities increased burst probabilities. Even without informing participants about the presence or absence of the regularity, sensitivity to both the simple deterministic and the most complex hybrid regularities emerged and influenced risk taking. Unpredictable outcomes of the middle phase did not deteriorate the acquired sensitivity to these regularities. When only a single probabilistic regularity was present, predictable and unpredictable outcomes were processed similarly. In conclusion, assuming the reappearance of the initially experienced regularity, the robustness of representations might serve fast adaptation in a volatile decision environment.

Genome-Wide Investigation and Analysis of Microsatellites and Compound Microsatellites in Leptolyngbya-like Species, Cyanobacteria

Microsatellites (simple sequence repeats, SSRs) are ubiquitously distributed in almost all known genomes. Here, the first investigation was designed to examine the SSRs and compound microsatellites (CSSRs) in genomes of Leptolyngbya-like strains. The results disclosed diversified patterns of distribution, abundance, density, and diversity of SSRs and CSSRs in genomes, indicating that they may be subject to rapid evolutionary change. The numbers of SSRs and CSSRs were extremely unevenly distributed among genomes, ranging from 11,086 to 24,000 and from 580 to 1865, respectively. Dinucleotide SSRs were the most abundant category in 31 genomes, while the other 15 genomes followed the pattern: mono- > di- > trinucleotide SSRs. The patterns related to SSRs and CSSRs showed differences among phylogenetic groups. Both SSRs and CSSRs were overwhelmingly distributed in coding regions. The numbers of SSRs and CSSRs were significantly positively correlated with genome size (p < 0.01) and negatively correlated with GC content (p < 0.05). Moreover, the motif (A/C)n and (AG)n was predominant in mononucleotide and dinucleotide SSRs, and unique motifs of CSSRs were identified in 39 genomes. This study provides the first insight into SSRs and CSSRs in genomes of Leptolyngbya-like strains and will be useful to understanding their distribution, predicting their function, and tracking their evolution. Additionally, the identified SSRs may provide an evolutionary advantage of fast adaptation to environmental changes and may play an important role in the cosmopolitan distribution of Leptolyngbya strains to globally diverse niches.

Fast and Slow Adaptations of Interlimb Coordination via Reflex and Learning During Split-Belt Treadmill Walking of a Quadruped Robot

Interlimb coordination plays an important role in adaptive locomotion of humans and animals. This has been investigated using a split-belt treadmill, which imposes different speeds on the two sides of the body. Two types of adaptation have been identified, namely fast and slow adaptations. Fast adaptation induces asymmetric interlimb coordination soon after a change of the treadmill speed condition from same speed for both belts to different speeds. In contrast, slow adaptation slowly reduces the asymmetry after fast adaptation. It has been suggested that these adaptations are primarily achieved by the spinal reflex and cerebellar learning. However, these adaptation mechanisms remain unclear due to the complicated dynamics of locomotion. In our previous work, we developed a locomotion control system for a biped robot based on the spinal reflex and cerebellar learning. We reproduced the fast and slow adaptations observed in humans during split-belt treadmill walking of the biped robot and clarified the adaptation mechanisms from a dynamic viewpoint by focusing on the changes in the relative positions between the center of mass and foot stance induced by reflex and learning. In this study, we modified the control system for application to a quadruped robot. We demonstrate that even though the basic gait pattern of our robot is different from that of general quadrupeds (due to limitations of the robot experiment), fast and slow adaptations that are similar to those of quadrupeds appear during split-belt treadmill walking of the quadruped robot. Furthermore, we clarify these adaptation mechanisms from a dynamic viewpoint, as done in our previous work. These results will increase the understanding of how fast and slow adaptations are generated in quadrupedal locomotion on a split-belt treadmill through body dynamics and sensorimotor integration via the spinal reflex and cerebellar learning and help the development of control strategies for adaptive locomotion of quadruped robots.

The Macrophage Iron Signature in Health and Disease

Throughout life, macrophages are located in every tissue of the body, where their main roles are to phagocytose cellular debris and recycle aging red blood cells. In the tissue niche, they promote homeostasis through trophic, regulatory, and repair functions by responding to internal and external stimuli. This in turn polarizes macrophages into a broad spectrum of functional activation states, also reflected in their iron-regulated gene profile. The fast adaptation to the environment in which they are located helps to maintain tissue homeostasis under physiological conditions.

Meta-Reinforcement Learning by Tracking Task Non-stationarity

Many real-world domains are subject to a structured non-stationarity which affects the agent's goals and the environmental dynamics. Meta-reinforcement learning (RL) has been shown successful for training agents that quickly adapt to related tasks. However, most of the existing meta-RL algorithms for non-stationary domains either make strong assumptions on the task generation process or require sampling from it at training time. In this paper, we propose a novel algorithm (TRIO) that optimizes for the future by explicitly tracking the task evolution through time. At training time, TRIO learns a variational module to quickly identify latent parameters from experience samples. This module is learned jointly with an optimal exploration policy that takes task uncertainty into account. At test time, TRIO tracks the evolution of the latent parameters online, hence reducing the uncertainty over future tasks and obtaining fast adaptation through the meta-learned policy. Unlike most existing methods, TRIO does not assume Markovian task-evolution processes, it does not require information about the non-stationarity at training time, and it captures complex changes undergoing in the environment. We evaluate our algorithm on different simulated problems and show it outperforms competitive baselines.

Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning

Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the difficulty in reproducing the data distribution hinder directly applying meta-learning techniques. Although the knowledge of governing partial differential equations (PDE) of the data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems. In this work, we propose a framework, physics-aware meta-learning with auxiliary tasks, whose spatial modules incorporate PDE-independent knowledge and temporal modules utilize the generalized features from the spatial modules to be adapted to the limited data, respectively. The framework is inspired by a local conservation law expressed mathematically as a continuity equation and does not require the exact form of governing equation to model the spatiotemporal observations. The proposed method mitigates the need for a large number of real-world tasks for meta-learning by leveraging spatial information in simulated data to meta-initialize the spatial modules. We apply the proposed framework to both synthetic and real-world spatiotemporal prediction tasks and demonstrate its superior performance with limited observations.