If you believe that breaking is possible, believe also that fixing is possible: a framework for ruptures and repairs in child psychotherapy

Safran and Muran’s classic theoretical framework of alliance rupture and repair suggests effective techniques for repairing alliance ruptures. Accumulating empirical evidence suggests that successful processes of rupture and repair result in better therapeutic outcome and reduced dropout rates. Although ruptures in the alliance in child psychotherapy are frequent, little is known about how to repair them. The present paper proposes a model for identifying and repairing ruptures in child psychotherapy based on Safran and Muran. It consists of four phases: i) identifying the rupture and understanding its underlying communication message, ii) indicating the presence of the rupture, iii) accepting responsibility over the therapists’ part in the rupture and emphasizing the children’s active role as communicators of their distress, and iv) resolving the rupture using change strategies and meta-communication by constructing a narrative story. The theoretical rationale of each phase is explained in detail, and practical clinical guidelines are provided. Empirical studies are needed to examine the effectiveness of the proposed framework

Revealing principles of autonomous thermal soaring in windy conditions using vulture-inspired deep reinforcement-learning

Abstract Thermal soaring, a technique used by birds and gliders to utilize updrafts of hot air, is an appealing model-problem for studying motion control and how it is learned by animals and engineered autonomous systems. Thermal soaring has rich dynamics and nontrivial constraints, yet it uses few control parameters and is becoming experimentally accessible. Following recent developments in applying reinforcement learning methods for training deep neural-network (deep-RL) models to soar autonomously both in simulation and real gliders, here we develop a simulation-based deep-RL system to study the learning process of thermal soaring. We find that this process has learning bottlenecks, we define a new efficiency metric and use it to characterize learning robustness, we compare the learned policy to data from soaring vultures, and find that the neurons of the trained network divide into function clusters that evolve during learning. These results pose thermal soaring as a rich yet tractable model-problem for the learning of motion control