Beyond kindness: a proposal for the flourishing of science and scientists alike.

We argue that many of the crises currently afflicting science can be associated with a present failure of science to sufficiently embody its own values. Here, we propose a response beyond mere crisis resolution based on the observation that an ethical framework of flourishing derived from the Buddhist tradition aligns surprisingly well with the values of science itself. This alignment, we argue, suggests a recasting of science from a competitively managed activity of knowledge production to a collaboratively organized moral practice that puts kindness and sharing at its core. We end by examining how Flourishing Science could be embodied in academic practice, from individual to organizational levels, and how that could help to arrive at a flourishing of scientists and science alike.

The lived experience of remembering a 'good' interview: Micro-phenomenology applied to itself.

Micro-phenomenology is an interview and analysis method for investigating subjective experience. As a research tool, it provides detailed descriptions of brief moments of any type of subjective experience and offers techniques for systematically comparing them. In this article, we use an auto-ethnographic approach to present and explore the method. The reader is invited to observe a dialogue between two authors that illustrates and comments on the planning, conducting and analysis of a pilot series of five micro-phenomenological interviews. All these interviews asked experienced researchers of micro-phenomenology to browse their memories to identify one successful and one challenging instance of working with micro-phenomenology. The interview then focused on this reflective task to investigate whether applying the method to itself might reveal quality criteria. The article starts by presenting a shortened and edited version of the first of these interviews. Keeping the dialogue format, we then outline the micro-phenomenological analysis procedure by demonstrating its application to part of this data and corresponding passages of other interviews. We focus on one unexpected finding: interviewed researchers judge the quality of an interview in part based on a connection or contact between interviewer and interviewee. We discuss these results in the context of the means and intentions of the method and suggest avenues for future research.

Socializing Sensorimotor Contingencies.

The aim of this review is to highlight the idea of grounding social cognition in sensorimotor interactions shared across agents. We discuss an action-oriented account that emerges from a broader interpretation of the concept of sensorimotor contingencies. We suggest that dynamic informational and sensorimotor coupling across agents can mediate the deployment of action-effect contingencies in social contexts. We propose this concept of socializing sensorimotor contingencies (socSMCs) as a shared framework of analysis for processes within and across brains and bodies, and their physical and social environments. In doing so, we integrate insights from different fields, including neuroscience, psychology, and research on human-robot interaction. We review studies on dynamic embodied interaction and highlight empirical findings that suggest an important role of sensorimotor and informational entrainment in social contexts. Furthermore, we discuss links to closely related concepts, such as enactivism, models of coordination dynamics and others, and clarify differences to approaches that focus on mentalizing and high-level cognitive representations. Moreover, we consider conceptual implications of rethinking cognition as social sensorimotor coupling. The insight that social cognitive phenomena like joint attention, mutual trust or empathy rely heavily on the informational and sensorimotor coupling between agents may provide novel remedies for people with disturbed social cognition and for situations of disturbed social interaction. Furthermore, our proposal has potential applications in the field of human-robot interaction where socSMCs principles might lead to more natural and intuitive interfaces for human users.

Model-Based Reasoning in Humans Becomes Automatic with Training.

Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load--a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders.