How stepping out helped us tune in: finding space and time to think as an early career researcher
Related Articles
Skill dependencies uncover nested human capital
Modern economies require increasingly diverse and specialized skills, many of which depend on the acquisition of other skills first. Here we analyse US survey data to reveal a nested structure within skill portfolios, where the direction of dependency is inferred from asymmetrical conditional probabilities—occupations require one skill conditional on another. This directional nature suggests that advanced, specific skills and knowledge are often built upon broader, fundamental ones. We examine 70 million job transitions to show that human capital development and career progression follow this structured pathway in which skills more aligned with the nested structure command higher wage premiums, require longer education and are less likely to be automated. These disparities are evident across genders and racial/ethnic groups, explaining long-term wage penalties. Finally, we find that this nested structure has become even more pronounced over the past two decades, indicating increased barriers to upward job mobility.
Self-reports map the landscape of task states derived from brain imaging
Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.
Conceptualizing space environmental sustainability
Recent advancements have significantly enhanced the capabilities for in-space servicing, assembly, and manufacturing (ISAM), to develop infrastructure in orbit and on the surface of celestial bodies. This progress is a departure from the traditional sustainability paradigm focused solely on Earth, highlighting the urgent need to define and operationalize the concept of “space sustainability” along with the development of an evaluation framework. The expansion of human activity into space, particularly in low-earth orbit, cis-lunar space, and beyond, underscores the critical importance of considering sustainability implications. Leveraging space resources offers economic growth and sustainable development opportunities, while reducing pressure on Earth’s ecosystems. This paradigm shift requires responsible and ethical utilization of space resources. A space sustainability assessment framework is essential for guiding ISAM capabilities, operations, missions, standards, and policies. This paper introduces an initial framework encompassing (1) pollution, (2) resource depletion, (3) landscape alteration, and (4) space environmental justice, with potential metrics (resources use and emissions, midpoint, and endpoint indicators) to measure impacts in the four domains.
Neural correlates of personal space regulation in psychosis: role of the inferior parietal cortex
Regulation of interpersonal distance or “personal space” (PS; the space near the body into which others cannot intrude without eliciting discomfort) is a largely unconscious channel of non-verbal social communication used by many species including humans. PS abnormalities have been observed in neuropsychiatric illnesses, including schizophrenia. However, the neurophysiological basis of these abnormalities remains unknown. To investigate this question, in this study, functional magnetic resonance imaging (fMRI) data were collected while individuals with psychotic disorders (PD; n = 37) and demographically-matched healthy control (HC) subjects (n = 60) viewed images of faces moving towards or away from them. Responses of a frontoparietal-subcortical network of brain regions were measured to the approaching versus the withdrawing face stimuli, and resting-state fMRI data were also collected. PS size was measured using the classical Stop Distance Procedure. As expected, the PD group demonstrated a significantly larger PS compared to the HC group (P = 0.002). In both groups, a network of parietal and frontal cortical regions showed greater approach-biased responses, whereas subcortical areas (the striatum, amygdala and hippocampus) showed greater withdrawal-biased responses. Moreover, within the PD (but not the HC) group, approach-biased activation of the inferior parietal cortex (IPC) and functional connectivity between the IPC and the ventral/limbic striatum were significantly correlated with PS size. This study provides evidence that PS abnormalities in psychotic illness involve disrupted function and connectivity of the PS network. Such brain-behavior relationships may serve as objective treatment targets for novel interventions for schizophrenia and related psychotic illnesses.
User-specified inverse kinematics taught in virtual reality reduce time and effort to hand-guide redundant surgical robots
Medical robots should not collide with close by obstacles during medical procedures, such as lamps, screens, or medical personnel. Redundant robots have more degrees of freedom than needed for moving endoscopic tools during surgery and can be reshaped to avoid obstacles by moving purely in the space of these additional degrees of freedom (null space). Although state-of-the-art robots allow surgeons to hand-guide endoscopic tools, reshaping the robot in null space is not intuitive for surgeons. Here we propose a learned task space control that allows surgeons to intuitively teach preferred robot configurations (shapes) that avoid obstacles using a VR-based planner in simulation. Later during surgery, surgeons control both the endoscopic tool and robot configuration (shape) with one hand. In a user study, we found that learned task space control outperformed state-of-the-art naive task space control in all the measured performance metrics (time, effort, and user-perceived effort). Our solution allowed users to intuitively interact with robots in VR and reshape robots while moving tools in medical and industrial applications.
Responses