Combinatorial thought in infancy: Language processing reveals conceptual combination

The unparalleled productivity of the human mind rests on our ability to combine a finite set of simple representations into an infinity of complex thoughts, a process often implemented in language. To investigate the developmental origins of combinatorial thought, we tested whether infants combine concepts linked to words they have just acquired. Across three eye-tracking experiments, 12-month-olds (N = 60) learned two novel quantity labels (e.g., “mize” for 1; “padu” for 2 items), and combined them with familiar nouns (e.g., “duck”) to identify referents of quantified noun phrases (e.g., “padu duck”) in an adult-like manner. Thus, combinatorial processes for setting up complex representations are available already in infancy and may support building complex models of experience for learning and language development

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: Fast Scattering/Crown and Low-frequency Blips. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that Fast Scattering/Crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that 27% of all transient noise at LIGO Livingston belongs to the Fast Scattering class, while 8% belongs to the Low-frequency Blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets

Gravity Spy: Lessons learned and a path forward

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.Comment: 33 pages, 5 figures, published in European Physical Journal Plus for focus issue on "Citizen science for physics: From Education and Outreach to Crowdsourcing fundamental research