Parallels in the sequential organization of birdsong and human speech.

Human speech possesses a rich hierarchical structure that allows for meaning to be altered by words spaced far apart in time. Conversely, the sequential structure of nonhuman communication is thought to follow non-hierarchical Markovian dynamics operating over only short distances. Here, we show that human speech and birdsong share a similar sequential structure indicative of both hierarchical and Markovian organization. We analyze the sequential dynamics of song from multiple songbird species and speech from multiple languages by modeling the information content of signals as a function of the sequential distance between vocal elements. Across short sequence-distances, an exponential decay dominates the information in speech and birdsong, consistent with underlying Markovian processes. At longer sequence-distances, the decay in information follows a power law, consistent with underlying hierarchical processes. Thus, the sequential organization of acoustic elements in two learned vocal communication signals (speech and birdsong) shows functionally equivalent dynamics, governed by similar processes

The Unreasonable Effectiveness of Machine Learning in Neuroscience: Understanding High-dimensional Neural Representations with Realistic Synthetic Stimuli

Parametrizing complex natural stimuli is a difficult and long-standing challenge. We used a generative deep convergent network to represent and parametrize a large corpus of song from European starlings, a songbird species, into a compressed low-dimensional space. We applied psychophysical methods to probe categorical perception of natural starling song syllables, which reveal a shared categorical perceptual space. Some categorical boundaries are sensitive to the category assignment of training syllables, indicating that the consensus is context dependent and that underlying dimensions of the space are not independent. We record simultaneous firing from populations of 10's of neurons in a secondary auditory cortical region of anesthetized starlings. By estimating how fast population level neural representation change with respect to the stimuli, we produce a measure along a path in stimuli space that is shared between birds and descriptive of the psychophysically determined parameters in other birds. Consistent with this, we predict the behavioral psychometric function along one dimension by fitting the behavior for other dimensions to the population level neural activity. Thus, knowing how the animal responds in one sub-region of the parametrized space informs responses in other sub-regions. Our results implicate the importance of experience in shaping shared perceptual boundaries among complex communication signals and suggest the categorical representation of natural signals in secondary sensory cortices is distributed much more densely than predicted by traditional hierarchical object recognition models. This thesis also explores other applications of machine learning to solve neuroscience problems, in particular, the curse of dimensionality and exploring predictive coding and surprise. A model explicitly designed to predict future states allows the compression of high-dimensional time-varying signals into a lower-dimensional representation encoding exclusively predictive and predictable information and has many practical applications

Making Friends and Buying Robots: How to Leverage Collaborations and Collections to Support STEM Learning

In a climate of increased interest in science, technology, engineering, and math (STEM), school libraries have unique opportunities to grow collections and cultivate partnerships in the sciences. At the federal level and in many states, STEM initiatives encourage hands-on exposure to technologies and open the door for student-led discovery of tools related to robotics, coding, programming, and electronics. Influenced by local STEM initiatives, the Learning Resource Center (LRC) at the University of Wyoming Lab School decided to create a circulating collection of STEM kits. (The UW Lab School is a tuition free charter school with a diverse population selected by lottery.) This school library also partnered with Lab School teachers to explore these STEM collections and to develop programming and a curriculum to teach digital literacies and STEM skills to students in kindergarten through ninth grade

TalkUp: Paving the Way for Understanding Empowering Language

Empowering language is important in many real-world contexts, from education to workplace dynamics to healthcare. Though language technologies are growing more prevalent in these contexts, empowerment has seldom been studied in NLP, and moreover, it is inherently challenging to operationalize because of its implicit nature. This work builds from linguistic and social psychology literature to explore what characterizes empowering language. We then crowdsource a novel dataset of Reddit posts labeled for empowerment, reasons why these posts are empowering to readers, and the social relationships between posters and readers. Our preliminary analyses show that this dataset, which we call TalkUp, can be used to train language models that capture empowering and disempowering language. More broadly, TalkUp provides an avenue to explore implication, presuppositions, and how social context influences the meaning of language.Comment: Findings of EMNLP 202

The Unreasonable Effectiveness of Machine Learning in Neuroscience: Understanding High-dimensional Neural Representations with Realistic Synthetic Stimuli

Parametrizing complex natural stimuli is a difficult and long-standing challenge. We used a generative deep convergent network to represent and parametrize a large corpus of song from European starlings, a songbird species, into a compressed low-dimensional space. We applied psychophysical methods to probe categorical perception of natural starling song syllables, which reveal a shared categorical perceptual space. Some categorical boundaries are sensitive to the category assignment of training syllables, indicating that the consensus is context dependent and that underlying dimensions of the space are not independent. We record simultaneous firing from populations of 10's of neurons in a secondary auditory cortical region of anesthetized starlings. By estimating how fast population level neural representation change with respect to the stimuli, we produce a measure along a path in stimuli space that is shared between birds and descriptive of the psychophysically determined parameters in other birds. Consistent with this, we predict the behavioral psychometric function along one dimension by fitting the behavior for other dimensions to the population level neural activity. Thus, knowing how the animal responds in one sub-region of the parametrized space informs responses in other sub-regions. Our results implicate the importance of experience in shaping shared perceptual boundaries among complex communication signals and suggest the categorical representation of natural signals in secondary sensory cortices is distributed much more densely than predicted by traditional hierarchical object recognition models. This thesis also explores other applications of machine learning to solve neuroscience problems, in particular, the curse of dimensionality and exploring predictive coding and surprise. A model explicitly designed to predict future states allows the compression of high-dimensional time-varying signals into a lower-dimensional representation encoding exclusively predictive and predictable information and has many practical applications

Understanding the Feasibility of Blockbuster Exhibits During and After COVID-19

Thesis (Master's)--University of Washington, 2021Blockbuster exhibits are extensive, revenue-building, popular topic exhibits that started in the museum field during the 1970s. These exhibits provide something new for the museum and community and increase a museum's prestige in the field, but they are expensive to host and develop and come at a risk for a return on investment. Some museums relied on these exhibits to bring in people and money to their institutions. This model was disrupted in 2020-2021 when the SARS-CoV-2 virus (COVID-19) caused financial uncertainty. The purpose of this research study was to explore the value of blockbuster exhibits for museums and their feasibility as a business model during and after the COVID-19 pandemic. This research was done through a qualitative descriptive study using semi-structured interviews with four sites and six museum professionals who have had experience with blockbuster exhibits. Preliminary results indicate that blockbuster exhibits are valued for providing something new and exciting for their community. However, their role and impact are changing due to COVID-19. These changes have brought about disappointment from visitor and show producers' perspectives. Museums had to adapt fast to the changing times, and it is thought that the changes these exhibits are going through now are going to be lasting. These results help the conversation and understanding of the relationship between blockbuster exhibits and COVID-19, but more time needs to pass to fully understand the impact of these exhibits

Animal Vocalization Generative Network (AVGN): A method for visualizing,understanding, and sampling from animal communicative repertoires

We propose here a set of machine-learning algorithms to produce a generative low-dimensional and visually-understandablespace of the communicative repertoire of vocal species such as songbirds. As opposed to human speech, where individualelements are well defined and grounded in principled ways, the methods for defining units of animal communication sys-tems are often more varied and rely on human-centric heuristics. Using our method, we can automatically discover latentstructure in the vocal repertoire of individuals and use these to define-well principled categorical boundaries between vocalelements in communicating species. Further, we can sample from latent representations to generate novel vocal units thatcan be used to probe perceptual and physiological representations of communication. We demonstrate two use cases: (1)automated labeling of songbird vocal repertoires showing novel structure in vocal communication, and (2) a perceptualtask demonstrating that behavioral and physiological representational spaces can be biased by contextual information.GitHub.com/timsainb/AVG

Identification of total and biologically sensitive forms of toxic metal inputs to an urban affected river

Office of Water Policy, USBR, and USG