More Like this

1. Fast behavioral feedbacks make ecosystems sensitive to pace and not just magnitude of anthropogenic environmental change

   https://doi.org/10.1073/pnas.2003301117  

   Gil, Michael A.; Baskett, Marissa L.; Munch, Stephan B.; Hein, Andrew M. (September 2020, Proceedings of the National Academy of Sciences)

   Anthropogenic environmental change is altering the behavior of animals in ecosystems around the world. Although behavior typically occurs on much faster timescales than demography, it can nevertheless influence demographic processes. Here, we use detailed data on behavior and empirical estimates of demography from a coral reef ecosystem to develop a coupled behavioral–demographic ecosystem model. Analysis of the model reveals that behavior and demography feed back on one another to determine how the ecosystem responds to anthropogenic forcing. In particular, an empirically observed feedback between the density and foraging behavior of herbivorous fish leads to alternative stable ecosystem states of coral population persistence or collapse (and complete algal dominance). This feedback makes the ecosystem more prone to coral collapse under fishing pressure but also more prone to recovery as fishing is reduced. Moreover, because of the behavioral feedback, the response of the ecosystem to changes in fishing pressure depends not only on the magnitude of changes in fishing but also on the pace at which changes are imposed. For example, quickly increasing fishing to a given level can collapse an ecosystem that would persist under more gradual change. Our results reveal conditions under which the pace and not just the magnitude of external forcing can dictate the response of ecosystems to environmental change. More generally, our multiscale behavioral–demographic framework demonstrates how high-resolution behavioral data can be incorporated into ecological models to better understand how ecosystems will respond to perturbations. 

   more » « less
2. Resilient and Sustainable Urban and Energy Systems

   https://doi.org/10.1061/9780784482445.062  

   Song, K.; Anderson, K.; Lee, S.; Raimi, K. T.; Hart, P. S. (June 2019, "Exploring the Effect of Data Granularity on Personalized Normative Messaging Interventions for Reducing Household Energy Consumption)

   Normative messaging interventions have proven to be a cost-effective strategy for promoting pro-environmental behaviors. The effectiveness of normative messages is partially determined by how personally relevant the comparison groups are as well as the lag of feedback. Using readily available energy use data has created opportunities to generate highly personalized reference groups based on households’ behavioral patterns. Unfortunately, it is not well understood how data granularity (e.g., minute, hour) affects the performance of behavioral reference group categorization. This is important because different levels of data granularity can produce conflicting results in terms of group similarity and vary in computational time. Therefore, this research aims to evaluate the performance of clustering methods across different levels of temporal granularity of energy use data. A clustering analysis is conducted using one-year of energy use data from 3,000 households in Holland, Michigan. The clustering results show that behavioral reference groups become the most similar when representing households’ energy use behaviors at a six-hour interval. Computationally, less granular data (i.e., six and twelve hours) takes less time than highly granular data which increases exponentially with more households. Considering the enormous scale that normative messaging interventions need to be applied at, using less granular data (six-hour intervals) will permit interveners to maximize the effectiveness of highly personalized normative feedback messages while minimizing computation burdens. 

   more » « less
3. Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

   https://doi.org/10.1371/journal.pcbi.1009439  

   Whiteway, Matthew R.; Biderman, Dan; Friedman, Yoni; Dipoppa, Mario; Buchanan, E. Kelly; Wu, Anqi; Zhou, John; Bonacchi, Niccolò; Miska, Nathaniel J.; Noel, Jean-Paul; et al (September 2021, PLOS Computational Biology)

   Theunissen, Frédéric E. (Ed.)

   Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone. 

   more » « less
4. A neurocognitive model for predicting the fate of individual memories

   Tubridy, S.; Halpern, D.; Davachi, L.; Gureckis, T.M. (July 2018, Proceedings of the 40th Annual Conference of the Cognitive Science Society.)

   One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for individual participants, which specific items in a list will be remembered at some point in the future. If you want to know if an individual will remember something, one commonsense approach is to give them a quiz or test such that a correct answer likely indicates later memory for an item. In this project we attempt to predict later memory without ex- plicit assessments by jointly modeling both neural and behavioral data in a computational cognitive model which captures the dynamics of memory acquisition and decay. In this paper, we lay out a novel hierarchical Bayesian approach for combining neural and behavioral data and present results showing how fMRI signals recorded during the study phase of a memory task can improve our ability to predict (in held-out data) which items will be remembered or forgotten 72 hours later. 

   more » « less
5. Daytime Data and LSTM Can Forecast Tomorrow's Stress, Health, and Happiness

   Umematsu, T; Sano, A; Picard, R (January 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   Accurately forecasting well-being may enable people to make desirable behavioral changes that could improve their future well-being. In this paper, we evaluate how well an automated model can forecast the next-day’s well-being (specifically focusing on stress, health, and happiness) from static models (support vector machine and logistic regression) and time-series models (long short-term memory neural network models (LSTM)) using the previous seven days of physiological, mobile phone, and behavioral survey data. We especially examine how using only a portion of the day’s data (e.g. just night-time, or just daytime) influences the forecasting accuracy. The results show that accuracy is improved, across every condition tested, by using an LSTM instead of using static models. We find that daytime-only physiology data from wearable sensors, using an LSTM, can provide an accurate forecast of tomorrow’s well-being using students’ daily life data (stress: 80.4%, health: 86.0%, and happiness: 79.1%), achieving the same accuracy as using data collected from around the clock. These findings are valuable steps toward developing a practical and convenient well-being forecasting system. 

   more » « less