An official website of the United States government
Can we predict the words a child is going to learn next given information about the words that a child knows now? Do different representations of a child’s vocabulary knowledge affect our ability to predict the acquisition of lexical items for individual children? Past research has often focused on population statistics of vocabulary growth rather than prediction of words an individual child is likely to learn next. We consider a neural network approach to predict vocabulary acquisition. Specifically, we investigate how best to represent the child’s current vocabulary in order to accurately predict future learning. The models we consider are based on qualitatively different sources of information: descriptive information about the child, the specific words a child knows, and representations that aim to capture the child’s aggregate lexical knowledge. Using longitudinal vocabulary data from children aged 15-36 months, we construct neural network models to predict which words are likely to be learned by a particular child in the coming month. Many models based on child-specific vocabulary information outperform models with child information only, suggesting that the words a child knows influence prediction of future language learning. These models provide an understanding of the role of current vocabulary knowledge on future lexical growth.
more »
« less
Laplace's demon in biology: Models of evolutionary prediction
Our ability to predict natural phenomena can be limited by incomplete information. This issue is exemplified by `Laplace's demon', an imaginary creature proposed in the 18th century, who knew everything about everything, and thus could predict the full nature of the universe forward or backward in time. Quantum mechanics, among other things, has cast doubt on the possibility of Laplace's demon in the full sense, but the idea still serves as a useful metaphor for thinking about the extent to which prediction is limited by incomplete information on deterministic processes versus random factors. Here we use simple analytical models and computer simulations to illustrate how data limits can be captured in a Bayesian framework, and how they influence our ability to predict evolution. We show how uncertainty in measurements of natural selection, or low predictability of external environmental factors affecting selection, can greatly reduce predictive power, often swamping the influence of intrinsic randomness caused by genetic drift. Thus, more accurate knowledge concerning the causes and action of natural selection is key to improving prediction. Fortunately, our analyses and simulations show quantitatively that reasonable improvements in data quantity and quality can meaningfully increase predictability.
more »
« less
- Award ID(s):
- 1844941
- PAR ID:
- 10409389
- Date Published:
- Journal Name:
- Evolution
- ISSN:
- 0014-3820
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Drought is one of the most destructive and expensive natural disasters, severely impacting natural resources and risks by depleting water resources and diminishing agricultural yields. Under climate change, accurately predicting drought is critical for mitigating drought-induced risks. However, the intricate interplay among the physical and biological drivers that regulate droughts limits the predictability and understanding of drought, particularly at a subseasonal to seasonal (S2S) time scale. While deep learning has demonstrated the potential to address climate forecasting challenges, its application to drought prediction has received relatively less attention. In this work, we propose a new dataset, DroughtSet, which integrates relevant predictive features and three drought indices from multiple remote sensing and reanalysis datasets across the contiguous United States (CONUS). DroughtSet specifically provides the machine learning community with a new real-world dataset to benchmark drought prediction models and more generally, time-series forecasting methods. Furthermore, we propose a spatial-temporal model SPDrought to predict and interpret S2S droughts. Our model learns from the spatial and temporal information of physical and biological features to predict three types of droughts simultaneously. Multiple strategies are employed to quantify the importance of physical and biological features for drought prediction. Our results provide insights for researchers to better understand the predictability and sensitivity of drought to biological and physical conditions. We aim to contribute to the climate field by proposing a new tool to predict and understand the occurrence of droughts and provide the AI community with a new benchmark to study deep learning applications in climate science.more » « less
-
The water content in the soil regulates exchanges between soil and atmosphere, impacts plant livelihood, and determines the antecedent condition for several natural hazards. Accurate soil moisture estimates are key to applications such as natural hazard prediction, agriculture, and water management. We explore how to best predict soil moisture at a high resolution in the context of a changing climate. Physics-based hydrological models are promising as they provide distributed soil moisture estimates and allow prediction outside the range of prior observations. This is particularly important considering that the climate is changing, and the available historical records are often too short to capture extreme events. Unfortunately, these models are extremely computationally expensive, which makes their use challenging, especially when dealing with strong uncertainties. These characteristics make them complementary to machine learning approaches, which rely on training data quality/quantity but are typically computationally efficient. We first demonstrate the ability of Convolutional Neural Networks (CNNs) to reproduce soil moisture fields simulated by the hydrological model ParFlow-CLM. Then, we show how these two approaches can be successfully combined to predict future droughts not seen in the historical timeseries. We do this by generating additional ParFlow-CLM simulations with altered forcing mimicking future drought scenarios. Comparing the performance of CNN models trained on historical forcing and CNN models trained also on simulations with altered forcing reveals the potential of combining these two approaches. The CNN can not only reproduce the moisture response to a given forcing but also learn and predict the impact of altered forcing. Given the uncertainties in projected climate change, we can create a limited number of representative ParFlow-CLM simulations (ca. 25 min/water year on 9 CPUs for our case study), train our CNNs, and use them to efficiently (seconds/water-year on 1 CPU) predict additional water years/scenarios and improve our understanding of future drought potential. This framework allows users to explore scenarios beyond past observation and tailor the training data to their application of interest (e.g., wet conditions for flooding, dry conditions for drought, etc…). With the trained ML model they can rely on high resolution soil moisture estimates and explore the impact of uncertainties.more » « less
-
Assessing perceived vulnerability to a health threat is essential to understanding how people conceptualize their risk, and to predicting how likely they are to engage in protective behaviors. However, there is limited consensus about which of many measures of perceived vulnerability predict behavior best. We tested whether the ability of different measures to predict protective intentions varies as a function of the type of information people learn about their risk. Online participants (N = 909) read information about a novel respiratory disease before answering measures of perceived vulnerability and vaccination intentions. Type-of-risk information was varied across three between-participant groups. Participants learned either: (1) only information about their comparative standing on the primary risk factors (comparative-only), (2) their comparative standing as well as the base-rate of the disease in the population (+ base-rate), or (3) their comparative standing as well as more specific estimates of their absolute risk (+ absolute-chart). Experiential and affective measures of perceived vulnerability predicted protective intentions well regardless of how participants learned about their risk, while the predictive ability of deliberative numeric and comparative measures varied based on the type of risk information provided. These results broaden the generalizability of key prior findings (i.e., some prior findings about which measures predict best may apply no matter how people learn about their risk), but the results also reveal boundary conditions and critical points of distinction for determining how to best assess perceived vulnerability.more » « less
-
Abstract Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/evo.14628
