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A combinatorial set of 3,125 cartoon characters based on five attributes for research on categorization, judgment, decision-making, and memory with adults and children
Get PDFA combinatorial set of pictorial stimuli generated from five five-valued attributes is presented (and shared freely under the CC BY-NC-ND license, hYps://osf.io/dq2k8) which can be used in various cognitive research areas like categorization, multiple cue probability learning, judgment, decision-making, memory, or metamemory. The stimuli consist of five different cartoon characters combined with four five-valued attributes, namely five different hats, shoes, equipment, and jackets. The characters were created in a way to make them suitable for research with children and adults alike. In three studies, we assessed the similarity structure and attribute salience via similarity judgments (Study 1) and validated the extracted attribute salience in a judgment task (Study 2) and the similarity in a recognition memory task with adults (Study 3) and 6- to 8-year-old children (Study 4). Obtaining a random sample of 12,251 similarity ratings from 51 online participants in Study 1 allowed us to quantify the salience of attributes by analyzing perceived similarity via multilevel regression. We provide similarity values extrapolated from the regression model for all 4,881,152 stimulus pairs to allow for similarity-controlled stimulus selection. Study 2 validated the salience estimates for all attributes by showing their influence on learning speed and accuracy in a cue learning paradigm. Study 3 demonstrated the validity of the extrapolated similarity values by showing their impact on recognition performance, and Study 4 showed the suitability of the stimuli for research on children
Learning tree-based models with gradient descent
Get PDFTree-based models are widely recognized for their interpretability and have proven effective in various application domains, particularly in high-stakes domains. In addition, their hierarchical structure and axis-aligned splits can provide a beneficial inductive bias, especially for heterogeneous tabular data. However, learning decision trees poses a significant challenge due to their combinatorial complexity and discrete, non-differentiable nature. As a result, traditional methods such as CART, which rely on greedy search procedures, remain the most widely used approaches. These methods make locally optimal decisions at each node, constraining the search space and often leading to suboptimal tree structures. Additionally, their demand for custom training methods precludes a seamless integration into modern machine learning approaches, such as those employed in multimodal or reinforcement learning, where optimization is typically achieved via gradient descent and backpropagation.
In this thesis, we propose a novel method for learning hard, axis-aligned decision trees through gradient descent. Our approach utilizes backpropagation with a straight-through operator on a dense decision tree representation, enabling the joint optimization of all tree parameters. We further extend this method to tree ensembles including a novel, instance-wise weighting scheme, allowing for a trade-off between performance and interpretability. By introducing gradient-based optimization for hard, axis-aligned decision trees, our method addresses the two primary limitations of traditional decision tree algorithms. First, gradient-based training is not constrained by the sequential selection of locally optimal splits but, instead, jointly optimizes all tree parameters. Second, by leveraging gradient descent for optimization, our approach seamlessly integrates into existing machine learning approaches e.g., for multimodal and reinforcement learning tasks, which inherently rely on gradient descent. These advancements allow us to achieve state-of-the-art results across multiple domains, including interpretable decision trees for small tabular datasets, advanced models for complex tabular data, multimodal learning, and interpretable reinforcement learning without information loss. By bridging the gap between decision trees and gradient-based optimization, our method significantly enhances the performance and applicability of tree-based models across various machine learning domains
Cognitive-linguistic development in childhood: Examining effects of multilingual competencies and German language skills on regulatory abilities in young learners
No full textHard to Reach Groups in der qualitativen Langzeitforschung: Herausforderungen und Lösungsansätze zur Rekrutierung und Bindung
No full textThe comfort of victimhood: Emotional engagement and media choices in alternative media spaces
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No place like home: Charging infrastructure and the environmental advantage of plug-in hybrid electric vehicles
Get PDFMany European companies face the challenge of lowering CO2 emissions from their company car fleets. A promising lever is to increase the notoriously low electric usage of Plug-in Hybrid Electric Vehicles (PHEVs). This paper examines whether home charging infrastructure can help achieve these goals. We leverage quasi-experimental variation in the delivery and installation of home chargers to quantify the impact of this technology on energy use and CO2 emissions of PHEV company cars held by 856 employees of a large German company. Since fuel and electricity expenditures for these cars are covered by the employer, home charging mainly changes the non-monetary costs to an employee. We find that access to home charging increases electricity consumption by 317.9 ((±23.3) kWh per quarter and decreases fuel consumption by 97.97 ((±36.5) liters, reducing CO2 emissions by 38%. Moreover, access to home charging increases the employee's propensity to choose a Battery Electric Vehicle (BEV) upon renewal of the lease by 28.4 ((±25.6) percentage points. We use these estimates to compute the private levelized abatement costs of home chargers for a range of scenarios characterizing the diffusion of BEVs and the effect of the program on vehicle choice. With current tax-inclusive energy prices, home chargers break even for the company within eight to 16 years
Pre-trained nonresponse prediction in panel surveys with machine learning
Get PDFWhile predictive modeling for unit nonresponse in panel surveys has been explored in various contexts, it is still under-researched how practitioners can best adopt these techniques. Currently, practitioners need to wait until they accumulate enough data in their panel to train and evaluate their own modeling options. This paper presents a novel “cross-training” technique in which we show that the indicators of nonresponse are so ubiquitous across studies that it is viable to train a model on one panel study and apply it to a different one. The practical benefit of this approach is that newly commencing panels can potentially make better nonresponse predictions in the early waves because these pre-trained models make use of more data. We demonstrate this technique with five panel surveys which encompass a variety of survey designs: the Socio-Economic Panel (SOEP), the German Internet Panel (GIP), the GESIS Panel, the Mannheim Corona Study (MCS), and the Family Demographic Panel (FREDA). We demonstrate that nonresponse history and demographics, paired with tree-based modeling methods, make highly accurate and generalizable predictions across studies, despite differences in panel design. We show how cross-training can effectively predict nonresponse in early panel waves where attrition is typically highest