Machine learning identifies the dynamics and influencing factors in an auditory category learning experiment

Leveraging AI techniques for predicting spatial distribution and determinants of carbon emission in China's Yangtze River Delta

This study focuses on the prediction and management of carbon emissions (CE) under the backdrop of global warming, with a particular emphasis on developing spatial planning strategies for urban clusters. In this context, we integrate artificial intelligence technologies to devise an optimized spatial analysis method based on the attributes of multi-source, urban-level spatio-temporal big data on CE. This method enhances both the accuracy and interpretability of CE data processing. Our objectives are to accurately analyze the current status of CE, predict the future spatial distribution of urban CE in the Yangtze River Delta (YRD), and identify key driving factors. We aim to provide pragmatic recommendations for sustainable urban carbon management planning. The findings indicate that: (1) the algorithm designed by us demonstrates excellent fitting capabilities in the analysis of CE data in the YRD, achieving a fitting accuracy of 0.93; (2) it is predicted that from 2025 to 2030, areas with higher CE in the YRD will be primarily concentrated in the 'Provincial Capital Belt' and the 'Heavy Industry Belt'; (3) the economic foundation has been identified as the most significant factor influencing CE in the YRD; (4) projections suggest that CE in the YRD are likely to peak by 2030.

Dorsal posterior cingulate cortex responds to negative feedback information supporting learning and relearning of response policies

Many challenges in life come without explicit instructions. Instead, humans need to test, select, and adapt their behavioral responses based on feedback from the environment. While reward-centric accounts of feedback processing primarily stress the reinforcing aspect of positive feedback, feedback’s central function from an information-processing perspective is to offer an opportunity to correct errors, thus putting a greater emphasis on the informational content of negative feedback. Independent of its potential rewarding value, the informational value of performance feedback has recently been suggested to be neurophysiologically encoded in the dorsal portion of the posterior cingulate cortex (dPCC). To further test this association, we investigated multidimensional categorization and reversal learning by comparing negative and positive feedback in an event-related functional magnetic resonance imaging experiment. Negative feedback, compared with positive feedback, increased activation in the dPCC as well as in brain regions typically involved in error processing. Only in the dPCC, subarea d23, this effect was significantly enhanced in relearning, where negative feedback signaled the need to shift away from a previously established response policy. Together with previous findings, this result contributes to a more fine-grained functional parcellation of PCC subregions and supports the dPCC’s involvement in the adaptation to behaviorally relevant information from the environment.

CatBoost for big data: an interdisciplinary review

Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.