We propose leveraging cognitive science research on emotions and
communication to improve language models for emotion analysis. First, we
present the main emotion theories in psychology and cognitive science. Then, we
introduce the main methods of emotion annotation in natural language processing
and their connections to psychological theories. We also present the two main
types of analyses of emotional communication in cognitive pragmatics. Finally,
based on the cognitive science research presented, we propose directions for
improving language models for emotion analysis. We suggest that these research
efforts pave the way for constructing new annotation schemes and a possible
benchmark for emotional understanding, considering different facets of human
emotion and communication.

借鉴情感和交流方面的认知科学研究，提出改进语言模型用于情感分析的方法，并探讨情感理论、自然语言处理中的情感标注方法及其与心理学理论的关联，以及认知语用学中的情感交流分析方法，最后提出了改进语言模型用于情感分析的方向。

优化情绪分析的语言模型：认知科学的见解

Improving Language Models for Emotion Analysis: Insights from Cognitive  Science

Understanding traffic participants' behaviour is crucial for predicting their
future trajectories, aiding in developing safe and reliable planning systems
for autonomous vehicles. Integrating cognitive processes and machine learning
models has shown promise in other domains but is lacking in the trajectory
forecasting of multiple traffic agents in large-scale autonomous driving
datasets. This work investigates the state-of-the-art trajectory forecasting
model Trajectron++ which we enhance by incorporating a smoothing term in its
attention module. This attention mechanism mimics human attention inspired by
cognitive science research indicating limits to attention switching. We
evaluate the performance of the resulting Smooth-Trajectron++ model and compare
it to the original model on various benchmarks, revealing the potential of
incorporating insights from human cognition into trajectory prediction models.

该论文研究了一种基于 Trajectron ++ 的 Trajectory Forecasting 模型，并通过在其 Attendant 模块中加入平滑项来增强该模型的性能，该注意机制模仿人类关注的限制，将认知过程和机器学习模型整合起来，从而提高自主车辆的规划系统的可靠性和安全性。此外，该研究还对平滑 Trajectron++ 模型在不同基准测试中的表现进行了评估并与原始模型进行了比较，揭示了将人类认知洞察力引入轨迹预测模型的潜力。