Given the rapid advancement of large-scale language models, artificial
intelligence (AI) models, like ChatGPT, are playing an increasingly prominent
role in human society. However, to ensure that artificial intelligence models
benefit human society, we must first fully understand the similarities and
differences between the human-like characteristics exhibited by artificial
intelligence models and real humans, as well as the cultural stereotypes and
biases that artificial intelligence models may exhibit in the process of
interacting with humans. This study first measured ChatGPT in 84 dimensions of
psychological characteristics, revealing differences between ChatGPT and human
norms in most dimensions as well as in high-dimensional psychological
representations. Additionally, through the measurement of ChatGPT in 13
dimensions of cultural values, it was revealed that ChatGPT's cultural value
patterns are dissimilar to those of various countries/regions worldwide.
Finally, an analysis of ChatGPT's performance in eight decision-making tasks
involving interactions with humans from different countries/regions revealed
that ChatGPT exhibits clear cultural stereotypes in most decision-making tasks
and shows significant cultural bias in third-party punishment and ultimatum
games. The findings indicate that, compared to humans, ChatGPT exhibits a
distinct psychological profile and cultural value orientation, and it also
shows cultural biases and stereotypes in interpersonal decision-making. Future
research endeavors should emphasize enhanced technical oversight and augmented
transparency in the database and algorithmic training procedures to foster more
efficient cross-cultural communication and mitigate social disparities.

对 ChatGPT 进行了心理特征和文化价值的测量，发现其与真实人类存在差异，表现出文化偏见和刻板印象。未来的研究应加强技术监督和算法训练的透明性，促进跨文化交流和减少社会差异。

ChatGPT 的高维心理特征和文化偏见

The high dimensional psychological profile and cultural bias of ChatGPT

In industrial scenarios, there is widespread use of collaborative robots
(cobots), and growing interest is directed at evaluating and measuring the
impact of some characteristics of the cobot on the human factor. In the present
pilot study, the effect that the production rhythm (C1 - Slow, C2 - Fast, C3 -
Adapted to the participant's pace) of a cobot has on the Experiential Locus of
Control (ELoC) and the emotional state of 31 participants has been examined.
The operators' performance, the degree of basic internal Locus of Control, and
the attitude towards the robots were also considered. No difference was found
regarding the emotional state and the ELoC in the three conditions, but
considering the other psychological variables, a more complex situation
emerges. Overall, results seem to indicate a need to consider the person's
psychological characteristics to offer a differentiated and optimal interaction
experience.

通过研究协作机器人的生产节奏对参与者的经验性控制和情绪状态的影响，本研究发现在情绪状态和经验性控制方面没有差异，但考虑到其他心理变量，结果表明需要考虑个人的心理特征以提供差异化和优化的交互体验。

合作装配场景中，协作机器人的生产节奏、控制定位与情绪状态之间的动态探索

Exploring the Dynamics between Cobot's Production Rhythm, Locus of  Control and Emotional State in a Collaborative Assembly Scenario

Driving assistance systems that support drivers by adapting individual
psychological characteristics can provide appropriate feedback and prevent
traffic accidents. As a first step toward implementing such adaptive assistance
systems, this research aims to develop a model to estimate drivers'
psychological characteristics, such as cognitive function, psychological
driving style, and workload sensitivity, from on-road driving behavioral data
using machine learning and deep learning techniques. We also investigated the
relationship between driving behavior and various cognitive functions including
the Trail Making test and Useful Field of View test through regression
modeling. The proposed method focuses on road type information and captures
various durations of time-series data observed from driving behaviors. First,
we segment the driving time-series data into two road types, namely, arterial
roads and intersections, to consider driving situations. Second, we further
segment data into many sequences of various durations. Third, statistics are
calculated from each sequence. Finally, these statistics are used as input
features of machine learning models to predict psychological characteristics.
The experimental results show that our model can predict a driver's cognitive
function, namely, the Trail Making Test version B and Useful Field of View test
scores, with Pearson correlation coefficients $r$ of 0.579 and 0.557,
respectively. Some characteristics, such as psychological driving style and
workload sensitivity, are predicted with high accuracy, but whether various
duration segmentation improves accuracy depends on the characteristics, and it
is not effective for all characteristics. Additionally, we reveal important
sensor and road types for the estimation of cognitive function.

使用机器学习和深度学习技术，从路上的驾驶行为数据中估计驾驶员的心理特征，建立具有自适应能力的驾驶辅助系统，研究结果表明，我们的模型能够以高准确度预测心理驾驶风格和负荷敏感度等特征，而不同持续时间的分割对准确性的影响因特征而异。