Incentive hope: A default psychological response to multiple forms of uncertainty

Potential categories of employment stress among rural college students and their relationship to employment psychology

Background

Psychological problems related to employment are among the most common psychological problems faced by rural college students. Employment stress is an important factor affecting the development of psychological health in employees; thus, reducing employment stress can improve the psychological state of employment.

Objective

This study aimed to understand the potential profiles of employment stress among rural college students to determine the relationship between different profiles and employment psychology.

Methods

This study was conducted in a higher education institution in Qingdao, Shandong Province, China between June and December 2023, and 249 rural college students participated. The Employment Stress Scale and Employment Psychology Scale were used to collect the data. Data were analyzed using latent profile analysis, independent sample t-tests, and binary logistic regression analysis.

Results

The results showed that rural university students were categorized into low-level (49.80%) and high-level (50.21%) employment stress groups. There was a statistically significant difference between the employment psychology of rural college students in the low- and high-level groups (p < 0.001). Juniors/seniors were more likely to be classified in the high-level group (OR = 0.477, p = 0.011).

Conclusion

Intervention programs should be developed and implemented to address the characteristics of employment stress among rural college students with different profiles to promote the healthy development of their attitudes toward employment.

Supervised machine learning aided behavior classification in pigeons

Manual behavioral observations have been applied in both environment and laboratory experiments in order to analyze and quantify animal movement and behavior. Although these observations contributed tremendously to ecological and neuroscientific disciplines, there have been challenges and disadvantages following in their footsteps. They are not only time-consuming, labor-intensive, and error-prone but they can also be subjective, which induces further difficulties in reproducing the results. Therefore, there is an ongoing endeavor towards automated behavioral analysis, which has also paved the way for open-source software approaches. Even though these approaches theoretically can be applied to different animal groups, the current applications are mostly focused on mammals, especially rodents. However, extending those applications to other vertebrates, such as birds, is advisable not only for extending species-specific knowledge but also for contributing to the larger evolutionary picture and the role of behavior within. Here we present an open-source software package as a possible initiation of bird behavior classification. It can analyze pose-estimation data generated by established deep-learning-based pose-estimation tools such as DeepLabCut for building supervised machine learning predictive classifiers for pigeon behaviors, which can be broadened to support other bird species as well. We show that by training different machine learning and deep learning architectures using multivariate time series data as input, an F1 score of 0.874 can be achieved for a set of seven distinct behaviors. In addition, an algorithm for further tuning the bias of the predictions towards either precision or recall is introduced, which allows tailoring the classifier to specific needs.

A deeper and distributed search for culture

The target article does not address the neural mediation of complex social behavior. I review evidence that such mediation may be compatible with proposed Bayesian information-processing principles. Notably, however, such mediation occurs subcortically as well as cortically, concerns reward uncertainty and information uncertainty, and impacts culture via group-level payoff structures that define individualism and collectivism.