Hong Li

State shyness refers to a transient emotional experience characterised by simultaneous approach and avoidance tendencies when individuals encounter novel stimuli or perceive social evaluations, representing a common phenomenon in daily social interactions. Current methods for eliciting state shyness, such as public speaking tasks, often involve complex indirect experimental designs and depend on post-hoc behavioural coding, making it difficult to precisely induce and synchronously measure state shyness, thus limiting their application in controlled experimental settings. To address this methodological limitation, this research developed and validated an audio-based corpus specifically designed for the direct induction of state shyness. The corpus consists of two subsets, each containing 25 standardised social scenarios. In Study 1, semi-structured interviews with 126 university students informed the creation of these scenarios, which were subsequently refined through thematic analysis. The efficacy of the corpus was then validated through self-reported state shyness ratings from 58 participants. Study 2 further validated the corpus’s reliability by employing Electrodermal Activity as an objective physiological measure with 37 participants. This research offers a standardised and easily deployable tool for inducing state shyness in controlled experimental contexts, enabling direct and precise inducing state shyness.

How do individuals with trait anxiety perform in an approach-avoidance (ap-av) conflict situation? To answer this question, we employed computational models to explore the effect of trait anxiety on decisions in ap-av conflict situations and uncover the computational mechanism involved in resolving such conflicts. Sixty-seven participants with high or low trait anxiety completed the ap-av conflict task, which was analysed using the hierarchical drift-diffusion model (HDDM). Anxiety levels were assessed during both baseline and six-month follow-up sessions. Results showed that, during ap-av conflict decision-making, the reaction time of the high trait anxiety group was slower than that of the low trait anxiety group. The slower reaction time in the high trait anxiety group was due to a longer non-decision time. However, the intercept of drift rate and the effect of reward and aversion on drift rate significantly increased in the high trait anxiety group compared to the low trait anxiety group. The high trait anxiety group had a starting point closer to avoidance decisions. This study provides evidence that individuals with high and low trait anxiety manifest distinct alterations in the underlying computational decision-making processes during ap-av conflict situations.

The whale optimization algorithm is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model, double diode model, and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules.

This study investigated Chinese university students’ technology-assisted self-regulated learning strategies and whether the technology-based SRL strategies mediated the associations between English language self-efficacy, English enjoyment, and learning outcomes. Data were collected from 525 undergraduate students in mainland China through three self-report questionnaires and the performance on an English language proficiency test. While students reported an overall moderate level of SRL strategies, they reported a high level of technology-based vocabulary learning strategies. A statistically significant positive relationship was noted between the use of technology-based SRL strategies and students’ English learning outcomes. English language self-efficacy and English language enjoyment were both related to technology-based SRL strategies. Furthermore, SRL strategies fully mediated the relationship between English enjoyment and English learning outcomes, but the association between English enjoyment and SRL strategies was only partially mediated by English language self-efficacy. Pedagogically, findings of this study suggest that training and instruction aimed at promotion of modern educational technology among students need to give attention to developing their strategic awareness of motivation regulation in optimizing effectiveness of their technology use in learning the target language.