ISSN 0439-755X
CN 11-1911/B


    25 July 2023, Volume 55 Issue 7 Previous Issue    Next Issue

    Reports of Empirical Studies
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    Reports of Empirical Studies
    The effect of reward prediction error on temporal order and source memory
    ZHANG Hongchi, CHENG Xuan, MAO Weibin
    2023, 55 (7):  1049-1062.  doi: 10.3724/SP.J.1041.2023.01049
    Abstract ( 217 )   HTML ( 28 )  
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    Although previous research has demonstrated that event boundaries enhanced source memory at boundaries and reduced temporal order memory across boundaries, there is little research on whether there is a mnemonic trade-off between temporal order and source memory and how intrinsic, socially meaningful change as event boundaries affects memory. The present study explored the effects of RPE event boundaries on temporal order and source memory by reward prediction errors (RPE) as event boundaries in two behavioural experiments and one ERP experiment. The results showed that RPE event boundaries enhanced source memory at the boundaries, and high RPE event boundaries produced mnemonic trade-off effect; compared to within-event/non-boundary conditions, a larger N400 amplitude was induced by across-event/ boundary conditions, and activation of temporal order memory was focused on anterior region, activation of source memory was focused on posterior region. The present study showed that the strength of segmentation of event boundaries is an important factor in mnemonic trade-off effects, and that the N400 component may be an important indicator of the integration and segmentation of episodic memory by event boundaries.

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    The role of dorsolateral prefrontal cortex on placebo effect of regulating social pain: A TMS study
    WANG Mei, CHENG Si, LI Yiwei, LI Hong, ZHANG Dandan
    2023, 55 (7):  1063-1073.  doi: 10.3724/SP.J.1041.2023.01063
    Abstract ( 121 )   HTML ( 23 )  
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    The developmental trajectory of oral vocabulary knowledge and its predictive effects on reading abilities among Chinese primary school students: A latent growth model
    CHENG Yahua, FENG Yao, LI Yixun, MA Jiaqi, SHEN Lanlan, ZHANG Wenjian, WU Xinchun, FENG Qiudi
    2023, 55 (7):  1074-1086.  doi: 10.3724/SP.J.1041.2023.01074
    Abstract ( 95 )   HTML ( 20 )  
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    The relationship between gratitude and social well-being: Evidence from a longitudinal study and a daily diary investigation
    YE Ying, ZHANG Linting, ZHAO Jingjing, KONG Feng
    2023, 55 (7):  1087-1098.  doi: 10.3724/SP.J.1041.2023.01087
    Abstract ( 135 )   HTML ( 13 )  
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    The positive psychological construct of gratitude is crucial for health and well-being. Previous studies have shown a significant positive correlation between gratitude and social well-being. However, to the best of our knowledge, no studies have examined this potentially reciprocal relationship from a longitudinal perspective. According to the broaden-and-build theory and gratitude amplification theory, we hypothesized that gratitude has a predictive effect on social well-being. In addition, based on the personality and social relationships model and self-determination theory, we proposed that social well-being is an antecedent to gratitude. In summary, this research combines a longitudinal study and a daily diary investigation to systematically explore the causal relation between gratitude and social well-being.

    Study 1 employs a two-wave cross-lagged design to explore the long-term relationship between trait gratitude and social well-being. The sample comprised 563 undergraduate students, who all participated online. Pursuant to the study purpose, participants were asked to complete the gratitude and social well-being scales twice, separated by a seven-month interval. The cross-lagged path analysis suggested reciprocal effects between trait gratitude and social well-being. To reduce recall bias and explore the short-term association between gratitude and social well-being, Study 2 employs a daily diary method. A total of 274 young adults completed daily gratitude and social well-being measures for 21 consecutive days.

    In Study 1, trait gratitude at T1 significantly positively predicted social well-being at T2, while social well-being at T1 also significantly predicted trait gratitude at T2. These effects remained significant after controlling for age and gender. Consistent with Study 1, Study 2 also revealed a reciprocal relationship: state gratitude on one day positively predicted social well-being the next day, while social well-being on one day also positively predicted state gratitude the next day. Moreover, these relationships were stable after controlling for time trends. Overall, the results of Study 1 and Study 2 support the hypotheses by showing reciprocal predictive effects between gratitude and social well-being.

    In summary, we predicted that experiencing gratitude would lead to higher social well-being, which would, in turn, result in higher gratitude, activating an upward spiral. This work deepens understanding of the interaction between gratitude and social well-being, paving the way for future intervention research to help increase both.

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    The role of cross-situational stimulus generalization in the formation of trust towards face: A perspective based on direct and observational learning
    YUAN Bo, WANG Xiaoping, YIN Jun, LI Weiqiang
    2023, 55 (7):  1099-1114.  doi: 10.3724/SP.J.1041.2023.01099
    Abstract ( 68 )   HTML ( 18 )  
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    Based on associative learning theory, three experiments were conducted to investigate the role of cross-situational (fairness-trust) stimulus generalization in formation of facial trust. From perspective of direct interaction and observational learning, respectively, Experiment 1a and Experiment 1b show that compared with medium unfair condition, as the perceptual similarity between the morphed trustee’s face and the face of the fair (unfair) allocator in the previous interaction increases, the degree of trust (distrust) towards the trustee gradually increases. In addition, this effect is asymmetrical, participants preferentially avoided more unfair morphs in comparison with fair morphs. This suggests an asymmetric overgeneralization toward individuals perceived to be morally aversive. Using drift-diffusion modeling (DDM), we found that drift rate v under unfair conditions was significantly smaller than that under medium or fair conditions, and most of them are in range of less than 0. This suggests that individuals are more likely to accumulate evidence of distrust when making trust decisions about unfamiliar faces that are similar to the allocator who was unfair in previous interactions. In Experiment 2, under an unintentional situation, the above-mentioned cross-situational generalization effect disappeared. These results indicated that individuals use associative learning mechanisms to generalize stimulus value acquired in different situations to new interactive situations, and then guide subsequent trust decisions.

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    Emphasizing recovery or improvement in charitable fundraising should depend on event controllability
    SONG Wenjing, CHEN Yixuan, HUANG Yunhui
    2023, 55 (7):  1133-1147.  doi: 10.3724/SP.J.1041.2023.01133
    Abstract ( 43 )   HTML ( 5 )  
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    The situation of the recipients is a critical element of charitable fundraising information, yet the impact of different descriptions of their situation on the efficacy of fundraising efforts has not been extensively studied. This research classified fundraising information into two types: recovery-description (emphasizing that the recipients were in a favorable situation but have since fallen into a disadvantaged one, and that the donation brings the recipients back to their previous state) and improvement-description (emphasizing that the donation helps the recipients transition from their current disadvantaged state to a better one). Using one secondary data analysis (N = 978, Study 1) and six experiments (N = 1163, Studies 2/3a/3b/4/5a/5b), it was found that the recovery-description (vs. improvement-description) led donors to perceive charity projects are better at reducing loss (vs. increasing gains) and donors are more concerned with reducing loss (vs. increasing gains) when faced with uncontrollable (vs. controllable) events. Thus, based on the matching on regulatory focus, when recovery-description (vs. improvement-description) was used to describe uncontrollable events, and improvement-description (vs. recovery-description) was used to describe controllable events, individuals’ willingness to donate (Study 5) and actual donation (secondary data) were higher. This research proposes a new theoretical classification of fundraising information and demonstrates different types of information have divergent subsequent impacts. Our findings suggest to design fundraising information according to event controllability.

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    Lost radiance: Negative influence of parental gender bias on women’s workplace performance
    XU Minya, LIU Beini, XU Zhenyu
    2023, 55 (7):  1148-1159.  doi: 10.3724/SP.J.1041.2023.01148
    Abstract ( 149 )   HTML ( 27 )  
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    Can leader gratitude expression improve employee followership behavior? The role of emotional expression authenticity
    ZHU Yanghao, LONG Lirong, LIU Wenxing
    2023, 55 (7):  1160-1175.  doi: 10.3724/SP.J.1041.2023.01160
    Abstract ( 111 )   HTML ( 15 )  
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    As a traditional virtue of the Chinese nation, gratitude has received much attention from scholars in recent years. This paper constructs a moderated mediation model by combining the social function theory of emotion and dual-strategies theory of social rank to explore the effect of leader gratitude expression on employee followership behavior. Using a scenario experiment and a multi-wave, leader-employee dyad survey, the findings confirm the proposed theoretical hypothesis that leader gratitude expression promote positive followership behavior by increasing perceived leader prestige and inhibit negative followership behavior by decreasing perceived leader dominance. The above relationship is especially strong when leader’s emotional expression authenticity is high. The findings of the study help to enlighten leaders to express appreciation to their employees more often and more sincerely.

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    Missing data analysis in cognitive diagnostic models: Random forest threshold imputation method
    YOU Xiaofeng, YANG Jianqin, Qin Chunying, LIU Hongyun
    2023, 55 (7):  1192-1206.  doi: 10.3724/SP.J.1041.2023.01192
    Abstract ( 79 )  
    In recent years, interest in cognitive diagnostic assessments (CDAs), as a new form of test, has increased drastically. Due to the specific design of the test, missing data is an inevitable problem in CDAs. Proper handling of missing data in CDAs is important to provide accurate diagnostic feedback to students and teachers. With the use of machine learning in education, relevant advancements have been made in missing data imputation. Research showed machine learning techniques have more desirable features for missing data imputation than traditional approaches. The random forest algorithm has been extended to become the random forest imputation (RFI) method in handling of CDAs missing data for CDAs. The method takes into consideration the characteristics of the data rather than assumes certain missing mechanism. RFI is a new non-parametric method that makes full use of the available response information and characteristics of response patterns to impute missing data.
    Making use of advantages of RFI in categorization/prediction and its non-reliant on missing mechanism type, we improved and proposed the new random forest threshold imputation (RFTI) method. It could be used to impute missing responses in the widely used DINA (Deterministic Inputs, Noise “And” Gate) model. This research proposed to apply the Response Conformity Index (RCI) in the missing data imputation to set the threshold of imputation and to develop a method for missing response treatment for CDAs without totally relying on imputation. Two simulation studies were conducted to compare the performance of the proposed method and traditional models. Study 1 began by introducing the theoretical background and algorithm implementation of RFTI. Then, RFTI and RFI were compared in terms of accuracy rate of imputation for data with different proportions of missingness (10%, 20%, 30%, 40%, 50%) and missing data mechanisms (MIXED, MNAR, MAR, MCAR). This was to affirm the necessity of including RCI during imputation. Study 2 aimed to investigate the performance of RFTI, as well as RFI and EM algorithm in imputing missing data under different conditions. The manipulated design factors were identical to those in Study 1. We evaluated RFTI in terms of its accuracy in assessing the model attributes and item parameters. We also compared RFTI against the traditionally better performed EM and RFI under various design conditions to explore the advantages and conditions of using RFTI.
    Results of Study 1 showed that RFTI, as compared to RFI, improved accuracy when imputation threshold was one. In various design conditions, RFTI imputation rate and accuracy were also better. Study 2 showed that RFTI outperformed other methods (RFI, EM algorithm) in accurately assessing the attribute pattern and attribute margin. This advantage was affected by the missing data mechanism and the proportion of missing data. Notably, RFTI was particularly better than other methods in handling mixed type of missing or MNAR data, and when the proportion of missing data was higher than 30%. However, RFTI was not any better than other methods in its accuracy of item parameter estimates. In most conditions, EM algorithm provided the most accurate parameter estimates.
    In sum, we propose a method to impute missing data in CDAs by applying machine learning methods in measurement models. The advantage of this new method is affirmed through its accurate assessment of attribute pattern and attribute margin of DINA model. Theoretically, the current study provides a missing data imputation approach with less assumptions, which extends the traditional methods to impute missing data in CDAs framework. Moreover, we investigate how to estimate the attribute pattern of students accurately through the responses of a few items. It sheds lights on imputing missing data due to particularly designs in assessment or teaching.
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