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CN 11-1911/B

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    25 January 2024, Volume 56 Issue 1 Previous Issue    Next Issue

    Reports of Empirical Studies
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    Reports of Empirical Studies
    Regional differences of large-scale spatial orientation ability in virtual environment
    SONG Xiaolei, LI Yiqian, ZHANG Kaige
    2024, 56 (1):  1-14.  doi: 10.3724/SP.J.1041.2024.00001
    Abstract ( 95 )   HTML ( 19 )  
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    Spatial orientation is one of the key capabilities of spatial navigation. Orientation in physical space, or large-scale spatial orientation, refers to the process by which an individual locates and navigates in a large-scale environment. Various geographic environments influence how individuals represent spatial orientation during navigation. Based on spatial reference frame theory, this study used a desktop virtual environment navigation task to explore the regional differences in large-scale spatial orientation abilities and their causes. The study findings offer valuable insights for designing navigation in different geographic areas to avoid safety accidents arising from navigation errors. Studies on the preferred reference frame and spatial orientation ability in different regions yield inconsistent results. Hence, it remains uncertain whether differences in spatial reference frame preferences are the sole reasons for regional variations in spatial orientation abilities. Moreover, the impact of factors other than spatial reference frame preferences on spatial navigation and orientation abilities remains unclear. Most prior studies primarily employed static spatial term experiments, and it remains unclear whether regional disparities exist in dynamic spatial tasks.

    Experiment 1 employed desktop virtual reality technology to clarify potential differences in large-scale spatial orientation abilities using the Route-repetition and Route-retracing tasks. Experiment 2 explored the underlying causes of regional disparities by utilizing the directional approach task, which assessed the flexibility of spatial reference frame transformation. Experiment 3 aimed to improve the large-scale spatial orientation abilities among participants from the southern region by activating the environmental spatial reference frame prior to the task.

    For Experiment 1, We performed a 2 (region: northern, southern) × 2 (task type: route-repetition, route-retracing) × 3 (route: 1, 2, 3) repeated measures ANOVA on the performance of the task. The result of the performance is shown in Figure 1. The performance of the northern participants was significantly better than that of the southern participants (F(1, 66) =18.16, p< 0.001, η2p = 0.22). The main effect of task type was significant (F(1, 66) = 113.77, p< 0.001, η2p = 0.63). The subjects' performance on the Route-repetition task was better than that on the Route-retracing task. The interaction between region and task type was significant (F(1, 66) = 5.08, p= 0.028, η2p = 0.07). In the route-retracing task, Northern participants significantly outperformed their southern counterparts.

    For Experiment 2, We performed a 2 (region: northern, southern) × 2 (direction of approach: same, different) × 3 (route: 1, 2, 3) repeated measures ANOVA on the accuracy rate and reaction time of the task. The results of the accuracy rate and reaction time are shown in Figure 2. The accuracy for the same direction in the directional approach task was higher than for different directions (F(1, 66) = 180.11, p< 0.001, η2p = 0.73). Furthermore, in the directional approach task, the interaction between region and direction of approach was significant (F(1, 66) = 8.78, p= 0.004, η2p = 0.12), participants from the northern region achieved a higher accuracy rate compared to their southern counterparts. All other main effects and interactions were insignificant (ps > 0.05).

    For Experiment 3, We performed a 2 (group: activation group, control group) × 2 (direction of approach: same, different) × 3 (route: 1, 2, 3) repeated measures ANOVA on the accuracy rate and reaction time of the task. The accuracy response rate for the same direction in the directional approach task was higher than for different directions (F(1, 66) = 187.28, p< 0.001, η2p = 0.74). The accuracy rate was significantly higher in the activation group than in the control group (F(1, 66) = 10.95, p= 0.002, η2p = 0.14). Notably, there was a significant interaction between the route and group (F(2, 132) = 6.64, p = 0.002, η2p = 0.09). Specifically, in Route 1, the activation group exhibited a significantly higher accuracy rate than the control group, suggesting that the continuous route knowledge of road signs and the environmental central reference provided by the synergistically improved task accuracy. Furthermore, orientation and group interactions were significant (F(1, 66) = 29.18, p< 0.001, η2p = 0.30). The accuracy rate of the activation group was significantly higher than that of the control group in different direction tasks. Regarding reaction time results, a significant main effect of direction was observed (F(1, 66) = 10.44, p= 0.002, η2p = 0.14), with reaction times being significantly longer for different directions compared to the same direction. Reaction times were also significantly longer in the activation group (F(1, 66) = 20.35, p< 0.001, η2p = 0.24). Additionally, there was a significant interaction between the route and the activation group (F(2, 132) = 11.56, p< 0.001, η2p = 0.15). The interaction between orientation and activation group was significant (F(1, 66) = 8.96, p= 0.004, η2p = 0.12), as detailed in Figure 3. All other main effects and interactions were insignificant (ps > 0.05).

    This study encompassed three experiments, yielding the following findings: (1) Spatial orientation abilities varied among participants from different regions. Participants from the northern region displayed superior performance in the Route-retracing task that required an environmental reference frame, while participants from the southern region preferred to utilize the egocentric reference frame. (2) These differences were attributed to disparities in the use and flexibility of spatial reference frames. Performance variations observed in the Route-retracing task between participants from different regions were linked to their capacity for flexible spatial reference frame switching during navigation tasks. (3) Activating the environmental reference frame for participants from the southern region enhanced their performance of large-scale spatial orientation tasks effectively. Specifically, incorporating a first-person perspective of the surrounding landmark structures in the navigation design facilitated the formation of an environmental reference frame for users. This study supports the spatial reference frame theory and embodied spatial transformation theory, offering valuable insights and recommendations for tailoring navigation interface designs to diverse geographic areas.

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    Persistence of part-list-cuing-induced forgetting: The role of item value
    LIU Tuanli, ZHANG Yajing, ZHOU Song, XING Min, BAI Xuejun
    2024, 56 (1):  15-28.  doi: 10.3724/SP.J.1041.2024.00015
    Abstract ( 69 )   HTML ( 9 )  
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    The part-list cuing effect refers to the phenomenon that when providing a subset of previously learned items as retrieval cues, people’s recall performance for the remaining items is often worse compared to when retrieval cues are absent. Memory research also showed that items with high value are generally better remembered than items with low value. However, it is unclear how the values of items affect the part-list cuing effect and its persistence. Through two experiments, this study investigated the influence of item value on the part-list cuing effect.

    Experiment 1 employed a part-list cuing paradigm in a value-directed memory task. During the learning phase, participants were asked to study 80 category exemplars which were assigned different values (1 or 10 points). Then, a distractor task of either 60s or 100s (in the part-list cuing or no-part-list cuing condition, respectively) was then given. Participants in the part-list cuing condition were then presented with 20 high- or low-value cues for 40 seconds, and were told to read these items aloud and use them to mentally recall the remaining items. Finally, participants completed an old/new recognition task. The procedure can be seen in Figure 1.

    The repeated-measures ANOVA of accuracy showed a significant main effect of cuing condition, F(2, 58) = 8.99, p < 0.001, η2 p = 0.24, and a significant main effect of targets value, F(1, 29) = 18.99, p < 0.001, η2 p = 0.40. An interaction cuing condition × targets value effect was found, F(2, 58) = 4.93, p = 0.01, η2 p = 0.15. A further simple effect analysis revealed that for the high value targets (F(2, 28) = 4.16, p = 0.026, η2 p = 0.23), the recognition accuracy under the no cues condition was significantly higher than that of high value (p = 0.048) and low value (p = 0.045) cues conditions; whereas for the low value targets (F(2, 28) = 7.48, p = 0.002, η2 p = 0.35), the recognition accuracy under the no cues condition (p = 0.002) and low value cues condition (p = 0.004) was significantly higher than that of high value cues condition (Table 1).

    Experiment 2 further manipulated the encoding condition (i.e., 1-study encoding vs. 2-study-test encoding) and the test schedule (i.e., immediate test vs. final test). In the 1-study condition, participants received only one study cycle, but went through two study-test cycles in the 2-study-test condition. The immediate test phase is the same as Experiment 1; the final test involved a final recognition test after a 5min distractor task. The procedure can be seen in Figure 2.

    For the 1-study encoding condition, the repeated-measures ANOVA of accuracy showed a significant main effect of cuing condition, F(2, 72) = 23.29, p < 0.001, η2 p = 0.39, a significant main effect of targets value, F(1, 36) = 12.68, p < 0.001, η2 p = 0.26, and a significant main effect of test schedule, F(1, 36) = 16.16, p < 0.001, η2 p = 0.31. An interaction cuing condition × targets value × test schedule effect was found, F(2, 72) = 3.89, p = 0.025, η2 p = 0.10. A further simple effect analysis revealed that for the high value targets, the recognition accuracy under the no cues condition was significantly higher than that of high value (p < 0.001) and low value (p = 0.008) cues conditions in the immediate test (F(2, 35) = 13.50, p < 0.001, η2 p = 0.44), the recognition accuracy under the no cues condition (p = 0.001) and low value cues condition (p = 0.014) was significantly higher than that of high value cues condition (F(2, 35) = 8.53, p = 0.001, η2 p = 0.33) in the final test; for the low value targets, the recognition accuracy under the no cues condition (p = 0.004) and low value cues condition (p = 0.017) was significantly higher than that of high value cues condition (F(2, 35) = 6.45, p = 0.004, η2 p = 0.27) in the immediate test, the recognition accuracy under the no cues condition (p < 0.001) and low value cues condition (p = 0.010) was significantly higher than that of high value cues condition (F(2, 35) = 10.59, p < 0.001, η2 p = 0.38) in the final test.

    For the 2-study-test encoding condition, the repeated-measures ANOVA of accuracy showed a significant main effect of cuing condition, F(2, 70) = 6.07, p = 0.004, η2 p = 0.15, and a significant main effect of targets value, F(1, 35) = 7.75, p = 0.009, η2 p = 0.18. An interaction cuing condition × targets value effect was found, F(2, 70) = 4.09, p = 0.021, η2 p = 0.11. A further simple effect analysis revealed that, for the high value targets (F(2, 34) = 2.67, p = 0.084), the recognition performance among no cues, high value cues and low value cues were not significant, whereas for the low value targets (F(2, 34) = 4.95, p = 0.013, η2 p = 0.23), the recognition accuracy under the no cues condition (p = 0.015) and low value cues condition (p = 0.020) was significantly higher than that of high value cues condition (Table 2).

    Results from the two experiments collectively showed both the assigned values of cued and test items affected the item recognition performance: cue items with high value resulted in poorer target item recognition performance than those with low value; however, the recognition accuracy was higher for target items with high- than low-value, and the high-value target items were more sensitive to the presentation of part-list cuing. The emergence and persistence of part-list cuing was also modulated by item values. Under the 1-study condition, the high-value cues led to worse target item recognition regardless of the values of the target items, and this detrimental effect was observed in both immediate and final tests. In contrast, the low-value cues only caused poorer recognition of high-value targets in the immediate test. Under the 2-study-test condition, only high-value cues caused recognition impairment of the low-value targets in both immediate and delayed tests. The above results partially validate the two-mechanism account of part-list cuing, and also are a key supplement to this hypothesis: the role of part-list cuing on memory retrieval is not necessarily manifested as a lasting impairment in the low associative coding condition, or a transient impairment in the high associative coding condition, and the item value also influences the strength and persistence of the role of part-list cuing, and it is also necessary to take into account the role of item value when defining the role of part-list cuing on memory retrieval from the perspective of item associative encoding.

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    The representational momentum effect and the reference dependence effect on the evaluation of dynamic happy expressions
    TIAN Yangyang, LI Dong, YAN Xiangbo, LI Zhao, CUI Qian, JIANG Zhongqing
    2024, 56 (1):  29-43.  doi: 10.3724/SP.J.1041.2024.00029
    Abstract ( 62 )   HTML ( 17 )  
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    Most of the existing studies on facial expressions have used static facial picture materials, and dynamic expressions have been relatively understudied. However, people's expressions are often dynamic in life. In order to investigate the emotional processing characteristics of dynamic expressions, the present study examined the effects of the direction of change and the average summary representation on the three-dimensional evaluation of emotion in three experiments that included both dynamic and static happy expression picture materials. It was found that the arousal ratings were higher for the dynamic happy expression with a higher average summary representation. Faces that went from strong to weak had lower valence scores and higher dominance scores than static faces with the same intensity of expression in the previous frame. Indicative of the the representational momentum effect, faces that went from weak to strong had higher valence scores. Furthermore, the dynamic happy expressions that moved from strong to weak had a larger impact on perceived representational momentum than the dynamic happy expressions that moved from weak to strong. In addition, when assessing a facial expression, the perceiver will make a relative assessment based on the internal reference standard: a lower standard is associated with a higher score, and vice versa. This finding is consistent with the reference dependence effect on expression perception. These processing characteristics are used as a reminder to academics to consider the difference between dynamic and static expressions and to think about the impact of various materials when using facial expression data in the future.

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    The gaze biases towards pain-related information during the late stages predict the persistence of chronic pain: Evidence from eye movements
    YANG Zhou, ZHU Jia-Wen, SU Lin, XIONG Ming-Jie, JACKSON Todd
    2024, 56 (1):  44-60.  doi: 10.3724/SP.J.1041.2024.00044
    Abstract ( 47 )   HTML ( 9 )  
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    Pain-related attention biases have a crucial role in the development and maintenance of chronic pain. Previous meta-analyses have demonstrated that individuals with chronic pain exhibit a sustained attentional biases toward pain-related stimuli. Several studies have also highlighted associations between the maintenance of pain-related attention biases and poorer long-term chronic pain outcomes. However, traditional measures used in previous studies including total fixation or duration indexes, cannot capture the dynamic nature of attention or variability in attentional processes between individuals. Some researchers have suggested that the attentional biases associated with chronic pain may exist at different stages of attention processing. Therefore, in order to gain a deeper understanding of the dynamic nature of visual attention biases toward pain-related stimuli and their potential predictive effects on responses to chronic pain, this study employed a time window segmentation analysis of eye movement data. Additionally, real pain stimuli were utilized in the visual task to elicit more authentic responses.

    GPower3.1 was utilized to estimate the required sample size for this study; 49 participants were needed to detect an effect size (f) of 0.17 with a significance level (α) of 0.05 and a power of 95%. A total of 94 participants (69 women) experiencing chronic musculoskeletal pain (e.g., neck pain, shoulder pain, or low back pain), were recruited for this study. During the experiment, participants completed two tasks while their eye movements were recorded using an Eyelink 1000 eye tracker. The eye tracker had a sampling rate of 500 Hz, a spatial accuracy greater than 0.5°, and a resolution of 0.01° in the pupil-tracking mode. After receiving instructions, participants began the first task comprising 16 pairs of pain-neutral pictures and 16 pairs of neutral-neutral pictures, each measuring 11 cm × 10 cm. The viewing angle of each picture was 8.99° × 8.17°. In this task, picture pairs were displayed for 2000 ms, during which participants were instructed to freely view the pictures. Following the disappearance of the stimuli, a detection point appeared at the location of one of the pictures, and participants had to quickly and accurately judge the location of the detection point. Task 2 was identical to Task 1, exception that, no detection point was presented following the offset of picture pairs; instead, there was a possibility that an actual somatosensory pain stimulus would be delivered. Specifically, participants had a 25% chance of receiving a painful stimulus after each pain-neutral picture pair appeared while there was no chance a painful stimulus delivery after neutral-neutral picture pairs appeared. Participants were instructed to quickly and accurately determine whether or not they experienced a painful stimulus. At the start of the experiment, baseline data was collected, including the participants' chronic pain grade, pain catastrophizing scale scores, center for epidemiologic studies depression scores, and demographic information. Additionally, after a period of 6 months, the experimenters followed up with the participants to gather information on their chronic pain intensity and interference.

    For Task 1 and Task 2, a 2 picture type (pain vs. neutral) ×4 epochs (0~500 ms, 500~1000 ms, 1000~1500 ms, and 1500~2000 ms) repeated measures ANOVAs assessing the attentional biases toward pain cues for patients with chronic pain was included. Then, bivariate correlation analyses evaluated the correlation between responses on baseline measures and follow-up levels of pain intensity and interference. Subsequently, significant attentional biases measures correlates of follow-up outcomes were assessed within separate machine learning and hierarchical standard multiple regression models for follow-up pain intensity and interference.

    In Task 1, the main effect for picture type was found, F(1, 93) = 88.36, p< 0.001, η2p = 0.49. Participants displayed significantly longer attentional biases toward pain pictures (M = 49.63 ms, SE = 4.97) than neutral pictures (M = 2.10 ms, SE = 1.79). The main effect for epochs was found, F(3, 91) = 54.88, p < 0.001, η2p = 0.64. Participants displayed significantly longer attentional biases on the second epoch (M = 56.87 ms, SE = 4.00) than the first epoch (M = 10.15 ms, SE = 1.40), the third epoch (M = 22.00 ms, SE = 4.89) and the fourth epoch (M = 14.43 ms, SE = 4.87). The picture type × epochs interaction was found, F(3, 91) = 59.62, p < 0.001, η2p = 0.66. Patients with chronic pain displayed attentional biases toward pain pictures than neutral pictures during the first three epochs (0~500 ms, 500~1000 ms, and 1000~1500 ms) (ps < 0.001, see Figure 1A), but not during the fourth epoch (p = 0.39). In Task 2, the main effect for picture type was found, F(1, 93) = 83.76, p < 0.001, η2p = 0.47. Participants displayed significantly longer attentional biases toward pain pictures (M = 52.40 ms, SE = 5.30) than neutral pictures (M = 4.28, SE = 1.13). The main effect for epochs was found, F(3, 91) = 22.53, p < 0.001, η2p = 0.43. Participants displayed significantly longer attentional biases on the second epoch (M = 55.76 ms, SE = 6.67) than the first epoch (M = 6.64 ms, SE = 1.25), the third epoch (M = 32.11 ms, SE = 4.69) and the fourth epoch (M = 18.86 ms, SE = 4.40). The picture type × epochs interaction was found, F(3, 91) = 19.37, p < 0.001, η2p = 0.39. Patients with chronic pain displayed attentional biases toward pain pictures than neutral pictures during all the four epochs (0~500 ms, 500~1000 ms, 1000~1500 ms and 1500~2000 ms) (ps < 0.046, see Figure 1B).

    By examining the magnitude of attentional biases across the four time windows in the two tasks, it was evident that attentional biases toward pain-related stimuli in patients with chronic pain were imbalanced. Attention was engaged in the first epoch of stimulus presentation (0~500 ms), reached its peak during the second epoch (500~1000 ms), and then gradually decreased during the third and fourth epochs (1000~1500 ms and 1500~2000 ms).

    The bivariate correlation analyses revealed significant correlations between attentional biases toward pain pictures in the third and fourth epochs of both Task 1 and Task 2, and the follow-up levels of pain intensity and interference (rs > 0.22, ps < 0.04). Subsequent hierarchical standard multiple regression analyses revealed that attentional biases towards pain-related stimuli during the third and fourth epochs (1000~1500 ms and 1500~2000 ms) of both tasks independently predicted the persistence of chronic pain intensity and interference levels at a six-month follow-up, β = 0.01, t = 2.45, p = 0.02, ΔR2 = 0.05 (see Table 1, model a), β = 0.02, t = 3.39, p = 0.001, ΔR2 = 0.08 (see Table 1, model b), β = 0.01, t = 2.94, p = 0.004, ΔR2 = 0.07 (see Table 1, model c), β = 0.01, t = 2.65, p = 0.01, ΔR2 = 0.05 (see Table 1, model d), β = 0.01, t = 1.88, p = 0.06, ΔR2 = 0.03 (see Table 1, model e), β = 0.01, t = 1.91, p = 0.06, ΔR2 = 0.03 (see Table 1, model f), β = 0.01, t= 2.46, p= 0.02, ΔR2 = 0.04 (see Table 1, model g), β = 0.01, t = 1.88, p = 0.06, ΔR2 = 0.03 (see Table 1, model h). To investigate the stability of the above results, we utilized machine learning regression models. The machine learning regression model (Random Forest) found the consistent results (see Table 2). The correlations between the attentional biases towards pain pictures in the third and fourth epoch of Task 1 and Task 2, and the predicted chronic pain intensity and interference 6 months later generated by the machine learning regression model, was found to be significant, rs ≥ 0.23, ps ≤ 0.02. The fitting index between the predicted results of the machine learning regression model and the actual results is good, R2≥ 0.88, MSE ≤ 2.25.

    In conclusion, attentional biases toward pain-related stimuli during the later stages (1000~1500 ms and 1500~2000 ms) predicted the maintenance of chronic pain intensity and interference levels at a six month follow-up. These effects were maintained even after controlling for baseline levels of pain intensity and interference and other baseline correlates of follow-up outcomes. The present study represents the first attempt to examine the impact of attentional bias towards pain-related stimuli on the maintenance of dysfunctional chronic pain outcomes from a dynamic perspective. These findings offer an explanation and valuable insights into attentional training, which holds significant importance in enhancing chronic pain management. Moving forward, training individuals to redirect their attention away from pain and associated cues during the later stages of attention may prove to be an effective approach for alleviating suffering due to chronic pain.

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    Effects of grammatical and semantic clues on verb acquisition in Chinese-speaking children
    CHEN Yongxiang, PEI Feifei, HUANG Jiali
    2024, 56 (1):  61-69.  doi: 10.3724/SP.J.1041.2024.00061
    Abstract ( 57 )   HTML ( 13 )  
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    Numerous studies have underscored the ability of young children to infer the meaning of novel words; however, the learning mechanisms in these children remain unknown. Although Chinese-speaking children acquire verbs at a younger age than their English-speaking counterparts, evidence suggests that they encounter greater challenges in verb acquisition compared with English- and Japanese-speaking children. Chinese children can acquire verbs with the aid of syntactic clues until the age of 5, though the effective clues for these children remain inadequately understood. This study investigated the grammatical and semantic clues that can facilitate verb acquisition in children, focusing on Chinese-specific markers such as word length and syntactic cues.

    To mitigate the effect of potential confounding factors, participants for each experiment were recruited from the same kindergarten. The sample sizes for experiments 1, 2a, and 2b were 49, 51, and 53, respectively, with all participants being native Chinese speakers. In both experiments, the Preferential Pointing Paradigm was employed to explore the impact of grammatical and semantic clues on children’s verb acquisition. This paradigm encompassed a learning phase and a testing phase. During the learning phase, the participants were presented with a standard event featuring an actress performing an unfamiliar action with an unfamiliar object, all while hearing an audio cue repeated as “Look, she is X. Look, she is X!” In the testing phase, the participants were tasked with selecting between two events displayed on the screen: an object-same event where “X” referred to the object (a noun) and an action-same event where “X” referred to the action (a verb). Accuracy was considered the dependent variable in this study.

    The results indicated that 5-year-old children could use a single syntactic clue for the acquisition of novel verbs, whereas those aged 4 years demonstrated the ability to utilize double clues, encompassing either double syntactic clues or one syntactic clue coupled with one semantic clue, in their verb learning process (see Table 1). However, 3-year-old children did not exhibit this capability (see Table 3). Furthermore, the length of words had an impact on verb acquisition among 3- and 4-year-old children. In Experiment 2a, 4-year-old children were more inclined to identify monosyllabic words as verbs (see Table 2), whereas in Experiment 2b, 3-year-old children displayed a greater tendency to recognize disyllabic words as nouns when semantic cues were introduced (see Table 3).

    As far as we know, this study is the first to examine Chinese participants, unveiling distinct verb acquisition mechanisms not previously observed in Western languages. Additionally, this study shows that 4-year-old children could learn verbs successfully with the introduction of semantic and more dependable syntactic clues, contradicting previous assumptions that only 5-year-old children possess this ability. Furthermore, the study highlights word length as a potential Chinese-specific factor affecting verb learning, particularly among children aged <4 years. These findings provide a robust foundation for future investigations into the unique mechanisms of verb learning in Chinese children and emphasize the importance of considering linguistic distinctiveness and the reliability of syntactic clues in word acquisition research.

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    Mechanisms underlying the effects of morphological awareness and rapid automatized naming (RAN) on the reading abilities of Chinese Children: An analysis of mediating effects across different stages
    ZHAO Ying, WU Xinchun, CHEN Hongjun, SUN Peng, WANG Haolan
    2024, 56 (1):  70-82.  doi: 10.3724/SP.J.1041.2024.00070
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    Reading is important for children’s future academic success. Clarifying the mechanisms underlying reading ability has been a heated issue in reading research for decades. Most previous studies have focused solely on reading comprehension but scarcely paid attention to the mechanisms underlying reading fluency throughout elementary school. Reading fluency at the text level has been acknowledged as one of the indicators of children’s overall reading competence. Therefore, the present study aimed to clarify the shareability and specificity of the mechanisms underlying Chinese children’s reading comprehension and reading fluency across different developmental stages.

    A total of 416 Chinese children in grades 2, 4 and 6 (lower, middle and higher stages) of elementary school were recruited and then followed up for half a year. Table 1 shows the basic demographic information of children in each stage. In the fall semester (Time 1), a series of tasks, including general cognitive ability; working memory; phonological, orthographic and morphological awareness; rapid automatized naming (RAN); word recognition accuracy; word recognition fluency and vocabulary knowledge, were administered. In the second or spring semester (Time 2), reading comprehension and reading fluency were administered. Three mediation models were fitted to the data with T1 morphological awareness and RAN as predictors, T1 word recognition accuracy, word recognition fluency, and vocabulary knowledge as mediators and T2 reading comprehension and reading fluency as outcomes. The remaining variables were controlled in all the three models.

    The skewness and kurtosis of scores on each task are shown in Table 2. The means, standard deviations, results of analysis of variance, and correlations among variables are reported in Tables 3-6. Besides, for lower, middle, and higher stages, the three mediation models with a completely standardized solution were presented in Figures 1-3, respectively. A bias-corrected bootstrap confidence interval for the indirect effect can be more informative than simply testing the statistical significance of each path. Therefore, such a 95% confidence interval for these data was constructed, and the results are displayed in Table 7. As shown in Figure 1 and the left half of Table 7, the results indicated that morphological awareness and RAN significantly predicted reading comprehension and reading fluency at T2 via word recognition accuracy among children in the lower stage after controlling for the effects of T1 general cognitive ability, T1 working memory and T1 phonological and orthographic awareness. The mediating effect of T1 word recognition fluency in the contribution of T1 RAN to T2 reading fluency was also significant. However, as shown in Figures 2-3 and Table 7, in the middle and higher stages, the indirect effects of T1 morphological awareness and T1 RAN on T2 reading comprehension were not significant; for T2 reading fluency, the mediating role of T1 word recognition accuracy in the effect of T1 morphological awareness was significant in both stages, but the mediated role of T1 word recognition fluency was only significant in the middle stage. Moreover, T1 RAN contributed to it via T1 word recognition accuracy and fluency.

    These findings attest to both the shareability and specificity in the mechanisms underlying reading comprehension and reading fluency across different developmental stages. These findings suggest that reading fluency should be incorporated as a legitimate index of children’s reading ability. They further imply that the developmental stages require consideration when exploring the mechanisms underlying the effects of morphological awareness and RAN on reading abilities (comprehension and fluency). This study provides empirical evidence for understanding the science of reading development among Chinese children and has important implications for future reading research and educational intervention.

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    The impact of Home Literacy Environment on Chinese children’s character recognition, vocabulary knowledge, and reading comprehension: A developmental cascade model
    CHENG Yahua, SHEN Lanlan, LI Yixun, WU Xinchun, LI Hong, WANG Tiequn, CHENG Fang
    2024, 56 (1):  83-92.  doi: 10.3724/SP.J.1041.2024.00083
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    Home literacy environment (HLE) can be more influential than reading intervention programs because HLE affects children’s language and literacy development from the very beginning. A better understanding of the developmental cascades of children’s HLE, character recognition, oral vocabulary knowledge, and reading comprehension is of great value to unpack and promote children’s reading development. The present study aimed to test these developmental cascades among Chinese children during their lower elementary grades.

    This work followed 149 children from Grades 1 to 3. Their HLE was estimated based on information provided by their parents in Grade 1. Their character recognition, oral vocabulary knowledge, and reading comprehension abilities were assessed with age-appropriate measures once per school year, three times in total. A structural equation model was carried out to examine the developmental cascades of HLE, character recognition, oral vocabulary knowledge, and reading comprehension over the three testing time points.

    Table 1 presented means and standard deviations for all variables. Obvious increases with grade were observed in character recognition and vocabulary knowledge. Table 2 presented the Pearson correlation matrix among all measures over time.

    Structural Equation Model was conducted to examine the developmental cascades of HLE, character recognition, oral vocabulary knowledge, and reading comprehension (Figure 1). Results suggested the excellent model fit of the developmental cascade model, χ2(44) = 58.596, p= 0.069, CFI = 0.984, TLI = 0.972, RMSEA = 0.047 (90% CI = 0.000~0.077), SRMR = 0.069.

    However, the path coefficients from Grade 1 vocabulary knowledge to Grade 2 character recognition (β = 0.09, p= 0.113), from Grade 1 reading comprehension to Grade 2 vocabulary knowledge (β = −0.07, p= 0.415), from Grade 2 character recognition to Grade 3 vocabulary knowledge (β = 0.12, p= 0.147), from Grade 2 reading comprehension to Grade 3 character recognition (β = 0.06, p= 0.216) were non-significant. To develop a more parsimonious model, the non-significant path was constrained to zero. We provided all the fit indices of the competing models in Table 3. Chi square difference testing was also conducted to compare the model fits.

    As shown in Table 4, no significant Chi square difference was found when comparing the competing models. Based on the parsimony principle, Model 6 was selected as the final model.

    Figure 2 showed this model with the estimated of the standardized path coefficients. Results showed that, (1) HLE spread through reading comprehension in Grade 1 (β = 0.19, p= 0.030) to character recognition in Grade 2 (β = 0.28, p< 0.001), then to reading comprehension in Grade 3 (β = 0.26, p= 0.002). (2) HLE spread through oral vocabulary knowledge in Grade 1 (β = 0.45, p< 0.001) to reading comprehension in Grade 2 (β = 0.20, p= 0.003), which in turn predicted oral vocabulary knowledge (β = 0.27, p< 0.001) and reading comprehension (β = 0.34, p< 0.001) in Grade 3. (3) The direct predictive effect of HLE on character recognition in Grade 1 (β = 0.16, p= 0.078) was marginally significant, but character recognition in Grade 1 predicted reading comprehension (β = 0.41, p< 0.001) and oral vocabulary knowledge (β = 0.14, p= 0.051) in Grade 2, which in turn predicted character recognition (β = 0.15, p< 0.001) and reading comprehension (β = 0.27, p< 0.001) in Grade 3.

    These results together demonstrated the strong predictive power of HLE to children’s early reading development, and clarified its complex direct and indirect impacts on children’s character recognition, vocabulary knowledge, and reading comprehension over time. These findings help elucidate the way in which HLE may affect children’s reading development, which leads to the theoretical advancement and practical implications for HLE and children’s reading development.

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    Differential effects of foreign language reading anxiety on the reading-related networks in the cerebellum and cerebrum
    DONG Lin, YE Yanghua, HUANG Huiya, LI Lina, LI Hehui, LUO Yue-Jia
    2024, 56 (1):  96-106.  doi: 10.3724/SP.J.1041.2024.00093
    Abstract ( 72 )   HTML ( 10 )  
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    “Neijuan” in China: The psychological concept and its characteristic dimensions
    ZHANG Wen, PAN Chao, YAO Shiming, ZHU Jiajia, LING Dong, YANG Hanchun, XU Jingsha, MU Yan
    2024, 56 (1):  107-123.  doi: 10.3724/SP.J.1041.2024.00107
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    With the deepening and spread of reform and opening-up, China has undergone rapid and unprecedented economic growth and societal transformations over the past few decades. Accumulating evidence has revealed the impacts of sociocultural changes on Chinese mental health. Since 2020, a popular buzzword, “Neijuan” (involution), has garnered significant attention and discussion in daily life. Neijuan could be traced back to agricultural involution, which refers to a process of inward over-elaboration in agricultural development. This concept was first identified by the anthropologist Geertz (1963), who observed that population growth failed to enhance productivity growth and economic development.

    Despite Neijuan's growing attention, it is still unclear about the connotation and characteristic dimensions of this social phenomenon. Cultural psychology provides a solid theoretical and empirical basis for exploring how social and cultural changes affect individuals’ psychological states and behaviors. In this context, we propose that Neijuan is a multidimensional psychological concept of great significance in this new era, closely connected to cultural changes in China’s rapid development and growth.

    To explore the psychological concept of Neijuan, Study 1 employed a grounded theory approach through in-depth interviews to clarify the intricate psychological components of Neijuan, including resource scarcity, social norm, psychological pressure, and competition (see Figure 1). At the macro level, limited resources of society and organization would make people conform to the implicit norms and perform irrational behaviors related to Neijuan. At the micro level, people would perceive intrinsic and extrinsic stressors to make them feel stressed and lead to no-benign competitive behaviors.

    Based on the results of Study 1, Studies 2 and 3 developed a measurement tool to validate the multiple characteristic dimensions of Neijuan in Chinese culture, utilizing exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). We first designed the measurement including 68 items to assess individuals’ perception of Neijuan. Based on the classical measurement theory, the discrimination ability of 68 items was analyzed by using the independent sample t test and the correlation test of total scores and each item score as the discrimination index. Through item analysis, we deleted only one item because of no difference between the low- and high-score groups. Then, principal component analysis (PCA) and the Procrustes variance maximum-oblique rotation method were used to analyze the factors of 67 items. The results showed that there are four factors for the feature value greater than 1, the cumulative total variation is 56.62%, and the load value of each item is between 0.45 and 0.88. Further, we explored the rationality of the four-factor model. The results among employees and undergraduates showed that χ2/df was less than 3, SRMR was less than 0.10, TLI and CFI were all more than 0.80, and RMSEA was less than 0.10, which suggested the model fits well. Thus, we supplied the effective 18-item measurement for assessing the individual perception of Neijuan and confirmed that Neijuan comprises four dimensions: resource scarcity, social norm, psychological pressure, and competition. Subsequently, Study 4 used a Neijuan scenario-based task in the university and workplace environments to assess participants’ behavioral tendencies related to Neijuan and examined the relationship between individuals’ perceptions of Neijuan and their actual behaviors. Results revealed that individuals with higher levels of perceived Nejuan exhibited a greater tendency to engage in behaviors associated with Neijuan among employees (r = 0.66, p < 0.001) and undergraduates (r = 0.61, p < 0.001).

    In summary, the series of studies sought to explore the psychological concept and multiple characteristic dimensions of Neijuan, which provides a theoretical and empirical basis for understanding this significant phenomenon in the contemporary era. The current research also offers an effective measurement tool to assess individuals’ perception of Neijuan and enlightens future research on the effect of Neijuan on psychological maladjustment and non-benign competition behaviors related to Neijuan.

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    Confidence interval width contours: Sample size planning for linear mixed-effects models
    LIU Yue, XU Lei, LIU Hongyun, HAN Yuting, YOU Xiaofeng, WAN Zhilin
    2024, 56 (1):  124-138.  doi: 10.3724/SP.J.1041.2024.00124
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    Hierarchical data, which is observed frequently in psychological experiments, is usually analyzed with the linear mixed-effects models (LMEMs), as it can account for multiple sources of random effects due to participants, items, and/or predictors simultaneously. However, it is still unclear of how to determine the sample size and number of trials in LMEMs. In history, sample size planning was conducted based purely on power analysis. Later, the influential article of Maxwell et al. (2008) has made clear that sample size planning should consider statistical power and accuracy in parameter estimation (AIPE) simultaneously. In this paper, we derive a confidence interval width contours plot with the codes to generate it, providing power and AIPE information simultaneously. With this plot, sample size requirements in LMEMs based on power and AIPE criteria can be decided. We also demonstrated how to run simulation studies to assess the impact of the magnitude of experiment effect size and random slope variance on statistical power, AIPE and the results of sample size planning.

    There were two sets of simulation studies based on different LMEMs. Simulation study 1 investigated how the experiment effect size influenced power, AIPE and the requirement of sample size for within-subject experiment design, while simulation study 2 investigated the impact of random slope variance on optimal sample size based on power and AIPE analysis for the cross-level interaction effect. The results for binary and continuous between-subject variables were compared. In these simulation studies, two factors regarding sample size varied: number of subjects (I= 10, 30, 50, 70, 100, 200, 400, 600, 800), number of trials (J= 10, 20, 30, 50, 70, 100, 150, 200, 250, 300). The additional manipulated factor was the effect size of experiment effect (standard coefficient of experiment condition = 0.2, 0.5, 0.8, in simulation study 1) and the magnitude of random slope variance (0.01, 0.09 and 0.25, in simulation study 2). In addition, we generated data under balance design (the number of trials for different levels of independent variable was equal) and unbalance design (the number of trials for different levels of independent variable was unequal). A random slope model was used in simulation study 1, while a random slope model with level-2 independent variable was used in simulation study 2. Data-generating model and fitted model were the same. Estimation performance was evaluated in terms of convergence rate, power, AIPE for the fixed effect, and the random effect.

    The results are as following. First, there were no convergence problems under all the conditions, except that when the variance of random slope was small and a maximal model was used to fit the data. Second, power increased as sample size, number of trials or effect size increased. However, the number of trials played a key role for the power of within-subject effect, while sample size was more important for the power of cross-level effect. Power was larger for continuous between-subject variable than for binary between-subject variable. Power was larger under balance design than unbalance design. Third, although the fixed effect was accurately estimated under all the simulation conditions, the width 95% confidence interval (95% width) was extremely large under some conditions. Lastly, AIPE for the random effect increased as sample size and/or number of trials increased. The variance of residual was estimated accurately. As the variance of random slope increased, the accuracy of the estimates of variances of random intercept decreased, and the accuracy of the estimates of random slope increased. To simplify the results of these simulation studies, a final set of summary guidelines are presented in the form of confidence interval width contours. Take Figure 1 for example, in Figure 1(a), the shaded area represents the conditions when power is higher than 0.8. While in Figure 1(b), the shaded area represents the conditions when power is higher than 0.8 and rbias of all the random effects are less than 0.1. Color represents different levels of width of 95% credible interval. The shaded area shows recommended sample sizes in terms of power. Practitioners can choose a sample size in the shaded area meets the requirement of width of credible interval, which evaluates the accuracy of parameter estimates for the fixed experimental effect.

    In conclusion, if sample size planning was conducted solely based on power analysis, the chosen sample size might not be large enough to obtain accurate estimates of effects size. Therefore, the rational for considering statistical power and AIPE during sample size planning was adopted. To shed light on this issue, this article provided a standard procedure based on a confidence interval width contours plot to recommend sample size and number of trials for using LMEMs. This plot visualizes the combined effect of sample size and number of trials per participant on 95% width, power and AIPE for random effects. Based on this tool and other empirical considerations, practitioners can make informed choices about how many participants to test, and how many trials to test each one for.

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