Acta Psychologica Sinica

The mechanism of visual processing for nonsalient stimuli in perceptual learning

ZHANG Qi, WANG Zile, WU Meijun

2024, 56 (6): 689-700. doi: 10.3724/SP.J.1041.2024.00689

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Previous studies have found that perceptual learning can improve the performance on visual search tasks. However, many cognitive processes are involved in visual search, and it is unclear at which visual processing stage perceptual learning facilitates search performance. The current study explored the mechanism of perceptual learning by dividing the eye movement metrics into three visual processing stages: search initiation time (the early visual processing stage), which represents the cognitive process of the time of processing the current location and selecting the next search location; scanning time (the middle visual processing stage), which includes the number and processing time of fixation positions; verification time (the late visual processing stage), which represents determining whether the current stimulus is the target and making a verification. A 2 (target type: trained vs. untrained triangle) × 2 (test stage: pretest vs. posttest) within-subjects design was used to address the above issue. 24 healthy young adults (5 males; mean age: 21.23 ± 2.02 years) participated as paid volunteers in this study. We randomly selected one of the four orientations of the triangle (Up, Down, Left, or Right) as the trained triangle, which would receive three days of training. Moreover, to ensure that the visual search training was specific to the trained triangle, the trained and untrained triangles were tested by recording the behavior results and eye movement before and after training (untrained triangle was randomly selected from the distractors). Each trial started with a fixation cross (When eye movement was recorded, the search display would not appear until the participants fixated on the center cross for more than 500 ms; when eye movement was not recorded, the central fixation cross was presented for 500 ms and then the search screen was presented). Then a search display was presented until the key response or the elapse reached 2000 ms since its onset. The response was recorded only before the fixation cross disappeared. The task of participants was to determine whether or not the target was presented as quickly as possible. Participants pressed the left arrow key to report the presence of a target or the right arrow key to report its absence. A two-way repeated-measures ANOVA was conducted with the factors of target type (trained vs. untrained triangle) and test stage (pretest vs. posttest). The behavior results (Figure 1) found the reduced response time (target present trial: Δ 0.50 ± 0.10 s, t(23) = 24.26, p < 0.001, Cohen’s d =5.06, BF₁₀= 7.29, 95% CI = [0.45, 0.54]; target absent trial: Δ 0.45 ± 0.20 s, t(23) = 11.06, p < 0.001, Cohen’s d = 2.28, BF₁₀= 8.74, 95% CI = [0.36, 0.53]) and increased accuracy (Δ -0.37 ± 0.14, t(23) = -13.31, p < 0.001, Cohen’s d = 2.77, BF₁₀= 2.99, 95% CI = [-0.43, -0.31]) when searching for trained stimuli after training. However, there was no significant difference in response time (target present trial: Δ -0.01 ± 0.18 s, t(23) = -0.40, p = 0.696; target absent trial: Δ 0.04 ± 0.17 s, t(23) = 1.13, p = 0.270) or accuracy between pretest and posttest for untrained stimuli(Δ 0.00 ± 0.18, t(23) = 0.07, p = 0.942). The results of eye movement tracking are as follows: (1) in the early visual processing stage (Figure 2), the search initiation time of the trained stimuli increased significantly after training(target present trial: Δ -32.43 ± 63.95 ms, t(23) = -2.48, p = 0.021, Cohen’s d = 0.52, BF₁₀= 2.65, 95% CI = [-59.43, -5.42]; target absent trial: Δ -45.16 ± 75.56 ms, t(23) = -2.93, p = 0.008, Cohen’s d = 0.61, BF₁₀= 6.12, 95% CI = [-77.06, -13.25]), and there was no significant difference in the search initiation time between pretest and posttest for untrained stimuli(target present trial: Δ 0.31 ± 83.42 ms, t(23) = 0.02, p = 0.986; target absent trial: Δ 13.51 ± 101.67 ms, t(23) = 0.65, p = 0.52). (2) In the middle visual processing stage, the number of fixations of trained stimuli (Figure 3) were significantly reduced after training(target present trial: Δ 2.04 ± 0.50, t(23) = 19.89, p < 0.001, Cohen’s d = 4.15, BF₁₀ = 9.37, 95% CI = [1.83, 2.26] ; target absent trial: Δ 2.23 ± 0.85, t(23) = 12.84, p < 0.001, Cohen’s d = 2.68, BF₁₀= 1.46, 95% CI = [1.87, 2.59]) and there was no significant difference for untrained stimuli before and after training(target present trial: Δ -0.02 ± 1.16, t(23) = -0.10, p = 0.919 ; target absent trial: Δ 0.18 ± 1.14, t(23) = 0.79, p = 0.437). The average fixation time of trained stimuli (Figure 4) was significantly reduced after training when target present (target present trial: Δ 63.40 ± 42.04 ms, t(23) = 7.39, p < 0.001, Cohen’s d = 1.54, log (BF₁₀) = 11.47, 95% CI = [45.65, 81.16]), but there was no significant difference for untrained stimuli before and after training(target present trial: Δ -4.82 ± 23.23 ms, t(23) = -1.02, p = 0.321). There was only a significant main effect of test stage in average fixation time when target absent (target absent trial: F (1, 23) = 10.06, p < 0.01, η2 p = 0.30). (3) In the late visual processing stage, there was no significant difference in verification time (Figure 5) between the pretest and posttest for both trained and untrained stimuli (target present trial: F(1, 23) = 1.25, p = 0.274; target absent trial: F(1, 23) = 0.37, p = 0.552). In conclusion, the accuracy and search initiation time of searching for trained stimuli was increased, while the number of fixations and the fixation time decreased. Moreover, the changes in behavior and eye movement indexes did not transfer to untrained stimuli. It is suggested that perceptual learning can affect the early and middle visual processing stages, and search performance may be improved by increasing the search latency, reducing the number of saccades, and reducing the fixation time.

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The impact of spontaneity and presentation mode on the ingroup advantage in recognizing angry and disgusted facial expressions

FANG Xia, GE Youxun

2024, 56 (6): 701-713. doi: 10.3724/SP.J.1041.2024.00701

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Previous research has found that individuals are more accurate at recognizing facial expressions of individuals from their own cultural background than those from a different cultural background, known as the ingroup advantage. However, most studies investigating the ingroup advantage have primarily focused on posed and static facial expressions, paying less attention to spontaneous and dynamic facial expressions. To investigate whether the ingroup advantage is influenced by spontaneity (posed and spontaneous) and presentation mode (static and dynamic) of facial expressions, we recruited participants from China, Canada, and the Netherlands to recognize posed and spontaneous facial expressions of anger and disgust displayed by Chinese and Dutch models (Experiment 1), as well as static and dynamic facial expressions (Experiment 2). The results showed that, in most cases, there was an ingroup advantage in the recognition of both posed and spontaneous expressions, with the ingroup advantage being significantly higher for posed expressions compared to spontaneous expressions. Additionally, an ingroup advantage was observed in the recognition of both static and dynamic expressions, although there was no significant difference between the two overall. These findings suggest that the ingroup advantage in facial expression recognition is influenced by the spontaneity of the expressions, but may not be affected by the mode of expression presentation. The implications of this research are significant in expanding our understanding of the ingroup advantage and deepening our knowledge of cross-cultural facial expression recognition.

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The development of symbolic and non-symbolic SNARC effects: The roles of phonological abilities, visuospatial abilities and working memory

JIANG Jiali, QI Yue, LEI Xiuya, LU Lifei, YU Xiao

2024, 56 (6): 714-730. doi: 10.3724/SP.J.1041.2024.00714

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The spatial-numerical association of response codes (SNARC) effect is a phenomenon in which the leftward response is faster than the rightward response for smaller numbers, whereas for larger numbers, the rightward response is faster than the leftward response. Although the existence of the SNARC effect has been examined in many studies, most of these studies focused on the symbolic SNARC effect and neglected to explore the non-symbolic SNARC effect. Little is known about how symbolic and non-symbolic SNARC effects develop and whether there are differences in the cognitive mechanisms involved in these two effects. The present study aimed to simultaneously investigate the developmental characteristics and cognitive mechanisms of symbolic and non-symbolic SNARC effects to contribute to the understanding of number processing.

In Experiment 1, a large-sample cross-sectional method was used with four age groups to explore the developmental characteristics of symbolic and non-symbolic SNARC effects. Thirty-six 6- to 7-year-old children (19 boys, mean age: 6.42 ± 0.47 years), 59 7- to 8-year-old children (30 boys, mean age: 7.56 ± 0.42 years), 69 8- to 9-year-old children (32 boys, mean age: 8.40 ± 0.38 years) and 31 adults (15 males, mean age: 21.76 ± 1.46 years) performed the symbolic and non-symbolic parity judgement task. Experiment 2 was based on dual coding theory and the findings from Experiment 1. In this experiment, 137 children aged 8 to 9 years (70 boys, mean age: 8.43 ± 0.75 years), the key age at which symbolic and non-symbolic SNARC effects are observed, were selected as participants and followed longitudinally for six months to explore whether the two SNARC effects had similar cognitive mechanisms. Phonological ability, visuospatial ability, visual working memory and phonological working memory were measured at T1. At T2 (after 6 months), the participants' symbolic and non-symbolic SNARC effects were measured. The symbolic and non-symbolic SNARC effects at T1 were controlled.

The findings of this study were as follows. (1) The non-symbolic SNARC effect emerged in 6- to 7-year-old children [t (35) = -4.20, p< 0.001], while the symbolic SNARC effect emerged in 8- to 9-year-old children [t(62) = -4.53, p< 0.001]. Thus, the non-symbolic SNARC effect emerged earlier than the symbolic SNARC effect (see Table 1, Table 2, Figure 1 and Figure 2). (2) There were no significant age differences in the symbolic or non-symbolic SNARC effects. (3) For 8- to 9-year-old children (r = 0.13, p = 0.33) and adults (r = -0.03, p = 0.86) with both symbolic SNARC effects and non-symbolic SNARC effects, these two effects were not significantly correlated. (4) Phonological ability (β = -0.81, SE = 0.19, p < 0.001) and phonological working memory (β = 0.45, SE = 0.09, p < 0.001) at T1 significantly predicted the development of the symbolic SNARC effect at T2 but not the development of the non-symbolic SNARC effect at T2. Visuospatial ability (β = -0.63, SE = 0.10, p < 0.001) and visual working memory (β = 0.29, SE = 0.10, p = 0.002) at T1 significantly predicted the development of the non-symbolic SNARC effect at T2 but not the development of the symbolic SNARC effect (see Table 3 and Figure 3).

In conclusion, 8 to 9 years is the critical age at which symbolic and non-symbolic SNARC effects emerge simultaneously, and there is no significant difference in the size of the SNARC effects according to age. Furthermore, phonological ability and phonological working memory contribute to the symbolic SNARC effect, whereas visuospatial ability and visual working memory contribute to the non-symbolic SNARC effect. These findings suggest a difference in the cognitive mechanisms of these two SNARC effects. These findings support the hypothesis of the separation of symbolic and non-symbolic SNARC effects and extend dual coding theory.

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The impact of instrumental feeding on picky eating behavior in children aged 9 to 12: Evidence from resting-state fMRI

CUI Yicen, ZHANG Yixiao, CHEN Ximei, XIAO Mingyue, LIU Yong, SONG Shiqing, GAO Xiao, GUO Cheng, CHEN Hong

2024, 56 (6): 731-744. doi: 10.3724/SP.J.1041.2024.00731

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Picky eating is a common dietary issue among children characterized by lack of variety of foods consumed due to rejection of familiar (or unfamiliar) foods. The influencing factor model of picky eating behavior in children indicates that environmental and cognitive factors are key elements influencing this. Studies have found that instrumental feeding exacerbates picky eating behavior in children. However, due to the relatively young age of children in previous studies, research on the relationship between instrumental feeding and picky eating behaviors in school-aged children is insufficient. Furthermore, the brain plays a central role in guiding eating behavior; however, to date, limited neuroscientific research on the neural basis of picky eating behaviors in school-aged children exists. This study aimed to utilize resting-state functional magnetic resonance imaging (rs-fMRI) data combined with a machine learning method to explore the neural basis of picky eating behaviors in children. Additionally, it attempted to show the neural mechanisms through which instrumental feeding influences picky eating behavior.

A total of 139 children were recruited for this study. Instrumental feeding and picky eating behaviors were assessed through parent-reported measurements and rs-fMRI was conducted. A total of 87 children were included in the formal analyses as those who did not participate in the two behavioral measurements and with unqualified rs-fMRI scans were excluded. This study utilized regional homogeneity and functional connectivity to evaluate the resting-state neural substrates of picky eating behaviors. Subsequently, a machine learning method is employed to validate the stability of our results. Additionally, a mediation model was constructed to investigate the mediating role of resting-state neural substrates in the relationship between instrumental feeding and picky eating behavior.

Results showed that picky eating behavior was positively correlated with regional homogeneity in the right caudate (see Figure 1, r = 0.43, p< 0.001, 95% CI = [0.25 0.59]). Functional connectivity results showed that picky eating behavior was positively correlated with functional connectivity between the right caudate and left putamen (see Figure 2, r = 0.43, p < 0.001, 95% CI = [0.24 0.59]). A prediction analysis based on a cross-validation machine learning method indicated a significant correlation between picky eating behavior scores predicted by the aforementioned neural substrates (i.e., regional homogeneity in the right caudate and functional connectivity between the right caudate and left putamen) and the actual observed picky eating behavior scores (regional homogeneity: r_{(predicted, observed)} = 0.37, p < 0.001; functional connectivity: r_{(predicted, observed)} = 0.35, p < 0.001). Shown as Figure 3, the mediation model further suggested that functional connectivity between the right caudate and left putamen could mediate the relationship between instrumental feeding and picky eating behavior (indirect effect: β = -0.16, standard error = 0.05, 95% CI = [-0.26 -0.06]).

Specifically, instrumental feeding might negatively influence the functional connectivity between the right caudate and left putamen, and further reduce picky eating behavior.

By combining resting-state regional homogeneity and functional connectivity analyses, this study detected altered functional brain activity related to picky eating behaviors in children aged 9 to 12. Specifically, hyperactive neural interactions within the brain areas involved in sensory sensitivity and reward processing may explain the manifestation of picky eating behavior in children. Additionally, instrumental feeding negatively influences picky eating behavior through brain activity in regions involved in sensory sensitivity and reward processing. This study provides new insights into the resting-state neural substrates of children's picky eating behavior, extends the influencing factor model of children's picky eating behavior, and provides theoretical support for interventions to improve poor picky eating behavior in children through parental feeding practices.

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Relationship between adolescents’ smartphone stress and mental health: Based on the multiverse-style analysis and intensive longitudinal method

HUANG Shunsen, LAI Xiaoxiong, ZHANG Cai, ZHAO Xinmei, DAI Xinran, QI Mengdi, WANG Huanlei, WANG Wenrong, WANG Yun

2024, 56 (6): 745-758. doi: 10.3724/SP.J.1041.2024.00745

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To explore the relationship and mechanisms between smartphone stress and adolescent mental health, Study 1 examined the robust relationship between smartphone stress and adolescent mental health in a sample of 74,182 adolescents using multiverse-style analysis; Study 2 conducted an intensive longitudinal survey over 35 days with 507 adolescents to investigate the mechanisms through which smartphone stress affects their mental health. Study 1 found that more than half of the adolescents reported experiencing stress from smartphones, and there was a robust negative correlation between smartphone stress and mental health, deserving attention from researchers and society. Study 2 identified that intensity/fluctuation of negative emotions and rumination mediate the effect between smartphone stress and mental health, with differences in how these factors affect positive or negative dimensions of mental health. This research extended, for the first time, the “stress-cognition/emotion” theory and the “media use-digital stress-mental health” model in depth and breadth, and provided new perspectives and a basis for promoting youth’s mental health development.

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Underestimating others’ fertility attitudes and behaviors hinders the fertility intentions of childless individuals in Gen Z

CHEN Sijing, SHEN Jiahui, JIANG Qiaojie, YANG Shasha

2024, 56 (6): 759-776. doi: 10.3724/SP.J.1041.2024.00759

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The “far dog, near cat” effect in stray animal charity rescue and its mechanism

LIU Wumei, WANG Lu

2024, 56 (6): 777-798. doi: 10.3724/SP.J.1041.2024.00777

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While past pro-social research has focused on charitable donations to humans, we know little about charitable assistance for stray animals. However, investigating the factors that promote human’s assistance to stray animals is of great practical importance. Currently, an increasing number of organizations and platforms are involving themselves in rescuing stray animals. When these organizations and platforms present animal rescue information, the information ad contains both animal type and spatial distance. Therefore, to address this research gap, this paper aims to study how animal type and spatial distance jointly influence consumers’ willingness to rescue stray animals, as well as the mechanisms and boundary conditions.

We propose a novel “far dog, near cat” effect. Specifically, we predict that under the near spatial distance, rescuing cat (versus rescuing dog) increases consumers’ rescuing willingness, whereas under the far spatial distance, rescuing dog (versus rescuing cat) increases consumers’ rescuing willingness. To test this effect, we conducted a total of nine experiments (N = 2848), including one implicit association test, one field experiment, one laboratory experiment, and six online experiments in different scenarios. We determined sample size of each experiment using G*power calculator.

Overall, this study found that cats were more compatible with proximal spatial distance, while dogs were more compatible with distal spatial distance (Experiment 1a, 1b). Therefore, presenting a stray cat (vs. a stray dog) in the proximal spatial-distance ad triggered consumers’ higher willingness to rescue the animal, while presenting a stray dog (vs. a stray cat) in the distal spatial-distance ad triggered consumers’ higher willingness to rescue the animal (Experiments 2-5). We further found that processing fluency mediated the “far dog, near cat” effect (Experiments 4-5). In addition, we found that the “far dog, near cat” effect was moderated by consumers’ thinking styles such that the "far dog, near cat" effect was evident when consumers adopted affective thinking style and disappeared when consumers adopted cognitive thinking style (Experiment 6).

Study 1a recruited 188 university students (76.1% female, M_age = 23.31 years, SD = 2.43 years) and conducted a 2 (animal type: cat vs. dog) × 2 (spatial distance: near vs. far) repeated-measures ANOVA with average response time as the dependent variable. The results showed a significant interaction effect, F(1, 186) = 16.08, p < 0.001, η²_p = 0.080. Further simple effects analysis revealed that participants responded faster to words indicating near spatial distance when viewing cats compared to dogs (M_cat = 819.86 ms, SD = 182.05 vs. M_dog = 908.99 ms, SD = 279.29), F(1, 186) = 6.72, p = 0.010, η²_p = 0.035. Additionally, participants responded faster to words indicating far spatial distance when viewing dogs compared to cats (M_cat = 961.97 ms, SD = 362.12 vs. M_dog = 862.05 ms, SD = 294.54), F(1, 186) = 4.31, p = 0.039, η²_p = 0.023. A similar analysis with average accuracy rate as the dependent variable also showed a significant interaction effect, F(1, 186) = 4.75, p = 0.031, η²_p = 0.025. Further simple effects analysis showed that compared to seeing cats, participants had a higher accuracy rate for far spatial distance words when seeing dogs (M_cat = 0.94 (94%), SD = 0.12 vs. M_dog = 0.98 (98%), SD = 0.07), F(1, 186) = 4.68, p = 0.032, η²_p = 0.025; whereas the accuracy rate for near spatial distance words was higher when seeing cats compared to dogs, but this difference was not significant (M_cat = 0.98 (98%), SD = 0.07 vs. M_dog = 0.97 (97%), SD = 0.08), F(1, 186) = 0.58, p = 0.448.

Study 1b involved 200 participants from the Credamo survey platform (M_age = 30.14 years, SD = 8.96 years, 68.5% female). The results indicated that participants who saw cat-related content chose images with closer spatial distances compared to those who saw dog-related content (M_cat= 1.24, SD = 0.90 vs. M_dog= 5.23, SD = 4.31), F(1, 198)= 82.26, p < 0.001, η²_p = 0.294. Similarly, the chosen images of participants in the cat group showed closer spatial distances between people and animals compared to those chosen by the dog group (M_cat= 1.63, SD = 0.96 vs. M_dog= 3.17, SD = 1.45), F(1, 198)= 78.41, p < 0.001, η²_p = 0.284.。

Study 2 had 310 participants (M_age = 20.51 years, SD = 1.81 years, 53.2% female) and conducted a 2 (animal type: stray cat vs. stray dog) × 2 (spatial distance: near vs. far) ANOVA, revealing a significant interaction effect on the willingness to participate in animal rescue activities, F(1, 306) = 8.57, p < 0.001, η²_p = 0.027. Further simple effects analysis indicated that at a close spatial distance, participants were significantly more willing to spend time on animal rescue activities for stray cats than for stray dogs (M_dog = 52.12, SD = 34.93 vs. M_cat = 63.28, SD = 38.87), F(1, 306) = 4.25, p = 0.040, η²_p = 0.014. Conversely, at a far spatial distance, participants' willingness to spend time on animal rescue activities was significantly higher for stray dogs than for stray cats (M_dog = 63.12, SD = 31.94 vs. M_cat = 51.04, SD = 32.65), F(1, 306) = 4.33, p = 0.038, η²_p = 0.014. When willingness to purchase animal food was used as the dependent variable, a 2 (animal type: cats vs. dogs) × 2 (spatial distance: close vs. far) factorial between-subjects analysis of variance showed a significant interaction effect, F (1, 306) = 10.88, p = 0.001, η²_p = 0.034. Further simple effects analysis revealed that at a close spatial distance, participants' willingness to purchase food was significantly higher for stray cats than for stray dogs (M_dog = 4.39, SD = 1.24 vs. M_cat = 4.79, SD = 1.14), F(1, 306) = 4.45, p = 0.036, η²_p = 0.014. However, at a far spatial distance, participants' willingness to purchase food was significantly higher for stray dogs than for stray cats (M_dog = 4.89, SD = 1.15 vs. M_cat = 4.37, SD = 1.39), F(1, 306) = 6.47, p = 0.011, η²_p = 0.021.

Study 3 recruited 317 participants (M_age = 31.45 years, SD = 8.48 years; 56.5% female) and used willingness to help as the dependent variable in a 2 (type of animal: stray cats vs. stray dogs) × 2 (spatial distance: near vs. far) factorial between-subjects ANOVA. The results indicated a significant interaction effect, F(1, 313) = 33.29, p < 0.001, η²_p = 0.096. Further simple effects analysis revealed that at a near spatial distance, participants' willingness to help stray cats was significantly higher than their willingness to help stray dogs (M_dog = 5.33, SD = 1.00 vs. M_cat = 5.79, SD = 0.98), F(1, 313) = 7.30, p = 0.007, η²_p = 0.023. Conversely, at a far spatial distance, participants' willingness to help stray dogs was significantly higher than their willingness to help stray cats (M_dog = 5.79, SD = 1.09 vs. M_cat = 4.87, SD = 1.16), F(1, 313) = 7.30, p < 0.001, η²_p = 0.087.

As a supplementary experiment to Experiment 3, 306 participants took part in study 3S on the Credamo platform (M_age = 30.95 years, SD = 8.01 years, 71.2% female). This experiment also focused on the willingness to adopt as the dependent variable, using the same 2 (type of animal: stray cats vs. stray dogs) × 2 (spatial distance: near vs. far) between-subjects factorial design. The results showed a significant interaction effect, F(1, 302) = 13.48, p < 0.001, η²_p = 0.043. Further analysis of simple effects indicated that at a near spatial distance, participants' willingness to adopt stray cats was significantly higher than their willingness to adopt stray dogs (M_dog = 5.67, SD = 0.84 vs. M_cat = 5.97, SD = 0.63), F(1, 302) = 4.13, p = 0.043, η²_p = 0.013. However, at a far spatial distance, participants' willingness to adopt stray dogs was significantly higher than their willingness to adopt stray cats (M_dog = 6.08, SD = 0.61 vs. M_cat = 5.65, SD = 1.20), F(1, 302) = 10.17, p = 0.002, η²_p = 0.033.

Study 4 was a pre-registered study involving 300 college students (M_age = 22.94 years, SD = 3.34 years, 64.7% female). It utilized a 2 (type of animal: stray cats vs. stray dogs) × 2 (spatial distance: near vs. far) between-subjects factorial design, with donation amount as the dependent variable. The results showed a significant interaction effect between the type of animal and spatial distance, F(1, 296) = 9.51, p = 0.002, η²_p = 0.031. Further simple effects analysis revealed that at a close spatial distance, participants donated significantly more to stray cats than to stray dogs (M_dog = 45.70, SD = 28.43 vs. M_cat = 56.93, SD = 34.50), F(1, 296) = 4.79, p = 0.029, η²_p = 0.016. Conversely, at a far spatial distance, donations were significantly higher for stray dogs than for stray cats (M_dog = 52.47, SD = 32.15 vs. M_cat = 41.54, SD = 29.12), F(1, 296) = 4.72, p = 0.031, η²_p = 0.016. Additionally, using donation amount as the dependent variable, type of animal as the independent variable, spatial distance as a moderator, and processing fluency as a mediator, a moderated mediation analysis was conducted using PROCESS (Model 8, 5,000 bootstraps; Hayes, 2018). The results indicated that processing fluency mediated the interaction between animal type and spatial distance on the donation amount (indirect effect = -3.64, SE = 1.79, 95% CI = [-7.6767, -0.6922], not including 0). Further analysis showed that the indirect effect of processing fluency was significant at a close spatial distance (indirect effect = 1.86, SE = 1.03, 95% CI = [0.2001, 4.2033], not including 0) and also significant at a far spatial distance (indirect effect = -1.78, SE = 1.13, 95% CI = [-4.4359, -0.0556], not including 0).

Study 4S, serving as a supplementary study to study 4, had 280 participants (M_age = 41.84 years, SD = 15.64 years, 58.9% female). The study conducted a 2 (animal type: stray cat vs. stray dog) × 2 (spatial distance: near vs. far) analysis of variance with the intention to help as the dependent variable. The results showed a significant interaction F (1, 276) = 9.42, p = 0.002, η²_p = 0.033. Further analysis of simple effects revealed that at a closer spatial distance, participants were more willing to assist stray cats over stray dogs (M_dog = 4.32, SD = 1.47 vs. M_cat = 4.93, SD = 1.64), F (1, 276) = 3.87, p = 0.050, η²_p = 0.014; whereas at a farther spatial distance, the preference shifted towards helping stray dogs rather than cats (M_dog = 4.94, SD= 1.58 vs. M_cat = 4.30, SD = 1.84), F (1, 280) = 5.87, p = 0.016, η²_p = 0.021. Additionally, with the willingness to help as the dependent variable, type of animal as the independent variable, spatial distance as a moderating variable, and processing fluency as a mediating variable, a moderated mediation analysis was conducted using PROCESS (Model 8, 5,000 bootstraps; Hayes, 2018). The results showed that processing fluency mediates the effect of the interaction between type of animal and spatial distance on the willingness to help (indirect effect = 0.13, SE = 0.08, 95% CI = [0.0036, 0.3206], not including 0).

Study 5 involved 308 participants (M_age = 30.95 years, SD = 10.53 years, 57.1% female) using adoption intention as the dependent variable, and conducted a 2 (animal type: stray cats vs. stray dogs) × 2 (spatial distance: near vs. far) between-subjects factorial ANOVA. The results showed a significant interaction effect, F(1, 304) = 51.49, p < 0.001, η²_p = 0.145. Further simple effects analysis revealed that at a close spatial distance, participants' willingness to adopt stray cats was significantly higher than for stray dogs (M_dog = 5.44, SD = 1.20 vs. M_cat = 6.25, SD = 0.65), F(1, 304) = 30.26, p < 0.001, η²_p = 0.091; whereas at a far spatial distance, participants' willingness to adopt stray dogs was significantly higher than for stray cats (M_dog = 6.25, SD = 0.65 vs. M_cat = 5.46, SD = 0.98), F(1, 304) = 21.68, p < 0.001, η²_p = 0.067. Additionally, using adoption willingness as the dependent variable, type of animal as the independent variable, processing fluency as the mediator, and spatial distance as the moderator, a moderated mediation analysis was conducted using PROCESS (Model 8, 5,000 bootstraps; Hayes, 2018). The results indicated that processing fluency mediated the interaction between animal type and spatial distance on adoption willingness (indirect effect = -0.24, SE = 0.10, 95% CI = [-0.4701, -0.0745], not including 0). The mediating effect of processing fluency was significant at a close spatial distance (indirect effect = 0.10, SE = 0.06, 95% CI = [0.0027, 0.2320], not including 0) and also significant at a far spatial distance (indirect effect = -0.14, SE = 0.07, 95% CI = [-0.2992, -0.0266], not including 0).

Study 6 aimed to examine the moderation of thinking styles, recruiting a total of 639 participants (M_age = 31.29 years, SD = 8.77 years, 65.9% female). The study used adoption intention as the dependent variable to conduct a 2 (animal type: stray cats vs. stray dogs) × 2 (spatial distance: near vs. far) × 2 (thinking style: affective vs. cognitive) three-factor between-subjects ANOVA. The results indicated a significant three-way interaction, F(1, 631) = 23.26, p < 0.001, η²_p = 0.036. Further simple effect analyses revealed that, after priming a cognitive thinking style, the interaction between animal type and spatial distance was not significant, F(1, 631) = 1.56, p = 0.180. When the spatial distance was near, participants' adoption intentions for stray cats and dogs were similar (M_dog = 5.66, SD = 1.28 vs. M_cat = 5.92, SD = 0.80), F(1, 631) = 2.98, p = 0.085. Similarly, when the spatial distance was far, participants' adoption intentions were also similar for both animal types (M_dog = 5.60, SD = 1.02 vs. M_cat = 5.58, SD = 0.92), F(1, 631) = 0.03, p = 0.865. After priming an affective thinking style, the interaction between animal type and spatial distance was significant, F(1, 631) = 57.50, p < 0.001, η²_p = 0.083. Specifically, when the spatial distance was near, participants' adoption intention for stray cats was significantly higher than for stray dogs (M_dog = 5.60, SD = 0.90 vs. M_cat = 6.25, SD = 0.56), F(1, 631) = 19.02, p < 0.001, η²_p = 0.029. Conversely, when the spatial distance was far, participants' adoption intention for stray dogs was significantly higher than for stray cats (M_dog = 6.30, SD = 0.39 vs. M_cat = 5.24, SD = 1.22), F(1, 631) = 51.37, p < 0.001, η²_p = 0.075. Additionally, with adoption intention as the dependent variable, animal type as the independent variable, processing fluency as the mediating variable, spatial distance as the first-level moderator, and thinking style as the second-level moderator, a moderated mediation analysis was conducted using PROCESS (Model 12, 5000 bootstraps; Hayes, 2018). The results showed that processing fluency mediated the effect of the interaction among animal type, spatial distance, and thinking style on adoption intention (indirect effect = -0.16, SE = 0.08, 95% CI = [-0.3334, -0.0157], not including 0). Under a cognitive thinking style: at near spatial distances, the mediating effect of processing fluency was not significant (indirect effect = -0.01, SE = 0.03, 95% CI = [-0.0638, 0.0712], including 0); at far spatial distances, the mediating effect was also not significant (indirect effect = -0.001, SE = 0.03, 95% CI = [-0.0680, 0.0668], including 0). Under an affective thinking style: at near spatial distances, the mediating effect of processing fluency was significant (indirect effect = 0.08, SE = 0.04, 95% CI = [0.0056, 0.1642], not including 0); at far spatial distances, the mediating effect was also significant (indirect effect = -0.08, SE = 0.05, 95% CI = [-0.1879, -0.0007], not including 0).

This paper has significant theoretical contributions and practical implications. Theoretically, this study focuses on stray animals as a novel object of charitable donations and builds the implicit linkage between animal type and spatial distance. Also, this study identifies the “far dog, near cat” effect in stray animal rescue, adding to past pro-social literature in general and donation literature in particular. Practically, the “far dog, near cat” effect we identified in this paper can guide charitable organizations how to present animal-rescue information appropriately.

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Differences in information processing between experienced investors and novices, and intervention in fund investment decision-making

XIN Ziqiang, WANG Luxiao, LI Yue

2024, 56 (6): 799-813. doi: 10.3724/SP.J.1041.2024.00799

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Many individuals now participate in online fund investment, but novice investors often struggle with the complex information they encounter due to the lack of professional guidance in traditional offline financing. Previous research on decision-making has primarily focused on outcomes and utilized statistical methods to construct decision models, which fail to provide direct evidence of information processing. To assist novices in developing the necessary skills for making investment decisions, this study employs process tracking technology in the field of fund investment for the first time. The aim is to explore the differences in information processing between experienced investors and novices, thereby identifying the advantages experienced investors possess in information processing. Additionally, this research investigates the relationship between the decision-making process and outcomes, proposing interventions based on information processing to aid novices in making accurate investment decisions.

To achieve the research objectives, two studies were conducted. Study 1 involved a comprehensive exploration that traced the fund investment decision-making process using Mouselab. It compared various information processing indicators between experienced investors and novices, including decision-making time, depth of search, variability of search, compensatory index, and SM (strategy measure) value of the search pattern. The study also examined the impact of the search pattern on decision quality for experienced investors and novices through grouping logistic regression. Study 1 revealed that: (1) Experienced investors, compared to novices, preferred to utilize attribute-based search pattern during fund investment decision-making (Experienced investors: n= 35, M= -5.48, SD= 4.34; Novices: n = 39, M= -2.88, SD= 5.67; t(72) = -2.20, p = 0.031, Cohen’s d = 0.51), and displayed a more non-compensatory approach to information processing (Experienced investors: M= 0.77, SD= 0.25; Novices: M= 0.88, SD= 0.19; t(72) = -2.15, p = 0.035, Cohen’s d = 0.50). (2) Only the decision quality of novices in fund investment was affected by the information search pattern (n = 37, B = -0.17, SE = 0.08, Wald χ²= 4.12, OR = 0.84, 95% CI = [0.72, 0.99], p = 0.042), indicating that their decision quality improved when they searched for information based on attributes. In contrast, the decision quality of experienced investors was unaffected by the information search pattern but positively influenced by working memory (n = 30, B = 0.16, SE= 0.08, Wald χ²= 4.15, OR = 1.18, 95% CI = [1.01, 1.38], p = 0.042).

Study 2 involved an intervention experiment utilizing a single-factor (structured intervention group vs. control group) between-subject design. Participants in the structured intervention group (n= 36) were provided a piece of form paper to guide them to structure information of funds, while participants in the control group (n= 37) were provided blank paper. Then all participants completed a simulated fund investment task and their decision quality was recorded. Study 2 demonstrated that participants who used form paper for intervention had higher decision-making quality (seventeen participants were correct) than those who used blank paper (nine participants were correct), χ² = 4.17, p = 0.041, φ = 0.24, indicating the effectiveness of the structured intervention.

This study makes theoretical and practical contributions to the literature. First, it explores the characteristics of the information processing process during fund investment decision-making and its relationship with decision outcomes, filling the research gap regarding the “process” of information processing and deepening the understanding of the essence of decision-making ability in fund investment. Second, it extends the "expert-novice" paradigm to the field of fund investment, summarizing the differences in the search pattern and compensatory behavior between experts and novices, further supporting the heuristic decision model. Third, it proposes effective interventions to assist novice investors in improving their online fund investment and inspires the interface design of fund applications.

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An exploration of the antecedents and consequences of judges’ time poverty at work: A qualitative study

ZHANG Nan, CAO Peiling, LI Ning, SUN Xiaomin, QIAO Zhihong, ZHAO Jingwu

2024, 56 (6): 814-830. doi: 10.3724/SP.J.1041.2024.00814

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In an era of “litigation explosion”, Chinese judges are faced with the challenge of effectively handling the overwhelming and increasing volume of court cases. To address the dilemma of “too many cases but too few judges”, previous research on judicial practice has mainly focused on improving the efficiency of litigation procedure. However, one critical aspect that has been largely neglected is the underlying psychological response of judges to this challenge, which may play a pivotal role in the effectiveness and quality of judicial decision making. To address this gap, the current research adopted a person-centered perspective, aiming to uncover the role played by the prevalent feeling of time poverty, the feeling of not having enough time to accomplish all work-related tasks, among judges. We delved into the antecedents that triggered judges’ perception of time poverty, explored the consequences it had on judicial work, and unraveled the mechanisms through which time poverty influences the quality and efficiency of judicial decisions.

Utilizing the grounded theory methodology, we conducted in-depth interviews with judges recruited through a purposeful sampling technique. Participants consisted of judges (N= 51) who came from various regions across North China, Central China, and Southeast China. These judges served at different tiers within the local People's Courts. Specifically, most participants (62%) came from the primary People's Court, 33% came from intermediate People's Court, and 5% came from high People's Court. About half of participants were male (51%). The average age was 39.89 years (SD = 9.08 years) ranging from 26 to 59 years, and the average job tenure was 6.96 years (SD= 6.04 years) ranging from 1 to 36 years. Most participants (62%) had one child, 22% had two children, and 16% had no child. We analyzed the data with QSR Nvivo 19.0. Adhering to the grounded theory’s established protocols, preliminary analysis, generic analysis, and theoretical construction were conducted. Additionally, examinations involving both participants and non-participants were undertaken to affirm the validity of our research findings.

The current study constructed an integrated model that elucidated the antecedents and consequences of the perception of time poverty within the realm of the judiciary (see Figure 1). Findings revealed that (1) a mismatch between job demands, which were increased due to the numerous and detailed workloads and the burden of assessment requirements, and resources, which were decreased due to insufficient staffing, contributed to judges’ time poverty at work, and (2) time poverty urged the judges to speed up judicial decisions as well as to prolong their working hours, which in turn damaged the quality and effectiveness of judicial decisions.

By examining judges’ feelings of time poverty at work, the current study employed a person-centered perspective that complements the normative approach of extant legal science research and elucidated the mechanism that underlies the formation of judges’ time poverty and its judicial consequences. Findings of the current study provide theoretical insight into the challenge of case overload in China through a psychological perspective and offer practical implications for policymakers to overcome the challenge by prioritizing the feelings and needs of judges.

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Automated scoring of open-ended situational judgment tests

XU Jing, LUO Fang, MA Yanzhen, HU Luming, TIAN Xuetao

2024, 56 (6): 831-844. doi: 10.3724/SP.J.1041.2024.00831

Abstract ( 119 )

Situational Judgment Tests (SJTs) have gained popularity for their unique testing content and high face validity. However, traditional SJT formats, particularly those employing multiple-choice (MC) options, have encountered scrutiny due to their susceptibility to test-taking strategies. In contrast, open-ended and constructed response (CR) formats present a propitious means to address this issue. Nevertheless, their extensive adoption encounters hurdles primarily stemming from the financial implications associated with manual scoring. In response to this challenge, we propose an open-ended SJT employing a written-constructed response format for the assessment of teacher competency. This study established a scoring framework leveraging natural language processing (NLP) technology to automate the assessment of response texts, subsequently subjecting the system's validity to rigorous evaluation. The study constructed a comprehensive teacher competency model encompassing four distinct dimensions: student-oriented, problem-solving, emotional intelligence, and achievement motivation. Additionally, an open-ended situational judgment test was developed to gauge teachers' aptitude in addressing typical teaching dilemmas. A dataset comprising responses from 627 primary and secondary school teachers was collected, with manual scoring based on predefined criteria applied to 6, 000 response texts from 300 participants. To expedite the scoring process, supervised learning strategies were employed, facilitating the categorization of responses at both the document and sentence levels. Various deep learning models, including the convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), C-LSTM, RNN+attention, and LSTM+attention, were implemented and subsequently compared, thereby assessing the concordance between human and machine scoring. The validity of automatic scoring was also verified.
This study reveals that the open-ended situational judgment test exhibited an impressive Cronbach's alpha coefficient of 0.91 and demonstrated a good fit in the validation factor analysis through the use of Mplus. Criterion-related validity was assessed, revealing significant correlations between test results and various educational facets, including instructional design, classroom evaluation, homework design, job satisfaction, and teaching philosophy. Among the diverse machine scoring models evaluated, CNNs have emerged as the top-performing model, boasting a scoring accuracy ranging from 70% to 88%, coupled with a remarkable degree of consistency with expert scores (r = 0.95, QWK = 0.82). The correlation coefficients between human and computer ratings for the four dimensions—student-oriented, problem-solving, emotional intelligence, and achievement motivation—approximated 0.9. Furthermore, the model showcased an elevated level of predictive accuracy when applied to new text datasets, serving as compelling evidence of its robust generalization capabilities.
This study ventured into the realm of automated scoring for open-ended situational judgment tests, employing rigorous psychometric methodologies. To affirm its validity, the study concentrated on a specific facet: the evaluation of teacher competency traits. Fine-grained scoring guidelines were formulated, and state-of-the-art NLP techniques were used for text feature recognition and classification. The primary findings of this investigation can be summarized as follows: (1) Open-ended SJTs can establish precise scoring criteria grounded in crucial behavioral response elements; (2) Sentence-level text classification outperforms document- level classification, with CNNs exhibiting remarkable accuracy in response categorization; and (3) The scoring model consistently delivers robust performance and demonstrates a remarkable degree of alignment with human scoring, thereby hinting at its potential to partially supplant manual scoring procedures.

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