Subjects: Psychology >> Cognitive Psychology Subjects: Psychology >> Statistics in Psychology submitted time 2024-04-17
Abstract： Cognitive modeling has gained widespread application in psychological research. Model comparison plays a crucial role in cognitive modeling, as researchers need to select the best model for subsequent analysis or latent variable inference. Model comparison involves considering not only the fit of the models to the data (balancing overfitting and underfitting) but also the complexity of the parameter data and mathematical forms. This article categorizes and introduces three major classes of model comparison metrics commonly used in cognitive modeling, including: goodness-of-fit metrics (such as mean squared error, coefficient of determination, and ROC curves), cross-validation-based metrics (such as AIC, DIC), and marginal likelihood-based metrics. The computation methods and pros and cons of each metric are discussed, along with practical implementations in R using data from the orthogonal Go/No-Go paradigm. Based on this foundation, the article identifies the suitable contexts for each metric and discusses new approaches such as model averaging in model comparison.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Statistics in Psychology submitted time 2024-04-16
Abstract： The application of Bayesian statistics to hypothesis testing - Bayes factors - is increasing in psychological science. Bayes factors quantify the evidence supporting the competing hypothesis or model, respectively, thereby making a judgment about which hypothesis or model is more supported by the data based on its value. The principles and applications of Bayes factor for ANOVA are, however, not available in China. We first present the theoretical foundation of Bayesian ANOVA and its calculation rules. It also shows how to perform Bayesian ANOVA and how to interpret and report the results of five common designs (one-factor between-group design, one-factor within-group design, two-factor between-group design, two-factor within-group design, and two-factor mixed design) using example data. Theoretically, Bayesian ANOVA is an effective alternative to conventional ANOVA as a powerful vehicle for statistical inferences.
Subjects: Psychology >> Other Disciplines of Psychology submitted time 2024-03-28
Abstract： Psychological and brain science study human behavior and the human brain by study volunteers who participate these studies. Given the mind and behavior of participants influenced by their own biological and social factors, the generalizability of findings in these fields largely depends on the representativeness of samples. However, the representativeness of samples in psychological and brain science has long been criticized as WEIRD (Western, Educated, Industrialized, Rich, and Democratic). In recent years, several meta-researches have surveyed the representativeness of samples in published studies across different subfields, but the overall understanding of sample representativeness in psychological and brain science is lacking. In this review, we analyze these meta-researches to provide a more comprehensive perspective on the current state of sample representativeness in the field.
Two major issues were found in these meta-researches. First, much important sample information was never reported in the published studies. Most psychological and brain science studies reported participants’ gender, age, and country, while participants’ race/ethnicity, education level, and socioeconomic status were less commonly reported. Other important demographic variables, such as rural/urban, were reported completely ignored. And from a temporal perspective, the reporting of these demographic variables has increased only slightly in recent years compared to the past. The current situation of neglect in reporting demographic information has not fundamentally changed.
Second, based on the reported information, the current sample in the field is far from being representative of the world population: most participants are young, highly educated Caucasian females in Western countries; middle-aged and older, less educated, disadvantaged people in and outside Western countries are less likely to be studied. In terms of countries, African, Latin American, and Middle Eastern countries appear fewer in psychological and brain science research.
These two issues may be due to the following reasons: convenience sampling as the main sampling method; Western researchers dominating the research of psychology and brain science, with most of the editors-in-chief, editorial board members, and authors coming from Europe and America; traditionally, psychology and brain science under-valued the effect of culture and various demographic factors; the assumption that findings from Western participants can be generalized to all human beings. Addressing the issue of sample representativeness in psychological and brain sciences requires a concerted effort by researchers, academic societies, journals, and funding agencies: Researchers should collect and report detailed demographic information about participants, state the limitations of generalizability, and use sampling methods that can increase representativeness whenever possible (e.g., probability sampling); academic societies should raise the awareness of the representativeness issues by organizing more academic symposium or workshops on this topic; journals should increase the representativeness of editorial board members and encourage more rigorous research with samples from underrepresented groups or studies that examine the generalizability of important findings; funding agencies can encourage researchers to pay more attention to study groups from underrepresented countries, and provide financial support for studying hard-to-research population. Improving sample representativeness will enhance the application of psychological and brain science knowledge to real-life setting and promote the building of a community with a shared future for mankind.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Other Disciplines of Psychology submitted time 2024-01-04
Abstract： In the past decade, researchers in psychological science have introduced new research practices to address issues such as publication bias: pre-registration without peer-review, peer-reviewed registered reports, and registered replication reports. Many journals in the field have accepted registered reports as a new article type, and the numbers of platforms and templates for pre-registration increased significantly. However, criticisms of pre-registration and registered reports still exist, some stemming from misunderstandings, while other criticisms pointed out practical challenges in implementing pre-registration and registered reports. Findings from meta-research revealed that registered reports alleviated the publication bias and improved the quality of research, while pre-registration without peer review failed to achieve similar results. Promoting a wider adoption of pre-registration and registered reports will further improve the openness, reproducibility, and rigor of research, and it requires the concerted efforts of all stakeholders, including individual researchers, academic institutes, and publishers.
Subjects: Psychology >> Cognitive Psychology submitted time 2024-01-01
Abstract： The evidence accumulation model is a widely used cognitive model of human decision-making, which assumes that decision-makers continuously gather and integrate information into evidence relevant to the decision and make a decision once the accumulated evidence reaches a predefined threshold. With the increasing popularity of evidence accumulation model, some researchers claim it has reached a theoretical plateau and can be considered as the standard model for analyzing response time and choices. However, the theoretical assumptions underlying these models lack rigorous testing. As an example, the drift-diffusion model (DDM) is an instantiation of evidence accumulation and has five underlying assumptions: (1) the universality of evidence accumulation; (2) the selectivity of evidence accumulation; (3) linear integration of evidence with noise; (4) a constant decision criterion; and (5) decision-making is independent of motor execution. DDM has been widely used in cognitive tasks, such as value-based decision-making, and social decision-making, probably due to the availability of user-friendly software for parameter estimation. However, only a few studies systematically examined to what extent these five assumptions of DDM were supported by empirical studies. To fill the gap, we reviewed studies that tested these five assumptions.
For the first assumption of DDM, the universality of evidence accumulation, we only found direct evidence from studies that employed perceptual decision-making tasks. For other studies that used DDM for modeling, such as value-based decision-making or social decision-making, we found few studies that directly tested the existence of evidence accumulation. The second assumption, the selectivity of evidence accumulation, suggested that only information related to the goal would contribute to evidence accumulation. We did not find empirical data supporting this assumption except for O’Connell et al. (2012). However, evidence from conflict tasks (e.g., flanker task) suggested that information irrelevant to the goal may also be incorporated into the evidence accumulation. Data from conflict tasks inspired new models related to evidence accumulation model and called for further investigation into the mechanism behind the selectivity of evidence. The third and fourth assumptions constitute the core assumptions of DDM, i.e., “evidence accumulate-to-bound”. Regarding the third assumption, which posits that evidence with noise is accumulated linearly, supporting data were found from animal studies and human EEG studies that employed perceptual decision-making. However, human EEG data from value-based decision-making tasks has challenged the validity of this assumption. The fourth assumption, that the decision criterion is constant, is controversial and has been challenged by several other evidence accumulation models, such as collapsing boundary models. The last assumption, that decision-making is independent of motor execution, has also been questioned by empirical data from both animal studies and human behavioral and electromyography data, despite support from EEG recording.
In summary, we found that, while the standard DDM is commonly used in many sub-fields of psychology and neuroscience, empirical studies that directly tested five assumptions of DDM were mainly from perceptual decision-making tasks. Also, we found that challenging these assumptions often resulted in new computational models. These findings call for studies to test these assumptions and develop new models. Besides, these findings suggest that researchers should be cautious when interpreting the parameters estimated from standard DDM. Finally, our review suggests that increasing transparency in model assumptions will accelerate the revision of models and theories, and ultimately deepen our understanding of human cognitive processes.
Subjects: Psychology >> Cognitive Psychology submitted time 2023-09-12
Abstract： Self-reference (or self-referential processing) refers to the cognitive processes underlying self-related information processing. It is widely studied in cognitive neuroscience to better understand the neural basis of self-cognition of human beings. However, does the term “self-reference” mean the same psychological processes across studies? This fundamental question has been largely disregarded and has not received the attention it deserves. To fill the gap, we built an ontological dataset based on neuroimaging studies of self-reference. We searched the literature and screened the articles following a standard protocol. Then, two independent coders extracted data and standardized operationalizations of self-reference on both behavioral and neural levels, resulting in a cognitive ontological dataset for neuroimaging studies of self-reference. This dataset consists of operationalizations of self-reference (in CSV file format) from 66 neuroimaging articles, coordinates data of brain areas activated by self-reference (in BrainMap format), and corresponding codebooks. The inter-rater reliability analysis indicates that the coding process exhibits an exceptional level of quality. Compared with automatic meta-analytical platforms, i.e., Neurosynth, the current dataset provides a fine-grained granularity in article selection, which allows the comparison of brain regions activated by different operationalizations of self-reference. This dataset lays a foundation for the understanding of neural mechanisms underlying self-cognition. It may also facilitate the study of cognitive ontology by serving as an exemplary model for the creation of similar metascience datasets.
Subjects: Psychology >> Developmental Psychology Subjects: Psychology >> Personality Psychology submitted time 2023-06-16
Abstract： How to develop the healthy personality of children and adolescents is a common concern in the society, among which family parenting style has received more and more attention, but the findings are inconsistent. To reveal the relationship between the two and its moderating mechanism, this study conducted a meta-analysis of 1054 effect values from 52 studies with 19,642 subjects. The results showed that (1) Positive parenting style is significantly and positively related to healthy personality, while negative parenting style is significantly and negatively related to healthy personality. (2) The effect of parenting style on healthy personality was moderated by the age of children and adolescents, with an inverted U-shaped trend of "small at the end and large in the middle," reflecting a strong influence at the junior high and high school levels, and a weak influence at the elementary and college levels. In addition, the effect of parenting style on the integrity of children and adolescents was also moderated by the gender, generation, and region of the children. The results of this study provide a scientific perspective and an empirical basis for the development and education of children and adolescents' sound personality in the context of Chinese family culture.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Network analysis models (or Network Psychometrics) have been widely used in psychology research in recent years. Unlike latent variable models which conceive observable variables as outcomes of unobservable latent factors, network analysis models apply the graph theory to construct a network to depict the associations among observable variables. The observable variables are treated as nodes and the associations between them are treated as edges. As such, network analysis models reveal the relationships among observable variables and the dynamic system resulted from the interactions between these observable variables. With indices reflecting individual nodes’ characteristics (such as centrality) and network structural characteristics (such as small-worldness), network analysis models provide a new perspective for visualization and for studying various psychological phenomena. In the past decade, network analysis models have been applied in the fields of personality, social, and clinical psychology as well as psychiatry. Future research should continue to develop and improve the methods of network analysis models, making them applicable to more types of data and broader research fields.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Background: P-value is the most widely used statistical index for inference in science. A p-value greater than 0.05, i.e., nonsignificant results, however, cannot distinguish the two following cases: the absence of evidence or the evidence of absence. Unfortunately, researchers in psychological science may not be able to interpret p-values correctly, resulting in wrong inference. For example, Aczel et al (2018), after surveying 412 empirical studies published in Psychonomic Bulletin & Review, Journal of Experimental Psychology: General, and Psychological Science, found that about 72% of nonsignificant results were misinterpreted as evidence in favor of the null hypothesis. Misinterpretations of nonsignificant results may lead to severe consequences. One such consequence is missing potentially meaningful effects. Also, in matched-group clinical trials, misinterpretations of nonsignificant results may lead to false “matched” groups, thus threatening the validity of interventions. So far, how nonsignificant results are interpreted in Chinese psychological literature is unknown. Here we surveyed 500 empirical papers published in five mainstream Chinese psychological journals, to address the following questions: (1) how often are nonsignificant results reported; (2) how do researchers interpret nonsignificant results in these published studies; (3) if researchers interpreted nonsignificant as “evidence for absence,” do empirical data provide enough evidence for null effects? Method: Based on our pre-registration (https://osf.io/czx6f), we first randomly selected 500 empirical papers from all papers published in 2017 and 2018 in five mainstream Chinese psychological journals (Acta Psychologica Sinica, Psychological Science, Chinese Journal of Clinical Psychology, Psychological Development and Education, Psychological and Behavioral Studies). Second, we screened abstracts of these selected articles to check whether they contain negative statements. For those studies which contain negative statements in their abstracts, we searched nonsignificant statistics in their results and checked whether the corresponding interpretations were correct. More specifically, all those statements were classified into four categories (Correct-frequentist, Incorrect-frequentist: whole population, Incorrect-frequentist: current sample, Difficult to judge). Finally, we calculated Bayes factors based on available t values and sample sizes associated with those nonsignificant results. The Bayes factors can help us to estimate to what extent those results provided evidence for the absence of effects (i.e., the way researchers incorrectly interpreted nonsignificant results). Results: Our survey revealed that: (1) out of 500 empirical papers, 36% of their abstracts (n = 180) contained negative statements; (2) there are 236 negative statements associated with nonsignificant statistics in those selected studies, and 41% of these 236 negative statements misinterpreted nonsignificant results, i.e., the authors inferred that the results provided evidence for the absence of effects; (3) Bayes factor analyses based on available t-values and sample sizes found that only 5.1% (n = 2) nonsignificant results could provide strong evidence for the absence of effects (BF01 > 10). Compared with the results from Aczel et al (2019), we found that empirical papers published in Chinese journals contain more negative statements (36% vs. 32%), and researchers made fewer misinterpretations of nonsignificant results (41% vs. 72%). It worth noting, however, that there exists a categorization of ambiguous interpretations of nonsignificant results in the Chinese context. More specifically, many statements corresponding to nonsignificant results were “there is no significant difference between condition A and condition B”. These statements can be understood either as “the difference is not statistically significant”, which is correct, or “there is no difference”, which is incorrect. The percentage of misinterpretations of nonsignificant results raised to 64% if we adopt the second way to understand these statements, in contrast to 41% if we used the first understanding. Conclusion: Our results suggest that Chinese researchers need to improve their understanding of nonsignificant results and use more appropriate statistical methods to extract information from nonsignificant results. Also, more precise wordings should be used in the Chinese context.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Substance addiction involves multiple factors, ranging from biological, social, to cultural. But the dominant biological reductionism-based explanations focus primarily on the brain, potentially hindering a more comprehensive and inclusive research of substance addiction and its recovery. We propose that network theory, focusing on feedback loops formed by interactions between myriad psychological disorder variables, will provide a better holistic framework to understand the complexity of substance addiction. Applying network theory to substance addiction may provide new insights in (1) understanding the interrelationships and interactions between symptoms, (2) understanding the systematic integrity and dynamic changes in symptom networks, and (3) integrating multiple levels of factors into a unified theoretical framework. Also, network theory may generate new approaches for future interventions and treatments. In sum, networktheory, as a theoretical model, provide a new perspective for understanding substance addiction and its intervention. We believe this reframing will encourage more empirical research toward various other hypotheses within this framework, thus, promoting the treatment and recovery of substance addiction.
Subjects: Psychology >> Cognitive Psychology submitted time 2023-01-10
Abstract：
A construct is a concept proposed by a researcher to represent an object of interest. Constructs serve as core media for scientific communication in research. In psychological science, the ontological commitment of constructs - whether a construct reflects a particular ontological entity - has received less attention. However, the progress of psychological science is compromised by three issues related to ontological commitments of constructs: the lack of a unified framework for defining and measuring psychological constructs, the confusion of ontological relations of psychological constructs, and the difficulty in identifying the similarities and deviations among different psychological constructs. We propose that one potential solution to the predicament is a consensus-based cognitive ontology framework that sort out the mapping relationship between psychological constructs and psychological entities. Cognitive ontology aims at answering the following questions: (1) what is the object of psychological science; and (2) how to accumulate the knowledge in the field? We suggest that based on evolutionary theory, psychological science should take human's psychological capacity, which is shaped by evolution, as the object of its study. Empirical studies of psychological capacity should be conducted under a consensus-based framework that provides a coherent logical flow from "psychological capacity” to “construct”, “measurement”, and “data", via mathematical or formal models. To build this framework, researchers need meta-science to re-evaluate the existing operationalization/measurements of psychological constructs; large-scale datasets from massive data collection using all available operationalization/measurements obtained in meta-science; and models, data-driven and/or theory-driven, to specify the relationships among measurements. By doing so, researchers will be able to update the measurement and theoretical models of the target construct and its related constructs. Iterating the above process, with the collaboration among researchers, the field as a whole will continue updating the measurements and theoretical models of its constructs, accumulating data, and, eventually, accelerating theoretical breakthroughs in psychological science.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Statistics in Psychology submitted time 2022-12-31
Abstract：
The key of experimental design is to balance between informativeness and efficiency. However, power analysis only focuses on informativeness and is difficult to implement. Sequential Bayes Factor analysis takes the advantage of Bayes Factor‘s ability and reach a trade-off between informativeness and efficiency by setting Bayes Factor criteria and the sequential analysis during data collection. The present primer demonstrates how to perform three steps of sequential Bayes Factor analysis using open-source software JASP and R. This method considers practical issues in real research practices and is easy to implement, which can help researchers to design more efficient experiments.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Statistics in Psychology submitted time 2022-07-30
Abstract： Meta-analysis is a crucial tool for accumulating evidence in basic and applied research. In the open science era, meta-analysis becomes an important way for integrating open data from different sources. Meanwhile, because of the great researchers’ degree introduced by multiple-step and multiple-choices in each step of meta-analysis, the openness and transparency are crucial for reproducing results of meta-analysis. To (1) understand the transparency and openness of meta-analysis reports published in Chinese journals and (2) improve the transparency and openness of future meta-analysis by Chinese researchers, we developed a Chinese version of checklist for meta-analysis, which was based on the Preferred Reporting Items for Systematic Review and Meta-Analysis protocols (PRISMA) and the principle of openness and transparency, and then surveyed the methods and results of 68 meta-analysis papers in mainstream Chinese psychological journals in last five years. Our results revealed that openness and transparency of Chinese meta-analysis reports need to be improved, especially in the following aspects: the date/time and limitation of literature search, the details of screening and data collection, the flow chart of article screening, the details of effect size transformation, and the evaluation of individual research bias. The checklist we present, which lists almost all aspects that an open meta-analysis should include, can be used as a guide for future meta-analysis.
Subjects: Psychology >> Clinical and Counseling Psychology Subjects: Psychology >> Psychological Measurement submitted time 2022-07-28
Abstract：
Substance addiction involves multiple factors, ranging from biological, social, to cultural. But the dominant biological reductionism-based explanations focus primarily on the brain, potentially hindering a more comprehensive and inclusive research of substance addiction and its recovery. We propose that network theory, focusing on feedback loops formed by interactions between myriad psychological disorder variables, will provide a better holistic framework to understand the complexity of substance addiction. Applying network theory to substance addiction may provide new insights in (1) understanding the interrelationships and interactions between symptoms, (2) understanding the systematic integrity and dynamic changes in symptom networks, and (3) integrating multiple levels of factors into a unified theoretical framework. Also, network theory may generate new approaches for future interventions and treatments. In sum, networktheory, as a theoretical model, provide a new perspective for understanding substance addiction and its intervention. We believe this reframing will encourage more empirical research toward various other hypotheses within this framework, thus, promoting the treatment and recovery of substance addiction.
Subjects: Psychology >> Statistics in Psychology submitted time 2021-04-25
Abstract： 在心理学研究中，以下两种情况下研究者可能需要对零效应进行评估：第一，推断某种效应不存在；第二，意外出现不显著结果，需要区分到底是效应不存在还是当前数据未能提供足够的证据。然而，常用的原假设显著性检验（Null hypothesis significance test, NHST）无法直接评估零效应。近年来，等价检验、贝叶斯估计和贝叶斯因子三种方法逐渐被用于评估零效应：在频率统计框架下，等价检验通过检验效应是否在最小感兴趣区内（Smallest effect size of interest, SESOI），通过p值来推断效应是否为零；在贝叶斯统计框架下，贝叶斯估计通过对比后验分布的最高密度区间和实际等价区的重叠情况，推断效应是否为零；而贝叶斯因子则是通过评估当前数据对原假设和备择假设的相对支持程度，推断当前数据对原假设的相对支持程度。文章通过分析两个真实的数据，展示三种方法的实际应用。三种方法各有其特点：等价检验在逻辑上是对NHST的拓展，易于从传统统计中延伸使用；贝叶斯因子的解读较符合直觉，逻辑上清晰；贝叶斯估计则具有较强的灵活性，可拓展于更多的研究问题。以上三种评估零效应的方法，可能能够帮助心理学研究者在实际研究中进行合理的统计推断和研究决策。
Peer Review Status:Awaiting Review
Subjects: Psychology >> Statistics in Psychology submitted time 2020-10-17
Abstract： P-value is the most widely used statistical index for inference in science. Unfortunately, researchers in psychological science may not be able to interpret p-value correctly, resulting in possible mistakes in statistical inference. Our specific goal was to estimate how nonsignificant results were interpreted in the empirical studies published in Chinese Journals. Frist, We randomly selected 500 empirical research papers published in 2017 and 2018 in five Chinese prominent journals (Acta Psychological Sinica, Psychological Science, Chinese Journal of Clinical Psychology, Psychological Development and Education, Psychological and Behavioral Studies). Secondly, we screened the abstracts of the selected articles and judged whether they contained negative statements. Thirdly, we categorized each negative statement into 4 categories (Correct-frequentist, Incorrect-frequentist: whole population, Incorrect-frequentist: current sample, Difficult to judge). Finally, we calculated Bayes factors based on the t values and sample size associated with the nonsignificant results to investigate whether empirical data provide enough evidence in favor of null hypothesis. Our survey revealed that: (1) 36% of these abstracts (n = 180) mentioned nonsignificant results; (2) there were 236 negative statements in the article that referred to nonsignificant results in abstracts, and 41% negative statements misinterpreted nonsignificant results; (3) 5.1% (n = 2) nonsignificant results can provide strong evidence in favor of null hypothesis (BF01 > 10). The results suggest that Chinese researchers need to enhance their understanding of nonsignificant results and use more appropriate statistical methods to extract information from non-significant results.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Legality Psychology Subjects: Psychology >> Social Psychology submitted time 2019-11-05
Abstract： Behavioral and neuroscientific methods have uniquely contributed to our understanding of human mind and behavior. The advance in neuroscience and its potential implications (e.g., in legal systems) have attracted attention from both academia and society. However, researchers found that, when providing statements supported by either neuroscientific or behavioral/psychophysiological results, even if these neuroscientific results were logically irrelevant to the statements, participants still considered statements with neuroscientific results as more trustworthy. This phenomenon was termed as neuroscience bias. By systematically reviewing empirical studies on neuroscience bias, we revealed that: (1) the reproducibility of neuroscience bias was debated, but the effect exists; (2) neuroscience bias could be attributed to people’s preference for the reductionism and psychological essentialism. Neuroscience bias is one of many biases people may have when interpreting scientific results; future studies should further explore the psychological mechanisms of these biases and thereby provide guidelines for correctly interpreting and using scientific results." " " " " " "
Subjects: Psychology >> Social Psychology submitted time 2019-09-12
Abstract： " Moral self, which is a core part of self-concept, is the overall self-evaluation in the moral domain. Previous studies in social psychology has shown that moral self is closely related to individual’s moral behaviors. Interestingly, recent studies found that moral self showed advantages over other aspects of self. For example, it was reported that self-enhancement effect in moral domain is stronger than that in other domains. However, it is still unknown whether moral self related information were processed preferentially during cognitive process. To answer this question, the current study investigated the enhancement effect of moral-self in perceptual processing, by adopting a cognitive neuroscience approach. Based on our behavioral (Chapter 2 ~ 4) and neural (Chapter 5) results, we proposed that moral self served as internal reference for individual’s cognitive processing. Firstly, the current study confirmed that the moral related information is processed preferentially during perceptual processing by three experiments (Chapter 2). Also, we excluded the cofounding factors such as familiarity of words. We reasoned that if moral self is the inner reference for information processing, then this effect should modulated by self-relatedness. Thus, if we explicitly compared the moral, neutral and immoral aspect of self and strangers, advantage of moral information should only occur on moral self. This hypothesis was confirmed by experiment 4. To further explore the interaction between morality and self-relatedness, we rendered the morality (experiment 5) or self-related (experiment 6) information as task-irrelevant information, and the interactions were found, suggesting that the moral self could be implicitly modulate the perceptual processing. To validate the stability of the effect , we conducted an meta-analysis of 6 studies, and found that the effect size of the modulation of moral self on perceptual processing is about Cohen’s d = 0.5, a moderate effect size. To further explore the mechanism underlying the facilitation effect of moral self, the current study employed cognitive modeling methods. We re-analyzed the data from the experiment 4, 5 and 6, by using drift diffusion model (DDM). The results showed that the facilitation effect of the moral-self occur primarily on drift rate, which means faster information accumulating speed. These results suggest that the perceptual salience of moral self is similar to those of physical salience. To examine the cross-task stability of the advantage of moral self, we conducted a new experiment in which participants required to finish a perceptual matching task and a perceptual categorization task. Using ex-Gaussian model and DDM, we found that the advantage of moral self is cross task stable. In the second part of this study, we explored the neural basis of moral self by Voxel-Based Morphometry (VBM) and meta-analysis. The VBM study explored the correlation between moral self-evaluation and volume of grey matters in the brain. We didn’t found any significant cluster that correlated to moral-self. We also used activation likelihood estimation (ALE) meta-analysis of fMRI studies of moral judgment and self-referential task, because these two tasks could potentially involve moral self-referential processing. Our meta-analysis found that dorsal medial frontal cortex and frontal pole are share by both moral judgment and self-referential processing, suggesting that these two brain regions may play an important role in moral self. In sum, the current study systematically investigated the cognitive mechanism and neural basis of moral self. From the perspective of computational modelling, we explore the mechanism of moral self form all three levels: function, algorithm and hardware. Our results showed that moral self is perceptual salient, and this saliency is cross-task stable. The cognitive mechanism behind this advantage lies in the higher information accumulation rate, and the dorsal medial frontal cortex and frontal pole maybe the neural basis of moral self. The study of moral self connected social psychology, cognitive psychology, social neuroscience, and computational modeling, providing solid foundation for future studies. " "
Peer Review Status:Awaiting Review
Subjects: Psychology >> Psychological Measurement submitted time 2019-08-13 Cooperative journals: 《心理科学进展》
Abstract： 变量间的网络分析模型近年来被广泛应用于心理学研究。本文目的在于介绍网 络分析的基本原理与常用指标，并进一步介绍此方法在多个领域中的实证研究，旨在推 进研究者对网络分析模型的理解与应用。不同于潜变量模型将潜变量作为观测变量的共 同先导因素， 网络分析模型将观测变量作为初级指标，采用图论的方法建立观测变量之 间的关系网络，故使观测变量之间的联系不再受到潜变量模型的局限。通过变量网络中 基于各个节点特征的指标(如中心性)以及基于整体结构特征的指标(如小世界性)，网络 分析为研究各种心理现象提供了新的可视化描述方式和理解视角。 本文详细介绍了此方 法目前在人格心理学、社会心理学和临床心理学等领域的应用， 进一步讨论了在未来研 究者可以发展和完善网络分析模型的方向，以使之运用到更多的数据类型和更多的研究 领域。
Subjects: Psychology >> Statistics in Psychology submitted time 2019-04-15
Abstract： The recent replication crisis in psychology has motivated many researchers to reform the methods they used in research, reporting effect sizes (ES) and their confidence intervals (CIs) becomes a new standard in mainstream journals. However, a practical tutorial for calculating CIs is still lacking. In this primer, we introduced theoretical basis of CIs of the two most widely-used effect size, Cohen's d and η2, in plain language. The CIs of both Cohen's d and η2 are calculated under the condition that the alternative hypothesis (H1) is true, and both rely on the estimation of non-centrality parameters of non-central distributions by using iterative approximations. More specifically, non-central t-distribution for Cohen's d and non-central F-distribution for η2. Then, we illustrated how to calculate them in R and JASP with real data. This practical primer may help Chinese psychological researchers understand the CIs better and report CIs in their own research. "
Peer Review Status:Awaiting Review
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